esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### bAuNPs Characterization  
#### 2.4.1 Ultraviolet-Visible Spectroscopy (UV-Vis Spectroscopy)  
To confirm the presence of AuNPs, the excitation spectra of the samples were measured by UV-Vis spectroscopy using the Multiskan SkyHigh spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA). The pellets of control and AuNPs solutions were resuspended in DEPC (diethyl pyrocarbonate)-treated water (Thermo Fisher Scientific, Waltham, MA, USA), mixed by vortexing, and the excitation spectra were recorded in a wavelength range of 300 to 700 nm. All measurements were done in a quartz cuvette (1 mm). Concentrations of gold ions that were reduced to AuNPs in the biosynthesis process were calculated following [PERSON] et al. [30] correlation, where, from the absorbance at 400 nm and for a 1 mm cuvette, an absorbance of 1.2 (OD\({}_{400}\)) corresponds to [Au0] = 0.5 mM:  
\[\mathrm{A}_{400\mathrm{nm}}=1.2\Leftrightarrow\mathrm{[Au^{0}]}=0.5\;\mathrm{mM} \tag{1}\]
# A Review of Neurotransmitters Sensing Methods for Neuro-Engineering Research
## 1 Introduction
### Monoamine Neurotransmitters
#### 1.2.2 Norepinephrine and Epinephrine  
Norepinephrine and epinephrine, also known as noradrenaline and adrenaline respectively, are monoamine molecules which act as hormones and neurotransmitters in the human body. As neurotransmitters, they are involved in the autonomic nervous system [56], which is composed of the sympathetic and parasympathetic systems. The autonomic nervous system is also known as the \"fight or flight\" system.  
In the brain, norepinephrine neurons are localized in the locus coerulus, a nucleus in the brainstem that projects to various regions of the brain, including the limbic system which is involved in the regulation of emotions and cognition [57; 58]. In the hypothalamus, amygdala and locus coerulus, the increase of norepinephrine release had been associated with anxiety [59]. Adrenergic neurons are also present in different regions of the brain, including the lateral tegmental system and medulla, but the epinephrine function as a neurotransmitter is poorly understood [57]. It is believed that it plays a role in the fight-or-flight response by increasing the heart rate, vasodilatation, pupil dilation, and blood sugar [60; 61].  
One of the major hypotheses for depression involves a deficit in monoamines, more precisely in serotonin and/or norepinephrine [58], and the antidepressant drugs currently used act on mechanisms involving monoamines regulations. Those mechanisms are: inhibition of reuptake of serotonin/norepinephrine, antagonism of presynaptic inhibitory serotonin/norepinephrine receptors and inhibition of monoamine oxidase [58]. More evidence exists on the role of serotonin at the origin of depression, but the evidence also shows a role for epinephrine. For instance, the locus coerulus has projection to the limbic system, including the amygdala, hippocampus and hypothalamus, which are all involved in emotion and cognition. Studies on animals have also shown that increasing norepinephrine level induces a protection from depression, while norepinephrine depletion studies have shown increased risk for depression. Drugs that act specifically on norepinephrine are effective to treat depression [58].
## 4 Experiments and Results  
### Dataset Generation for LTE and IEEE 802.11 Family  
We focused on Wi-Fi signals operating in the 5 GHz industrial, scientific, and medical (ISM) band in our experimentation. The selection of this unlicensed band is due to LTE, which can operate in the 5 GHz band, based on the operators' preference [68]. To the best of the authors' knowledge, there is no available dataset for wireless local area network (WLAN) 802.11 protocols or LTE, especially under multiple noise scenarios and propagation models; therefore, we resorted to data generated through simulation. We set up the simulation of the following five protocols operating in ISM bands: LTE, IEEE 802.11 ac, IEEE 802.11 ax, IEEE 802.11n, and IEEE 802.11a under different channel conditions. Protocols and conditions will be further detailed in Section 4.2. IEEE 802.11 ax is the high throughput and efficiency WLAN amendment, which will replace both IEEE 802.11n and IEEE 802.11 ac [69; 70]. The end-to-end radio signal environment is built using the MATLAB WLAN System Toolbox for various WLAN 802.11 standards such as IEEE 802.11a, IEEE 802.11n, IEEE 802.11 ac (Wi-Fi5), and IEEE 802.11 ax (Wi-Fi6). MATLAB also includes LTE System Toolbox, which is used to build, design, and generate LTE waveforms and model end-to-end LTE communication links. The propagation of channel modeling functions allows fading and noise to the transmitted LTE and WLAN signals. In the following subsections, the implementation system for WLAN IEEE 802.11 protocols and unlicensed LTE is explained. The simulation setup details for LTE and IEEE 802.11 standards, the characteristics, the channel conditions, and the received signal for each protocol or RAT are all detailed in Appendix B.
#### Directed Diffusion  
Directed diffusion [4, 8] developed at ISI/USC and UCLA is a novel network protocol built for information retrieval and data dissemination. It is task specific and provides good support for event-driven applications like wireless sensor networks. One of its novel ideas is that it is \"data-centric\", meaning routing is based on data contained in the sensor nodes rather than traditional IP theme where end-to-end delivery method is used based on unique identifications. In directed diffusion, data generated by sensor nodes are identified by their attributes. Sinks or nodes that request data send out _interests_ into the network. If the attributes of the data generated by source nodes match these interests, a _gradient_ is setup within the network and data will be _pulled_ toward the sinks. Therefore, it is essentially a _receiver-initiated_ routing protocol. Intermediate nodes are capable of caching and transforming data. Take target tracking as an example, when there is a target moving through the network, the sensor nodes could publish the signal energy and geographical information of the sensor as an attribute to the network, thus facilitating other nodes to know about the existence of the target in a certain geographical area.  
Directed diffusion also facilitates the design of energy-efficient distributed sensing applications [8]. It provides Geographic and Energy Aware Routing Protocol (GEAR) which helps define a closed geographic region for propagating _interests_ that improves performance by avoiding the _interest_ messages to flood the entire network [23].
###### Abstract  
Petroleum hydrocarbon (PHC) contamination of soil is a widespread global environmental concern due to the persistence and recalcitrant nature of PHCs. The PHCs are highly toxic and their removal from the terrestrial ecosystem is necessary to maintain soil as well as human health. Here, a pot experiment was performed to examine the impact of _Enterobacter_ sp. MN17 and biochar addition on the growth of mungbean plants and PHCs removal from diesel-polluted soil. For this purpose, soil was contaminated artificially with diesel to achieve a final concentration of 5000 mg kg\({}^{-1}\). Untreated and _Enterobacter_ sp. MN17 treated mungbean seeds were sown in pots. Sugarcane bagasse biochar was applied as an amendment in respective pots along with the recommended levels of essential nutrients. Results showed that PHCs significantly suppressed the seedling emergence as well as agronomic and physiological attributes of mungbean as compared to un-contaminated controls. However, the co-application of _Enterobacter_ sp. MN17 and biochar significantly reduced the phytotoxicity of PHCs to mungbean plants and effectively increased the seedling emergence, shoot and root length, shoot fresh and dry biomass, root fresh and dry biomass of plants up to 24%, 54%, 52%, 52%, 54%, 55% and 60%, respectively as compared to controls. Similarly, 30%, 57%, 64%, 36% and 57% increase in chlorophylls contents, transpiration rate, stomatal conductance, sub-stomatal conductance, and photosynthetic rate, respectively were observed in their combined application as compared to respective controls. Furthermore, the co-addition of biochar and _Enterobacter_ sp. MN17 could remove 69% and 85% higher PHCs from unplanted and planted pots, respectively, than that of their respective controls. Our results suggest that the co-application of biochar and _Enterobacter_ sp. MN17 may be useful in enhancing plant growth and eliminating PHCs from contaminated soil.  
petroleum hydrocarbons; phytotoxicity; bioremediation; _Enterobacter_ sp. MN17; biochar; mungbean
###### Abstract  
This study describes an inexpensive, simple and green method to form silver nanoparticles from different leaf extracts of _Achyranthes aspera_ and _Scoparia dulcis_ plants. The silver nitrate is reduced by _Achyranthes aspera_ and _Scoparia dulcis_ leaf extracts respectively to generate two silver nanoparticle types symbolized as AA. AgNPs and SD. AgNPs. The optical absorption, size and morphology of silver nanoparticles are significantly impacted by extract types. The ultraviolet visible spectrum of AA. AgNPs shows a 433-nm peak being more broadened than that of SD.AgNPs. The Fourier infrared transform spectra of two of these silver nanoparticles revealed that their surface is modified by organic constituents from extracts, and thus they are stabilized in solution without any additional reaction. Images from transmission electron microscopy and scanning electron microscope indicate that AA.AgNPs are in clusters with the size of 8-52 nm almost possessing oval shape, while SD.AgNPs are smaller size of 5-45 nm separated well in diversified shapes (spherical, triangle, quadrilateral and hexagonal). Moreover, both AA.AgNPs and SD.AgNPs exhibit the highly antifungal effect against _Aspergillus niger_, _Aspergillus flavus_ and the most strong impact on _Fusarium oxysporum_. For these obtained results, two new silver nanoparticles are promising fungicides for various applications of medical and agricultural fields.  
silver nanoparticle; fungicide; plant extract; _Achyranthes aspera; Scoparia dulcis_
## 1 Introduction
In this study, we employed EO-1 Hyperion data and measured EC flux tower data in a pine and broad-leaved mixed forest to evaluate narrowband indices for monitoring C fluxes in Northeastern China. The primary goals of this study were:  
1. To evaluate effectiveness of narrowband bio-indicators for monitoring temporal variations of forest C fluxes;
2. To map regional patterns of C fluxes using the optimal bio-indicators and validate inversed GPP spatial patterns;
3. To develop methods for the use of narrowband bio-indicators for hyperspectral remote sensing of terrestrial ecosystem C fluxes across forest stand and regional spatial scales.
###### Abstract
Furthermore, availability, update rate, and latency of the NLP were assessed under different conditions. Effectively, the results of this paper aim to highlight the expected performance of the out-of-the-box Android NLP under various drone operating conditions. Overall, the main contributions of this paper include:  
* Characterization of the NLP accuracy in several environments with varying numbers of visible Wi-Fi and cell tower access points;
* Accuracy assessment of the altitude measurements provided by the NLP;
* Determining the correlation between the NLP accuracy and the number of visible access points;
* Validation of the position accuracy estimates provided by the NLP;
* Investigation of the NLP availability, update rate, and latency.
# Biophotonics in Dentistry
## 3 Applications in Dentistry
### Photodynamic Therapy  
#### 3.5.1 Management of Symptomatic Oral Lichen Planus  
Lichen Planus is a chronic inflammatory disease clinically classified as reticular lesions, which are generally asymptomatic, whereas its erosive and atropic forms, presented as white lines (Wickham's striae), are painful and require immediate medical attention, as they have the potential to undergo a malignant transformation [139]. Corticosteroids, invasive surgical procedures and laser therapy are the therapeutical procedures used, each of which have their own adverse effects, including mucosal atrophy, candidiasis, GIT disorders and adrenal insufficiency [140]. Therefore, photodynamic therapy is highly recommended as an alternative treatment plan.  
\begin{table}
\begin{tabular}{l} \hline \hline
**Summary of Advantages of Laser Therapy in Dentistry** \\ **Compared to Conventional Therapies** \\ \hline Reduced need of anaesthesia \\ Reduces pain with analgesic effects \\ Haemostatic effects; reduces bleeding and tissue inflammation \\ Very precise cutting; minimises damage to healthy tissues \\ Ability to ablate and vaporise dental hard tissues \\ No issue of painful vibrations caused by conventional equipment, such as dental drills \\ Microbial and bacterial inhibition \\ Improved cell metabolism and biostimulation \\ Patients with higher sensitivity can be treated with photomodulation therapy \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of advantages of laser therapy in dentistry compared to conventional therapies.  
Figure 12: Advantages of laser in periodontal therapy in contrast to conventional periodontal surgeries.  
Figure 13 illustrates the mechanism of action of PDT. The molecules of photosensitizer are activated by the laser light and are converted into an excited triplet state, which then reacts with oxygen molecules to form reactive oxygen species. These reactive oxygen species have the potential to kill the target cells and therefore have tissue-healing and regenerative properties [141,142]. Various studies were conducted to investigate the efficacy of PDT in contrast to corticosteroids. A systemic review by [PERSON] et al. confirmed the effectivity of PDT for the management of oral lichen planus; however, further research fortified on standardized parameters of PDT, such as fibre diameter and lesion size, is essential [120].
# Effective Strategies for Enhancing Real-Time Weapons Detection in Industry
## 4 Results
### Second Phase
#### 4.2.2 Scale Match  
This experiment aims to assess the performance impact in terms of precision, recall, and inference time using both versions of the Scale Match method for training a model. The distinction between the two versions lies in the way the extra dataset is transformed; in the first version, the images are re-scaled using the average size of their bounding boxes, whereas in the second version, the bounding boxes are copied, resized, and pasted onto another image (as illustrated in Figure 10). As observed in Table 8, utilising the Scale Match base version's dataset for training improves every metric compared to the current baseline. When examining objects with smaller aspect ratios (those whose area is less than or equal to \(100\ px^{2}\)), it also enhances every metric, although it cannot reduce the false positives, which are most common among smaller sizes. The performance of the copy-and-paste version is inferior across all metrics. This outcome can be attributed to the technique of pasting the bounding box into another image, which can potentially alter the original structure of the image. Another issue might be the images used for the pasting process, as most of the weapons present appear in dark environments, and there are images used where the predominant colour is black, so changing the images used to brighter and more colourful images might yield better precision and recall.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Augment** & **AP50** & **AP75** & **AP** & **F1** & **FP** \\ \hline no\_blur\_noise & 77.26 & 72.40 & 62.34 & 72.60 & 287 \\ no\_blur\_cutout & 77.35 & 72.30 & 62.69 & 73.20 & 245 \\ no\_blur\_bright & 76.13 & 71.80 & 61.26 & 71.60 & 117 \\ no\_flip & 76.89 & 70.55 & 59.15 & 73.00 & 223 \\ no\_blur & 77.23 & 73.06 & 62.77 & 73.10 & 202 \\ _Default_ & 75.90 & 71.21 & 59.89 & 71.60 & 157 \\ Baseline & 77.21 & 73.18 & 62.81 & 72.50 & 293 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Median results grouped by dataset. The metrics used were Average Precision (AP), AP50, AP75, F1 score (F1), and False Positives (FP). These were calculated using an IoU of 0.7 and a confidence threshold of 0.5. Note that the results in red are the best and those in blue are the second best.
# Building Damage Assessment Based on Siamese Hierarchical Transformer Framework
## 2 Methods
### Two-Stage Framework
#### 2.2.1 Stage 1: Building Localization  
The building segmentation is performed in Stage 1 to locate the buildings. As shown in Figure 1a, in Stage 1, only the pre-disaster images are input to train the network for the localization of buildings. Compared with the post-disaster images, the building contours in the pre-disaster images are more distinct to perform building segmentation. A single-branch U-Net-like network is used, which is shown in Figure 2. The Swin Transformer block [41] is the basic unit of the network. Patch partition is conducted for the encoder, in which the inputs are partitioned into non-overlapping patches of size 4 \(\times\) 4 pixels to generate sequence embeddings. Then, a linear embedding layer is introduced to flatten and project patches into dimension \(C\). These transformed patch tokens are put into the encoder for feature extraction, which consists of patch merging layers and the Swin Transformer blocks embedded with the SF module. The output feature map of the encoder is fed into a bottleneck consisting of two consecutive standard Swin Transformers, in which the number of channels and resolution of the feature map are kept constant. The decoder consists of standard Swin Transformer blocks and patch expanding layers, in which the patch expanding layer is responsible for up-sampling. By skip connections, the multi-scale features from the encoder can be fused with the extracted features to compensate for the loss of spatial information due to down-sampling. The network outputs a binary segmentation mask to locate the building objects before the disaster, and the results are used to guide the damage classification in the next stage.
# Mobility Management Based on Beam-Level Measurement Report in 5G Massive MIMO Cellular Networks
## 5 Numerical Results  
### Simulation Environment  
We consider mmWave massive MIMO networks with two-deployment scenarios: (1) single-gNB-single-sector with 23 dBm gNB Tx power case and (2) an ultradense-networks (UDN) environment with 23 dBm gNB Tx power case. In the UDN case, we consider 50 gNBs are deployed in \(0.1\times 0.1\) km\({}^{2}\) area, and each gNB has 3-sectors; i.e., 3-cells. Each cell has 18-candidate beams in the scenario of the single-gNB and 6-candidate beams in the UDN scenario. Each cell activates at least one beam corresponding to the attached users. We assume that an \(8\times 8\) ULA antenna of the cell and a \(4\times 4\) ULA antenna of the user are aligned for the maximum gain at the receiver. That is, the user always can find the best Rx beam to the Tx beam from the cell. There is always one stationary user per cell. Furthermore, there are one or five moving users with a uniform distribution in the area. The speed of all users is 16.7 m/s. Table 2 lists the other parameters considered for the simulation.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter Description** & **Symbol** & **Value** \\ \hline Carrier frequency & \(f_{c}\) & 28 GHz \\ System bandwidth & \(BW\) & 500 MHz \\ Pathloss probability coefficients & \(a_{\text{out}}\), but, \(a_{\text{LOS}}\) & \(1/30.0\) m, 52, 1/67.1 [32; 37; 38] \\ Pathloss coefficients & \(a_{\text{LOS}}\), \(f_{\text{LOS}}\), \(a_{\text{NLOS}}\), \(f_{\text{NLOS}}\) & 61.4, 2, 72.0, 2.92 [32; 37; 38] \\ Standard deviation for log-normal shadow fading & \(\sigma_{\text{LOS}}\), \(\eta_{\text{NLOS}}\) & 5.8 dB, 8.7 [32; 37; 38] \\ Time to trigger & \(TTT\) & 40 ms [16] \\ Entering and escaping threshold of state S1 & \(Q_{\text{out}k_{s}}\), \(Q_{\text{in}k_{s}}\), \(-8\) dB, \(-5\) dB [16] \\ Waiting timer to decide to escape from state S1 & \(T310\) & 50 ms [16] \\ Bits for SSB and CSI-RS ID & \(K_{\text{s}}^{\text{SBB}}\), \(K_{\text{s}}^{\text{CSI-RS}}\) & 6 bits, 4 bits [40] \\ Bits for RSRP and default MR & \(M_{\text{RSRP}}\), \(M_{\text{default}}\) & 7 bits, 184 bits [25; 40] \\ Bits for downlink BS command & \(M_{\text{BS}}\) & 28 bits [25] \\ Bits for downlink HO command & \(M_{\text{HO}}\) & 288 bits [25] \\ \hline \hline \end{tabular}
\end{table}
Table 2: System parameters.
# DOSS Protocol for Data Caching and Sharing Security in a 5G Network
## 3 Data Caching and Data Sharing Security in 5G
### Federated Authorization  
The end-user will be able to access services from numerous SPs controlled by Identity Provider (IdP) using the same digital identification [7]. Following a successful federatedauthentication between the UE and the IdP, the IdP will be able to send the FId and security context to the SP for end-user verifications and access authorization, [36]. This is supported by authentication and authorization mechanisms such as access controls, security protocols and the OAuth2 framework [37], which also supports Single Sign On (SSO) to provide a common login credentials for users across multiple systems.  
The access controls facilitate authorization by granting the end-user access to the services, verified against the user's profile and a permission list [38]. Some of the access controls used are Role-based Access Control (RBAC) [39], Encryption-based Access control EBAC [40], Attribute-based access control ABAC [41], and Capabilities-based Access control CBAC [42]. These access control mechanisms use different techniques to give different access rights, attributes, capabilities, and encryption to a subject to access an object in the form of security tokens.
## 4 Discussion
### Input Variables
1—air surface pressure (hPa), 2—air temperature (\({}^{\circ}\)C), 3—incoming photosynthetically active radiation (\(\mathrm{\SIUnitSymbolMicro m}\)/\(\mathrm{m}^{2}\)/s), 4—relative air humidity (%), 5—wind speed (m/s), 6—wind direction (deg), 7—net radiation (W/\(\mathrm{m}^{2}\)), 8—soil temperature at 5 cm (\({}^{\circ}\)C). Various models (LMI, GPRexp, GPR2, and NN) demonstrate similar results with comparison of the importance on the number of input variables. The air and soil temperature always show a better performance. The wind direction and wind speed are the most difficult variables for reproduction by any model. According to Figure 6 the increase in PCA input variable numbers above 16 variables does not result in any changes of the model's quality. [PERSON] et al. [34] demonstrated the importance of the input variable dataset for the gap-filling of the eddy-covariance derived methane fluxes for 17 FLUXNET [8] wetland sites. The marginal distribution sampling model had a good median performance even with a limited set of input variables, and artificial neural networks showed comparable performance when using all predictors. The configuration of the NN model is more complicated than other regression models. The number of hidden layers and number of neurons (nodes) can potentially change the model performance. The optimum architecture (number and size of a hidden layer) is related to the complexity of the input and output mapping, along with the amount of noise and the size of the training data [48]. The influence of the number of hidden layers and the size of the hidden layers on the NN model's performance were tested in the numerical experiments. The single layer, bilayered, and trilayered NN models demonstrate very close results in reproduction of meteorological variables. The number of neurons in the hidden layers does play a small role in the construction of the good NN model. Figure 8 shows the change in performance of the bilayered NN model with an increase in the size of the hidden layers from 1 to 100 neurons. It was found that a rise in the _KGE_ indicator occurs at an increase in the number of neurons from 1 to 5. A further increase in the hidden layer size does not lead to the model's improvement.