esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 System Model
### Technical Layer
#### 4.1.3 Cloud Layer  
The cloud layer enables higher authorities to access comprehensive data from registered institutions. This layer employs intelligent algorithms to allow top authorities to effectively oversee all the institutions. The cloud layer includes cloud servers and educational applications.  
(a) Cloud server: Education systems are interconnected by a network of fog servers and other specialized servers. This network enables the exchange of institutional data, records, videos, and other resources among the systems.  
(b) Educational applications: These applications run policies and formulas to efficiently manage the resources.
# A Low Phase Noise Frequency Synthesizer with a Fourth-Order RLC Loop Filter
## 4 Simulation Analysis
### Filter Performance Analysis  
The transfer functions of the two filters and their zero-pole point distributions are as follows:  
\[F_{1}(s)=\frac{1+R_{1}C_{2}s}{R_{3}R_{2}C_{4}C_{3}R_{1}C_{2}s^{3}+(R_{2}C_{3}R _{1}C_{2}+R_{2}C_{4}R_{1}C_{2}+R_{3}C_{4}R_{1}C_{2}+R_{3}R_{2}C_{4}C_{3})s^{2}+ (R_{1}C_{2}+R_{2}C_{3}+R_{2}C_{4}+R_{3}C_{4})s+1} \tag{7}\]  
\[F_{2}(s)=\frac{1+R_{1}C_{2}s}{C_{1}R_{1}L_{1}C_{2}s^{3}+(C_{1}L_{1}+L_{1}C_{2}+ C_{1}R_{1}R_{2}C_{2})s^{2}+(C_{1}R_{2}+R_{2}C_{2}+R_{1}C_{2})s+1} \tag{8}\]  
The filter's performance significantly depends on its zero-pole point distribution. In Table 3, the zero-pole point distributions of both filters indicate that the poles are placed in the left half-plane, demonstrating that both are stable systems. However, one pole of the passive fourth-order RC filter is closer to the imaginary axis than the passive fourth-order RLC filter. Thus, its decay time is relatively longer, leading to a slower transient response and degrading system performance [19]. The poles are determined only by the filter topology. The imaginary part of the poles corresponds to sinusoidal oscillations. The larger the value of the imaginary part, the higher the oscillation frequency, which means the more violent the oscillation. Accordingly, the superiority of the passive fourth-order RLC filter to the passive fourth-order RC filter is demonstrated.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Passive Fourth-Order RC** & **Passive Fourth-Order RLC** \\  & **Filter** & **Filter** \\ \hline \multirow{4}{*}{poles} & \(-4.2670\times 10^{7}\) & \((-8.1664+8.6381i)\times 10^{6}\) \\  & \(-0.6242\times 10^{7}\) & \((-8.1664-8.6381i)\times 10^{6}\) \\ \cline{1-1}  & \(-0.0348\times 10^{7}\) & \((-0.3503+0.0000i)\times 10^{6}\) \\ \cline{1-1}  & 0 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The closed-loop system poles.  
Figure 4: Circuit design: (**a**) circuit design of the PLL section and (**b**) the circuit board.  
In practice, the filter's amplitude-frequency characteristic curve cannot reach the rectangular shape in the ideal case. Therefore, the roll-off characteristic can be defined to measure the ideal degree of a filter, determining the transition zone between the passband and the stopband and the verticality degree of this transition zone, that is, the approximation degree of the rectangular response. As shown in Figure 5, the roll-off values of the passive fourth-order RC and RLC filters are 34.62 dB/decade and 39.95 dB/decade, respectively. The roll-off characteristic of the passive fourth-order RLC filter is more significant than that of the passive fourth-order RC filter, demonstrating the better filtering effect of the former.
# Towards Resolving Challenges Associated with Climate Change Modelling in Africa
## 2 Current Issues Facing Climate Change Modelling in Africa  
Many studies, including the Intergovernmental Panel on Climate Change (IPCC) report [30], have discussed or noted the challenges of climate change modelling for past and future scenarios. Some of the major limitations--especially in Africa--are related to data quality, availability, and accessibility [31; 32; 33]. The paucity of data sources directly affects research in the assessment of climatic conditions and changes, which directly impacts livelihoods [14; 34]. One of the primary sources of most climate data in Africa is a network of weather stations, which are scattered disproportionately across the landscape. The continent has a very low density of weather stations, with data not readily available [31]. In addition, the historical data gathered from this network span but a few decades and the records are typically riddled with missing information, incorrect capture, and incomplete conversion between the metric and imperial system. Moreover, most ground stations are concentrated in or near major cities or easily accessible locations, disregarding regions with rough or inhospitable terrain (e.g., mountains and deserts) [32]. Additionally, this observational network may be poorly maintained and rarely serviced, mostly due to limited investment in the respective countries' climate infrastructures [32].  
In addition to a low spatial resolution of stations, the storage of data is usually undertaken by the relevant government branch or even by private groups, which introduces accessibility challenges, either by legal restrictions, lack of knowledge of the pertinent branch that hosts the data repository, and/or high access costs [32; 35]. Thus, sharing of data beyond the initial user is rather limited. Furthermore, due to low financial investment,the availability of the latest products and tools is minimal, leading to a lack of dissemination of skills. Alternative options to the observation network include computational modelling, remote sensing, and geographical information systems (GISs). Over the last few decades, developments in geospatial techniques have aided researchers in visualizing the impacts of climate change. Furthermore, [PERSON] [36] highlighted challenges that African countries are facing with regards to the usage of earth observation data; these include poor investment in information and communication technologies along with infrastructure, a lack of capacity to process or use the available data, a lack of access to available data, and limited awareness in the private and public sectors of the mini-satellites launched by some African countries, amongst other challenges. The use of earth observation repositories for climate change modelling requires high-resolution data (i.e., spectral, temporal, spatial, and thematic resolution), which can be costly. In most cases, freely available earth observation data do not have high resolution, and this limits predictions in modelling activities.
## 6 Conclusions  
In this work, the performance of coherent THz links was investigated. Both closed-form solutions for BER and outage probability were obtained for such THz wireless links. Asymptotic solutions were also derived and revealed some interesting insights into the coherent THz wireless links. The presented results can be used to estimate coherent THz links performance effectively and rapidly over LN and GG composited channels. Numerical studies demonstrated that the derived closed-form solutions are highly accurate for coherent THz link system performance estimation. In conclusion, our exploration of coherent THz transmission technology provides valuable insights into its performance under various channel conditions. Incorporating green communication principles underscores the potential of THz systems in achieving low-power, efficient, and sustainable wireless communication.  
Conceptualization, methodology, original manuscript writing, supervision, funding acquisition, Writing--review & editing, M.N.; Validation & analysis, H.L.; Writing--original draft, analysis, H.W.; analysis, Writing--original draft & review & editing, R.J. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the Fundamental Research Funds for the Central Universities, CHD (Grant No. 300102384901), a Provincial High Level Thousand-Talent Project, Shaanxi, China (Grant No. 300201000156), and a National Key RD Program of China (Grant No. 2021 YFB1600205).  
Not applicable.  
The original contributions presented in the study are included in the article.  
The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# An Intelligent Real-Time Object Detection System on Drones
## 5 Conclusions  
In this research, we propose an intelligent object detection system on drones. We implement a power-efficient neural network engine on the FPGA and use it as the NN model accelerator. The image data are transferred by the host from the camera to the FPGA. To improve the accuracy of object detection, our intelligent detection approaches the target and generates images with more pixels for the target. Experimental results indicate that our system can achieve real-time detection at the speed of 8 FPS. Compared to other approaches, our work achieves low power consumption at 3.529 W with a lower latency of 130 ms. In addition, it can increase the detection confidence score from 0.74 to 0.90 and from 0.70 to 0.84 for persons and cars, respectively. In future work, we plan to apply our approach to various intelligent applications--such as smart city monitoring and fault diagnosis using AI techniques--and combine our design with state-of-the-art edge-computing and blockchain technologies to deal with data security issues while capturing images in real-time [34; 35].  
Conceptualization, C.C. and Z.W.; methodology, C.C.; software, H.M.; validation, Y.P.; data curation, Y.Y.; writing--original draft preparation, C.C.; writing--review and editing, Z.W. and Y.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Key-Area Research and Development Program of Guangdong Province (grant number 2019B010155003), National Natural Science and Foundation of China (NSFC 61902355), and the Guangdong Basic and Applied Basic Research Foundation (grant number 2020B1515120044).  
Not applicable.  
The authors declare no conflict of interest.  
Figure 12: The intelligent car-detection result.
## 4 Results
### Development Phase  
During the development phase, we distributed properly designed questionnaires concerning the identified KPIs shown in Table 1 to a group of two domain experts in academia and the maritime industry, respectively. The experts were tasked with conducting pairwise comparisons based on the six categories outlined in Table 1 that impact a smart port's performance in a neutrosophic environment. This comparison was undertaken using linguistic variables that were transformed into single value neutrosophic numbers based on a specific scale as shown in Table 2. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Perspective** & **Evaluation Indicator** & **Reference** \\ \hline \multirow{5}{*}{**Operational efficiency (OE)**} & Vessel turnaround time: the time taken for a ship to dock, unload/load cargo, & [51; 52; 53; 54; 55] \\  & and depart the port & \\ \cline{2-3}  & Dwell time: the duration cargo spends at the port, including storage and & \\  & processing times & [54; 55] \\ \cline{2-3}  & Berth utilization: the effectiveness of berth allocation and utilization, measured & [56; 57; 58] \\  & as a percentage of total available time & \\ \hline \hline \multirow{5}{*}{**Financial (F)**} & Revenue and profitability: financial metrics such as revenue, profit, and return & \\  & on investment, e.g., & \\ \cline{1-1}  & Total revenue = revenue from port operations + revenue from ancillary services & \\ \cline{1-1}  & Net profit = total revenue \(-\) total expenses & \\ \cline{1-1}  & ROI = (net profit/total investment) * 100\% & [54; 55; 56] \\ \cline{1-1}  & Profit margin = (net profit/total revenue) * 100\% & \\ \cline{1-1}  & Revenue growth rate = ((current year revenue \(-\) previous year revenue)/previous year revenue) * 100\% & \\ \cline{1-1}  & Operating expense ratio = (total operating expenses/total revenue) * 100\% & \\ \cline{1-1} \cline{2-3}  & Cost efficiency: cost per unit of cargo handled or cost per vessel serviced & [55; 56; 57; 58] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Classification of smart port evaluation indicators. During this stage, perspectives affecting smart port performance were drawn as nodes of the NCM to be analyzed. Next, according to their knowledge, each decision group member specified causal links between the identified concepts. Group members use neutrosophic language phrases to represent the strength of inter-concept causal linkages, which are subsequently assigned numerical weights. It is important to recall that in NCMs, the relation between two vertices might be considered indeterminate (unknown or unidentified), marked by \"_I_\".
# Dual-Band Wearable Antenna Integrated with Glasses for 5G and Wi-Fi Systems
## 2 Materials and Methods
### Antenna Design--Assumptions  
The primary objective of this project was the development of a dual-band antenna suitable for integration with eyewear. A key requirement was the non-destructive attachment of the antenna to existing spectacles, necessitating its placement on the temples. The height of the antenna should not exceed 14 mm, and its length should not be greater than 85 mm. The antenna's thickness should be less than 5 mm. This application demands a compact and lightweight antenna exhibiting satisfactory impedance matching and a potentially high gain, despite its proximity to the human body. To achieve these characteristics, a viable geometric configuration amenable to fabrication using thin-layer laminate technology with single-sided metallization was investigated.
# Energy-Efficient Check-and-Spray Geocast Routing Protocol for Opportunistic Networks
## 5 Results Analysis Using Synthetic Models
### Varying the Buffer Size  
The number of hosts is fixed at 195 and TTL is set to 120 min. The buffer size is increased from 65 to 520 Mb and the impact of this variation on the message delivery ratio and average latency are investigated. The results are given in Figures 6 and 7 respectively.  
GSAF. This is due to the fact that when the buffer size is increased, more messages are stored in a node, which leads to a better delivery ratio of messages to their final destinations.  
In Figure 7, it is observed that when the buffer size is increased, the average latency also increases, and in terms of latency performance, EECSG is about 5.6% better than CGOPP and 16% better than GSAF. This is due to the fact that a bigger buffer size stores more messages. This causes the messages to stay longer than usual and hence leads to an increase in average latency.
## 4 Results
### Understanding of the Results  
The vector-based state representation shows excellent results; however, a trained model with this approach cannot be used for solving any other networks since it does not know anything about the network layout. It just learns the given one correctly, hence does not carry the potential for generalization. Still, the network infrastructure in the real world does not change too often. Consequently, the generalization potential is not the only aspect in the assessment since the safe and efficient control always carries significant value. Furthermore, if the particular network has changed, it can be easily retrained with the new layout. In the meantime, the MCTS algorithm is considered a general solution for such a problem. Still, more extensive network architectures will clearly lack the required computational needs, which prevents the algorithm from the real-time operation. On the contrary, the image-like representations significantly support generalization. The first component comes from the action space. Still, as the results showed, the Flatland global observation is not sufficient when learning more networks at once, not to mention further generalization tests, such as operating on a totally unknown network. In the meantime, the proposed method successfully solved the control problem on both network layouts simultaneously. The sole and most important reason for that is the layout channel in the representation, whose existence enables the agent to understand the concept of the free path. Through that, it does not overfit a single network but learns which paths can be used or not. Furthermore, the goal channel also allows learning how to navigate the network flexibly. With all these advances, the proposed network architecture, in theory, can be capable of solving any network layout. However, this approach also has limitations that  
Figure 10: Comparison of the different solutions for both networks.  
arise from the fact that the image's height and width predetermine the size of the network that can be represented in the given grid-world.
# A Novel Transfer Learning-Based OFDM Receiver Design for Enhanced Underwater Acoustic Communication
## 4 Experimental Setup
### Benchmark Methods  
The benchmark models LS [9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19], MMSE [20; 21], and DNN [26; 27; 28; 29; 30; 31; 32; 33; 34] estimators offer different approaches to UWA channel estimation, each with its own strengths and limi  
Figure 12: Hardware connection block diagram of proposed system.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{227.6 pt}} \hline \hline
**Step** & **Details** \\ \hline Datasets Used & Watermark channels data for pre-training \\ \hline \multirow{2}{*}{Label Data} & Concatenated categorical labels from the corresponding datasets \\ \hline \multirow{2}{*}{Data Concatenation} & Training features of each watermark channel are concatenated \\ \hline \multirow{2}{*}{Training/Validation Split} & 80\% training, 20\% validation \\ \hline \multirow{2}{*}{Fine-tuning Data} & 50\% of data Qingdao data used for fine-tuning, split further into 80\% training and 20\% validation \\ \hline \multirow{2}{*}{Testing Data} & Remaining 50\% of input data Qingdao Lake data used exclusively for testing \\ \hline \hline \end{tabular}
\end{table}
Table 8: Training and validation data preparation.  
tations. These state-of-the-art methods are used as benchmark schemes for comparison, which are explained and analyzed in the following subsections.
# Designing a Cleavable Cell Surface Protein for Cytotherapy and Drug Delivery Applications
## 3 Results
### cmSA Cleavage by uPA  
To demonstrate the ability of cmSA to be cleaved by uPA, cmSA-C17.2 cells were incubated for 30 min in 10 \(\mu\)g/mL biotin (5-fluorescein) and washed thoroughly. Cells were then incubated with or without uPA and medium was removed at 5-min intervals to determine the amount of fluorescein released. Data showed significant release of fluorescein fluorescence within 45 min from the uPA treated cells and minimal release of fluorescence from the control cells (Figure 5, \(p=0.0014\)) showing that uPA is able to cleave the cmSA protein and release the N-terminal domain into the media.  
Figure 4: The cmSA protein can bind small molecules or nanoparticles containing biotin. (**A**): cmSA-C17.2 labeled with biotin (5-fluorescein); (**B**): C17.2 labeled with biotin (5-fluorescein); (**C**): cmSA-C17.2 labeled with PLGA-rhodamine-biotin; (**D**): C17.2 labeled with PLGA-rhodamine-biotin. **Green** = fluorescein fluorescence. **Red** = rhodamine fluorescence. **Insets**: A/B: fluorescein fluorescence without bright field overlay. C/D: rhodamine fluorescence without bright field overlay.  
Figure 5: Urokinase can cleave the cmSA protein. (**A**): IVIST\({}^{\text{TM}}\) images of media harvested from biotin (5-fluorescein) labeled cmSA-C17.2 incubated with or without uPA. (**B**): Numerical data from panel A with best fit curves to demonstrate the trends. The repeated measures statistical test demonstrated the difference between uPA and no uPA was highly significant (\(p=0.0014\)).
## 3 Results and Discussion
### Films Characterization
#### 3.1.2 Colour  
The colour of food packaging materials is an important index to be considered in terms of general appearance as it could influence the consumer acceptance [49]. Films containing ASE showed a visual slight yellow colour (Figure 2) in accordance with the significant (\(p<0.05\)) decrease in \(L^{*}\) (lightness) values and increase in \(a^{*}\) and \(b^{*}\) values observed with ASE addition (Table 1). Accordingly, the incorporation of ASE to PCL resulted in a remarkable increase in the total colour difference (\(\Delta E^{*}\)) which was more pronounced with increasing ASE concentration (\(p<0.05\)). These differences could be attributed to the natural yellowish colour of ASE. As a result, PCL6 film showed a higher \(\Delta E^{*}\) value compared to PCL3 formulation. The addition of plant extracts has been reported to modify the original colour of the biopolymers-based films to a certain extent; being the magnitude of this modification determined by the origin of the plant extracts and concentrations used. Phenolic pigments present in plant extracts are likely to contribute to the different colours observed in these biopolymer-based films [50]. Similar results were recently obtained for PCL-based films loaded with sag extract, suggesting that the lower lightness (\(L^{*}\)) values obtained with the addition of the extract (from 5 to 20 wt.%) might be helpful to prevent oxidative deterioration in packaged  
Figure 1: Surface (**a**,**c**,**e**) and cross section (**b**,**d**,**f**) SEM micrographs (1000\(\times\)) of films ((**a**,**b**): neat PCL; (**c**,**d**): PCL3; (**e**,**f**): PCL6).  
foodstuffs [51]. Moreover, the transparency of the developed films in this work was maintained even for PCL6 film compared to neat PCL, as it can be seen in Figure 2, which is an important issue in food packaging applications.