esa-sceva/satcom-chunk-collection huggingface dataset:
# Modeling of Historical Marine Casualty on S-100 Electronic Navigational Charts
## 5 Discussion and Limitation
### Future Development Opportunities  
The limitations identified in this study also point toward several promising directions for further development. Most notably, the reliance on manual data mapping highlights the need for automated structuring of unstructured narrative reports. Advances in Natural Language Processing (NLP), particularly in multilingual and domain-specific contexts, can support automated extraction of incident attributes from text.  
Additionally, expanding the model's geometry support to include lines and polygons will allow for richer spatial analysis. Coupled with real-time integration capabilities and context-aware filtering, this will enable dynamic updates and personalized information delivery based on vessel position, route, or risk thresholds.  
By addressing these areas, the proposed model can evolve into a core component of next-generation maritime information infrastructures--supporting proactive risk monitoring, navigational safety, and smarter vessel operations.
## 3 Deep-Learning-Empowered Security Solutions for IoT Systems
### Detecting Threats and Taking Preventative Action
\begin{table}
\begin{tabular}{p{14.2 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Ref.** & **Algorithm** & **Results** & **Limitations** \\ \hline
[69] & Security for the IoT based on deep learning, with support for both deep model (DM) and shallow model (SM) attack detection & Accuracy: 99.20\% Precision: 95.22\% of IoT traffic and related attacks. \\ \hline
[73] & IoT malware detection using a deep-learning-based long-short-term memory (LSTM) algorithm trained on Opcodes. & Accuracy: 98\% & The dataset used in this work was extremely small. \\ \hline
[61] & Recurrent neural networks for intrusion detection using deep learning (RNN-IDS). & Accuracy: 97.09\% Precision: 83.28\% & A long testing time is required for detection. \\ \hline
[74] & Federated DL-based method for multiparty computation that takes into account the safety of IoT devices. & Accuracy: 56\% & The detection accuracy achieved by the algorithm is unsatisfactory. \\ \hline
[75] & D’IoT, which is a proposed autonomous self-learning system for identifying vulnerable IoT devices. & Accuracy: 95.6\% Precision: 92.10\% & The reliability of detection is poor. To improve detection accuracy, a method of feature selection is required. \\ \hline
[73] & A recurrent neural-network-based anomaly detection system. & Accuracy: 98\% & It is not possible to factor in testing time. \\ \hline
[76] & A new technique for packet-level detection in the IoT and networks was developed using deep learning and bidirectional long short-term memory (LSTM). & Accuracy: 99\% Precision: 98\% & Testing time was not calculated. \\ \hline
[77] & Method for mobile malware detection using Q learning for efficient offloading. & Accuracy: 67\% Predictability is too low. \\ \hline
[78] & Model based on deep learning that uses LSTM to detect bots by leveraging content and metadata. & Accuracy: 90\% & The reliability of detection is poor. \\ \hline
[66] & Technique for detecting intrusions using deep learning based on the combination of a deep belief network and a probabilistic neural network. & Accuracy: 99.14\% missing. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Deep learning for detecting threats in the IoT.
# Characterization of Tropical Cyclone Intensity Using the HY-2B Scatterometer Wind Data
## 3 Method
### TC Wind Radii  
Taking the identified HSCAT TC center as the origin of Polar coordinates, one may estimate the TC wind radius as follows [29]. The wind speed profiles along a set of equally spaced azimuth angles (e.g., \(\Delta\phi\) = 15\({}^{\circ}\), 24 intervals in total) are calculated using spatial bilinear interpolation. Practically, the radial profile is calculated at a set of discrete grid  
Figure 2: Illustration of the divergence (**a**) and the curl (**b**) of HSCAT wind stress (TC Faxai) acquired on 7 September 2019 at UTC 16:34.  
points (\(\Delta d\approx 5\) km) starting from TC center. At the \(i\)th azimuth angle, the radial extent of 17-m/s wind is recorded as \(d_{i}\). As such, the azimuthally averaged radius (R17) is given by,  
\[\text{R17}=\frac{1}{24}\sum_{i=1}^{24}d_{i} \tag{1}\]  
The azimuthal average removes most of the asymmetry associated with TC motion [30], and makes the analysis in Section 4 rather straightforward. However, due to rain contamination and radar measurement noise, the wind speed profile may have spurious oscillations, leading to multiple peaks, and, therefore, multiple 17-m/s intersections. For example, in Figure 3a,b, one may get an inaccurate result if the 17 m/s intersection closest to the TC center (the origin of the used coordinate) is used to estimate R17. In this case, the 17-m/s intersection whose distance (the blue marker in Figure 3b) to the TC center is closer to the 17-m/s radial extent at neighboring azimuth angles is selected, as shown in Figure 3c. Although such adjustment only happens about 2.7% of the times within the 32 \(\times\) 24 HSCAT wind profiles, it proves to be generally efficient.  
Note that the rainfall associated with TCs has remarkable impact on the Ku-band pencil beam scatterometer winds, generally resulting in overestimated low winds and underestimated high winds when heavy rain is present [19]. The operational quality control (flag) of PenWP shows difficulty in discriminating true high winds (w > 17 m/s) from the rain contaminated retrievals using [20], and generally removes too many high winds around the TC core. Consequently, the QC flag is ignored in the calculation of wind radii.
# Towards Harmonious Coexistence in the Unlicensed Spectrum: Rational Cooperation of Operators
## 1 Introduction  
### The Next Generation Wireless Technologies and Spectrum Scarcity  
Ever increasing traffic demand and ultra-low latency requirements have driven technological advancements in wireless communications, namely 5G New Radio (NR) [1]. The previous 4G network deployments with Long-Term Evolution-Advanced (LTE-A) have successfully offered not only great quality of service (QoS), high spectral efficiency, and long-range coverage [2; 3], but also a myriad of new business opportunities in different industries such as smart grid, healthcare, and Internet of Things (IoT) [4; 5]. Along with the Fourth Industrial Revolution, to support ultra-high capacity and ultra-low latency wireless systems, key enabling technologies such as ultra-dense small cell networks, massive multiple-input multiple-output (MIMO), and full-duplex communications are just around the corner [6]. In addition, new spectrum usage paradigms have been considered to make more spectrum resources available to 5G-related services because spectrum scarcity is expected to be one of the fundamental bottlenecks in 5G NR [7].
## 2 Material and Methods
### Radiation Dose Measurement  
An acrylic phantom was placed at the examination table and made to lean against the isocenter of the C-arm X-ray machine for simulating the left chest of patients, as shown in Figure 1. The maximum distance between the two X-ray tubes and the image intensifier was 100 cm. During the image collection process, the image intensifier was placed as close as possible to the phantom. The operator measurement point was located to the right of the patient table, to simulate transradial cardiac catheterization. The linear distance between the image intensifier and operator was 80 cm. The operator radiation dose measurements were performed by measuring radiation exposure at the lens of the eyes, thyroid glands, and gonads, with and without a ceiling-suspended lead glass shield (30 \(\times\) 50 cm, lead equivalent 0.5 mm, Mavig, Germany), and an FLS or CLS. The distance between the X-ray tubes and lens of the eyes, thyroid glands, and gonads was 160 cm, 145 cm, and 90 cm, respectively. A nanodot OSLD was placed at the location corresponding to each of the aforementioned organs to measure their radiation doses. Three measurements were performed at each organ, and the average radiation dose of each radiation angle was obtained by averaging the readings from three CAG procedures.  
Figure 1: The geometry of measurement of the coronary angiography for cardiology: (**a**) clean lead glass shielding (CLS); (**b**) fogged lead glass shielding (FLS).  
Repeated radiation dose measurements were taken with the ceiling-suspended lead glass shield being positioned in four different placements (near or far from the image intensifier, and horizontal or vertical). Regardless of whether the lead glass shield was near or far from the image intensifier, it was always placed close to the patient, at an angle of 90\({}^{\circ}\). The distance between the lead glass shield and image intensifier was set to 20 cm or 70 cm, to realistically simulate a clinical setting. In the former case, the lead glass shield was close to the patient and placed in the horizontal position (see Figure 1(a)); in the latter, the lead glass shield was close to the operator, and the shield was either placed in the horizontal or vertical position, as shown in Figure 1(b). The experimental conditions of geometrical configuration for radiation dose measurements were summarized in Table 1.
## 3 Results
### Evaluation of the Biological Effects of the Isolates from the Hulls of T. aestivum  
All compounds were evaluated for their anti-inflammatory activities and cell viabilities in LPS-induced RAW 264.7 macrophage cells. The LPS treatment markedly increased NO production in RAW 264.7 cells compared with that in the control cells, whereas the **1**-**4** and **6**-**8** pretreatment inhibited NO production in a concentration-dependent manner (Figure 2). We could not observe any NO production reduction in the case of **5**. All compounds except for **4** exhibited no obvious cytotoxicity at 40 \(\upmu\)M; however, **4** showed slight toxicity. These results are shown in Table 1. In the structures of **2**-**4** in which the \(O\)-acetyl group was substituted to C-9\", **2** and **4** with 7**\",8\"-_throe_-configuration inhibited NO production at lower inhibitory concentrations than the _erythro_ type of **3**. However, in the structures of **5**-**8** in which the hydroxy group was substituted to C-9\", **6** and **8** with 7**\",8\"-_erythro_-configuration were more effective than the _throe_ types of **5** and **8**. Therefore, it is expected that the substituents at C-9\" and the stereochemistry of C-7\" and C-8\" influence the activity of the compounds. Among the tested compounds, two compounds (**4** and **6**) showing IC\({}_{50}\) values less than 40 \(\upmu\)M were considered as active components of the _T. aestivum_ hull for targeting inflammation-related diseases. However, in order to verify the potential benefit of **4** and **6** as an anti-inflammatory agent, further mechanism and in vivo inflammation disease studies are required.  
Figure 2: The effects of **1**-**8** on NO production in RAW 264.7 cells. The values are expressed as the mean \(\pm\) SD of three independent experiments. \({}^{\#}\)\(p\)\(<\) 0.05 vs. negative control. \({}^{*}\)\(p\)\(<\) 0.05 vs. the group treated with LPS only.  
Anti-inflammatory compounds have been discovered in natural products [49; 50]. The laboratory models for anti-inflammatory screening of naturally occurring compounds are important tools for discovering drug candidates related to various inflammations [51]. To the best of our knowledge, our study is the first report evaluating the anti-inflammatory activities of compounds **2-4**, **7**, and **8**, even though compounds **1**, **5**, and **6** have been reported to exhibit inhibitory effects on LPS-induced nitrite or NO production in RAW 264.7 cells [24; 27; 34; 48]. In particular, **5** has inhibited NO production with an IC\({}_{50}\) of 10.8 uM [34] and has shown 43.76% inhibition at a concentration of 10 ug/mL [27] or 64.0% inhibition at 20 ug/mL [24] in the previous reports. However, its inhibitory activity was not observed in this study. In addition, there have been reports that **2** and **5** exhibited DPPH scavenging activities [45] and **5-8** showed phytotoxic effects against plant seedlings [28]. However, **3** and **4** have never been associated with any biological activity.
## 2 Related Work in WBAN MAC Protocols  
Most of the devices used in WBAN applications store the recorded data or transmit it to a monitoring station using IEEE 802.15.1 (Bluetooth) or IEEE 802.15.4 (ZigBee) protocols. These wireless standards  
Figure 1: Example of WBAN integration.  
are well documented and tested, but are not an ideal choice for the wireless BAN because they target more flexible networks and are used for longer transmission ranges. This makes them less energy efficient than protocols that are specifically targeting a BAN [3]. A comparison and optimisation of two popular WBAN technologies, Bluetooth and ZigBee, is given in the comparative study [4], in terms of design, cost, performance and energy efficiency.  
As mentioned in [5] the long term health monitoring of patients requires low power techniques. Medium Access Control (MAC) protocols play a significant role in determining the energy consumption in wireless communication. Traditional MAC protocols mainly focus on improving bandwidth utilisation, throughput, and latency. However, they lack energy conserving mechanisms, which is one of the most important constraints of a WBAN. The main sources of energy waste are collision, idle listening, overhearing, and control packet overhead. MAC protocols maximise the network lifetime by controlling the aforementioned sources of energy waste. Some contention based protocols such as WiseMAC [6] and BMAC [7] use low power listening and preamble sampling techniques to reduce idle listening. Other protocols such as SMAC [8], TMAC [9] and PMAC [10] reduce idle listening by applying a synchronised schedule between the nodes. Contention-based solutions are not suitable for WBAN because most of the traffic is correlated.  
Recent works have proposed TDMA-based MAC protocols for wireless sensor networks [11; 12; 13; 14] and protocols especially for WBAN [15; 16; 17; 18; 19]. Some are directed by the type of transceiver used [15; 16].  
Newer WBAN protocols incorporate the concept of Wake Up Receiver (WUR) in their design. A Traffic-adaptive MAC protocol (TaMAC) for WBAN that improves energy efficiency by exploiting the traffic information (traffic-patterns) of the nodes is presented in [5]. Other solutions that incorporate WUR for energy efficiency is presented in [20; 21]. These solutions are based on a fictional (concept) WUR, but prove that if presented with a WUR device, power consumption of any protocol can be significantly reduced.  
We expand the work on wireless wake-up functionality protocols by including a developed and tested WUR, and present a formalisation in the context of Wireless Health monitoring.
# Removal of Metals and Dyes in Water Using Low-Cost Agro-Industrial Waste Materials
## 2 Materials and Methods
### Adsorption Isotherms  
The adsorption isotherms describe the interaction between adsorbate and adsorbent in the equilibrium. In accordance with the interaction between the surface of the material, the adsorbate particles, and the nature of the process, the results can be described by established models for determining the adsorption mechanism and measuring the adsorption capacity of any adsorbent [27]. The equilibrium of adsorption was studied by varying the initial concentration of MB (10, 20, 40, 60, 80, and 100 mg/L) and Cr (VI) (5, 10, 15, 20, 30, 40, 50, 75, 100, 150, 200, 250, and 300 mg/L) and using 30 mg of adsorbent at room conditions and 200 rpm for 24 h. The experimental data were adjusted to the models of [PERSON] and [PERSON].  
[PERSON]'s model (Equation (4)) describes monolayer adsorption based on specific homogeneous sites in the adsorbent's surface, forming a single surface layer [28].  
\[q_{e}=\frac{q_{max}^{L}k_{L}C_{e}}{1+k_{L}C_{e}} \tag{4}\]  
where \(C_{e}\) is the remaining concentration of pollutant in the equilibrium [mg/L], \(q_{e}\) is the amount of pollutant adsorbed in the equilibrium [mg/g], \(q_{max}^{L}\) is the maximum adsorption capacity of [PERSON]'s model in the equilibrium [mg/g], and \(k_{L}\) is [PERSON]'s constant associated with the affinity of the adsorbent for the pollutant [L/mg]. [PERSON]'s model (Equation (5)) is based on monolayer adsorption but considers the heterogeneity of the active sites with different activation energies for the adsorption process [29].  
\[q_{e}=K_{F}C_{e}^{1/n} \tag{5}\]  
where \(K_{F}\) and \(n\) are constants of the model.
## 2 Background and Literature Survey
### Preliminaries
#### 2.1.5 Activation Functions  
Each layer's neuron has an associated activation function. Non-linear and differentiable functions are also part of the equation. The model will be able to learn more complicated features and use backpropagation to update the weights for better models if these traits are present [14]. ReLU and log SoftMax were the activation functions employed in this model. Equation (1) expresses ReLU mathematically as follows:  
\[Z(x)=\max(0,x) \tag{1}\]  
where \(x\) represents the input to the function, and \(Z(x)\) is the ReLU activation function.  
Multi-class classification issues can be solved using the SoftMax activation function, which is a neural transfer function. Equation (2) expresses the SoftMax function as follows:  
\[Z(x_{i})=\frac{exp(x_{i})}{\sum_{j}exp(x_{j})} \tag{2}\]  
Here, \(Z(x_{i})\) is the SoftMax activation function, and represents the feature values from the neuron to the output layer. Here also, x represents the function's input, and j indicates the quantity of classes. A new log SoftMax function expands the previous log SoftMax function. This is how Equation (3) expresses the logarithmic SoftMax function:  
\[Z(x_{i})=log\frac{\exp(x_{i})}{\sum_{j}\exp(x_{j})} \tag{3}\]  
Log SoftMax function outperformed the sigmoid function in terms of performance since each node's probability outputs were higher.
# A Novel Continuous-Discontinuous Multi-Field Numerical Model for Rock Blasting
## 2 Numerical Method
### Explosion Load  
[PERSON] et al. [34] suggests a time-dependent explosion pressure curve with two peaks, see Figure 3a. The first peak corresponds to the blasting wave, and the second peak corresponds to the gas pressure. As mentioned before, comparing to the explosion stress wave which acts as dynamic loading, the explosive gas permeation process lasts longer, which is considered as a quasi-static loading.  
Figure 2: The iterative procedure for calculating the coupled deformation/pressure fields of cracked rock mass.  
The first peak pressure \(P_{w}\) can be determined base on C-J detonation theory, which is  
\[P_{w}=\frac{\rho_{0}\,D^{2}}{2(\gamma+1)}. \tag{12}\]  
Furthermore, the action time \(t_{w}\)  
\[t_{w}=\frac{r_{s}}{V_{cp}}. \tag{13}\]  
According to [35], time of blast pressure ascent stage \(t_{ur}=0.1\)\(t_{w}\) and descent stage \(t_{wf}=t_{w}-t_{wr}\).  
The second peak pressure of explosive gas \(P_{gmax}\) was calculated based on [PERSON] et al. [36]:  
\[P_{gmax}=\left\{\begin{array}{ll}\frac{\rho_{0}D^{2}}{8}(\zeta)^{-6},&P_{d} \leq P_{k}\\ P_{k}\left(\frac{\rho_{0}D^{2}}{1.6\times 10^{9}}\right)^{0.467}(\zeta)^{-2.8},&P_{d} >P_{k}\end{array}\right. \tag{14}\]  
where \(\zeta\) is the uncoupling coefficient of blasting hole, \(\zeta=r_{b}\) / \(r_{e}\) where \(r_{b}\) is the radius of hole, and \(r_{e}\) is the radius of charge. Based on [37], we assume a sine curve for the second peak. The complete time dependent explosion pressure curve used in this work is shown in Figure 3b.  
A personal computer with an AMD Ryzen Threadripper 3960X 3.90 GHz processor and 32 GB memory was used to obtain the numerical results. A benchmark model with 10,000 triangular elements will take 2 h, and a complex case such as Section 3.2 of this paper (with 21,896 triangular elements and 32,972 interface elements) will take about 6 h.
## 6 Experimental Results
### Experimental Results in Different Environments  
To verify that if the proposed method had migration issue, the gesture classification recognition process was performed at three different areas: classroom, hall, and office (denoted as Room1, Room2, and Room3, respectively, see Figure 12). The results revealed high migration and the accuracy of Room2 was lower than the other two rooms mainly because Room2 was equipped with more indoor furnishing, appeared to be more complex, and had varied multipath.
# Embedded Federated Learning for VANET Environments
## 6 Conclusions  
Federated learning is a promising candidate to address the gap in centralized ML solutions. It has lower latency and communication overhead when performing most of the processing on the edge devices; it improves privacy, as data do not travel over the network; and it facilitates the handling of heterogeneous data sources and expands scalability. To assess how federated learning (FL) can effectively contribute to smart city scenarios, we presented FedFramework and built a testbed that integrates the components of the city infrastructure, where edge devices such as NVIDIA Jetsons were connected to a cloud server. We deployed our lightweight container-based federated learning (FL) framework in this testbed where we evaluated the performance of devices, the effectiveness of machine learning and aggregation algorithms, the impact on the communication between the edge and the server, and the consumption of resources. To carry out the evaluation, we opted for the scenario in which we estimated vehicle mobility inside and outside the city, using the real data collected from the Aveiro Tech City Living Lab infrastructure in the city of Aveiro.  
In the context of smart cities, several services are used in edge devices, where great diversity is expected in relation to the consumption of resources by these services. Taking into account the scale of a smart city, the important thing is to properly assess the scope of use in each one of them to have the best cost/benefit ratio, thus reaching an ideal project. In this way, our study contributes to the field by showing the limits of each of these options, facilitating the adequacy of services \(\times\) devices. The better performance of Jetson Xavier compared with the Nano has its price; thus, the Nano can meet several situations where services with softer requirements can be used.  
Figure 14: Jetson’s CPU consumption.  
In future work, we aim to scale our framework to more edge devices and to mobile nodes such as OBUs.  
All authors designed the solution, analyzed the results, and wrote the paper. [PERSON] implemented the solution, prepared the evaluation scenarios, and evaluated the results. [PERSON], P.R., and [PERSON] supervised the entire research process. All authors have read and agreed to the published version of the manuscript.  
This work was funded by FEDER, through POR LISBOA 2020 and COMPETE 2020 of the Portugal 2020 Project CityCatalyst POCI-01-0247-FEDER-046119, and by the Urban Innovation Action EU/H2020 Aveiro STEAM City.  
The authors declare no conflict of interest.