esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey on Parameters Affecting MANET Performance
## 5 Related Work

Table 7: Percentage of routing parameter usage in MANETs. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Reference Name** & **Simulator** & **Network Area** & **Simulation Time** & **Mobility Speed (m/s)** & **Number of Network Nodes** & **Number of Malicious Nodes** & **Attack Type** & **Packet Rate (Packet/s)** & **Mobility Model** \\ \hline Performance Analysis of MANET under Grayhole Attack Using AODV [1] & NS-2 & 1000 m \(\times\) 850 m & 1200 s & - & 10 & 1 & Grayhole & - & Random waypoint \\ \hline A Comparative Study of Reactive, Proactive, and Hybrid Routing Protocol in Wireless Sensor Network Under Wormable Attack [7] & QualNet 5.0 & 400 m \(\times\) 400 m & 17 min & 10 & 50 & 1, 8 & Wormhole & - & Random waypoint \\ \hline Securing Backhole Attacks in MANETs using Modified Sequence Number in AODV Routing Protocol [10] & NS-2 & 500 m \(\times\) 500 m & 100 s & - & [10–50] & [1–10] & Blackhole & - & Random waypoint \\ \hline Simulation-Based Study of Backhole Attack under AODV Protocol [12] & NS-2 & 500 m \(\times\) 500 m & [20–100] s & 0, 50 & [20–100] & 0–1 & Blackhole & [5–25] & Random waypoint \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Survey on simulation and attack parameters usage in MANETs. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
Table 8 shows a wide variety in the simulation and attack parameters used to set up the MANET environment. All research papers share in common the random waypoint mobility model. The network area for small networks was found to be 200 m \(\times\) 200 m, while for extensive networks, the network area does not exceed 2500 m \(\times\) 2500 m. The range of simulation time was found to be from 5 s up to 1 h, and the mobility speed range is between 0 for static nodes up to 50 m per second. Additionally, from Table 8, the number of network nodes for small networks is between 3 and 50 nodes, and for very large networks, the number of nodes reaches 600 nodes with a varying number of malicious nodes inside--the number of malicious nodes varies between 0 and 60 malicious nodes. Table 9 presents a conclusion of the ranges used in the literature for simulation and attack parameters.
## 3 Materials and Methods
### Defects Detection
#### 3.5.1 Data Processing  
The previously described approach of dividing the images in patches generated the inputs for this module as it ensures that the defects are adequately captured and preserved,allowing for more accurate detection and analysis within the defect detection model. The overlap between patches was set to 50% to guarantee that an entire defect was present in the selected area (except for the Defect-12 class which presented bigger dimensions in some cases, as it represents cracked pieces).  
Offline data augmentation techniques were also performed to enhance the model's generalization and robustness. The generation of additional image data by applying transformations in contrast, brightness and sharpness, and adding Gaussian and motion blur helps to increase the diversity of the training data.
## 3 The MQTT/MQTT-SN Based Cyber System of the UEMS
### The Advantages of the Co-Use of MQTT/MQTT-SN  
As one of the typical CPSs, the UEMS requires that the communication mechanism should be real-time, reliable, compatible, and scalable. Thus, the co-use of MQTT/MQTT-SN, which could satisfy the above requirements, is proposed. The advantages of MQTT/MQTT-SN are as follows:  
1. MQTT/MQTT-SN supports publish-subscribe mechanism. Various studies have proven that publish-subscribe-based communication outperforms the client/server (C/S)-based communication [16; 17; 18]. MQTT/MQTT-SN provides topic-centric publish/subscribe mechanism to realize one-to-many data transmission, which improves the expansibility of the protocol as well as the instantaneity of the data transmission. Hence, a MQTT/MQTT-SN-based UEMS gives full expression of data-driven concept. The subsystems and modules are fully decoupled and only keep loose connections via the topic.  
2 The co-use of MQTT/MQTT-SN could support not only IP-based networks but also non-IP networks. Due to the outperformance of publish-subscribe mechanism marked by MQTT/MQTT-SN, MQTT has attracted increasing attention in smart grid [19; 20]. However, the above research only focuses on the relatively resourceful and IP-based scenarios while resource limited, and non-IP IoT devices are hardly applicable.
3 The transformation between MQTT and MQTT-SN is tractable. Since it is formidable to design a communication standard which is compatible with various IoT devices and suitable for every running scenario (IP-based networks and non-IP networks), a practical approach is to design a communication protocol that is transplantable and independent of any specific wireless technique, while the gateway is responsible for protocol transition to support interconnection among IoT devices. Considering the similarity and simple effective conversion between the MQTT and the MQTT-SN, the co-use of the MQTT and the MQTT-SN is a cost-effective solution.  
In conclusion, we believe that MQTT/MQTT-SN is a more appropriate solution for cyber systems of the UEMS.
# Energy-Efficient De-Duplication Mechanism for Healthcare Data Aggregation in IoT
###### Abstract
This paper presents a de-duplication mechanism for the Energy Efficient Fuzzy Data Aggregation System (EE-FDAS) to enhance network lifetime in heterogeneous environments by mitigating data duplication issues. The novelty of the proposed EE-FDAS lies in its mechanism of handling healthcare data. The EE-FDAS focuses on handling the data in different formats generated by various sensors in healthcare environments. It is based on the three-tiered approach, In the first case, when data lies in a normal range and has no variation, instead of transmitting full 16 or 32-bit data, only Boolean digit 0 is sent. In the second case, when there is a partial irregularity in data readings, only those values are sent in full form, while normal data is transmitted in Boolean digits. In the third scenario, when all data readings lie in the critical range then all values are sent in their original format, it occurs rarely. This approach demonstrates the adaptability of EE-FDAS to diverse healthcare data scenarios. Moreover, the proposed scheme contributes to the sustainability of healthcare monitoring systems by reducing redundancy issues, ensuring a more robust and resource-efficient solution in the domain of IoT-enabled Wireless Sensor Networks (WSNs). This improvement assists in enhancing the usefulness of remote patient monitoring, mainly in resource-constrained and rural healthcare settings, eventually improving the quality and accessibility of healthcare services. The contributions of this research are presented as follows:  
1. We thoroughly studied the most relevant and latest literature on data aggregation approaches in the domain of IoT. This comprehensive study forms the foundation for understanding the existing landscape and identifying gaps for improvement in the field of the healthcare sector. 2. This work presents the EE-FDAS scheme, employing a three-tiered approach to data transmission based on the nature of the healthcare data. Next, the proposed mechanism is applied at the sensor level to transmit Boolean digits for in-range values, whereas the complete data readings are shared in case of variation. The EE-FDAS, by enhancing the efficiency of data aggregation in healthcare WSNs, contributes to the progression and sustainability of modern healthcare technologies. * An extensive number of simulations are performed by using NS 2.35. Results are extracted from trace files to compare with counterparts. The rest of paper is organized as follows. Section 2 presents the latest existing schemes on data aggregation. In Section 3, the problem statement and system of the proposed solution are described. In Section 4, the solution of the proposed scheme is described in detail. Section 5 illuminates results and analysis while Section 6 presents the conclusion and future work.
## 3 Modeling Procedures of Extended Finite Element Method (XFEM)-Based Multiscale Analysis
### Bond-Slip Effect and Spring Connector Elements  
As present in Figure 5, the bond-slip effect between concrete and steel reinforcement is considered. The theoretical model of the bond stress-slip relationship reported in the Europe concrete code is employed [30], where \(s_{1}=s_{2}=0.6\) mm, \(s_{3}=1.0\) mm, \(\alpha=0.4\), \(\tau_{max}=2.0\sqrt{f_{ck}}\) and \(\tau_{f}=0.15\tau_{max}\), as presented in Figure 8. However, a large number of non-linear connection springs can easily lead to severe convergence problems. In order to simplify the FEM models, the bond-slip between stirrups and concrete is neglected in this study. The spring stiffness of the connector element in the longitudinal direction is defined according to the analytical bond stress-slip relationship presented in Figure 8. The connection between concrete and longitudinal steel bars in their lateral cross-section plane is simulated using rigid spring connectors.  
In general, in order to improve calculation efficiency, a reliable bonding condition between the steel reinforcements and the concrete is usually assumed in most FEM analysis. In this section, the influence of bond-slip on the hysteretic behavior of RC columns is investigated. Figure 9 shows the comparison of the hysteretic behavior of specimen C3 with and without considering the bond-slip in a solid element model and two-scale model. As shown in Figure 9, the pinching effect in hysteresis loops becomes more obvious and the structural stiffness of RC columns is obviously reduced while considering the bond-slip effect. When steel bars are directly embedded into concrete, the peak load increases by 8.6%. Crack distribution patterns corresponding to a solid element model and two-scale model are shown in Figure 9. The crack patterns present limited differences in either the solid element model or two-scale model whether bond-slip is considered or not. However, the specific location of the maximum fracture is slightly different. When considering bond-slip behavior, the maximum fracture appears at the column foot. The position of the maximum crack ascends and is mainly located around 8-10 cm above the column foot while ignoring the bond-slip effect between concrete and steel bars. Therefore, in order to improve calculation accuracy, two-scale models in this study are established using non-linear spring elements to consider the bond-slip effect in RC columns.  
Figure 8: Analytical bond stress–slip relationship.
## Nomenclature  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} \(d\) & Demand index (\(d\in\mathcal{N}\)). \\ Parameters & \\ \(\Delta h\) & Duration of a single time period. \\ \(\eta\) & Maximum number of FACTS device available. \\ \((\cdot)^{*}\) & Conjugate of the complex number. \\ \(\omega_{1}\), \(\omega_{2}\), \(\omega_{3}\) & FACTS cost function coefficients. \\ \(\text{imag}(\cdot)\) & Imaginary part of the complex number. \\ \(\text{real}(\cdot)\) & Real part of the complex number. \\ \(A^{+}\) & Positive values of the node-to-branch incidence matrix \(A\). \\ \(A^{-}\) & Negative values of the node-to-branch incidence matrix \(A\). \\ \(C\) & Average costs of energy losses. \\ \(k_{1}\) & Annual investment costs. \\ \(k_{2}\) & Annual investment costs. \\ \(p_{l}^{d}\) & Active power demanded at node \(k\) and time \(t\). \\ \(q_{\text{max}}^{\text{FACT}}\) & Maximum reactive power of the FACTS device. \\ \(q_{kl}^{d}\) & Reactive power demanded at node \(k\) and time \(t\). \\ \(s_{l}^{\text{max}}\) & Maximum power flow in branch \(l\). \\ \(T\) & Number of days in a year. \\ \(v^{\text{max}}\), \(v^{\text{min}}\) & Maximum and minimum voltage allowed in the grid. \\ \(v^{\text{nom}}\) & Voltage at the slack node. \\ \(y_{l}\) & Admittance of the branch (or line) \(l\). \\ Sets and indices & \\ \(\mathcal{B}\) & Set of branches (or lines). \\ \(\mathcal{H}\) & Set of time periods under analysis. \\ \(\mathcal{N}\) & Set of nodes. \\ \(g\) & Generation index (\(g\in\mathcal{N}\)). \\ \(h\) & Time index (\(h\in\mathcal{H}\)). \\ \(k\), \(m\) & Node indices (\(k\), \(m\in\mathcal{N}\)). \\ \(l\) & Branch index \(km\) (\(km\in\mathcal{B}\)). \\ Variables & \\ \(q_{kl}^{\text{FACT}}\) & Reactive power of a FACTS device at node \(k\) and time \(t\). \\ \(p_{kl}^{y}\) & Active power generated at node \(k\) and time \(t\). \\ \(p_{lk}^{r}\) & Receiving active power flow at branch \(l\) and time \(t\). \\ \(p_{jl}^{i}\) & Sending active power flow at branch \(l\) and time \(t\). \\ \(q_{kl}^{u}\) & Reactive power generated at node \(k\) and time \(t\). \\ \(q_{k}^{\text{FACT}}\) & Optimal size for a FACTS device at node \(k\). \\ \(q_{k}^{\text{FACT}}\) & Size of a FACTS device at node \(k\). \\ \(q_{lk}^{r}\) & Receiving reactive power flow at branch \(l\) and time \(t\). \\ \(q_{lh}^{s}\) & Sending reactive power flow at branch \(l\) and time \(t\). \\ \(v_{kl}\), \(v_{mh}\) & Voltage at the node \(k\) (or \(m\)) at time \(t\). \\ \(z_{k}\) & Binary variable for the location of the FACTS device at node \(k\). \\ \end{tabular}
# A Lightweight Small Target Detection Algorithm for UAV Platforms
## 2 Preliminary Work  
### The YOLOv8 Network  
Ultrayltics YOLOv8 is a YOLO-based target detection model developed by Ultralytics in 2023, introducing new features and improvements based on previous YOLO versions to further enhance performance. YOLOv8 includes five model sizes: n, s, m, l, and x, for different tasks. The overall structure is divided into three parts: backbone, neck, and head.  
The backbone part is used to perform feature extraction on the preprocessed input image. This part adopts the gradient flow-rich C2f module as the basic constituent unit, which considers inference speed and accuracy, and the fast spatial pyramid pooling (SPPF) structure is used to maintain the ability to extracting multi-scale features. The neck part is based on the idea of the Path Aggregation-Feature Pyramid Network (PA-FPN), where a series of upsamplings and downsamplings is used to extract features from the input image. Feature maps of different sizes are aligned and spliced through these processes to achieve feature fusion between different levels of semantic information. The head part uses the Decoupled-Head structure, which separates classification and localization, alleviating the inherent conflict between classification and regression tasks. The detection head is changed from Anchor-Based to Anchor-Free, with Task Alignment Learning (TAL) adopted for dynamic matching. Distribution Focal Loss (DFL) combined with CIoULoss is introduced as the loss function of the regression branch, ensuring a good relationship between classification and regression tasks [16].
## 4 Results
### Experiments in the Simulated Environment
#### 4.2.2 Overall Results in the Simulated Environment for a Linear and Circular Trajectory  
Figure 10 reports the errors in the x and y axis, respectively, for all 20 experiments. Each box within a boxplot represents five experiments, where for each experiment we computed the mean error value of the whole trajectory (from takeoff until landing).  
Note that the mean error in all cases was always lower than 0.12 m. Note as well how for both trajectory types (linear and circular) and for both the error in the x and y axes, the variant w/ prediction achieved lower error than its non-predictive counterpart.  
In particular, the error along the x axis (Figure 10a) for a linear trajectory was around 0.10 cm on average for the system w/o prediction and around 0.04 cm for the system w/ predictive action; for a circular trajectory, this error was smaller in general terms: around 0.04 cm for the system w/o prediction and close to zero when employing the predictive height-adaptive PI controller.  
With regards to the error along the y axis (Figure 10b), for a linear trajectory the mean error for all ten experiments (five for each mode, i.e., w/o and w/ prediction) was very small with almost no dispersion of the data at all. For the circular trajectory, the error produced by the non-predictive tracking algorithm was in the range of 0.06 cm, while the system leveraging the Kalman filter achieved lower errors in the range of 0.03 cm.  
Figure 10: Comparison of the errors between linear and circular trajectories, both for the non-predictive (w/o pred) and predictive (w/ pred) variants.  
Figure 9: Errors for a circular trajectory sequence.  
Overall, the system performed robustly in all tests, managing to land flawlessly in all cases. We have therefore demonstrated how an approach based on a predictive height-adaptive PI controller outperforms its non-predictive counterpart, allowing the UAV to follow and land on the moving landing platform consistently, performing a more stable and accurate flight.
# Synchronous Generator Stability Characterization for Gas Power Plants Using Load Rejection Tests
## 3 Experimental Tests
### Test Procedure  
The first step in the audit process was obtaining the project dossier of the power plant, i.e., the summary description of the entire project of the power plant construction and its outputs. It contains all the technical information and a description of each component of the plant.  
Next, individual controller tests were performed, which can be classified into two types: AVR set point changes without load and speed governor set point changes without load. To carry out these tests, the power plant's load was gradually reduced until the generator becomes idle, after which the set points of the AVR and speed governor can be changed (either automatically or manually through the control panel) to observe how the controllers respond to voltage and frequency variations. This no-load test provides information about how the controllers behave as separate entities.  
A second set of tests was conducted to observe how the AVR and speed governor controllers behave when working together. Load rejection tests were then carried out to evaluate the transient response to power grid faults. For these tests, the load was set to between 10% and 15% of the generator's rated load to prevent overstressing the system, which could potentially damage the plant. Once the load reached steady-state operation, the main circuit breaker was turned off to isolate the generator and create an islanded condition.
## 4 Results and Discussion
### WRF-Alone Forecasts of Solar Radiation
\begin{table}
\begin{tabular}{l l c c c c} \hline \hline
**Site** & **Parameters** & **Clear-Day** & **Cloudy-Day** & **Overcast-Day** & **All-Year** \\ \hline \multirow{6}{*}{Maizuru} & Frequency of occurrence & 57\% & 17\% & 26\% & — \\  & MBE & 124.7 & 47.1 & \(-\)18.1 & 70.4 \\ \cline{1-1}  & MAE & 145.8 & 116.2 & 62.9 & 117.4 \\ \cline{1-1}  & RMSE & 247.6 & 210.3 & 130.0 & 213.3 \\ \cline{1-1}  & nRMSE & 0.2 & 0.2 & 0.2 & 0.2 \\ \hline \multirow{6}{*}{Qinghai} & Frequency of occurrence & 41\% & 39\% & 20\% & — \\ \cline{1-1}  & MBE & 89.9 & 0.33 & \(-\)1.1 & 24.9 \\ \cline{1-1}  & MAE & 113.7 & 161.9 & 132.8 & 96.1 \\ \cline{1-1}  & RMSE & 204.7 & 298.7 & 251.2 & 181.1 \\ \cline{1-1}  & nRMSE & 0.2 & 0.3 & 0.23 & 0.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance statistics of hourly WRF (Weather Research and Forecasting) raw forecasts of solar radiation (W/m\({}^{2}\)) under different sky conditions from 1 January to 31 December 2018 for the Maizuru site, and from 1 January to 31 December 2017 for the Qinghai site, respectively.
## 2 Related Work
### End-to-End Model
Authors in [32] presented a semantic and temporal attention-based technique for a video description in textual formation. The model can expressly join with the high-level visual idea to create temporal attention and included an encoder to extricate the highlights from a visual substance and decoder for the sentence generation. A scene-edge GRU strategy for video descriptions was presented by [PERSON] et al. [33]. The technique presented another encoding framework that can experience the skeleton of a video outline, and the GRU can be ready to search for suspending video frames, which can be prepared with no prior annotation information. The method also outperformed in terms of S2 VT models. \"Spatio-temporal ranked attention network\" for the video description generation in textual format has been performed by [PERSON] et al. [34]. The two-way strategy was utilized such as spatio-temporal and temporal-spatio. The model is comprised of LSTM based temporal ranking capacity that can powerfully catch activities from the visual frames. The setting outperformed in various datasets and broke the previous top-tier benchmark records. It can be observed that most of the related models incorporate single layered bi-LSTM, LSTM, or GRU strategies to generate video captions. However, the novelty of this research work is that it proposes a two-layer attention-based bi-directional LSTM as an encoder and a dedicated single layer forward pass LSTM as a decoder for generating video description semantically. While different CNN models of other studies mine the video structures' features to suckle into the encoder and decoder model for the enriched video description generation, our video depiction model, in contrast with the others, is fit for extricating different high-level features. This study also includes an efficient word embedding with a soft attention mechanism in the encoder section to generate qualitative results. We have positioned towards better extricating visual data from the frames, which is revealed in the outcomes. As far as multifaceted computational nature, the proposed model is very reasonable for predicting inconspicuous information.