esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Engineering geology
#### 3.3.2 Analysis of Intensity and Hazard-Formation Processes
Zone II (Hengduan Mountains and Western Sichuan): Tectonic activity (30.531%) modifies rock structures; high-relief canyon terrain (27.034%) provides gravitational energy; precipitation (25.551%) accelerates weathering and saturation; and human activity contributes to a \"tectonic modification-geomorphic potential-precipitation erosion-engineering disturbance\" mechanism. Zone III (Qilian Mountains): Freeze-thaw cycles and tectonic activity (24.705%) cause rock fragmentation; precipitation (33.134%) triggers instability; and engineering geological fragility (22.13%) heightens susceptibility, forming a \"precipitation-triggered-freeze-thaw fragmentation-tectonic activity\" mechanism. Zone IV (Qaidam-Gonghe-Hehuang Basins): Intense weathering (27.454%) weakens lithology; basin-edge terrain (26.889%) provides topographic potential; snowmelt and short-duration precipitation (21.814%) serve as triggers; and tectonic activity (23.844%) contributes to a \"weartheed lithology-geomorphic relief-precipitation triggering-tectonic influence\" mechanism. Zone V (Eastern and Western Kunlun Mountains): Freeze-thaw processes (26.34%) generate loose surface materials; glacial meltwater and episodic rainfall (30.164%) act as erosional forces; and geomorphology (24.627%) contributes to a \"freeze-thaw accumulation-precipitation erosion-geomorphic potential\" mechanism, primarily leading to debris flows and periglacial mass wasting. Zone VI (Qiangtang Plateau and Tanggalu Mountains): Freeze-thaw and aeolian erosion processes (23.353%) reshape lithology; precipitation (26.885%) further erodes weakened materials; and gentler terrain (29.88%) reduces the likelihood of large-scale disaster formation, forming a \"freeze-thaw/aolian degradation-precipitation erosion-low-relief terrain\" mechanism. Zone VII (Karakoram Mountains and Qiangtang Plateau): Alpine freeze-thaw cycles result in lithological fragility (42.077%); sporadic precipitation (26.731%) triggers instability; the resulting \"freeze-thaw weakening-precipitation triggering\" mechanism leads to sparse disaster occurrences due to limited hydmeteorological activity and human disturbance. Zone VIII (Lake Basins of the Qiangtang Plateau): Drought-induced weathering (31.868%) leads to soft, loose lithology; precipitation (28.52%) causes erosion; and flat terrain (20.218%) limits gravitational energy. The resulting \"weathered lithological fragility-precipitation erosion-gentle topography\" mechanism accounts for the minimal hazard occurrence observed in this zone. In summary, the hazard formation mechanisms across zones reflect a multifactorial coupling of \"tectonic structure-lithological properties-geomorphology-climate-human activity\". Tectonic activity and lithology provide the material basis, while precipitation and terrain offer the dynamic driving forces, and anthropogenic factors serve as critical triggers. Together, these elements co-regulate the spatial differentiation of landslide, collapse, and debris flow hazards on the Qinghai-Tibet Plateau.
## 3 State of the Art
### Requirements Management
#### 3.1.1 Research Activities Concerning Requirements Management
In this paper, requirements management and requirements engineering are understood in a holistic sense as a systematic and disciplined approach for specifying and managing requirements with the following objectives:  
* to identify the relevant requirements, achieve consensus between the stakeholders, document the requirements in accordance with given standards, and manage the requirements in an systematic manner;
* to understand and document the wishes and needs of the stakeholders; and
* to specify and manage requirements in order to minimize the risk that a system will not correspond to the wishes and needs of the stakeholders (compare [PERSON] and [PERSON] [16]). Similarly, the ISO standard 29148 [9] defines requirements management as \"activities that ensure requirements are identified, documented, maintained, communicated, and traced throughout the life cycle of a system, product, or service\".
# Wavelet-Based Analysis of Motor Current Signals for Detecting Obstacles in Train Doors
## 1 Introduction  
Rail transportation is a key method of mass passenger transit. In most rail systems, numerous people enter and exit train cars rapidly through side doors. A literature review found that 30% to 40% of the operation failures of trains occur in the train door systems [1]. In this study, we focus on double sliding powered doors. These doors protect passengers from the train while it is in motion and can reduce train delays significantly. However, automatic train doors can also injure a passenger if they become trapped between the doors, such as if a passenger attempts to pass through the doors while they are closing, obstructing the door's movement. This may result in minor or severe injuries to the passenger and also delay the train. To prevent these incidents, train doors are often equipped with obstacle detection equipment that comprises a sensor at the leading edge of the door; such sensorsare called sensitive-edge sensors. Some train stations are installed with Platform Screening Doors (PSDs) to enhance safety. [PERSON] [2] proposed a video-based deep differentiation segmentation neural network to improve the detection effect for objects in the gap between train doors and PSDs. Another study proposed by [PERSON] et al. [3] detected the anomaly in the rail train with 97.8% precision based on door tracking (key frame extraction and AnoDet network), outperforming the state-of-the-art. [PERSON] and [PERSON] [4] developed an object detection system for the gap between train and PSD doors based on deep learning. This system was able to detect large objects at an accuracy up to 100%. But none of the studies published have been on object detection within the train doors. Existing methods use sensitive-edge sensors or infrared sensors. However, their performance and robustness are limited by various conditions. In addition, these sensors are expensive and substantially increase the overall cost of the system used to control train doors. The present study proposes a sensorless current-based obstacle detection method.
# Robustness Analysis for Multi-Agent Consensus Systems with Application to DC Motor Synchronization
## 2 Multi-Agent Consensus System Preliminaries
### Graph Theory  
A _graph_\(\mathcal{G}\) is a pair \((\mathcal{N},\mathcal{E})\) which consists of a set of nodes \(\mathcal{N}\) and a set of edges \(\mathcal{E}\) on \(\mathcal{N}\). Every link between a pair of nodes \((n_{i},\,n_{j})\) is called an _edge_, \(e\in\mathcal{E}\). A graph is said to be _bidirectional_ if nodes \(n_{i}\) and \(n_{j}\) can get information from each other, i.e., \((n_{i},\,n_{j})\in\mathcal{E}\Leftrightarrow(n_{j},\,n_{i})\in\mathcal{E}\). A graph is called _digraph_ if the information between nodes only flows from node \(n_{i}\) to \(n_{j}\). Thus, information exchange between agents can be modeled as a digraph but also as an undirected graph. A graph is said to be _balanced_ if its _in-degree_ (the number of communication links arriving at the node) is equal to its out-degree (the number of communication links leaving the node). A graph is _connected_ if, for every pair \((n_{i},\,n_{j})\) of distinct vertices, there is a path from \(n_{i}\) to \(n_{j}\). A connected graph allows the communication between all agents through the network. This leads us to the fact that a graph \(\mathcal{G}\) has a _spanning tree_ if and only if \(\mathcal{G}\) is connected [27]. We assume that every communication link \((n_{i},\,n_{j})\) has a gain (weight) \(g_{ij}\). A matrix representation of the information flow \(\mathcal{G}\) among agents can be obtained using _Laplacian matrix_. The definition of the _Laplacian matrix_ is \(\mathcal{L}=[l_{ij}]\) where \(l_{ii}=\sum_{j\in\mathcal{N}_{i}}g_{ij}\) and \(l_{ij}=-g_{ij}\) for \(i\
eq j\) and \(\mathcal{N}_{i}\) is the set of neighbors of the \(i^{th}\) agent.
## 7 Conclusions  
An efficient target-detection network structure, namely, FDA-SSD, was proposed in this study to achieve efficient and fast target-detection problems. The key idea of FDA-SSD is to train samples with the normalization depth. Firstly, we proposed a method for building OTMP datasets with spatial parameters and open-source datasets. Then, we proposed a fusion model of FFN and SSD, which realizes the data fusion of sample image information and spatial normalization parameters of the model in the training stage, so that the model can filter detectors of different scales. Finally, the appropriate scale in the SSD was selected for detection to significantly reduce the number of calculations. Based on FDA-SSD, a mono-visual positioning-and-tracking framework integrating target feature detection and dynamic target tracking was designed.  
Indoor experiments verified that the speed under the computing platform based on GTX1060 was about 18.1% faster than the original SSD, while the accuracy of FDA-SSD maintained a consistently high level. The method proposed in this study was not limited to monocular-vision applications. In the future, it would be extended to binocular and depth-camera models and implanted in various applications such as obstacle avoidance, detection, and follow-up of various UAVs and self-driving cars.  
Conceptualization, Z.W.; Data curation, Z.W.; Formal analysis, Z.W. and S.Y.; Investigation, S.Y. and M.S.; Methodology, Z.W.; Project administration, K.Q.; Resources, Z.W.; Software, Z.W.; Supervision, K.Q.; Visualization, Z.W.; Writing--original draft, Z.W.; Writing--review and editing, Z.W. and S.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Science and Technology Department of Sichuan Province under Grant No. 2020 YJ0044, 2021 YFG0131, the Fundamental Research Funds for the Central Universities under Grant No. ZYGX2020J020, and the National Numerical Wind Tunnel Project, China under Grant No. NNW2021 ZT6-A26.  
The complete dataset of benchmarks for object tracking with motion parameters (OTMP) is available at [EMAIL_ADDRESS]. Part of the dataset has been uploaded to GitHub: [[https://github.com/6](https://github.com/6) wa-car/OTMP-DataSet.git]([https://github.com/6](https://github.com/6) wa-car/OTMP-DataSet.git) (accessed on 30 December 2021).  
The authors declare that there are no conflict of interest regarding the publication of this study.
## 3 Proposed Network Traffic Predictor and Traffic Classification Agents
### Proposed Network Traffic Classifier Agent Architectural Framework  
The general architecture of NTCA is similar to NTPA. However, the actual values, parameters, and algorithms used are different. That means the input parameter, knowledge base, cognition algorithms, validation technique and the output for NTCA are different from NTPA. In general, taking the network traffic as input, NTCA classifies them into the classes to which the traffic belongs. NTCA helps other network agents in making network decisions, including QoS provisioning and monitoring based on SLA, resource provisioning, and network slicing. The NTCA design and implementation framework is depicted in Figure 5 (top).  
In particular, simpler than NTPA, NTCA has an \"input-unit\" that can be used in receiving a service request and network state sampling/measuring points. In addition, NTCA has facts, i.e., incoming traffic features, which could be the size of a packet, source and destination IP addresses, protocols, etc. The \"cognition/reasoning unit\" is the component that gives the central intelligence for NTCA. It uses it to identify and classify traffic. The \"planning strategy unit\" is the steps/procedures in providing the class of the traffic. Another component of NTCA is the validation unit. It is composed of validation rules for the final classification decision, i.e., knowing the amount and type of arriving network traffic of given traffic classes that helps make decisions. Finally, the remaining NTCA component is an \"output unit\", which could be a classification result to be sent to other agents.
# A Review of Research on Secure Aggregation for Federated Learning
## 4 Aggregation Techniques for Federated Learning
### Discussions
However, traditional blockchain technology in federated learning lacks robust privacy guarantees for model information, as client-uploaded models remain in plaintext. Thus, it typically needs to be integrated with additional privacy protection mechanisms. For example, methods like homomorphic encryption and differential privacy can be applied, requiring clients to encrypt trained models before uploading them to the blockchain to prevent information leakage. Integrating blockchain with federated learning inevitably introduces additional communication and computational overhead, largely influenced by the blockchain's consensus algorithm. The computational overhead varies significantly with the consensus algorithm. PoW-based blockchains demand substantial computational resources, whereas PoS-based systems reduce overhead at the cost of decreased decentralization. Thus, research primarily focuses on enhancing model accuracy and minimizing resource consumption in untrusted environments. In practical applications, high-energy consensus mechanisms are generally avoided. Instead, techniques such as model compression, gradient pruning, and asynchronous updating are employed to reduce communication frequency and on-chain interactions, thereby lowering overall energy consumption. To address scalability challenges, a collaborative on-chain/off-chain storage strategy is adopted. In this approach, model parameters and large-scale transaction data are stored off-chain, while only hash values or metadata are recorded on-chain, ensuring data verifiability while enhancing scalability. Furthermore, scalability can be improved through asynchronous federated learning and local model fusion. This method eliminates the need for all participants to upload their models simultaneously. Instead, partial model fusion is first conducted within partitions, followed by global aggregation, effectively reducing synchronization overhead. Blockchain-based secure aggregation is commonly applied in fields requiring incentives, transparency, and decentralization, making it suitable for untrusted multi-party scenarios such as smart homes, smart grids, and supply chain management [137; 138].
## 3 Continuous to Discrete Dynamics: Return Map
### Identification of Switching Pattern for Expertise via the State Transition Probability  
Figure 4 shows that the offensive and defensive maneuvers could be classified into six coordination patterns and that these patterns switched over short time scales. To clarify the characteristics of the expert and intermediate switching patterns, the histograms of the return maps for each group were calculated. As a result, we identified two discrete states in each histogram: the \"farthest apart\" high-velocity state (F) and the \"nearest (closest) together\" low-velocity state (N). We identified four trajectories, namely \(\{X_{n}=F,X_{n+1}=F\},\{X_{n}=N,X_{n+1}=N\},\{X_{n}=F,X_{n+1}=N\}\), and \(\{X_{n}=N,X_{n+1}=F\}\), as second-order transitions. Figure 5 shows the second-order state transition diagrams for experts and intermediat  
Figure 4: Examples of a well-fitted series of points by each function using the return map analysis. (**a**–**d**) Linear functions, \(X_{n+1}=aX_{n}+b\), with four different slopes for \(0<a<1,-1<a<0,1<a\), and \(a<-1\), respectively. (**e**) Exponential function, \(X_{n+1}=b\ \exp(aX_{n})\), and logarithmic function, \(X_{n+1}=a\log(X_{n})+b\). (**f**) Examples of switching functions in one scene. The red lines show attractors, blue lines show repellers, and cyan lines show intermittencies. Modified from [37].  
The conditional probabilities for second-order state transitions of the experts were \(\{Pr(F|F)=0.96,Pr(N|F)=0.04\}\), and \(\{Pr(N|N)=0.19,Pr(F|N)=0.81\}\). The probabilities for the intermediates were \(\{Pr(F|F)=0.82,Pr(N|F)=0.18\}\), and \(\{Pr(N|N)=0.69,Pr(F|N)=0.31\}\). The transition probabilities between the experts and intermediates were clearly different; the experts were more often in the \"farthest apart\" high-velocity F-state. In contrast, the intermediate players generally remained in the \"nearest (closest) together\" low-velocity N-state.  
The return map analysis revealed that the coordination patterns repeated over short time scales during interpersonal competitive behavior. In addition, the state transition probability analysis revealed differences in the switching patterns between experts and intermediates. Nevertheless, all patterns were shown for both levels of players. However, we considered two players as one system in these analyses. In other words, the two players were an autonomous, self-excited system. As a result, this approach clarified how the coordination pattern between the two players behaved rather than how the individuals behaved. The manner of the individual's adaptation to abrupt changes in the external environment must be examined for the individual perspective.
## 1 Introduction  
### Structure of the Article  
The structure of this article is divided into five chapters. It begins with an introduction to the topic, followed by an overview of common methods and techniques for detecting defects in aircraft structures. The discussion chapter highlights this work's potential contributions to the field and concludes with suggestions for future research on automating aircraft inspections.  
Section 1: Introduction--This chapter introduces the challenges of inspecting aircraft structures and explains the importance of automation. It highlights the need to improve safety and efficiency and reduce operating and maintenance costs.  
Section 2: Materials and Methods--This chapter analyses the tools, methods, and techniques used, such as thermography, visual, and 3D scanning. It also explores common materials used in the aviation industry, including strengths and limitations, as well as the most common types of defects that appear during inspection. Special attention is given to how artificial intelligence could support these processes to enhance accuracy and reliability.  
Section 3: Discussion--This chapter emphasises the intellectual contributions of this research and its potential impact on the aviation industry, the non-destructive testing (NDT) field, and academia.  
Section 4: Conclusions--This chapter summarises the findings, highlighting the value of automated inspection. It stresses the importance of integrating AI with advanced tools to enhance outcomes and reduce human errors.  
Section 5: Future Directions--This section looks ahead, discussing the scientific aim and the specific objectives this work is willing to address.
# Interest Flooding Attacks in Named Data Networking and Mitigations: Recent Advances and Challenges
## 5 Applications and Use Cases of NDN
### IFA Defense in IoT Networks (NDN of Things)  
The IoT domain often involves numerous low-power devices (sensors/actuators) that use NDN to fetch commands or report data. These devices (and their gateways) usually have limited memory (small PIT size) and processing capability, making them even more vulnerable to PIT exhaustion. At the same time, introducing heavy defense algorithms can be impractical. One notable effort to target this domain is the NDN of Things (NDNoT) framework and the _IfNoT_ mechanism. IfNoT is an IFA mitigation approach specifically designed for NDNoT environments [48]. Identify attacker nodes within the IoT network and filter out their interest traffic before it can propagate widely. The IfNoT mechanism monitors the interest behavior of IoT nodes and pinpoints those that send suspiciously large numbers of unsatisfied interests. Once identified, requests for these nodes are curtailed. IfNoT essentially pushes detection to the network edge; by quickly detecting malicious IoT devices, the gateway can cut off their traffic, protecting both the local network resources and the rest of the NDN infrastructure.
## 3 Results
### Computational Efficiency and Resource Requirements  
The proposed methodology for semi-blind watermarking is robust and safe thanks to secret keys and the parameters of the model. However, the contribution of factors such as computational efficiency and resource requirements also plays a significant role in real-world application scenarios.  
The training phase is carried out through deep convolutional generative adversarial networks (DCGANs), which would generally require heavy computational resources owing to the sustainable generator and discriminator networks employed for iterative training. All the above tests were carried out on a system that has an NVIDIA RTX 2060 SUPER GPU, 16 GB of RAM, and an Intel i7-9700 CPU.  
The extracted hidden watermark is computed in an inference phase based under the trained model parameters along with secret keys. This computation is relatively less demanding compared to training, taking only a fraction of the hardware resources.  
Depthwise separable convolution (DSC) has been used in some models, such as DSC-1 and DSC-2, to enhance computational efficiency. The model parameters accounted were reduced significantly, with a nearly 50% reduction from 1.27 MB to 0.69 MB, and yet still offered an acceptable level of robustness (PSNR: -48-49 dB, BER: 0.01-0.014). However, there was a minor drop in SSIM (from 0.95 to approximately 0.90), which might adversely affect perceptibility for pictures with a higher level of detail.
# A Comprehensive Study of the Use of LoRa in the Development of Smart Cities
## 4 Results of the Analysis of the Selected Works
### Analysis of the Application Layer
#### 4.1.6 Waste Management  
The increasing population density generates more garbage every day, which increases the need for more physical space for waste and the associated costs to clean, bury, and burn it. In waste management, three important aspects to define are: (i) the collecting routes; (ii) an adequate waste classification system; and (iii) the management of the physical space for garbage. A waste management system can consider a compression device; when a garbage container is full, a step motor is activated to compress the garbage and save space [49]. Additionally, with the use of a variety of sensors, such as infrared, ultra-sonic, and methane sensors, it is possible to generate an automatic classification of garbage and distinguish between recyclable and non-recyclable garbage. The use of the RFID technology also allows the identification of waste to indicate the correct garbage bin where it should be deposited. An important aspect to be considered in this work is data confidentiality, since the garbage of a  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Pollutants** & **Health Disease** \\ \hline NO\({}_{2}\) & Lung damage \\ \hline CO & \\ PM 2.5 & Respiratory and hearth diseases \\ PM10 & \\ \hline SO\({}_{2}\) & \\
**O\({}_{3}\)** & Asthma \\ \hline \hline \end{tabular}
\end{table}
Table 4: Diseases due to pollutants. PM: particulate matter.  
person or family can contain sensitive information. Regarding this issue, the authors of some studies considered the usage of secure communication protocols such as https in their solutions [50].  
Wastewater overflow monitoring is another important issue for the modern city, and consists, for example, in detecting possible problems in the flow of wastewater and in analyzing the effect of waste in sensitive areas. To control wastewater, IoT systems can uses radar sensor platforms, sewer pump stations, and LoRa to cover a large portion of a city [51]. With a single gateway, it is possible to collect the information of water-waste levels of sewer pump stations and then analyze the data to estimate the pump flow; this allow identifying when sewer pump stations are full, avoiding the overflow of wastewater.