esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Location Selection Model of 5G Micro Base Station Based on Smart Street Lighting System
## 4 Location Selection Model of 5G Micro Base Station
### Basic Assumption and Location Selection Principle
#### 4.1.2 Location Selection Principle  
The deployment of 5G base stations meets the network service requirements of intelligent city construction, and the objective function was constructed according to the following location selection principles.  
_The priority principle of coverage requirement:_ The coverage requirement proposed by the operator is the minimum to ensure that the base station can provide standard services for users and is the most fundamental goal of building a base station, which serves as the priority of the location selection principle.  
_Principle of lowest economic cost:_ On the premise of meeting the coverage requirements, it is usually expected to build the least number of base stations, improve the utilization rate of base stations, and save construction and operation, as well as maintenance costs to the maximum extent.  
_Principle of highest coordination:_ Mounting as many devices as possible on the same light pole can reduce construction costs and land resource occupation. The high degree of coordination between the mounted devices benefits the synergy effect between subsystems and reduces the cost of equipment deployment operation and maintenance. The high degree of uniformity can reduce the production cost of smart street lighting system functional devices and beautify the city's image. On the premise of meeting coverage requirements and economic costs, the degree of coordination is a crucial consideration for location selection.
# A Dense Small Object Detection Algorithm Based on a Global Normalization Attention Mechanism
## 4 Conclusions  
This paper proposes the GNYL (Global Normalization Attention Mechanism You Only Look Once) object detection network based on the YOLOv8l detection network, specifically targeting the task of detecting dense small objects in aerial images. Firstly, the GNAM is proposed, which processes the input features using a channel attention unit, which utilizes scale factors from batch normalization (BN) to highlight features based on the variance measurement of the training model weights, and then inputs them into a spatial attention unit, which incorporates spatial information using two convolutional layers, further emphasizing the spatial information. The interaction ability between the channel and spatial information is improved, fully exploits effective information for classification and localization tasks within the input features, and enhances the richness of detail in the GNAM's output features. In order to increase the richness of detail of GNAM's output features, the backbone network and feature enhancement network are redesigned to address the challenges posed by small objects in aerial images. This involves increasing the amount of feature information inputted into the detection heads and adjusting the number and density of anchor boxes. These modifications aim to improve the accuracy of small object detection.  
The performance of the proposed GNYL network is evaluated using the publicly available aerial dataset VisDrone2019. The GNYL network significantly improves the detection accuracy compared to the baseline network. GNYL achieves the highest mean average precision (mAP) for 9 of 10 object categories in the VisDrone2019 dataset among various object detection networks. Experiments have shown that GNYL has strong practicality in dense small object detection tasks.  
Conceptualization, Y.Z. and H.W.; methodology, Y.Z.; software, Y.Z.; validation, H.W., Y.Z. and L.W.; formal analysis, Y.Z.; investigation, H.W.; resources, H.W.; data curation, Y.Z.; writing--original draft preparation, Y.Z.; writing--review and editing, Y.Z.; visualization, Y.Z.; supervision, H.W.; project administration, H.W.; funding acquisition, H.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflict of interest.
# Integration of Microstrip Slot Array Antenna with Dye-Sensitized Solar Cells
A compatible integration of a circularly polarized omnidirectional metasurface antenna with solar cells has been reported in [20]. While the antenna gain of type IV is 4.1 dBi, it can be predicted that the output power generated by the solar cell array is not high due to the fact that the solar cells are not connected to each other, which limits its practical application. In this paper, an integration of a \(2\times 2\) circularly polarized microstrip slot array antenna with dye-sensitized solar cells is designed. A novel stack design method makes the solar cell and array antenna well integrated and the simulation and measurement results show that the gain of the array antenna increased by 0.12 dB, reaching 6.60 dBi, and the axial ratio was reduced to 1.50 dB after the integration of dye-sensitized solar cells. The solar cells and the microstrip slot array antenna are perfectly integrated. The integration saves a lot of space, especially when the proposed antenna is used in satellite communication. Compared with the existing circularly polarized microstrip slot array antenna, the proposed antenna adds the output of dye-sensitized solar cells into the voltage regulation circuit to form a stable power supply for the radio frequency system, which ensures the operation of the microstrip slot array antenna. In other words, the antenna has the ability of self-sustaining power generation capabilities, so as to provide reliable and long-term communication for the communication system when the power is not easy to obtain.
## 4 Discussion
### Smartphone and Gait
#### 4.2.4 Effect of Smartphone Use on Cognitive Function  
Three studies have investigated the impact of smartphone use on cognitive function [25; 26; 41]. One such study evaluated text comprehension and cognitive flexibility among 30 participants engaged in various tasks, including sending text messages, reading texts on smartphones, and writing tasks [26]. The results indicated that engaging in dual tasks involving texting while walking resulted in a reduction of approximately 23% in text comprehension compared to single-task writing or dual-task conditions involving simultaneous writing and texting with a smartphone [26]. The resultant decrease in gait stability during dual-task performance may contribute to diminished text comprehension, thereby impairing executive function and reflecting an overall decline in cognitive performance.  
Moreover, additional research has shown that continuous smartphone use adversely affects cognitive function, correlating with decreased ability to walk in a straight line, attention deficits, heightened collision risk, and an increased likelihood of traffic accidents [25]. These findings suggest that activities such as reading or sending text messages may exacerbate gait variability, indicating a reduction in cognitive flexibility. Other studies have similarly reported a heightened risk of falls associated with cognitive impairment, particularly noting challenges in executing motor tasks, such as ascending and descending stairs [41].
# Passive Detection of Low-Altitude Signal Sources Using an Improved Cross-Correlation Algorithm
## 5 Conclusions  
Passive detection of low-altitude signal sources was proposed using an improved cross-correlation algorithm. The DOA and communication frequency estimation of these signals was studied. An algorithm was developed to estimate the SNR and the distance between the signal source and receiver. A UAV was considered in the experiments as a typical low-altitude signal source. The results obtained show that the proposed method has a low false alarm probability as well as a low missed alarm probability. UAV parameters such as communication frequency, DOA, distance, and SNR were estimated accurately in both outdoor and indoor environments using this method. In addition, the proposed algorithm provides better performance than several well-known passive detection algorithms in the literature. Thus, the proposed method is more suitable for the long-distance passive detection of low-altitude signal sources.  
[PERSON] conceived and designed the passive detection algorithm, implemented the experiments, and analyzed the data; [PERSON], H.Z., [PERSON], and T.A.G. provided comments on the paper organization, contributed towards the performance results and evaluation, and were involved in writing the manuscript.  
This work was supported by the National Natural Science Foundation of China (61871354, 6172780176, 61701462, 61727806, and 41527901), the Qingdao National Laboratory for Marine Science and Technology (2017 ASKJ01), the Qingdao Science and Technology Plan (17-1-1-7-jch), the Nature Science Foundation of Shandong Province (ZR2017 MD027), and the Fundamental Research Funds for the Central Universities (201713018).  
The authors declare no conflicts of interest.
# Recent Advances on Surface Modification of Halloysite Nanotubes for Multifunctional Applications
## 3 Application of Surface Modification of HNTs
### As the Nanocarriers for Drug Delivery  
HNTs are environmentally friendly natural nanomaterials with low cost, high porosity, adjustable surface chemistry structure [40], good biocompatibility [41], and a large surface area. HNTs have huge development prospects in the field of drug capacity as a sustained manner. Hence, HNTs attracted a lot of attention in biological medicine, biological science, and technology. HNTs were used as multi-purpose excipient to improve the stability of drugs and to achieve controlled release [42]. They possess a special periodic multilayer with the structure of gibbsite octahedral (Al-OH) in internal surface and siloxane (Si-O-Si) on external surface [43]. HNTs have great application value in alternative modification with organic and inorganic functional molecules at different surfaces.  
Some meaningful research advances were successively reviewed in the drug delivery of HNTs. For example, the chemical or physical modified HNTs as nanocontainers for encapsulation the bioactive molecules, such as dexamethasone, tetracycline, furosemide, gentamicin, and nifedipineas. The loaded capacities and sustained drug delivery were demonstrated by [PERSON] et al. [44]. Except for drugs, the protein or nucleic acids are also loaded into the lumen surface of HNTs [45]. In addition, the outer surface covalent modified HNTs have improved the loading capacities of bioactive molecules, such as DNA, proteins, and other macromolecules [46].  
The modified HNTs showed better effect of drug loading than unmodified ones. [PERSON] et al. [47] used octadecylphosphonic acid (ODP) to modify halloysite nanotubes (halloysite-ODP) to load ferrocene by the crosslinking method. The results showed that halloysite-ODP exert more accolloidal stability in the aqueous suspension than the unmodified HNTs. When comparing with HNTs, the halloysite-ODP possesses higher adsorption capacity and faster assimilate for hydrophobic molecules of ferrocene. There have a small initial burst release for unmodified HNTs because of the dissolved ferrocene to the HNTs surface. Halloysite-ODP showed a two-step release with a non-Fickian model.  
Besides, HNTs were modified with \(\gamma\)-aminopropyltriethoxysilane (\(\gamma\)-APTES) to enhance the ability of loading analgesic [48]. The results demonstrated that the modified HNTs showed a much higher capacity. Furthermore, the modified HNTs have a long time sustaining release that reached 115 h at different pH values. In addition, the functionalized HNTs crosslinked with the APTES to load ibuprofen [49], because of the low loading capacity and burst release of HNTs. The results showed that the modified HNTs possess higher capacity to load ibuprofen increasing by 25.4% [50]. The release behavior of ibuprofen indicated that the modified and unmodified HNTs put up two-step release in vitro. However, the modified HNTs showed slower releasing than unmodified ones due to strong electrostatic interactions.
# Automatic TV Logo Identification for Advertisement Detection without Prior Data
## 2 Literature Review
This means that a fine-tuning step is required when applying this method to different channels that are not part of the initial training process. A recent approach that does not require previous trained models is presented in [3]. It also follows the general premise that the presence of the channel logo is correlated to the content being a program and not an advertisement. The logo identification process is based on an analysis of digital on-screen graphics, which are watermark-like digital objects that are displayed on top of the transmitted content. It uses an unsupervised approach for detecting logo candidates on the image through an analysis of edge maps obtained from the frame corners. Edge maps are calculated at each frame and are inserted into a database depending on their characteristics. The reasoning between distinguishing general logos from channel logos is made through a series of thresholds that need to be fine-tuned to provide the best performance. Results show that the method is able to obtain interesting results on the author's testing dataset. However, the videos used in the testing are very short and contain manually introduced advertisements, which is not a good representative of real broadcasts. Furthermore, results are not compared with state-of-the art methods. This analysis of state-of-the-art methods for advertisement detection shows that the general consensus is that the channel logo can be a good indicator for identifying programming content. The dual of this observation is that whenever the channel logo is absent, the content belongs to the advertisement. Even though it is a common trait in Europe, this concept is not followed by every television station worldwide. Methods for logo detection mostly follow a supervised approach, where it is assumed that a database of target channel logos can be either produced manually or given as input, which implies less flexibility or a heavy burden in data preparation. This shows that there is a need for a truly automatic procedure that self-learns the characteristics of the logo and automatically trains a classifier targeted for each processed stream.
# A Dense Neural Network Approach for Detecting Clone ID Attacks on the RPL Protocol of the IoT
## 3 Related Work
\begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Category** & **Description** & **Taxonnies** & **Memory Complexities Reported \({}^{*}\) \\ \hline \multirow{4}{*}{ Centralized} & Uses a powerful central Base Station (BS) to track each node position and its neighbours identity & Key usage-based & \(O(d),O(d\sqrt{\pi}),O(log(n)),O(1),O(N)\) \\  & Uses a powerful central Base Station (BS) to track each node position and its neighbours identity & Base station-based & \(O(n)\) \\  & when joining to the network & Neighbourhood social signature-based & \(O(t)\) \\  & When joining to the network & Cluster head-based & \(O(t)\) \\  & & Zone-based & \(O(t^{2})\) \\  & & Neighbour ID-based & \(O(n)\) \\ \hline \multirow{4}{*}{Distributed} & \multirow{4}{*}{Clone replication is applied to all network nodes with no central Base Station (BS)} & Node to network broadcasting & \(O(1)\) \\  & & Witness node-based & \(O(g),O(d),O(\sqrt{\pi}),O(\omega)O(tk+t^{\prime}k\sqrt{\pi}),O(t+t^{\prime} \sqrt{\pi^{\prime}})\) \\  & & & \(O(\sqrt{\pi}\log(n)),O(1)^{2},O(h),O(1)^{2}\) \\ \cline{1-1}  & Clone replication is applied to all network nodes with no central Base Station (BS) & Generation- or group-based & \(O(1),O(m),O(d+2m),O(2+22m(1+D_{max})),O(r,\sqrt{\pi}),O(r)\) \\ \cline{1-1}  & & Neighbour-based & \(O(r)\) \\ \cline{1-1}  & & Clustered-based & \(O(k,e)\) \\ \cline{1-1}  & & Whiteness path-based & \(O(\frac{1}{2})\) \\ \cline{1-1}  & & Cluster head-based & \(O(\frac{1}{2})\) \\ \hline \hline \end{tabular}  
* results expressed in BIG-O notation.  
\end{table} 
Table 3: Centralized and distributed techniques for corrective controls in the detection of Clone ID Attacks. hence, the RPL protocol was out of scope of their paper. In a more conscientious work [45], a DNN in consolidation with a Self-Taught Learning (STL) framework was developed, with two main tasks to cluster different RPL attacks: Feature learning and dimensional reduction, with different Autoencoder configurations. The results revealed that the STL can group attacks into four categories with an improved feature analysis: DoS, Probe, R2L, and U2R attacks. Notwithstanding its recommendation statuses, impersonation remained undetected. Although former approaches have tackled some RPL attacks on various DODAG fields using ML, the Clone ID attack has been noted as warranting subsequent exploration. Table 4 summarizes the algorithms and data acquisition from the previously discussed work.
# 5G/B5G Service Classification Using Supervised Learning
###### Abstract  
The classification of services in 5G/B5G (Beyond 5G) networks has become important for telecommunications service providers, who face the challenge of simultaneously offering a better Quality of Service (QoS) in their networks and a better Quality of Experience (QoE) to users. Service classification allows 5G service providers to accurately select the network slices for each service, thereby improving the QoS of the network and the QoE perceived by users, and ensuring compliance with the Service Level Agreement (SLA). Some projects have developed systems for classifying these services based on the Key Performance Indicators (KPIs) that characterize the different services. However, Key Quality Indicators (KQIs) are also significant in 5G networks, although these are generally not considered. We propose a service classifier that uses a Machine Learning (ML) approach based on Supervised Learning (SL) to improve classification and to support a better distribution of resources and traffic over 5G/B5G based networks. We carry out simulations of our proposed scheme using different SL algorithms, first with KPIs alone and then incorporating KQIs and show that the latter achieves better prediction, with an accuracy of 97% and a Matthews correlation coefficient of 96.6% with a Random Forest classifier.  
5G/B5G service classification; ML; predictive model; KPI; KQI; QoS; QoE; SLA
# A3C-R: A QoS-Oriented Energy-Saving Routing Algorithm for Software-Defined Networks
###### Abstract  
With the rapid growth of Internet applications and network traffic, existing routing algorithms are usually difficult to guarantee the quality of service (QoS) indicators such as delay, bandwidth, and packet loss rate as well as network energy consumption for various data flows with business characteristics. They have problems such as unbalanced traffic scheduling and unreasonable network resource allocation. Aiming at the above problems, this paper proposes a QoS-oriented energy-saving routing algorithm A3C-R in the software-defined network (SDN) environment. Based on the asynchronous update advantages of the asynchronous advantage Actor-Critic (A3C) algorithm and the advantages of independent interaction between multiple agents and the environment, the A3C-R algorithm can effectively improve the convergence of the routing algorithm. The process of the A3C-R algorithm first takes QoS indicators such as delay, bandwidth, and packet loss rate and the network energy consumption of the link as input. Then, it creates multiple agents to start asynchronous training, through the continuous updating of Actors and Critics in each agent and periodically synchronizes the model parameters to the global model. After the algorithm training converges, it can output the link weights of the network topology to facilitate the calculation of intelligent routing strategies that meet QoS requirements and lower network energy consumption. The experimental results indicate that the A3C-R algorithm, compared to the baseline algorithms ECMP, I-DQN, and DDPG-EEFS, reduces delay by approximately 9.4%, increases throughput by approximately 7.0%, decreases the packet loss rate by approximately 9.5%, and improves energy-saving percentage by approximately 10.8%.  
A3C; QoS; SDN; energy saving; routing strategy +
Footnote †: journal:  
_Article_  
A3C-R: A QoS-Oriented Energy-Saving Software-Defined Networks
# Resilience Assessment of Beijing Subway Lines under Extreme Precipitation Weather
## 4 Case Study
### Risk Analysis of Changping Line
#### 4.2.3 Risk Matrix of Changping Line  
After obtaining the corresponding failure probability and the importance evaluation results of the Changping Line, the risk matrix under extreme rainfall can be drawn according to the method in Section 2.1. For the Changping Line, the comprehensive evaluation results of the electromechanical system are shown in Table 8. The results of other systems are shown in Appendix A.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Failure Mode** & **Failure Cause** & **Failure Mode Probability** \\ \hline Environment control failure & Extreme rainfall & 0.029 \\ \hline Elevator & Extreme rainfall & 3.345 \\ \hline Lighting & Extreme rainfall & 0.000 \\ \hline Fire alarm & Extreme rainfall & 0.044 \\ \hline Failure of fire extinguishing equipment & Extreme rainfall & 0.000 \\ \hline Platform door & Extreme rainfall & \(-0.274\) \\ \hline Water supply and drainage & Extreme rainfall & 0.000 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Failure mode probability of electromechanical system of each line—examples.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Rating** & **1** & **2** & **3** & **4** & **5** \\ \hline
**Probability of failure mode** & \textless{}0.3\% & 0.3\(\sim\)1\% & 1–3\% & 3–8\% & \textgreater{}8\% \\ \hline
**Specific description** & Usually does not happen & Rarely happens & Happens in some cases & Happens in more cases & Happens frequently \\ \hline \hline \end{tabular}
\end{table}
Table 7: Classification levels of failure probability.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**System** & **Coding** & **Secondary Sub-Function** & **Failure Mode** & **Failure Mode** & **Importance** \\ \hline \multirow{4}{*}{**Electromechanical system**} & 0101 & Environment and equipment monitoring system & Environment control failure & 0.03\% & 9 \\ \cline{2-5}  & 0102 & Integrated monitoring system & & 10 \\ \cline{1-1} \cline{2-5}  & 0103 & Escalators, moving walks, and barrier-free access & Elevator failure & 3.35\% & 6 \\ \cline{1-1} \cline{2-5}  & 0104 & Low-voltage lighting & Lighting failure & 0.00\% & 8 \\ \hline \hline \end{tabular}
\end{table}
Table 8: The comprehensive results of the electromechanical system of the Changping Line.  
Combined with the results of the risk matrix, for the Changping Line, in extreme rainfall weather, the important indicators are escalators, moving walks, and barrier-free access, low-voltage power distribution system, traction power supply system, switch equipment, and vehicle connection system. The resilience assessment of the Changping Line can be carried out based on the important indicators.