esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Dynamics Modeling
### Rigid-Flexible Coupled Dynamics Model  
Generating flexible body structures is pivotal in modeling vehicles with rigid-flexible coupling. First, finite element analysis software was employed to perform finite element discretization of the bogie structure. Then, modal analysis and substructure analysis were performed. The substructure analysis resolves the issue of excessive computation and the need for extensive computational space caused by the structural complexity. By simplifying the model of the structure to retain the essential components of the structure while maintaining the integrity of the overall structure, computational workload is significantly reduced [22]. Subsequently, a flexible frame was generated through the flexible body generation interface in the dynamics analysis software. Finally, the flexible frame was coupled with other components for simulation. The detailed process of generating the rigid-flexible coupling model is shown in Figure 3.  
Figure 3: Flowchart of the generation of the rigid–flexible coupling model.  
Figure 2: Full rigid body dynamics simulation model of the bogie (**a**) and the entire vehicle (**b**).  
A 3D model of the bogie frame was constructed in 3D modeling software. The model of the simplified architecture is shown in Figure 4. Modal and substructure analyses of the frame were conducted using finite element analysis software to generate process documents of the modal analysis, stiffness matrix, and mass matrix. The architectural model generated a total of 34,480 elements and 46,372 nodes. The details of the delineated completed framing grid are shown in Figure 5. The material density of the frame was 7850 kg/m\({}^{3}\). Its Young's modulus was 210 GPa, and the Poisson's ratio was 0.3. Among them, the first six modes of the frame were rigid, while the dynamics calculation primarily considered the flexible modes. Table 2 shows a comparison of the modal data ranging from the 7 th to the 12 th order, both before and after the substructural analysis was performed, and Figure 6 shows the corresponding vibration patterns for modes 7 through 12.  
The bogie frame was replaced with a flexible body frame in the fully rigid body vehicle model. Force elements were established between the master nodes on the flexible frame and the mark points on other rigid components, such as the vehicle body and wheel pairs, to simulate a suspension system and positioning instrument. The bogie rigid-flexible coupling model is shown in Figure 6(a), and the entire vehicle rigid-flexible coupling model is shown in Figure 6(b).  
Figure 6: Plot of vibration shapes corresponding to modes of order 7 to 12.  
Figure 7: Rigid–flexible coupling dynamics simulation model of the bogie (**a**) and the entire vehicle (**b**).
# Application in Nanomedicine of Manganese-Zinc Ferrite Nanoparticles
## 2 Synthetic Strategies of Mn-Zn Ferrites
### Thermal Decomposition Method  
Highly monodispersed nanoparticles can be synthesized via thermal decomposition of organometallic precursors method. Through this method, organic iron compounds, such as ferric acetylacetonate or iron oleate, are decomposed at a high temperature inside the non-polar boiling solvent in the presence of capping agents [30]. The precursors are heated to the solvent boiling point at a constant heating rate and are kept at this temperature for the desired time. The nanoparticles are finally coated with a capping ligand, which is not only the colloidal stabilizer of the suspension but also an important tool to control the size and the shape of the crystals. Nanoparticles prepared by this technique are hydrophobic and can be stored in hexane, cyclohexane, toluene or other non-polar solvents [5]. Generally, the method produces high-quality nanoparticles. One of the most significant difficulties of this route is to establish a constant heating rate, especially in the range where the nucleation and growth occurs, and the production of a large-scale amount of nanomaterial.  
In general, Mn-Zn ferrite nanoparticles were obtained by partially replacing the iron precursor (Fe(acac)3) with the respective manganese and zinc acetylacetonates, in different proportions, in the typical protocol used for the synthesis of iron oxide nanoparticles. This method produces very regular particles with accurate control over the size distribution [31, 32, 33]. It has been observed that the introduction of doping elements (Mn and Zn) is more difficult in this method, with detected values significantly lower than the theoretical/expected ones [31,32]. This implies, in the X-ray diffraction (XRD) analysis of the obtained crystals, the presence of the pure magnetite-based phase. The magnetic characterizations of these nanoparticles reveal that, in fact, zinc is the element that is more responsible for the increase in Ms and SAR values, while the contribution of manganese is slightly negligible [32], partially in contrast with the analyses of nanoparticles obtained by co-precipitation.
# Recognition of Urdu Handwritten Characters Using Convolutional Neural Network
## 3 Literature Review
### Urdu Numerical Recognition
Dataset consisted of 80 pages having 687 handwritten Urdu text lines, and printed dataset consisted of 48 pages having 495 printed text lines. The algorithm performed significantly well on printed documents and handwritten Urdu text documents with well-separated lines and moderately well on a document containing overlapping words. The literature related to the Urdu text recognition at the character level proved that the ANN outperformed other machine learning approaches. The results generated by the character recognition system based on ANN were two-fold, i.e., the system was not only applicable for Latin script, but also for handwritten cursive characters of the Arabic-base script. We present a novel approach of CNN in order to recognize Urdu handwritten characters embedding both pixel- and geometrical-based features. The geometrical features were extracted for each text image using hybrid approaches of connected-components labeling [57] and the upper-lower profile [58]. The upper-lower profile works by dividing the image into four columns, then by detecting the position of both the first and last black pixels on each column, and provides the bounding box covering the area of interest. The extracted features are then embedded with pixel-based features, making a feature vector and then processed by our proposed model (discussed in the subsequent section) in order to recognize and classify using the variable size of the test set and invariant font.
## 5 Conclusions
The model was trained on 70% of the dataset, with 15% for validation and testing, achieving high accuracy (R\({}^{2}\): 0.9253-0.9695) across all scenarios. Scenario 2 had the lowest MSE (0.0018 kWh), while Scenario 3 had the highest value (0.0156 kWh). The artificial neural network (ANN) model accurately predicted energy balance for Scenarios 1 to 5, demonstrating its effectiveness. This would enable ports to make rapid energy predictions using the EMTv1, HY4 RES, or HOMER(r) 3.16.2 Pro models, without requiring expertise or time-consuming simulations. An additional analysis was conducted for an 80% increase in demand, analyzing the impact of extra energy beneficiaries on the hybrid energy module. The predictive model remained effective, with Scenario 3 having the highest MSE (0.0166 kWh) and Scenario 2 having the lowest R\({}^{2}\) (0.9289), confirming the model's reliability in predicting energy surplus and deficit under different renewable combinations. The main limitation of ML is its ability to handle increasing demand, as accurate predictions require proper training, validation, and testing. Without these steps, the model's reliability may be compromised. Conceptualization, H.M.R., J.S.T.C., E.B., T.X.A. and O.E.C.-H.; methodology, H.M.R., J.S.T.C., T.X.A., O.E.C.-H. and M.P.-S.; software and calculus, J.S.T.C., E.B., T.X.A. and O.E.C.-H.; writing--original draft preparation, H.M.R. and O.E.C.-H.; review, M.P.-S., K.K., A.M. and R.E.-V.; supervision, H.M.R., M.P.-S., K.K., A.M. and R.E.-V.; editing and final preparation, all authors. All authors have read and agreed to the published version of the manuscript. The authors are grateful for the project HY4 RES (Hybrid Solutions for Renewable Energy Systems) EAPA_0001/2022 from INTERREG ATLANTIC AREA PROGRAMME, as well the Foundation for Science and Technology's support to UIDB/04625/2020, the research unit CERIS. The used data are available in the manuscript. **Acknowledgments:** This work was supported by FCT, UIDB/04625/2025 CERIS, in the Hydraulic Laboratory, for experiments on pumped storage performance, and the project HY4 RES (Hybrid Solutions for Renewable Energy Systems) EAPA_0001/2022 from the INTERREG ATLANTIC AREA PROGRAMME. **Conflicts of Interest:** The authors declare no conflicts of interest.
## 4 Experiments
### Experiment Results  
#### 4.4.1 Comparison with Large-Scale Networks  
To demonstrate the competitiveness of the proposed RPS-YOLO network compared to other object detection networks, we conducted further comparative experiments on the VisDrone-DET2021 dataset. The networks compared include the classical networks Faster R-CNN, CenterNet, and SABL, the UAV object detection network RSDD, and Drone-Yolo, as well as networks TOOD and RT-DETR. The experimental results are presented in Table 1.  
As shown in Table 1, the RPS-YOLO network maintains strong competitiveness compared to other advanced networks. Among all these models, RPS-YOLO provides the second-highest detection accuracy while using the minimum number of parameters and computational resources. This outstanding detection performance is mainly attributed to the two-stage \"processing\" of the image and four-scale detection.
## 1 Introduction and Background
### Bandwidth Availability and Download Times  
Bandwidth availability can be considered a social issue as well as a technological one. In this section we will provide a brief documentation of current state of the art from a technology perspective. We will touch upon the social and political aspects in the discussion section later. In the most basic sense, bandwidth is limited by speed of light, that is, latency cannot be reduced below \(t=\) (distance)/(speed of light). The maximum amount of (error free) data that can be transferred is often determined based on available bandwidth and signal-to-noise ratio. In information science this limit is formally defined by the _Shannon-Hartley theorem_[22, 23].  
For common _Internet access technologies_, the current bandwidth (net bit rate) varies from 56 kbit/s for modem and dialup connections at the lower (although not the lowest) end and 100 Gbit/s for Ethernet connections at the maximum. Between these two bit rates various technologies offer differing speeds, such as ADSL (Asymmetric Digital Subscriber Line) at 1.5 Mbit/s, T1/DS1 at 1.544 Mbit/s at the lower ends, and OC48 (Optical Carrier 48) at 2.5 Gbit/s, and OC192 at 9.6 Gbit/s.  
Current data transfer rates for _mobile communication_ vary from GSM (Global System for Mobile communication) at 9.6 kbit/s, GPRS (General packet radio service) up to 40 kbit/s, UMTS (Universal Mobile Telecommunications System) up to 384 kbit/s, HSDPA (High Speed Downlink Packet Access) and HSUPA (High Speed Uplink Packet Access) at 14.4 Mbit/s with High Speed Downlink Packet Access (HSDPA, 3.5G, 3G+ or UMTS-Broadband) and 600 Mbit/s for Wireless LAN (802.11n). The next generation of mobile network standards, such as the Long Term Evolution (LTE) will offer 300 Mbit/s for downlink and 75 Mbit/s for uplink. Another standard, similar to Wireless LAN is WiMAX (Worldwide Interoperability for Microwave Access) that currently offers data transfer rates up to 40 Mbit/s, but will reach with 802.16m expected rates up to 1 Gbit/s. To appreciate what these bit rates mean for geographic data volumes as suggested by [PERSON] _et al._ (2005) [12] and reported in Section 1.2., we can calculate download times for a set of common bandwidths (Table 1).  
While there are many additional factors that may affect download times (e.g., how many users are sharing the bandwidth, how far is the receiving device from the access point, channel inferences, etc.), it quickly becomes clear that for someone who would like to use high quality geographic data ubiquitously, bandwidth poses a serious problem. In other words; a reasonable assumption is this: using an average mobile device with a bit rate of 256 kbit/s, you will have to wait for more than eight hours (to be precise 08:40:50 and this number is without overhead) to download an \"entire street network of a large city or small country\", which \"weighs\" 1 gigabyte.
## 2 Related Work
### Mask Wearing Detection  
There are two primary approaches to mask-wearing detection. The first one utilizes face detection techniques to locate faces. The detected faces are then cropped and fed into a classifier to determine if the masks are worn correctly. Despite offering the advantage of reusing a pre-deployed face detection model and thus lowering the deployment costs, this method has a disadvantage in that established face detection techniques have all been optimized for uncovered faces and may not be able to detect faces covered with masks well. [PERSON] [34] employed a multi-task convolutional neural network (MTCNN) to segment face regions in images and used MobileNetv2 for mask-wearing classification. Alternatively, one-stage object detection algorithms are developed to detect faces and simultaneously detect and tell mask-wearing. [PERSON] [35] enhanced the mask-wearing detection model by introducing inverse convolution and feature fusion to the original SSD model. Additionally, an attention mechanism was incorporated to filter and retain relevant information. These improvements significantly enhance the model's ability to detect mask-wearing. [PERSON] [36] improved YOLOv4 for mask-wearing detection by integrating the CBAM attention module and substituting ordinary convolution with depthwise over-parameterized convolution. These methods can classify images into two categories: with or without masks, but cannot distinguish between correct and incorrect mask-wearing. Although [PERSON] [37] can tell between correct, incorrect, and no mask-wearing, they have not taken into account the impact of frequency domain features on the detection results, which is significant, as shown later in this paper.
# AUV Adaptive Sampling Methods: A Review
## 5 Multiple Platforms Networking
### A Pair System  
When more than a single vehicle is utilised in an adaptive sampling mission, several more challenges must be considered. They include inter-AUV acoustic communication, autonomous coordinated control, prioritised criteria for adaptive behaviours and real-time data exchange.  
[PERSON] et al. [98] proposed a safe tracking method using chasing behaviours between an AUV and an ASV. Staying within a predetermined safe zone, the ASV autonomously tracked the AUV, although difficulties were encountered with an intermittent modem communication. In order to solve  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Direction** & Each transect is directed orthogonal to the average of plume flow direction. \\ \hline \multirow{2}{*}{**Length**} & Length is the sum of the distance the vehicle travels from the ambient, through the \\  & plume (above thresholds), to the point where ambient samples (below thresholds) are \\  & consecutively detected \\
**Distance** & Distance to the next transect is a percentage of the length of the previous transect \\ \hline \hline \end{tabular}
\end{table}
Table 13: Three features of the proposed adaptive transect mapping algorithm.  
this, they developed real-time self-security and self-prediction modules. The first safety measure was to build a fence, referred to as a safe-zone, so that the vehicle did not exit the area even when the communication was lost. The second method was the autonomous slide model [99] that was capable of dynamically predicting the next possible locations of the AUV for a short period of time, utilising a fitting polynomial based on the updated sample positions.  
[PERSON] and [PERSON] [21] used two AUVs, the Bluefin 21\" Unicorn and the NURC OEX Harpo, to collaboratively monitor the temperature field (thermocline) in order to capture the presence of internal waves in the Tyrrhenian Sea, Italy, during the GLINT10' internal wave detection experiment. Their work is of significance in adaptive collaboration in the sense that in-situ data and status updates were shared through the acoustic communication that they used to coordinate their relative motions adaptively. While Harpo maintained a constant depth just below the thermocline, Unicorn autonomously trailed Harpo, performing vertical yo-yos and adapting its speed (deceleration) to maintain a relative distance from [PERSON]. The two AUVs demonstrated track-and-trail behaviour, performing a loiter pattern around the acoustic communication buoy.
## 3 Results and Discussion
### Variation of Doppler Signal
#### 3.6.2 The Propagation Trajectory of Sounding Radio Wave  
Next, based on the profile of electron concentration determined in the previous step, the propagation of the sounding radio wave was considered, taking into account the Earth's magnetic field and using the IGRF16 model for the ordinary component [44]. As an illustration, the trajectories of radio waves calculated for daytime (5 h, 9 h, 12 h, and 13 h UT) on the date 28 February 2024 are presented in Figure 9, right panel. It is seen in this plot that in the time interval from 5:00 to 13:00 UT, the height of reflection has varied from 129 km to 208 km.  
As mentioned above, monitoring of DFS was fulfilled at an extended inclined radio path of 970 km in length. This stipulated a specific reflection of the sounding radio wave from within a low-altitude but fairly large domain in the ionosphere, of about \(\pm 250\) km in size relative to the middle of the radio path (Figure 9, right frame). In Figure 8, the region of radio wave reflection is schematically limited by a black ellipse. As it is seen there, nearly half of this region falls inside the Dobrovolsky circle of the 4 March 2024 earthquake.  
At higher altitudes, the reflection region covers a significantly smaller area in the ionosphere.
## 1 Introduction
In this case, extra insulation designs for the manipulator on the mechanical arm need to be added to enhance insulation performance, and most arms of this type are limited to a voltage level of 10 kV. However, the self-insulated manipulator uses epoxy resin as the rigid material and polyester fiber as the flexible material for the manipulator body. Consequently, this manipulator not only requires no additional insulation design but can also adapt to working environments with a high voltage level of 220 kV. Second, in terms of manipulator mass: Conventional manipulators typically need to mount drive motors at the joints, which increases the mass of the mechanical arm. The self-insulated manipulator has significantly reduced its own mass because the design separates the drive motors from the body. This results in minimal changes in the center of gravity during the movement of the entire arm, and its small inertia tensor imposes low performance requirements on the lower mobile platform. This also means that during the operation of the entire machine, the multi-rigid-body kinematic analysis of the manipulator body can be almost omitted. Finally, although the self-insulated mechanical arm cannot complete the tasks such as cable cutting and splicing due to its relatively small load capacity, its insulation performance makes it more suitable for sensing, monitoring, and fault detection in high-voltage environments. The specific comparisons between the conventional live-working manipulator and the self-insulated cable-driven manipulator are shown in Table 1.
# A Deep Learning Approach for Intrusion Detection Systems in Cloud Computing Environments
## 3 Materials and Methods
### Data Preprocessing
#### 3.3.2 Label Encoding  
Label encoding is a technique that is used to transform categorical feature values to numerical values so that they can be fitted by deep learning models that only accept numerical data. The features can be encoded by many methods. Among them, one-hot  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Class** & **Traffic Type** & **Count** \\ \hline Normal & Normal & 13,484,708 \\ DDoS & Attack & 1,263,933 \\  & (HOIC, LOIC-UDP, and LOIC-HTTP) & 1,263,933 \\  & Attack (Hulk, GoldenEye, Slowloris, and SlowHTTPTest) & 654,300 \\  & & \\ Brute Force & Attack (FTP and SSH) & 380,949 \\ Bot & Attack (Botnet) & 286,191 \\  & & \\ Infiltration & Attack (Infiltration) & 161,934 \\  & Web & Attack (Web, XSS, and SQL Injection) & 928 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Attack classes of CES-CICIDS2018 dataset.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Scenarios** & **Contents** \\ \hline Scenario 1: Brute Force Attack Simulation & Benign: 667,626 instances \\  & FTP Brute Force: 193,360 instances \\  & SSH Brute Force: 187,589 instances \\  & Benign: 996,077 instances \\ Scenario 2: DoS Attack Types & DoS GoldenEye: 41,508 instances \\  & DoS Slowloris: 10,990 instances \\ Scenario 3: DDoS LOIC-HTTP & Benign: 7,372,557 instances \\  & DDoS Attacks-LOIC-HTTP: 576,191 instances \\ Scenario 4: Advanced DDoS Techniques & DDOS attack-LOIC-UDP: 360,833 instances \\  & DDOS attack-HOIC: 1730 instances \\  & Benign: 1,048,213 instances \\ Scenario 5: Web Application Attacks & Brute Force-Web: 249 instances \\  & Brute Force-XSS: 79 \\  & SQL Injection: 34 instances \\ Scenario 6: Infiltration Tactics & Benign: 1,048,009 instances \\  & Infiltration: 151 instances \\ Scenario 7: Botnet Detection & Benign: 762,384 instances \\  & Bot: 286,191 instances \\ \hline \hline \end{tabular}
\end{table}
Table 3: All seven scenarios.  
encoding is the most popular method; thereby, we applied one-hot encoding to convert the categorial feature values into numerical values.