esa-sceva/satcom-chunk-collection huggingface dataset:
# Towards Crowdsourcing Internet of Things (Crowd-IoT): Architectures, Security and Applications
## 6 Applications for Crowd-IoT
### Specific Applications
#### 6.2.3 Ocean/Marine & Environmental
The suggested method was feasible for gathering air quality data in city areas, according to the algorithm's evaluation and performance when compared to ground truth. [PERSON] (2019) presented a framework for processing sensor data (GPS location and depth sounder) generated by leisure boat to create and update a comprehensive 3D seabed (bathymetry) map. The data collection was executed through DYNAMO, an IoFT ecosystem, while the cloud hosted FACE-IT Galaxy workflow engine was used to organize and combine the data gathered through DYNAMO. The system employed CUDA-accelerated algorithms on virtual machine (VM) instances that support NVIDIA CUDA to process the highly voluminous data. GVrtus was used to virtualize the CUDA calls, allowing the interpolation method to run on ordinary VMs, and the CUDA-enhanced algorithms to run on GPU-enabled VMs when needed in order to minimize cost. The result of the experiment indicated that the system successfully collected and processed data and can readily scale to handle bigger inputs at moderate cost. [PERSON] and [PERSON] [PERSON] and [PERSON] (2019) constructed a low-cost weather monitoring system that obtains weather conditions for any place from a cloud database management system and displays the results on an OLED display. The suggested system employed an ESP8266-EX microcontroller-based Wemos D1 board that was built on the Arduino platform to fetch the data from the cloud.
## 2 Designing Pepita
### The Form of Pepita  
The appearance of social robots can be divided into four categories: anthropomorphic, zoomorphic, caricatured, or functional [15]. It was found that the appearance factor has a strong effect on the perception of a robot's abilities, and it might also be linked to the expectation of its capabilities [16; 17]. Each of the four categories for a social robot's appearance has been perceived to be more suitable for specific tasks [18].  
Anthropomorphic robots are designed with a human-like appearance, and they are meant to interact with humans in a human-like way, using different gestures, facial expressions, and body postures. They are appropriate for service applications and public spaces [19]. Because these robots can manage non-verbal communication, they can be found in robot-assisted therapies for individuals with developmental disorders [20]. Robots with a high degree of anthropomorphism can potentially elicit human-like communication, making them the most appropriate to use when investigating human behavior [21; 22]. Because these robots have a human-like appearance, designers work hard to develop behaviors and interactions that can meet the user's expectations, resulting in engaging and meaningful interaction.  
Living creatures inspire the design of zoomorphic robots. Their social application is related to the potential for creating a feeling of pet-ownership and companionship [23]. There are many cases in which the benefits of animal-robots have been exploited, including applications in hospitals and nursing homes [6; 24]. Concerning appearance, zoomorphic robots face challenges similar to those for anthropomorphic robots. Those with a more realistic appearance need to display a behavior that matches the user's expectations; otherwise, it might lead to a reduced level of engagement [25].  
A functional robot is designed to allow a user to understand the robot's functions just by its appearance. These robots are not meant to imitate a living creature. Instead, they are usually designed with a machine-like appearance. They interact with people in a very task-oriented manner, making the interaction with humans less complex concerning social aspects. An example of this category is robots with parallel arms or bars to support nurses lifting patients [26]. Robots that help to carry luggage [27] and robot suits can also be considered functional robots [28]. These robots are designed to complete specific tasks safely, and the display of emotional behavior is not as relevant in this context.  
The proposed companion robot Pepita was designed with a caricatured appearance with simplified anthropomorphic features. Caricatured robots have a simpler appearance and are capable of expressions in their unique way. Giving a robot a caricatured appearance reduces the user's expectations and makes it possible to design a robot behavior with believable results [15; 29]. This type of robot is considered to be especially suitable for home environments [30]. We consider that, among the different appearances for robots, a caricatured one is more suitable for the design of Pepita.
## 3 Results and Discussions
### Electrochemical and Photophysical Properties
At 1200 \(\mathrm{cd}\)/\(\mathrm{m}^{2}\), TPA-1A exhibited an excellent current efficiency of 49.13 \(\mathrm{cd}\)/A when compared to NPB (27.21 \(\mathrm{cd}\)/A). Consequently, the current efficiency of the TPA-1A-based device dramatically increased with the luminescence value of 1282 \(\mathrm{cd}\)/\(\mathrm{m}^{2}\). Interestingly, the TPA-1A-based device showed an outstanding external quantum efficiency (EQE) of 13.74%, while the NPB-based reference device showed a lower EQE of 7.64%. We believe that the higher triplet energy of TPA-1A helped to improve the efficiencies when compared to the NPB-based device. All data are summarized in Table 2. As a result, the TPA-1A-based green PhOLED revealed excellent device efficiencies than the reference (NPB) device. Additionally, we have studied the hole-injecting property of TPA-1A in which we used NPB as the hole-transporting material. The device characteristics and efficiencies are shown in Figure 5 and summarized in Table 2. The current efficiency was noticed as 37.09 \(\mathrm{cd}\)/A, while the power efficiency was 32.37 \(\mathrm{lm}\)/W at the turn-on voltage (3.6 V). Consequently, the TPA-1A (HIL) device showed an external quantum efficiency of 14.59, 8.53% at the turn-on voltage and 1200 \(\mathrm{cd}\)/\(\mathrm{m}^{2}\), respectively. TPA-1A (HIL) device exhibited better performance when compare to other devices which used HATCN as the hole-injecting layer with the NPB hole-transporting material. Finally, we found that overall performance of TPA-1A as a hole-transporting and hole-injecting material enhanced the device efficiencies. Figure 5: J-V-L, luminescence-current, and power efficiency curves of TPA-1A-based devices. The electroluminescence spectra of TPA-1A and NPB-based green PhOLEDs are shown in Figure 6. Both devices exhibited an identical EL emission peak, which was attributed to the CIE (x,y) colour coordinate values of 0.33, 0.62 and 0.33, 0.63 for TPA-1A- and NPB-based PhOLED, respectively.
###### Abstract  
Using GNSS observable from some stations in the Asia-Pacific area, the carrier-to-noise ratio (CNR) and multipath combinations of BeiDou Navigation Satellite System (BDS), as well as their variations with time and/or elevation were investigated and compared with those of GPS and Galileo. Provided the same elevation, the CNR of B1 observables is the lowest among the three BDS frequencies, while B3 is the highest. The code multipath combinations of BDS inclined geosynchronous orbit (IGSO) and medium Earth orbit (MEO) satellites are remarkably correlated with elevation, and the systematic \"V\" shape trends could be eliminated through between-station-differencing or modeling correction. Daily periodicity was found in the geometry-free ionosphere-free (GFIF) combinations of both BDS geostationary Earth orbit (GEO) and IGSO satellites. The variation range of carrier phase GFIF combinations of GEO satellites is \(-2.0\) to \(2.0\) cm. The periodicity of carrier phase GFIF combination could be significantly mitigated through between-station differencing. Carrier phase GFIF combinations of BDS GEO and IGSO satellites might also contain delays related to satellites. Cross-correlation suggests that the GFIF combinations' time series of some GEO satellites might vary according to their relative geometries with the sun.  
BDS; GNSS; CNR; multipath; GFIF; elevation; hardware delay +
Footnote †: journal: Sensors
# The Design and Implementation of a Novel Open Source Massive Deployment System
## 5 Discussion
### Cost Comparisons  
The feasibility of Clonezilla live to deploy one source image to multiple drives in a single machine has been verified. When using other deployment software, users must deploy the source image one by one. Otherwise, users must write their own script(s) or software for this kind of massive deployment. However, users' writing of their own scripts makes the deployment more complicated. Hardware solutions are also available for deploying one source image to multiple drives. However, such hardware solutions are much more expensive than using Clonezilla live. The overall cost to assemble the USB drive duplicator by using four USB hubs and Clonezilla live in this experiment was about $200. (The cost of the PCs and the time cost for learning how to use the Clonezilla live are excluded.) Furthermore, the PC is easily available and Clonezilla live is ready to use; no installation and configuration are required. Hence, the cost of the PC and the software learning can be ignored when comparing Clonezilla live versus other solutions. In Table 4, the prices of the solutions that are similar to Clonezilla live are summarized. Meanwhile, the ratios of Clonezilla live to the costs of each solution are provided. In general, the average price of other similar solutions was $2561, which is about 13 times the cost of the Clonezilla live solution.  
In addition, Clonezilla live is an open source program. All of the source codes are easily available on the GitHub [48; 49] platform. The developer can modify and add new features based on this paper easily. Thus, this USB drive duplicator based on Clonezilla live has higher values than other commercial proprietary products from the aspects of cost, upgradability, and modification.
# Reliability of NAND Flash Memories: Planar Cells and Emerging Issues in 3D Devices
## 2 The NAND Memory Array
### Array Operation
#### 2.2.3 Erase  
Erase operation is performed on blocks, i.e., a set of pages that can be 4-8 MB in size, exploiting the same FN mechanism used in program. As NAND chips do not allow the use of high negative voltages, WLs are grounded and a high positive voltage is applied to the \(p\) and \(n\) wells of the array. A verify phase follows, aimed at ensuring that all cells have negative \(V_{T}\), which triggers another erase pulse if the condition is not met: the technique is similar to what is used during program, and is called _incremental step-pulse erasing_. Floating WLs are used to inhibit erase on unselected blocks.  
Figure 2: Pictorial view of the threshold voltage (\(V_{T}\)) distribution in a TLC cell. A three-bit code is associated to each of the \(V_{T}\) levels.
# Towards Hierarchical Workflows in SysML to Support Virtual Validation of Technical Systems
## 4 Method
### The Framework of Virtual Testing Workflows Based on the Functional Architecture
#### Adding a new virtual validation workflow  
Modular simulation activities, organized according to domain model classification, offer significant flexibility in designing virtual testing workflows, which ensures that domain model simulation activities can be easily reused in different virtual testing workflows. Therefore, workflows can be easily adapted when the solution is used in different system contexts.  
As shown in Figure 7, corresponding to the system architecture [23], the system scopes should be regarded at the highest level within the nested structure. In the framework,  
Figure 6: Adding virtual validation workflows & virtual testing workflows.  
each system scope contains domain models with different model purposes. In addition, analogous to the hierarchical design of the functional virtual validation workflow, a large system scope contains small system scopes. As an example, the activity in the scope \"Wind Turbine\" is divided into two parts:  
* The first part is a parallel arrangement of actions that, respectively, invoke the simulation activities regarding the domain models of the solution \"Wind Turbine\". These domain models are classified according to the purpose of the model, such as Load analysis models and AEP models of the wind turbine, etc.
* The second part is the action, which contains sub-actions arranged in parallel. These sub-actions are used to invoke the activities regarding its sub-scopes (e.g., Nacelle) of the \"Wind Turbine\". These sub-scope activities follow the same design as above, and also contain their own sub-scopes (e.g., Gearbox, Bearing), and so on to form a hierarchical structure.  
Figure 7: The nested framework of virtual testing workflows corresponding to the hierarchical system scopes.  
Ref. [19] mentioned that model fidelity also needs to be considered in the virtual testing workflow. This work further develops simulation activities with specific model purposes. As shown in Figure 8, similar to the nested structure design of the activities with a specific system scope, an activity for the same purpose includes sub-simulation activities with different model fidelities. For example, the \"AEP Models\" activity is used to calculate the annual energy production of a WT and this activity contains two actions arranged in parallel. These two actions, respectively, invoke two simulation activities with different model fidelity, which are named the \"MATLAB mathematical model (Fidelity-A)\" and the \"Simpack simulation model (Fidelity-B)\" to calculate the values of the annual energy production.  
In the virtual testing workflow, the activity with specific model fidelity can perform external simulations by triggering constraints in ibds; details are already published in the related paper [19].
## 5 Design Overview  
Figure 1 illustrates our overall architecture. Each _patient_ has one wireless _mote_ attached to his/her body. The mote is connected (wired) to several _medical sensors_, which take samples of the patient's health data when they are activated. A _medical professional_ issues queries for a patient's data through one or a few _base stations_. Queries issued from base stations may activate the patient's medical sensors or adjust their sampling frequency and other parameters. Before sending the queries, the base station verifies that the medical professional is a legitimate user with the necessary privileges to access the particular patient's data. After the mote receives the query from the base station, it activates the appropriate sensor(s) or adjusts its parameters. It also continuously sends the resulting patient data from the sensors to the base station until the sensors are deactivated. Wireless _relay nodes_ forward the queries and patient data between the base stations and the motes. Note that since our design focuses on _securing_ the communication between the motes and base stations, we do not consider the routing protocol used to transfer data through the relay nodes.
## 3 Methodology
### Data Set Compilation  
In this work, the CICIoT2023 [11] data set was used for the proposed model. The data set was created to meet the growing demand for reliable security analytics applications in real-world IoT contexts. The experimental setup comprises a complete IoT architecture with 105 networked devices that simulate a genuine operational environment. Within this network, 33 different cyberattacks were carried out, each meticulously planned to mirror the changing threat landscape of IoT security. We have distributed the data set in four parts containing 467,449 data at each end, containing 340,557 data of DDoS, 81,205 data of DoS, 26,268 data of Mirai, 3113 data of Recon, 11,011 Benign data, and 5275 data of other classes combined. In order to provide diversity and authenticity in attack situations, compromised IoT devices were intentionally used as hostile entities, attacking other IoT devices in the network. The data set encompasses a wide range of harmful activities, with the goal of serving as a standard for the research community in developing and assessing intrusion detection and mitigation solutions. The assault diversion is shown in Figure 1.  
The recorded attacks are systematically categorized into seven major classes, each representing a fundamental aspect of IoT security threats:  
1. Distributed Denial of Service (DDoS): Large-scale flooding assaults meant to harm multiple IoT gadgets simultaneously to exhaust computational resources and disrupt network availability.
2. Denial of service (DoS): single-source attack tactics designed to overwhelm a specific IoT device, making it unresponsive to valid queries.
3. Reconnaissance (Recon): passive and active network scanning techniques are used to obtain information on vulnerable IoT devices, services, and network setups.
4. Web-based attacks: exploitation of IoT web interfaces using security holes in IoT web interfaces, including SQL infiltration, command insertion, and cross-website scripting to gain unauthorized access.
5. Brute-force attacks: systematic password-guessing attacks targeting IoT authentication mechanisms to compromise credentials and gain illicit control over devices.  
VI Spoofing attacks: identity forging techniques, such as ARP and IP spoofing, are used to masquerade as legitimate IoT organizations in order to eavesdrop or manipulate communications. VII Mirai-based attacks: malware-driven attacks use the Mirai botnet to exploit vulnerabilities in IoT device security, allowing for large-scale infections and subsequent coordinated cyber attacks.  
This data set is a crucial asset for cyber security research, allowing for the creation and testing of advanced machine learning models, intrusion detection systems, and anomaly detection approaches designed specifically for IoT security applications.
# Light Propagation and Multi-Scale Enhanced DeepLabV3+ for Underwater Crack Detection
## 4 Results
### Computational Efficiency  
To assess the real-time applicability of the proposed method, this paper measured the inference speed of the enhanced DeepLabV3+ model. As summarized in Table 4, our model achieves an average frame rate of 68 FPS on an NVIDIA RTX 4090 GPU, with a mean inference time of 14.7 ms per image. This exceeds typical real-time thresholds (e.g., 30 FPS), making it suitable for integration into ROV-based inspection systems or real-time video stream analysis. To validate practical deployment viability, this paper benchmarked the proposed model against real-world ROV (Remotely Operated Vehicle) inspection requirements. Industry standards mandate: minimum 30 FPS for continuous video inspection; maximum 50 ms latency for real-time feedback. The proposed method's enhanced DeepLabV3+ achieves 68 FPS (14.7 ms/image) on an NVIDIA RTX 4090 GPU, exceeding minimum requirements by 2.3\(\times\). This performance enables real-time 4K video processing at 30 FPS (66% headroom); synchronized inspection with ROV navigation speeds up to 3 knots; and immediate defect flagging with <20 ms decision latency.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
\begin{tabular}{c} RTX 4090, 24 GB \\ VRAM \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of computational efficiency.  
Figure 11: Performance under challenging conditions: (**a**) original images; (**b**) labels; (**c**) enhanced DeepLabV3+; (**d**) original DeepLabV3+.  
While the enhanced model introduces minor additional complexity over the baseline DeepLabV3+, it maintains high inference efficiency. For edge deployment, future work may explore TensorRT optimization, quantization, or lightweight backbone substitution to further reduce latency on devices like Jetson AGX Xavier.
## Appendix A Maximum Fiber Fronthaul Distance Corresponding to Round-Trip Latency Barrier  
In Section 2.1, we briefly mentioned the 5G fronthaul latency requirements imposed for low-latency services. This Appendix provides the calculation of the maximum fiber fronthaul distance which satisfies this latency constraint.  
Table 11 includes the latency components considered for our study, by adopting the available parameters within [28].  
The additional QKD post-processing delay component was calculated by assuming the use of the high-speed postprocessing modules reported by Toshiba recently [73], to realize the operations of sifting, error correction and privacy amplification for the key distillation process of an AES-256 key. Following the methodology reported in [28], for a round-trip processing of less than 3 ms, the fiber fronthaul distance is limited to be less than  
\[D=\frac{3\;\text{ms}-40\;\text{$\mu$s}-10\;\text{$\mu$s}-2700\;\text{$\mu$s}-4 \;\text{$\mu$s}-2\times 34\;\text{$\mu$s}-11\;\text{$\mu$s}}{10\;\text{$\mu$s}/ \text{km}}\cong 17\;\text{km}. \tag{1}\]
## 3 Results
### Estimation of Forest Degradation Indicators
#### 3.1.2 Net Primary Productivity (NPP)  
The Normalized Difference Vegetation Index (NDVI) was highly correlated (\(r=0.88\), \(p<0.05\)) to fPAR, according to the Pearson correlation coefficient. Therefore, we fitted a linear equation (a = \(-1.05\), b = 2.45, R\({}^{2}\) = 0.76) that estimates fPAR as a function of NDVI.  
PAR (MJ/m\({}^{2}\)) estimated from the meteorological station network (Figure 3) was used to derive NPP shown monthly. Its variability over time is shown in Figure 2. Given that Landsat imagery was not consistently acquired for all months of the years evaluated, where there was a lack of imagery, the closest Landsat imagery date to the required date was used to estimate NPP for a specific month. Therefore, we assumed that conditions of fPAR were similar on both dates.  
Figure 3: Temporal variation of average Photosynthetic Active Radiation (PAR) over the years in the study area.  
Figure 2: Methodological path to monitor and assess forest degradation in a dry semi-deciduous forest.  
NPP estimated from the period 2007-2013 showed a maximum value of 843 gC/m\({}^{2}\)/year and a mean of 547 gC/m\({}^{2}\)/year.