esa-sceva/satcom-chunk-collection huggingface dataset:
# Resource Management in Cloud Radio Access Network: Conventional and New Approaches
## 4 Challenges and Open Research Issues
### Inter-Cell Interference  
The C-RAN incorporates robust computing capability and large data storage, which requires proper resource allocation and interference mitigation. Notwithstanding the design of a near-optimal algorithm for resource allocation, the performance may be significantly sub-optimal and the measurement of inter-cell interference tedious, time-consuming, and highly inaccurate, owing to the incorrect modeling of interference among different cells [76]. It is necessary to design an algorithm that solves this dilemma so that the optimization of resource management is achieved in conjunction with interference mitigation with appropriate measurement and reduced cost. This task can be challenging because, over time, the interference estimated would be dynamic. Therefore, multiple measurements would be required to ensure the accuracy of the measurements within each connection. The authors in [77] investigated the resource management in a NOMA based F-RAN, focusing on the power and sub-channel allocation considering the co-channel interference. The problem is designed as a non-cooperative game, and many-to-many two-sided matching algorithm is studied for sub-channel allocation.
# Pico-Sat to Ground Control: Optimizing Download Link via Laser Communication
## 4 Results
### Link Budget for FSO Nano-Satellite
#### 4.1.3 RF Link-Budget Analysis  
We now present the link-budget analysis using RF modeling:  
**Link Margin:** The link margin is a calculation that shows the likelihood of creating wireless communication under certain conditions. In our case, we show the possibility of creating a link between relatively simple communication components given optimal conditions, i.e., a clear night sky without clouds, rain, or haze. Our calculation mainly considers the geometric position, the elevation angle, and the receiver's sensitivity and transmission power. The link margin is computed for two wavelengths: 850 mm and 1550 mm. The link-budget equation is  
\[LinkMargin=Tx_{power}-R_{s}-G_{loss}-Tur_{loss}-Atm_{loss} \tag{11}\]  
where \(Tx_{power}\) is the transmitter power, \(R_{s}\) is the receiver sensitivity, \(G_{loss}\) is the geometric attenuation, \(Tur_{loss}\) is the turbulence attenuation, and \(Atm_{loss}\) is the atmospheric attenuation (assuming a clear night with no clouds, rain, or haze).  
**Parameters:** In this model, the \(Tx_{power}\) is 100 mW, i.e., 20 dBm, and the receiver sensitivity \(R_{s}\) is \(-65\) dBm. The geometric attenuation can be calculated using this formula:  
\[G_{loss}=10 log_{10}\left(\frac{\frac{\pi}{4}(d\varphi)^{2}}{R_{a}}\right) \tag{12}\]  
where \(R_{a}\) is the receiver area (0.1 m\({}^{2}\), representing a 35 cm or 14\({}^{\prime\prime}\) telescope). Assuming that there is a satellite located 1000 km from the telescope, then there is an expected 8 dB margin for FSO communication. Table 2 presents the link budget, margin parameters, and total link loss.  
Based on both analyses (photon counting and RF path loss), we conclude that on clear nights a 14'' robotic telescope with a sensitive APD receiver should be sufficient to receive a bit stream of 10 Mbps with a bit error rate lower than \(10^{-4}\), allowing FSO packets of size 120 bytes to have a packet error rate of 10%; such error rate can be handled using modern FEC (forward error correction) methods. Taking into account the additional FEC and packet headers, this results in a reliable bit stream of 6-8 Mbps.
## 5 Deposition Methods
### Atomic Layer Deposition (ALD)  
In non-volatile memories, high integration density desired GST thin films with good conformality. The self-limiting film growth mechanism of Atomic Layer Deposition (ALD) ensures a superior growth conformality. This deposition method is considered as a subclass of chemical vapor deposition. In earlier demonstrations, researchers have attempted atomic layer deposition of tellurium-containing phase changing materials by using plasma-assisted, partially CVD-like process [129; 130]. However, the high reactivity required in ALD for Telluride can raise safety issues. To overcome this limitation, people have developed the alkylsilyl tellurium-based ALD precursors for tellurium which is sufficiently volatile, thermally stable and highly reactive [35; 131]. In 2012, [PERSON] et al. reported a robust thermal ALD of the G-S-T films by using silyl-Te and alkoxy-Ge and alkoxy-Sb metal-organic precursors at low temperature (\(\approx\)70\({}^{\circ}\)) without using any reactive gas [132]. The cross-section transmission TEM images shows good uniformity along the contact hole surface and the composition is close to Ge\({}_{2}\)Sb\({}_{2}\)Te\({}_{5}\), as shown in Figure 22.  
Figure 21: **(a)** Schematic of the hot wire chemical vapor deposition for GST thin films. **(b)** Cross-sectional TEM images of deep holes filled with GST made by radical-assisted MOCVD. Adapted with permission from Reference [128]. ©2015 Elsevier.
###### Abstract
The core of such a framework is the integration between mobile devices, edge nodes, and central servers to ensure resource management and optimization with power efficiency. This integration allows computational functions or processes locally performed in mobile devices to reduce data transfer to central servers and thus preserve bandwidth while enhancing data privacy. The edge nodes near the servers can handle the processing of intermediate data; this decreases latency and reduces the load on the central servers. Central servers maintain the overall network in coordination for resource allocation and for the periodic updating of the global model that is needed to keep network performance at its optimum. The framework also integrates dynamic trust management that calculates trust scores for IoT devices based on their history, integrity of data provided, and compliance with security protocols. Moreover, the framework utilizes dynamic pricing for economic optimization techniques in real-time and algorithms for cost efficiency. The major contributions of the proposed approach can be summarized as follows:  
1. The proposed framework introduces a novel trust management system that computes real-time trust scores for IoT devices by incorporating historical behavior, data integrity, and security. 2. The TEO-IoT framework also integrates advanced economic models, including real-time dynamic pricing strategies and cost-efficiency algorithms. 3. Novel routing protocol is implemented to adapt to varying network conditions and priorities to ensure data transmission reliability and energy efficiency. The structure of the rest of the article can be summarized as follows: In Section 2, we review the existing literature on 6G-enabled IoT networks. Section 3 presents the trust-optimized 6G-IoT framework, including the system architecture, trust management, economic optimization, and delay-tolerant routing mechanisms. In Section 4, the simulation setup is presented and the proposed framework's performance is evaluated. Finally, Section 5 concludes the articles with potential future work.
## 2 Literature Review
### Contribution of the Paper  
The main contribution of the paper is twofold: to deal with the uncertainties of the company's forecasts of the market demand and to reduce the computational time while improving the search in the solution space. The most relevant works in this research area rely on a correct estimation of the market demand, which means the optimization process always revolves around a given value. However, the market demand in the real situation is of course uncertain, and it is more reasonable to give the decision maker a set of solutions that span a range of market demands rather than referring to a single value. To consider a range of market demands, the problem must be modified with the consequence that the solution space can expand considerably, thus increasing the difficulty in solving the overall problem.  
The second issue refers to the searching speed and ability of the algorithm. Due to the inner operators from the evolution algorithms, newly generated solutions have the chance to be feasible but are obviously far from the optimum. However, all solutions of each generation, even the worst, need the simulation-based evaluation of the throughput. This could lead to a waste of computing time. In addition, it may result in the request for applying again and again the simulation-based evaluation of the throughput on the identical solutions, which actually leads to overestimations: the production rate may be improved just by running the simulation on the same solution due to the natural variability of this result. This may lead to issues related to both the local optimization of the objective function and a waste in computational time.  
Therefore, we propose a combination of an evolutionary algorithm and a heuristic-based algorithm to solve the problem considering the market demand interval. Firstly, new heuristic principles in the decoding stages were designed to fulfil the industrial requirement of the expected demand interval, which not only ensured each individual could be decoded into a feasible solution, but also improved the quality of the solutions during the decoding stage, thereby improving the final performance of the algorithm. At the same time, the heuristic principles gave each solution classic evaluating criteria from the line balancing side, which also provided a direct and effective basis for swiftly screening the worse solutions among the entire population. Based on this, a pre-screening strategy is proposed to eliminate the obviously worse or previously evaluated solutions in each generation. With the help of this strategy, the algorithm may reduce invalid searches and waste of computing time and thus improve the optimizing performance. In addition, a simulation strategy is introduced and was tested to enhance stability when estimating the throughput of a single solution.
# Data Sensing and Processing Tensioning System Based on the Internet of Things
## 3 Intelligent Tensioning System Design
### Pushing and Receiving
#### 3.3.2 Transport Flow  
The sequence diagram of the packet transport based on the above-mentioned transport mechanism is shown in Figure 8. The specific transport flow is divided into seven steps. First, startup commands are sent out from the PC console before tensioning jobs to start or wake up the sensing layer and data-pushing service. A \"connecting request\" is sent to the remote server by using a remote pushing request. Second, after receiving the connecting request, the monitoring service identifies the request information in accordance with the request rules in rule base and returns the result. Third, when the response result is \"err\", the exception-handling service is called to record the exception information and return the connection failure result to the PC console; if it is \"ok,\" then the pushing control service is called to send the \"request for establishing channels\". Fourth, after receiving the request, the  
Figure 6: Packet arrangement order.  
receiving control establishes a data transport channel and is ready to receive data. Fifth, the header of the packet queue is read by the pushing control and pushed to the receiving control service. Sixth, after receiving the data, the receiving control service identifies the legality and type of the data and returns the result to the pushing control service. Seventh, the result is received and identified by the pushing control service. When it is \"yes,\" the packet queue is dequeued, and a \"true\" is returned to the PC console; when it is \"no,\" the exception-handling service is called, and a \"false\" is returned to the PC console.  
In the transport layer, the following measures are taken to ensure the integrity of the tensioned data. First, at the sending side, the packet queue control mechanism is used to ensure orderly pushing of data, and the exception handing service is started when an abnormality occurs. Second, CRC method is used to check the integrity of data packets at the data receiving side to prevent data block loss in data packets.
###### Abstract  
Three-dimensional models are required to virtualize heritage sites. In recent years, different techniques that ease their generation have been consolidated, such as photogrammetry with Unmanned Aerial Vehicles (UAVs). Nonmetric cameras allow relatively inexpensive data collections. Traditional aerial photogrammetry has established methodologies, but there are not commonly used recommendations for the selection of parameters when working with UAV platforms. This research applies the Taguchi Design of Experiments Method, with four parameters (height of flight, forward and lateral overlaps, and inclination angle of the sensor) and three levels (L\({}_{9}\) matrix and nine flights), to determine the set that offers the best metric goodness and, therefore, the most faithful model. The Roman _civitas_ of Julibriga (Cantabria, North of Spain) was selected for this experiment. The optimal flight results of the average signal-to-noise ratio analysis were height of 15 m, forward and lateral overlaps of 80%, and inclination of 0\({}^{\circ}\) (nadiral). This research also highlights the noticeable contribution of the inclination in the accuracy of the model with respect to the others, which is 16.4 times higher than that of the less relevant one (height of flight). This leads to propose avoiding inclination angle as a variable, and the sole development of nadiral flights to obtain accurate models.  
Taguchi; DOE; survey; drone; mission; design; parameters; accuracy Article
# Enhanced Localisation and Handwritten Digit Recognition Using ConvCARU
## 1 Introduction
To clearly delineate our novel contributions from established methods, we highlight the following key innovations:  
**New gradient-regularised GAN loss**: A modified GAN loss function is proposed that stabilises the adversarial training process and improves the quality of feature extraction, thus enhancing the visual fidelity of the predicted frames. **Parameter-efficient ConvCARU gate**: By replacing traditional linear operations with CNN-based computations in the gating mechanisms, our ConvCARU design captures extended spatio-temporal dependencies while reducing computational overhead. **Ablation-verified performance gain**: Systematic ablation studies confirm that each component of our approach--from the adaptive fusion of features to the decoupling of foreground and background information--significantly contributes to performance improvements measured in SSIM, PSNR, and memory efficiency compared to state-of-the-art methods. These contributions collectively establish a novel framework that not only addresses the limitations of its predecessors but also sets a new benchmark in video prediction for handwritten digit recognition.
# A Comparative Analysis of High Availability for Linux Container Infrastructures
## 1 Introduction
### Contribution  
The originality of the paper lies in the comparative analysis and performance evaluation of high-availability solutions in Linux container infrastructures. While other studies address various aspects of container technologies such as containerization, orchestration, security, and virtualization, this paper specifically focuses on evaluating the performance characteristics of different high-availability solutions. It fills a gap in the existing literature by providing detailed information on service recovery time, failed calls, data transfer rate, and average call time for popular container platforms Docker, Kubernetes, and Proxmox through experiments and analysis of results. This new contribution enables readers to make informed decisions when selecting the most appropriate high-availability solution for their specific use cases.
# A Review of Fault Diagnosis Methods for Key Systems of the High-Speed Train
## 2 Fault Diagnosis of High-Speed Train Bogie System
### Fault Diagnosis of Train Suspension System
[60] proposed a real-time monitoring method based on a nonlinear constraint model. To change the maintenance of train suspension systems from scheduled repair to condition repair, [PERSON] et al. [61] proposed a method based on cubic Kalman filtering, which was verified experimentally. To improve the diagnostic accuracy and robustness of suspension systems for high-speed trains, [PERSON] et al. [62] presented a method based on a functional model (FM), which was experimentally verified to have better performance compared to the traditional method. Therefore, the fault diagnosis of the train's suspension system is mainly accomplished by feature extraction and pattern recognition. The main methods applied in feature extraction include permutation entropy, LLTSA, wavelet packet, VMD, MDF, FM, singular value decomposition, EEMD, etc. In pattern recognition, DNN, SVM, LSSVM, CNN, Kalman filters, etc., are mainly used.
# Robust Simulation of Cyber-Physical Systems for Environmental Monitoring on Construction Sites
## 3 Research Framework
Considering that belief functions can determine the confidence level of the monitoring data to improve the accuracy and stability of the fusion results, this study integrates the belief function and the fuzzy-index belief function, in order to develop a sensor data-fusion belief function that improves the accuracy and stability of the fusion results. The Dempster-Shafer (D-S) evidence theory is a method of uncertainty inference, proposed by [PERSON] and developed by [PERSON] [47]. It can fuse uncertain information. However, it sometimes displays poor robustness [48]. Therefore, this study combines the fuzzy theory and the D-S evidence theory, according to the combination approach of conflict evidence, in order to conduct conflict detection and the processing of experts' decisions on evaluating environmental monitoring results [49]. Finally, the proposed ontology-based E-CPS model is applied in a construction project in Nanjing, Jiangsu Province, China. The environmental monitoring indicators in this model are determined using the literature review, the questionnaire survey, and the expert consultant. In this context, the simulation experiments are performed to examine the improvements of the system robustness using the recognition algorithm based on spatio-temporal correlation, the data-level fusion algorithm based on belief functions, and the decision-fusion algorithm based on the improved D-S evidence theory.
## 2 Materials and Methods
### Transciptomic Analyses  
The collected fungal samples containing fresh culture cells were used for further RNA extraction using an AurumTM Total RNA Mini Kit (Bio-Rad, Hercules, CA, USA), according to the manufacturer's instructions. Thereafter, the extracted total RNAs were employed to perform the RNA evaluation followed by poly(A)+ RNA purification and cDNA library synthesis using a TrueSeq stranded mRNA Sample Prep kit (Illumina). Then, libraries were multiplexed with Illumina barcodes and sequenced per lane using an Illumina HiSeqTM 4000 system (PE 100 bp sequencing lane) based on the massively parallel sequencing protocol [35] at McGill University and Genome Quebec Innovation Center (Montreal, QC, Canada).  
The obtained raw reads were managed in Trimmomatic [36] for the trimming and clipping process, and then the clean reads were utilized for transcriptome de novo assembly which was accomplished by applying a general pipeline previously described [37,38] through the Trinity assembly software suite consisting of Inchworm, Chrysalis, and Butterfly at the Canadian Centre for Computational Genomics. The normalization counts, practically transcripts per million (TPM), were used as information on relative expression to compare different conditions. Further, the transcriptome BLAST search against GeneBank (NCBI) was performed with the BlastX program to identify protein-coding genes in cDNAs experimentally obtained and further predict potential functionality of the genes. Computational alignments were carried out by placing transcripts against the UniProtKB/Swiss-Prot (Swissprot) protein database (uniprot_sprot.trinotate_v2.0.pep) providing the highest level of annotation with a minimal level of redundancy along with NCBI non-redundant protein database and UniRef90. E-values less than \(10^{-5}\) and sequence similarity greater than 60% were regarded as high homology. Further functional annotation was achieved through the Trinotate pipeline including Hummer v.3.1b1 and PFAM for protein domain identification, SignalP v.4.1 and Tmhmm v.2.0c for protein signal peptide and transmembrane domain prediction, and EMBL Uniprot eggnog/GO pathways to search for orthologous groups.
## 3 Proposed Framework (Fed-DTB)
### Network Status Score  
The network status score, \(\tau_{N_{i}}(t)\), quantifies the availability and stability of the network connections established by vehicle \(v_{i}\) at time \(t\). Reliable network connectivity is critical for ensuring timely communication and effective participation in the federated learning process.  
Let \(NS_{i}(t)=\{NS_{i1}(t),NS_{i2}(t),\ldots,NS_{i_{{HN_{S}}}}(t)\}\) denote a set of normalised network status indicators, such as signal strength, connection stability, and bandwidth usage. Each indicator \(NS_{ik}(t)\) is assigned a weight \(W_{k}\), reflecting its relative importance. The network status score is computed as a weighted average:  
\[\tau_{N_{i}}(t)=\frac{\sum_{k=1}^{{{{{{\tau}_{N_{i}}}}}}}{W_{k}} \cdot NS_{ik}(t)}{\sum_{k=1}^{{{{{\tau}_{N_{i}}}}}}{W_{k}}} \tag{13}\]  
_Impact on Trust Score:_ A lower network status score indicates unreliable connectivity, leading to potential delays or failures in transmitting model updates. Such circumstances reduce the vehicle's reliability in the federated learning process, resulting in a corresponding adjustment to its trust score. Conversely, vehicles with stable and strong network connections are more likely to be selected for participation due to their dependability.