esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Time Series of Surface Deformation  
Based on 64 diverse SAR images in the time series from 2018 to 2023, a series of cumulative surface deformation maps relative to the initial image from 6 January 2018 have been generated. To present more detailed and clear surface deformation results, 16 typical maps with approximately the same time interval of four months have been selected (Figure 6). As shown in the figure, mining-induced subsidence is evident in every zone across the eight mining fields and four small coal mine reconstruction areas. The subsidence areas exhibit a consistent pattern of expansion and intensification over time, which is visually confirmed by the progressively widening boundaries of blue tones representing surface subsidence and the deepening discoloration into blue or even lilac hules, indicating the worsening extent of subsidence as time progresses. A closer analysis reveals that subsidence in the southeastern zones of the study area is more severe compared to other zones. This is evidenced by the lilac areas, where subsidence exceeds \(-250\) mm, appearing consistently in every southeastern zone, while these extreme subsidence levels are scarcely observed in the northwestern zones.  
Figure 5: Annual average velocity of surface deformation in the Wanli mining area.  
To further analyze the detailed surface subsidence conditions in these 12 zones, 12 feature points were selected within the subsidence center of each zone, based on the cumulative subsidence map (Figure 7).  
Figure 6: Time series of mining-induced surface deformation from 2018 to 2023. (**a**) Surface subsidence on 6 May 2018; (**b**) Surface subsidence on 3 September 2018; (**c**) Surface subsidence on 13 January 2019; (**d**) Surface subsidence on 13 May 2019; (**e**) Surface subsidence on 10 September 2019; (**f**) Surface subsidence on 8 January 2020; (**g**) Surface subsidence on 25 April 2020; (**h**) Surface subsidence on 4 September 2020; (**i**) Surface subsidence on 26 January 2021; (**j**) Surface subsidence on 2 May 2021; (**k**) Surface subsidence on 11 September 2021; (**l**) Surface subsidence on 9 January 2022; (**m**) Surface subsidence on 9 May 2022; (**n**) Surface subsidence on 18 September  
Subsequently, we plotted the time series of cumulative subsidence for each feature point over the entire monitoring period (Figure 8). Notably, surface subsidence is first observed in the curve of feature point F1, located in northern zone R1, while it appears latest in the curve of feature point F11, situated in southern zone C8. Moreover, the curves clearly highlight multiple subsidence events that occurred during the monitoring period. For instance, the curve of feature point F11 shows three significant subsidence events in April 2020, April 2021, and May 2022. These events correspond to the initiation of coal excavation at three mining working faces within this panel, progressing from distant to near relative to the location of feature point F11.  
Figure 7: Distributional location of twelve feature points.
## 2 Related Work
### Research Gap and Contribution  
Table 1 presents a comparative analysis of representative works in anomaly detection for IoT and 5G environments. Many existing methods are either centralized and unsuited for real-time deployment, or insufficiently robust to non-IID data and poisoning attacks in federated settings.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Study** & **Focus Area** & **Approach** & **Strengths** & **Limitations** \\ \hline [PERSON] et al. & Deep learning & Centralized & High detection accuracy & Centralized and not scalable to IoT \\ (2019) [22] & for IDS & autoencoder + CNN & & & \\ [PERSON] et al. & Federated learning & Consensus-based FL for & Decentralized & No real-time evaluation \\ (2020) [25] & in IoT & anomaly detection & and scalable & and assumes IID data \\ [PERSON] et al. & Non-IID handling & Prototype-based & Addresses client & Not tested on IoT or \\ (2023) [23] & in FL & personalization (FedNH) & heterogeneity & edge environments \\ [PERSON] et al. & Blockchain for AV & Hyperledger + secure & Privacy, trust, and & Limited ML integration \\ (2022) [14] & edge sharing & data contracts & realistic deployment & \\ [PERSON] et al. & D2D edge & & Fully distributed & High communication \\ (2024) [27] & anomaly Detection & WAFL-autoencoder + D2D & & and unsupervised & load and new method \\ [PERSON] et al. & FL security & Backdoor attacks in FL & Highlights threat model; & No mitigation strategies \\ (2020) [13] & & & baseline for defenses & and not IoT-specific \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of representative related works on anomaly detection and security in IoT and 5G-enabled systems. The table highlights each approach’s scope, strengths, and limitations, and contrasts them with the goals of the proposed Edge-FLGuard framework.  
Edge-FLGuard aims to address these gaps by combining  
* Lightweight, edge-deployable deep learning models (autoencoders and LSTMs);
* A privacy-preserving and communication-efficient federated learning pipeline;
* Robustness evaluations under both synthetic and public attack datasets, with explicit measurement of detection accuracy, adversarial resilience, and communication cost.  
This approach unifies key dimensions of effective 5G-IoT security--scalability, privacy, robustness, and real-time inference--into a single, replicable framework suitable for deployment in modern IoT ecosystems.
# Coarse-Resolution Satellite Images Overestimate Urbanization Effects on Vegetation Spring Phenology
## 3 Methods
### Data Preprocessing
#### 3.1.2 Processing NPP VIIRS Nighttime Light Data  
A smooth urban boundary is needed to quantify the urbanization effects on vegetation phenology. Previous studies of urbanization effects on vegetation phenology used nighttime light data to extract  
Figure 3: An example of generating fused 10 m image (18 April 2016): (**a**) raw Landsat-8 image (30 m); (**b**) cloud and cloud shadow detection by ATSA; (**c**) cloud mask after manual correction; (**d**) cloud removal by NSPI; (**e**) fusion with Sentinel-2 images by ESTARFM (10 m), (**f**) and (**g**) zoom-in sub-image marked by a red frame in (**d**) and (**e**).  
Figure 2: Steps of data preprocessing to generate fused 10 m time series and urban extent boundary.  
the urban boundary because it is closely related to human activities [24; 45]. Compared to the urban extent extracted from the classification of daytime satellite images, the usage of nighttime images has several advantages: (1) it is more directly linked to the intensity of urbanization, (2) it is not affected by the classification errors of artificial surfaces, which are easily confused with bare soil and rocks, and (3) it can capture a smooth and meaningful rural-urban boundary. In this study, monthly NPP VIIIRS nighttime light data were averaged to obtain the annual nighttime light over the study area in 2016. The urban boundary will be extracted from this annual nighttime light data. It should be noted that the extracted urban boundary has 500 m resolution, but it will be resampled to other resolutions for computing the rural-urban phenology difference at other scales. Given the large size of Beijing city and surrounding rural areas, a resampled urban boundary will not lead to large errors in the calculation of rural-urban phenology difference.
# Applying Vision-Based Pose Estimation in a Telerehabilitation Application
## 2 Materials and Methods  
For the purpose of this research, the ExerCam application was developed. The main objective of this system is to stimulate patients to perform the therapy prescribed by specialists. In addition, we want to offer specialists (doctors, therapists) a tool to track the progress of their patients.  
ExerCam is designed as an application for telerehabilitation-based exergames. For this, we make use of the augmented reality mirror-type technology based on 2D body pose detection. The system, as seen in Figure 1, makes use of an RGB camera (for example, webcam type) installed in the patient's computer. This camera provides the necessary video input to be able to apply techniques of human pose estimation and augmented reality performed in the ExerPlayer module, which allows the gamified scene to be created. ExerPlayer will show the video on the patient's screen, along with the different virtual elements of augmented reality, the main element being a virtual skeleton superimposed on the patient's body. Depending on the mode of use (calibration, game or task), other virtual objects (objects to hit, decoration, instructions, user interface, etc.) are also superimposed on the video. All the information about the results of the tasks, exercises and patient data is stored in a database on the cloud, together with the information of the tasks and exercises prescribed by the therapist, which will allow the information to be available and updated in both the ExerPlayer and ExerWeb modules. At the other end of the system is the ExerWeb module, where thanks to its web interface and the different algorithms implemented, the therapist can monitor the patient's evolution from a distance and set new types of exercises or tasks.  
In the following subsections, the libraries and modules used in the ExerCam architecture are explained in greater detail.
## 2 Materials and Methods
### Testing Methods  
#### 2.2.1 Selecting Cement Dose and Sample Preparation  
The testing program involved initial trials to investigate the possible concentrations of cement and calcium chloride that were likely to provide good results and were economical to use. Cement is a well-known stabilizer, but the addition of high amounts of cement will make the soil so rigid and it is non-reusable material, apart from being expensive. The initial addition of 1% cement led to minor improvements. The addition of 2% was selected based on previous studies [34]. This amount will not significantly affect the stabilization cost, and it will be sufficient to enhance the stabilization using calcium chloride. The clay samples were oven dried for 24 h and mixed with different proportions of calcium chloride powder to form mixtures of 4%, 6%, and 8% by dry weight of the sample. A further 2% cement by the dry weight of clay was added to each sample. The prepared samples were mixed with distilled to achieve a 32% water content and placed in an oedometer ring in three layers by hand tamping, to achieve a dry unit weight of 12 kN/m\({}^{3}\). The water content and dry unit weight were selected to be close to the optimum values of Al-Qatif clay. Samples were then kept in plastic bags for curing. A curing time of 3, 7, and 28 days was considered for this study.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \(\mathbf{K^{+}}\) & \(\mathbf{K_{2}O}\) & \(\mathbf{Al}\) & \(\mathbf{Al_{2}O_{3}}\) & \(\mathbf{Si}\) & \(\mathbf{SiO_{2}}\) & \(\mathbf{Ca^{2+}}\) & \(\mathbf{CaO}\) \\ (\%) & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) \\ \hline
1.8 & 2.2 & 3.3 & 6.3 & 8.1 & 17.3 & 0.7 & 0.9 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The chemical composition of the Al-Qatif clay. After [27].  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Oxides** & **Percentages** \\ \hline SiO\({}_{2}\) & 19.97 \\ Al\({}_{2}\)O\({}_{3}\) & 5.55 \\ Fe\({}_{2}\)O\({}_{3}\) & 3.96 \\ TiO\({}_{2}\) & 0.36 \\ MnO & 0.06 \\ MgO & 0.75 \\ CaO & 65.98 \\ Na\({}_{2}\)O & 0.08 \\ K\({}_{2}\)O & 0.13 \\ P\({}_{2}\)O\({}_{5}\) & 0.05 \\ SO\({}_{3}\) & 3.08 \\ L.O.I. & 4.00 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The chemical properties of a typical OPC.
## 5 D-MSR Management Phases
### Constructing the Routes (Phase-3)  
In this phase, the routing layer finds the routes between the endpoints. In D-MSR we have used RPL in the routing layer. RPL specifies how the new device finds a path toward the gateway. By generating the RPL control messages, the routing entries in the intermediate nodes will be constructed as well as a complete path toward the new device. Several control messages, e.g., DAO (Destination Advertisement Object control message is used to construct routes to the other intermediate or leaf nodes) message, are forwarded through the network periodically to maintain and update the \"up\" (multipoint-to-point) and \"down\" (point-to-multipoint) routes.
# A Systematic Review of Virtual Reality Interfaces for Controlling and Interacting with Robots
## 5 Future Directions
### Robot Control and Motion Planning  
**Further development in shared-control interfaces.** Currently, the state-of-the-art focuses primarily on using controllers or motion planners that allow operators to teleoperate the robot, but there is a plethora of research on shared-control planners that could be utilized. Results in [48] show that users were faster and more accurate when placing waypoints for a robot to traverse through (shared-control), rather than having the robot directly follow the user's hand (teleoperation). There is opportunity for additional development into integrating these types of shared-control methods as they could remove workload from the operator to handle tasks the robot is well suited for, and allow users to focus on the high-level operation.
## 4 Characterising SDN-Based IoT Architecture
### The Proposed Algorithm for Mitigating DDoS
**Switch Mitigation:** As the switch is compromised, the controller is unable to manage that switch. Thus, SECOD instructs the controller not to process the _Packet_In_ messages from that malicious switch, but to keep counting the number of requests from that switch. When its \(\Sigma_{j}\gamma_{ij}\) is lower than \(\omega\), we regard it as DDoS attack is over, and then the flow entry which blocks the switch is removed. SECOD aims to mitigate DDoS attacks without involving extra components, such as middleware, and we try to automatically localise the source of attack, either from the host or switch, via the control plane. Through the validation in the SDN-based IoT network, we also verified its performance which will be discussed in Section 6. \begin{table}
\begin{tabular}{p{85.4 pt} p{284.5 pt}} \hline \hline
**Symbol** & **Definition** \\ \hline \(i\) & Port ID on a switch, where i = 1, 2... n \\ \hline \(j\) & Switch ID in the network, where j = 1, 2...m \\ \hline \(k\) & IP address ID under a specific switch port, if an IP address is the first IP who generates a request towards the controller under a switch port, then k = 1 for this IP address under switch m port n. \\ \hline \(\gamma\) & Count of _Packet_In_ messages, where \(\gamma_{ijk}\) indicates the \(k\)th IP address from \(i\)th port in \(j\)th switch. \\ \hline \(\omega\) & The maximum number of _Packet_In_ messages that is allowed from one switch within a time period. It is the switch threshold. As the target of DDoS attack is to exhaust resources, \(\omega\) is defined based on the maximum system resources that users would like to assign to the switch for new flow processing, i.e., a switch can generate up to 1000 _Packet_In_ messages per minute, the administrator can set \(\omega\) to 500 per minute, which means the switch is allowed to use up to 50% of its resources to generate _Packet_In_ messages. This value varies according to the application and customer requirement. \(\omega\) is defined before a switch joins the network, and this threshold will not change over time. \\ \hline \(\alpha\) & The maximum number of _Packet_In_ messages that is allowed from one switch port within a time period. It is the port threshold. The initial port threshold is \(\alpha^{\prime}\), it is defined by \(\omega\) and the number of ports under a switch, i.e., a switch has 5 ports and \(\omega\) is 500 per minute, then \(\alpha^{\prime}\) is 100 per minute. \(\alpha\) will update according to the network behaviour. \\ \hline \(\rho\) & The maximum number of _Packet_In_ messages that is allowed from one IP address under a specific switch port within a time period. It is the IP threshold. \(\rho\) is also dependent on the performance of the switch, because a more powerful switch could allow a user to make more requests. This threshold is predefined by the administrator and will not change over time. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Parameters in the SECOD algorithm. Figure 2: Workflow of SECOD algorithm.
# Ontology-Based Context Event Representation, Reasoning, and Enhancing in Academic Environments
## 3 Event-Driven Approach Based on an Ontology Model
### Event Basis  
Events happen in any daily process. People know and perceive the world through events. They help to organize the perceived information and give a spatial and temporal order; in addition, they situate the protagonists that appear. In this way, events have been conceived as an activity that involves a change of state, a location, and temporal and cause-effect aspects [37; 38]. A definition from the point of view of the knowledge representation field is presented in [39], which affirms that all events are given according to intervals and instants of time and that they involve a causality. On the other hand, according to [40], an event is an entity that can be involved in causality and that is identified by its location in a region of space-time. Finally, The authors of [6] affirm that events can be organized into five issues: Location, temporality, protagonists, causality, and intentionality.  
The definitions of events presented above come from different perspectives. However, they share a common meaning, which is considered to be the concept of an event as an action or activity that is characterized by the following dimensions: Actors, location, temporality, and causality. In this paper, we adopt such definitions and we rely on the events being the core of the context information in academic environments.  
According to this cognition, we obtain a formal definition of the event as follows. An event E is defined as E = (E, A, L, T), where E is the event category, A is the actor involved in the event, L is the location information, and T is the time associated with the event.  
The assumption of events as a core of the academic context is part of the basis in our context modeling since they help to build a representation model for supporting the reasoning and enrichment process. Therefore, they provide the fundamental knowledge for context modeling using ontologies in the academic environment.  
According to [PERSON] [41] \"an ontology is a formal description of the concepts and the relationships between them\". Thus, ontologies provide the mechanism to represent concepts formally, they are human-understandable and readable by computers. Ontology elements are concepts (classes), taxonomic relations, non-taxonomic relations (data or object with domain and range), and individuals. We propose that each component of the event becomes a context module or individual ontology. With the purpose of integrating a final customized ontology system for an academic domain, where all modules are connected.
## 1 Introduction  
Fistulas of the lower urinary tract are serious complications that can occur after treating various pelvic abnormalities [1, 2, 3, 4]. Surgical management, such as ureterostomy and ureteral clipping, is the standard palliative treatment of such fistulas; however, most fistulas are inoperable [1, 4]. Various minimally invasive techniques have been described for ureteral occlusion, using various materials; however, primary success rates fall within 40-100%, with a reintervention rate of 66.7% for ureters that received embolization [5, 6, 7, 8, 9, 10, 11, 12, 13]. Nephrostomy, for interventional transcranial ureteral occlusions, has been proposed as an alternative therapeutic method [4]. Percutaneous occlusion of urinary flow has been investigated using various materials, such as detachable balloons, embolization coils, Amplater vascular plugs, n-butyl cyanoacrylate, and combinations thereof [2, 3, 4, 7, 14, 15, 16, 17, 18]. Despite recent advances in occlusive materials and technical approaches, each method has its own limitations. Furthermore, additional studies are required to prove their efficacy and safety in preclinical research.  
The covered self-expandable metallic stents (SEMSs) have been considered as an alternative therapeutic option to percutaneous drainage and double J stents for malignant ureteral occlusion [19, 20]. In 2015, a silicone-covered SEMS, designed in accordance with the occlusive principle to have a candy-wrapper configuration, was first introduced for treating ureteral leakage [14]. Newly developed transrenal ureteral occlusion SEMSs are safe and effective in patients with inoperable urinary leakage or fistulas [14; 15]. The two commonly used occlusion SEMSs for ureteral fistulas include M- and D-types, whose middle and distal portions are constricted with a nylon thread, respectively [14]. However, the occlusive properties of the occlusion SEMSs have not been evaluated, and further investigation is required in preclinical trials. Herein, the pressure changes that maintain the ureteral occlusion were quantitatively analyzed to validate the occlusion SEMS. Therefore, this study aimed to evaluate the occlusive properties of M- and D-type occlusion SEMSs using 3D-printed phantom and extracted porcine urinary tracts.