esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Connection with Limbic Structures
#### 3.4.2. Connection to the Hippocamp  
In addition to connections with the striatum and cortex, the SN/VTA also appears to be structurally connected to the hippocampus via specific fibers identified by probabilistic tractography. [PERSON] et al. (2021) provided a concise overview of the literature on this subject, concluding that SN/VTA projection neurons contribute only marginally to the formation of the hippocampus [47]. Nonetheless, indirect interactions have been demonstrated. For instance, the hippocampus, via the subiculum, exerts influence on the VTA/SNc, and conversely, dopamine from the midbrain modulates hippocampal function, which is important for novelty and memory consolidation [47]. A study by [PERSON] et al. (2022) [67] demonstrated that the degree of SN/VTA-hippocampus connectivity exhibited a correlation with performance on a task requiring motivated memory encoding. That is to say, the study revealed that learning was influenced by the expectation of reward or punishment. Conversely, SN/VTA connectivity to the striatum was not significant in this cognitive context. The results of this study indicate that individual dopaminergic mesencephalic projections are not only anatomically distinct, but also exhibit functional specialization, in this case to influence memory processes [67].
# Artificial Intelligence of Things for Solar Energy Monitoring and Control
## 4 Discussion
### Future Aspects
#### 4.2.2 Integration of Emerging Technologies  
The future of AIoT-powered solar energy systems will be shaped by the convergence of emerging technologies such as Edge AI, Federated Learning, Blockchain, Digital Twins, and 6G networks--enabling smarter, more secure, and efficient energy management. Edge AI will minimize reliance on cloud infrastructure by processing data locally on IoT devices, reducing latency and energy use while enabling real-time decision-making. Federated Learning will address privacy concerns by training AI models across distributed solar installations without sharing raw data, enhancing both security and model accuracy. Blockchain will play a critical role in securing energy transactions, supporting transparent peer-to-peer trading, and enabling decentralized grid operations. Digital Twins--virtual replicas of PV systems--will allow operators to simulate performance, predict failures, and test control strategies without physical risks. The rollout of 6G networks will further enhance device connectivity, delivering ultra-fast data transfer, improved synchronization across distributed systems, and more reliable AI-based automation. Together, these technologies will drive the next generation of AIoT-enabled solar systems, making them more autonomous, scalable, and resilient to both environmental and operational challenges.
## 2 Height Factor Model for GNSS Tomography
### Dynamicity of the Tomography Top Boundary  
The top boundary serves as a key factor for the GNSS water vapor tomography model. On the one hand, signals are classified into two categories (top and side signals) according to the positional relationship between themselves and the top boundary. It is worth noting that a higher top boundary leads to a lower number of top rays in the traditional tomography model, according to [PERSON] and [PERSON] [36]. On the other hand, redundant voxels where the WVD is close to zero will be introduced into the tomography system with an increasing top boundary. As a consequence, it is essential to identify an appropriate tomography top boundary to utilize more signals and avoid unnecessary voxels.  
Water vapor, having an approximately exponential decreasing trend with altitude, is considered to be a criterion for determining the top boundary. In [PERSON] and [PERSON] [14], the principle that a certain height with a WVD of less than 0.2 g/m\({}^{3}\) can be considered as the top boundary of the tomography model is proposed.  
In this study, the idea of determining the optimal tomography top boundary based on the water vapor vertical distribution derived from 30 years (1989-2018) of radiosonde data at Hong Kong King Park station (114.17\({}^{\circ}\)E, 22.31\({}^{\circ}\)N) is introduced. Accordingly, the water vapor profiles of different months in Hong Kong are shown in Figure 1.  
Figure 1: The distribution of water vapor derived from 30 years of radiosonde data in the atmosphere for different months. (**a**) The vertical distribution of atmospheric water vapor. The black ellipse in (**a**) represents the outliers; (**b**) enlarged view corresponding to the blue rectangle in (**a**). Blue vertical lines in (**b**) correspond to the principle that the water vapor density (WVD) is 0.2 g/m\({}^{3}\), and the blue horizontal lines denote the optimal top boundary during different months.  
It can be seen that the distribution trend of water vapor in the atmosphere varies greatly in different seasons. Evidently, the optimal top boundary in summer and autumn is higher than that in spring and winter. The mean height values of the tomography top boundary for the four seasons (spring corresponding to March to May, summer corresponding to June to August, autumn corresponding to September to November, and winter corresponding to December to February) are approximately 10.2, 10.9, 10.6, and 9.6 km, respectively. As a consequence, the tomography top boundary changes in different seasons, even for the same region, which can be deduced from this research. What is more, the dynamic top boundary of the tomography area, particularly in regions with complex weather conditions, should be determined from the local water vapor profiles in different months.
## 2 Materials and Methods  
### Study Design and Imaging Data  
This was a retrospective study with methods based on similar studies of comparison of performances between the atlas-based and DL auto-contouring for the OARs segmentation in the head and neck RT [5; 6; 10; 27]. Planning CT datasets of 45 head and neck cancer patients who had RT treatments between 2018 and 2021 at Pamela Youde Nethersole Eastern Hospital in Hong Kong Special Administrative Region were retrospectively collected from Eclipse treatment planning system (Varian Medical Systems, Palo Alto, CA, USA). Patient inclusion criteria were: 1. head and neck cancer histologically proven and 2. radical radiotherapy received. Patients with pre-radiotherapy surgery were excluded. Table 1 shows the patient characteristics. The study was conducted in accordance with the Declaration of Helsinki, and approved by the Human Research Ethics Committee of Curtin University (approval number: HRE2022-0582 and date of approval: 18 October 2022), Institutional Review Board of The Hong Kong Polytechnic University (approval number: HSEARS20220815001 and date of approval: 7 October 2022), and Research Ethics Committee of Hong Kong East Cluster of Hospital Authority of Government of Hong Kong Special Administrative Region (approval number: HKECREC-2022-054 and date of approval: 6 October 2022). Patient consent was waived due to the retrospective nature. The collected CT datasets were acquired with the following parameters, slice thickness of 2 mm, 400 mAs, 120 kV and field of view of 600 mm in accordance with the routine protocol of Pamela Youde Nethersole Eastern Hospital [27]. Figure 1 shows the overview of study design.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Characteristics** & **Value** \\ \hline _Gender (n = 45)_ & \\ Male & 35 (78\%) \\ Female & 10 (22\%) \\ \hline _Age (n = 45)_ & \\
18–65 years & 29 (64\%) \\ \textgreater{}65 years & 16 (36\%) \\ \hline _Tumor site_ & \\ Nasopharynx & 45 (100\%) \\ \hline _Tumour classification_ & \\ T1 & 13 (29\%) \\ T2 & 3 (6\%) \\ T3 & 17 (38\%) \\ T4 & 12 (27\%) \\ \hline _Node classification_ & \\ N0 & 13 (29\%) \\ N1 & 13 (29\%) \\ N2 & 9 (20\%) \\ N3 & 10 (22\%) \\ \hline _Systemic treatment_ & \\ Yes & 37 (82\%) \\ No & 8 (18\%) \\ \hline _Treatment technique_ & \\ Volumetric arc therapy & 45 (100\%) \\ \hline _Neck irradiation_ & \\ Bilateral & 45 (100\%) \\ \hline \hline \end{tabular}  
* Figures in parentheses are proportions.  
\end{table}
Table 1: Patient characteristics (\(n=45\)).
## 3 Results and Discussion
### Adsorption Isotherms
This stronger interaction indicates that these MPs have a higher potential for the stable adsorption of 4-MBC, although the environmental persistence of microplastics raises concerns regarding their use as adsorbents. In conclusion, the isothermal adsorption models applied in this study offer a comprehensive understanding of the mechanisms governing 4-MBC adsorption on both biochar and microplastics. Biochar shows promise as an efficient, sustainable adsorbent with adjustable properties, while microplastics, despite their adsorption potential, present environmental risks. The study highlights the need for tailored adsorbents based on the specific pollutant and environmental conditions, with biochar emerging as a more environmentally friendly and efficient option for organic pollutant removal [52; 53; 60; 61; 62; 63].
## 3 Link Level Simulation Model for Interference Evaluation in 5G UDN with LAB  
The distinguishing feature of the proposed simulator is that gNB knows current UE location and both UE moves along predefined trajectories, which are set by the parameters of terrestrial and angular separation. Such approach allows to scientifically substantiate practically realizable limits of UE SDMA. Section 3.1 considers SIR dependence evaluation on terrestrial separation and Section 3.2 on angular separation of two gNB-UE links. Section 3.3 describes briefly transition from the link to system level simulation model for interference evaluation in 5G UDN with LAB.  
It should be noted, that general structure of presented link level simulation model was already detailed in [1]. However, investigation [1] utilized built in functions of MathWorks Phased Array System Toolbox [75]. Presented in the current work open-source simulator utilizes only primitive MathWorks functions and is available at [76].
# Grid Anchor Lane Detection Based on Attribute Correlation
## 4 Experimental Results
### Ablation Study
#### 4.3.3 Impact of Loss Function on Model Performance  
Section 3.3 introduced the anchor-based loss function used to converge the model. The loss function consists of two parts: one part is the intra-anchor loss function \(L_{si}\), which is constructed using cross-entropy and L1 smooth loss functions, and the other part is the inter-anchor loss function \(L_{so}\), constructed using the L1 smooth loss function. To verify the effectiveness of the inter-anchor loss function, we selectively incorporated \(L_{so}^{ed}\) and \(L_{so}^{ok}\) on top of \(L_{si}\) to converge the model. The experiments were performed on the fully improved FPN model using different combinations of loss functions.  
Table 8 shows the model's detection accuracy under different loss functions. It can be observed that both \(L_{so}^{ed}\) and \(L_{so}^{ok}\) improve the model's detection accuracy. This is because both of these loss functions calculate the difference between predictions and labels, and their gradients for backpropagation are similar to those of \(L_{si}\), thus driving the model towards the same direction for convergence. Unlike \(L_{si}\), \(L_{so}^{ed}\) and \(L_{so}^{ok}\) consider the loss values of neighboring anchors, balancing the gradients across all anchors, and ensuring that the model learns consistently across different image regions. Therefore, when both \(L_{so}^{ed}\) and \(L_{so}^{ok}\) are applied, the convergence effect is the best, resulting in a 0.61% increase in detection accuracy.
# Detection and Classification of Bearing Surface Defects Based on Machine Vision
## 4 Experiment  
### Experimental Design  
The test object of this paper is the outer ring of 6204 deep groove ball bearings (6204 bearings, outer diameter 47 mm, inner diameter 20 mm, height 14 mm, weight 0.11 kg, Cr: 15.8 KN, Cor: 7.88 KN, DW: 9.525 mm, Z: 7). Based on the experimental requirements, the detection system consisted of a lower machine and the main computer. The lower machine included a programmable logic controller (PLC), DALSA linear array camera, linear light source, and self-made loading and unloading control mechanism. The main computer was a Dell Precision T3630 graphics workstation (Intel i7-10700K, 8-core, 16 lines, 3.8 G, 32 G memory, and a Windows operating system), The programming languages used were MATLAB and C++. The lower machine was responsible for digital IO (input and output) and motion control, and the main computer was responsible for image acquisition, image processing, and the resulting output. In operation, the bearing to be inspected was automatically placed on the turntable and centered, the image acquisition switch was turned on, the encoder drove the turntable to rotate at a constant preset speed, the line light source was turned on, and the line array camera automatically-collected images and sent them to the main computer, which performed various operations and processing on the image, positioned the image under test, detected and identified the inspected object, and generated motion control decisions based on the processing results.  
In the test of model 6204 bearing, three types of commonly occurring surface defects in the machining process of the outer ring, namely over-grinding, scratches, and bruises, were selected as identification objects. A total of 900 defect samples were collected, with 300 of each type of defect. Some examples of the defects are shown in Figure 7.  
Figure 11: Region segmentation process with multi-neural network: (**a**) initial segmented region; (**b**) morphological processing result; (**c**) boundary polygon; (**d**) multi-neural network region.  
Figure 10: Multi-scale feature images.  
classified were classified using the trained neural network to obtain pixels belonging to the target region, as shown in Figure 12d. By performing this operation on all small regions, we obtained the target pixels in the region to be classified. By combining all the target pixels with the original region, the final segmentation result was obtained.
# The Challenges and Compatibility of Mobility Management Solutions for Future Networks
## 5 Recent MM Techniques and 5G Architecture
### Considerations for SDN-based MM Solutions
#### Considerations for phantom cell approach  
In the phantom cell approach, parameters DL4 and DL5 are fulfilled for the dependability criteria. Next, for the adaptability criteria, parameters AL2, AL4 and AL5 are satisfied. Further, regarding fulfillment of extensibility criteria, the phantom cell technique fulfills parameters EL2, EL3, EL4 and EL5. Furthermore, regarding power-saving conditions, the phantom cell technique fulfills the parameters PL1, PL2, and PL3.
# A Study on Design Requirement Development and Satisfaction for Future Virtual World Systems
## 2 Trend Analysis
### Immersion  
The user's feeling of immersion is an important trend to study, for it generates a sense of presence [13]. By observing the evolution of immersive technologies and cross-sensory technologies, two trends can be found. First, the market evidence in consumption (Figure 5a, data source: [35]) and revenue (Figure 5b, data source: [36]) shows the increasing trend of immersive hardware adoption, as VR, AR, and MR are three typical immersive peripheral technologies. Second, the recent innovation in multiple sensorial media (mulsemedia) [37] provides users more sensorial experience than visual and audio delight, leading to a deeper experience of immersion. Figure 5c (data source: Supplementary Materials) shows the trend of new mulsemedia device release in the haptic rendering, olfactory rending, and gustatory rendering market. Overall, the figures for all device categories are growing in the long run, especially since 2012. Haptic devices gain the largest traction in growth, since they cover a much wider market with many types, including haptic gloves, chair, vest, etc., which is consistent with the haptic device market value growth (Figure 5d, data source: [38]). Some VR headsets integrating the multisensory function can provide multiple sensorial experiences (besides audio and video experience). For example, OhRoma (camsoda.com/products/ohroma/) provides different smells during video or audio playing. Feelreal (feelreal.com) can simulate both smell and wind by changing airflow. These trends show the increasing adoption of immersive rendering techniques, which enriches user experience.  
Figure 4: Trends of interconnectivity: (**a**) wearable device sales revenue worldwide from 2016 to 2022; (**b**) forecast unit shipments of wearable devices worldwide from 2017 to 2022; (**c**) social gaming revenue worldwide from 2010 to 2014; (**d**) value of the social gaming industry worldwide from 2011 to 2020.
# Assessment of Energy Use and Photovoltaic Energy Potential in Saudi Arabian Governmental Schools
###### Abstract  
Adopting photovoltaic (PV) systems in government schools across Saudi Arabia presents an opportunity to reduce energy costs and contribute to the country's RE goals. In this paper, the energy consumption and energy consumption indicators of 3 schools in Qassim region (the central region of the Kingdom of Saudi Arabia) were determined. The integration of PV systems into the buildings of these schools was also studied to achieve zero energy and zero bills along the system's life cycle. The analysis considered the effects of temperature and other factors on PV system output and a projected 1% annual increase in school load. Energy use intensity ranged from 22 to 48 kWh per square meter, while per capita energy use varied between 337 and 630 kWh. Values for end-use of electricity of 80%, 11%, and 9% were obtained for air conditioning, lighting, and others, respectively. The results note that the zero-energy scenario's technical, economic, and environmental indicators are appropriate. The indicators in the zero-billing case were similar to the energy-zero scenario except for the payback period, which was longer and, in some cases, not economically feasible. The results show that economic evaluation must be revisited by reviewing the tariff value for selling surplus energy to the distribution network. The study also recommends scaling this model to other educational institutions, contributing to sustainable energy transitions in Saudi Arabia.  
energy consumption; photovoltaics; Saudi Arabia; school; solar energy Article
# Lemongrass Essential Oil Components with Antimicrobial and Anticancer Activities
## 3 LEO Therapeutics
### Antimicrobial Potential  
Lemongrass oil and extract are effective against a wide variety of disease-causing microbes. LEO has been used as an antibacterial [23; 34; 35], antifungal [32; 36], and an antiviral [23; 37; 38] agent on many occasions. Similarly, lemongrass extract inhibited the growth of _Bacillus cereus_, _Escherichia coli_, _Klebsiella pneumoniae_, _Candida albicans_, and _Staphylococcus aureus_ with different levels of susceptibility [39]. The EO components with different functional groups exhibit different levels of antimicrobial potential, where phenols and aldehydes have the highest activities, while esters and hydrocarbons have the least [40]. However, the antimicrobial activity of lemongrass is extensively attributed to the citral (aldehyde) present in its oil [28; 41; 42]. Yet, when a mixture of principal oil components and the whole EO were tested, the whole EO exhibited enhanced antimicrobial efficacy [43].  
[PERSON] et al. [43] studied 1114 strains of different microbes including moulds, yeasts, and bacteria from 29 genera and 105 species, and circled out about 425 LEO-sensitive microbial isolates. It is suggested that a low concentration of LEO inhibits microbial growth and development (bacteriostatic, fungistatic, and virstatic), while a higher concentration renders irreversible destruction leading to microbial death (bactericidal, fungicidal, and virucidal) [43; 44]. Furthermore, one study addressed LEO antimicrobial potential against 42 microorganisms, including 20 bacteria, 15 fungi, and 7 yeasts [45].  
In microbial studies, IC\({}_{50}\) (half maximal inhibitory concentration) and MIC (minimum inhibitory concentration) values are two important markers that can be considered to determine the antimicrobial potential of any chemical. The IC\({}_{50}\) of a drug is the concentration that can bring a 50% reduction in the microbial activity, therefore it may be cytostatic but bacteria can recover soon thereafter. In addition, concentrations are not always assessed precisely but are usually tested on a scalar dilution, from 1 to 10, and so on. However, it is more informative to know the MIC of a drug or plant extract, i.e., the concentration at which no visible growth of a microbe is detected. The MIC of LEO and citral against planktonic _Staphylococcus aureus_ were noticed to be 0.0781% (\(v/v\)) and 0.0313% (\(v/v\)), respectively [44]. Similarly, LEO had higher efficacy (MIC--0.65 % \(v/v\)) against _Acinetobacter baumannii_ strains over citral (MIC--0.14% \(v/v\)) [45]. This implies that its minor components, including limonene, linalool, and myrcene have specific as well as a synergistic mechanism with major components and can play a decisive role in augmenting oil effectivity [45; 46; 47]. In this regard, Table 1 addresses IC\({}_{50}\) and MIC of _CymDopogon citratus_ (DC.) Stapf EO and citral against some common microbes as a variable for their antimicrobial efficacies.
# Development and Optimization of Deep Learning Models for Weapon Detection in Surveillance Videos
## 3 Methodology
### Dataset Selection  
In machine learning and computer vision applications, datasets play a significant role because without good datasets the algorithms will not be able to perform well because it is the only way a model learns to see the world with an eye. Therefore, the collection of a good quality dataset is an important task. The dataset used for the training and testing is the same as the previous weapon detector model consisting of a total of 8327 labeled images split into training and test datasets, as given in Table 1.  
The dataset is collected by keeping in view the constraints of real-time detection scenarios where different kinds of colors, backgrounds, occlusion, flipped, and rotated objects might appear. This dataset is generalized because images captured have all kinds of background, resolutions, and occlusion that will help the model to generalize well and perform better in an unseen and unknown environment.  
The dataset is categorized into two main categories; the first is the pistol and the second is the non-pistol category. The mAP of a model can be improved by reducing the number of false negatives and false positives. Therefore, along with the weapon class, we also included the other objects with which our model could confuse the pistol class. For the weapon category, the dataset includes revolvers, pistols, shotguns, and rifles, as shown in Figure 1 below.  
Likewise, in the non-pistol class, we included items such as bicycles, cars, purses, selfie sticks, smartphones, etc. to help the model understand other similar things so that it can generalize well. Some samples of the dataset from non-pistol category are shown in Figure 2 below.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Category** & **Data Distribution** \\ \hline Total Dataset & 8327 \\ Training Data & 7328 \\ Test Data & 999 \\ Split Size & 12\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dataset Distribution.