esa-sceva/satcom-chunk-collection huggingface dataset:
# An Effective Algorithm for Finding Shortest Paths in Tubular Spaces
## 4 The ESP Searching Algorithm Based on Visibility
### Oriented Drilling Process  
To effectively determine the longest length of sight from an arbitrary point \(\boldsymbol{X}\) inside the tube, we employ Lemma 2. The operation scheme of finding the longest-length-of-sight direction illustrated in Figure 9 comprises a series of expanding and deepening processes. Without loss of generality, we assume that \(\boldsymbol{C}_{i-1}\) can see a point \(\boldsymbol{T}\) in a forward section \(S_{j}\). By employing Lemma 2, we discretely expand the examined direction from the direction passing through \(\boldsymbol{T}\) to others passing through its nearby nodes on the same mesh \(S_{j}\) until discovering a point \(\boldsymbol{T}_{new}\) in a farther disk \(S_{k}\) (\(k>j\)). As a deepening process, we then update \(\boldsymbol{T}\) by \(\boldsymbol{T}_{new}\) and also the examined cross-section \(S_{j}\) by \(S_{k}\). The operation is then repeated until the expanding process is over. The condition to stop the expanding process is when the boundary of the visited area in \(S_{j}\) just comprises the invisible nodes and the boundary points of \(S_{j}\). Finally, we compare the length of sight corresponding to all visible points in the farthest visible cross-section and find the longest one. The next correct point \(\boldsymbol{C}_{i}\) is the intersection point between this line of sight and the next cross-section \(S_{i}\).  
One advantage of this method is the fact it represents a significant improvement in computation time as we do not need to visit all the nodes of the graph. For the expanding process, on the examined disk \(S_{j}\), we just need to expand the investigated nodes until the current exact point \(\boldsymbol{C}_{i-1}\) can see farther; then we jump further into the more in-depthcross-section. Otherwise, if there is no new disk observed by \(\mathbf{C}_{i-1}\), we stop the process and indicate the next correct point of the ESP \(\mathbf{C}_{i}\). Moreover, as this is an inheritable algorithm, in the next searching process, we can directly use the previous correct direction as the initial examined orientation. Thus, we skip the disks that were examined in the previous loop. For several circumstances such as points on the straight-line segments of the shortest path (the ESP consists of straight-line and geodesic curve segments, see Theorems 1 and 2), the next searching process can stop right after choosing this initial examined orientation. That is also the reason why we call this method Oriented Drilling. Imagine that every time we find the correct direction for point \(\mathbf{C}_{i-1}\) such as when we drill a hole in that direction. For the next searching process, as there was already a hole, the searching is simplified. The whole process becomes an adjustment the direction of the drill so that it can drill deeper. Consequently, the drilling direction will be oriented closer and closer to the deepest drill hole (the longest length of sight).
## 3 Overview of IoT System Security Applications in the Industrial Field
### Comparative Analysis Referred to Cybersecurity
The second combines hybrid routing and IoT gateways using technologies such as BLE and LoRa to extend the reach of networks while facilitating data processing in edge devices. Figure 15 briefly outlines these different strategies and technologies. Table 3 summarizes the characteristics of the IoT paradigm proposed by the authors about cybersecurity to protect acquired data. In addition to the entity of the IoT platform and the communication technology, the application scope, i.e., whether it concerns data security, threat detection, or authentication methods. Any data decryption algorithms and ML algorithms used for data processing are also highlighted. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reference** & **IoT Entity** & \begin{tabular}{c} **Communication** \\ **Technologies** \\ \end{tabular} & **Application** & \begin{tabular}{c} **Crypting** \\ **Algorithm** \\ \end{tabular} & \begin{tabular}{c} **Detection** \\ **Algorithm** \\ \end{tabular} \\ \hline \begin{tabular}{c} [PERSON]. [PERSON] \\ et al. [73] \\ \end{tabular} & \begin{tabular}{c} IoT Framework (IoT \\ sensing devices, \\ gateways, and fog \\ services nodes) \\ \end{tabular} & LoRa, ZigBee, Sigfox, WiFi, Bluetooth & Data security & RSA & SoftMax-DNN \\ \hline \begin{tabular}{c} [PERSON]. [PERSON] [PERSON] \\ et al. [75] \\ \end{tabular} & \begin{tabular}{c} IoT platform \\ (Raspberry Pi 3b) \\ \end{tabular} & Ethernet & Threat detection & Security protocol block (SPB) & N.A. \\ \hline \begin{tabular}{c} [PERSON]. [PERSON] \\ [76] \\ \end{tabular} & IoT field and control & N.A. & Threat detection & N.A. & Fuzzy clustering \\ \hline \
\begin{tabular}{c} [PERSON] \\ et al. [77] \\ \end{tabular} &
\begin{tabular}{c} Secure master and \\ slave (Raspberry Pi) \\ \end{tabular} & WiFi network & Data security & FSoE & N.A. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparative analysis of references previously discussed related to IoT-based data security applications. Figure 15: Summary of comparative analysis of IoT approaches in cybersecurity. Differently from Table 2, an additional column (column 5) relating to the decryption algorithms used to convert encrypted data into their original readable format has been added. The decryption algorithm is a fundamental process that ensures data security during transfer. During the analysis of the various research papers reported in this section, it was possible to observe that different AI algorithms were used; in fact, it is possible to come across supervised learning algorithms, unsupervised learning algorithms, and deep learning algorithms.
## 5 Discussion  
A successful EMS is vital to guarantee the smooth and enduring operation of FCHEVs. The EV-SimKit modeling framework provides a fast and efficient method of comparing different EMSs for various types of vehicles. The ECMS and SMC have a considerable impact on FC degradation caused by sudden and large load changes. The RLP approach leads to minimal degradation but also reduces the FC system's efficiency. The FC operates efficiently using both standard FLC and optimized FLC [123; 124; 125], but FLCS had a significant impact on the degradation of FC. The optimization reduced this impact to 0.028 USD/km, ranking it as the second-best result among all EMSs. In addition, the MFLC can reduce this impact, where the output membership functions were altered based on a metric called state-of-degradation (SOD) [82].  
In the early stages, both FCS + battery and FCS + battery + UC hybridization were equally prevalent. However, FCS + battery + UC hybridization has recently gained prominence. While FCS + UC boasts a smaller size and weight than FCS + battery, its limited capacity poses challenges in meeting continuous high-load power demands. The integration of all three sources, FCS, battery, and UC, introduces complexity to the EMS. Nevertheless, this complexity has a minimal impact on rule-based EMSs (e.g., TCS, PFC, and FLC) for simplicity and low computational cost. Conversely, optimization-based EMSs (DP, ECMS, and MPC) and learning-based EMSs (NN, Q-learning, and DQN) are preferred to use for FCS + battery hybridization due to their inherent complexity.  
Early investigations of EMSs primarily concentrated on the comparative analysis of two conventional EMS categories: rule-based EMSs and optimization-based EMSs, with a specific emphasis on fuel economy. In the period 2017-2019, the prevalence of rule-based EMSs outpaced that of optimization-based EMSs, driven by the former's advantages in real vehicle applications. Within the domain of rule-based EMSs, FLC garnered significant attention due to its flexibility, which facilitates performance enhancement through integration with other methods. The prevailing optimization methodology predominantly revolved around enhancing critical FLC parameters via algorithms such as genetic algorithms. Nevertheless, despite enhancements in the FLC's optimized performance, its control effectiveness remained unsatisfactory.  
In EMS studies, the attention directed towards DP steadily diminished after 2017. The primary reason behind this decline is the impracticality of DP's application in real-time ve  
Figure 24: DQN schematic diagram.  
hicle scenarios, as it relies on comprehensive global operating conditions. Nevertheless, DP stands out for its capability to achieve theoretically optimal economic outcomes, positioning it as the gold standard for evaluating the performance of alternative EMS methodologies.  
As evidenced by the data presented in Table 5, we can gain insights into the research trends and developments pertaining to FCHEV topology and EMS over the past decade.
# A Closed-Form Method for Simultaneous Target Localization and UAV Trajectory Optimization
## 3 Simultaneous Target Localization and Unmanned Aerial Vehicle (UAV) Trajectory Optimization
### Target Localization with the Attitude Measurement of Line of Sight (LoS)
Therefore, the measurement model for the moving target can be expressed as:  
\[\mathbf{z}=\mathbf{h}(\mathbf{x})+\mathbf{w} \tag{8}\]  
where \(\mathbf{x}=[x_{g},y_{g}]^{\top}\) denotes the horizontal position vector of the target; \(\mathbf{z}=[\gamma,\mathbf{\theta}]^{\top}\) represents the measurement vector; and the expression of \(\mathbf{h}(\mathbf{x})\) is exactly the equation in (4). As \(z_{g}\) is known, there are only two variables to be estimated, and hence the target position can be estimated by (4). Thus, the target is observable with the attitude measurement of the LoS.
###### Abstract  
Wireless sensor networks (WSNs) have recently been viewed as the basic architecture that prepared the way for the Internet of Things (IoT) to arise. Nevertheless, when WSNs are linked with the IoT, a difficult issue arises due to excessive energy utilization in their nodes and short network longevity. As a result, energy constraints in sensor nodes, sensor data sharing and routing protocols are the fundamental topics in WSN. This research presents an enhanced smart-energy-efficient routing protocol (ESEERP) technique that extends the lifetime of the network and improves its connection to meet the aforementioned deficiencies. It selects the Cluster Head (CH) depending on an efficient optimization method derived from several purposes. It aids in the reduction of sleepy sensor nodes and decreases energy utilization. A Sail Fish Optimizer (SFO) is used to find an appropriate route to the sink node for data transfer following CH selection. Regarding energy utilization, bandwidth, packet delivery ratio and network longevity, the proposed methodology is mathematically studied, and the results have been compared to identical current approaches such as a Genetic algorithm (GA), Ant Lion optimization (ALO) and Particle Swarm Optimization (PSO). The simulation shows that in the proposed approach for the longevity of the network, there are 3500 rounds; energy utilization achieves a maximum of 0.5 Joules; bandwidth transmits the data at the rate of 0.52 MBPS; the packet delivery ratio (PDR) is at the rate of 96% for 500 nodes, respectively.  
wireless sensor networks; Internet of Things; energy efficient; routing protocol; performance metrics +
Footnote †: journal: Sensors  
**Keywords**: Wireless sensor networks; Internet of Things; energy efficient; routing protocol; performance metrics +
Footnote †: journal: Sensors
# Development of Patient Specific Conformal 3D-Printed Devices for Dose Verification in Radiotherapy
## 3 Results and Discussion
### In Vitro Dose Verification Using 3D-Printed Devices
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Difference from Prescribed Dose 2 Gy**} \\ \cline{2-7}
**IMRT Plan** & \multicolumn{2}{c}{**D95\%**} & \multicolumn{2}{c}{**D97\%**} & \multicolumn{2}{c}{**D99\%**} \\ \cline{2-7}  & **Gy** & **\%** & **Gy** & **\%** & **Gy** & **\%** \\ \hline Without bolus & 0.1 & 5.14 & 0.17 & 8.64 & 0.52 & 26.04 \\ With 0.5 cm bolus & 0.007 & 0.35 & 0.008 & 0.4 & 0.02 & 0.98 \\ With 1.0 cm bolus & 0 & 0 & 0 & 0 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Calculated difference between doses calculated by TPS and prescribed dose for the IMRT plan. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Difference from Prescribed Dose 2 Gy**} \\ \cline{2-7}
**A Single-Field Plan** & \multicolumn{2}{c}{**D95\%**} & \multicolumn{2}{c}{**D97\%**} & \multicolumn{2}{c}{**D99\%**} \\ \cline{2-7}  & **Gy** & **\%** & **Gy** & **\%** & **Gy** & **\%** \\ \hline Without bolus & 0.85 & 40.0 & 0.98 & 50.0 & 1.04 & 52.0 \\ With 0.5 cm bolus & 0.01 & 0.5 & 0.04 & 2.0 & 0.06 & 3.0 \\ With 1.0 cm bolus & 0.00 & 0.0 & 0.01 & 0.5 & 0.03 & 1.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Calculated difference between doses calculated by TPS and prescribed dose for single-field plan. Figure 9: Isodose distributions in five-field intensity-modulated radiation therapy plans: (**A,B**) sagittal and frontal views of CT head phantom with attached patient-specific phantom; (**C**) patient-specific phantom with attached 0.5 cm thick bolus; (**D**) patient-specific phantom with attached 1.0 cm thick bolus.
## 6 Future Prospects and Outlook for Grain-Drying Machine Control Systems
### Advanced Technology Integration and Intelligent Automation Sector  
6.1.1. Deep Integration of AI and ML  
Grain dryer control systems will gradually integrate AI and ML technologies. These advanced algorithms can monitor key parameters in the drying process, such as grain moisture, temperature, and air speed, in real-time and make dynamic adjustments based on historical data and real-time feedback. Through adaptive control algorithms, the system can identify and predict potential uneven drying issues, thereby automatically optimizing the drying curve to improve drying efficiency and product quality.
# LDPC-Coded CAP with Spatial Diversity for UVLC Systems over Generalized-Gamma Fading Channel
###### Abstract  
In this paper, low-density parity-check (LDPC)-coded carrierless amplitude and phase (CAP) modulation with spatial diversity is proposed to mitigate turbulence-induced fading in an underwater visible-light communication (UVLC) channel. Generalized-gamma (GG) distribution was used to model the fading, as this model is valid for weak- and strong-turbulence regimes. On the basis of the characteristic function (CHF) of GG random variables, we derived an approximated bit-error rate (BER) for the CAP modulation scheme with spatial diversity and equal-gain combining (EGC). Furthermore, we simulated the performance of the CAP system with diversity and LDPC for various turbulence conditions and validated the analysis. Obtained results showed that the combination of LDPC and spatial diversity is effective in mitigating turbulence-induced fading, especially when turbulence strength is strong.  
UVLC; CAP; LDPC; turbulence-induced fading; GG distribution +
Footnote †: journal: _Acticle Journal of the Optical Society_
## 4 Experiments and Analyses
### Validation of the Nonlinear Ultrasonic Harmonic Detection Method Based on HHT  
In the nonlinear ultrasonic detection method, WPT and HHT were used to process the signals. To verify the effectiveness of the method, a comparison of the processing process with or without WPT and HHT used was performed. The Discrete Fourier transform (DFT) was used to calculate the nonlinear coefficient for the signal processed by WPT only. The spectrum processed by WPT+DFT and HHT of different delamination damages are shown in Figures 13 and 14. The amplitude of the fundamental frequency also decreased slightly with the increase of delamination damage. The amplitude of second-order harmonic is buried in the surrounding frequency component. The amplitude of the third-order harmonic that was processed by WPT+DFT has a growing trend with the increase of delamination damage. Because of the difference of the nonlinear coefficient obtained by different methods, the detection results were normalized by min-max normalization.  
The changing curves of the normalized second-order and third-order nonlinear coefficient are shown in Figures 15 and 16. The nonlinear coefficient resulted being increased with the delamination damage, except for the second-order nonlinear coefficient which was processed by HHT, had a negative growth at 16 mm. The second-order nonlinear coefficient which was processed by WPT+DFT grows evenly at first, but the second-order nonlinear coefficient of 16 mm and 20 mm is very close, which is difficult to distinguish. The growth rate of the second-order nonlinear coefficient that was processed by HHT is quite different, which is unfavorable to the characterization of the damage. The growth rate of the second-order nonlinear coefficient that was processed by WPT+HHT increased evenly compared to WPT+DFT and HHT. The growth rate of the third-order nonlinear coefficient that was processed in all three ways increased evenly. To further compare three processing methods, the mean squared  
Figure 11: The changing curves of the second-order nonlinear coefficient.  
Figure 12: The changing curves of the third-order nonlinear coefficient.  
error (MSE) was calculated. The MSE of three processing process is shown in Table 1. The MSE of a nonlinear coefficient that was processed by WPT+HHT was much less than WPT+DFT and HHT. The MSE of the third-order nonlinear coefficient is smaller than a second-order nonlinear coefficient. The result obtained by using WPT+HHT was found to be the best one.  
Figure 14: Marginal spectrum processed by HHT of different delamination damages. (**a**) 0 mm; (**b**) 4 mm; (**c**) 8 mm; (**d**) 12 mm; (**e**) 16 mm; (**f**) 20 mm.  
Figure 13: Marginal spectrum processed by WPT+DFT of different delamination damages. (**a**) 0 mm; (**b**) 4 mm; (**c**) 8 mm; (**d**) 12 mm; (**e**) 16 mm; (**f**) 20 mm.
# Few-Shot Knowledge Graph Completion Model Based on Relation Learning
## 3 Preliminaries
### Problem Formulation  
A knowledge graph \(\mathcal{G}\) is composed of various facts, each of which can be represented as a set of triples in the form \(\mathcal{G}=\{(h,r,t)\subseteq\varepsilon\times\mathcal{R}\times\varepsilon\}\), where \(\varepsilon\) and \(\mathcal{R}\) represent the entity set and relation set, respectively. The problem of knowledge graph completion is to infer one fact by giving two facts in a triple. The focus of this study is predicting the tail entity \(t\) by giving the head entity \(h\) and relation \(r\), which involves determining whether \((h,r,?)\) holds true. Different from general knowledge graph completion problems,  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Symbol** & **Description** \\ \hline \(\mathcal{G}\) & knowledge graph \\ \(\mathcal{G}^{\prime}\) & background knowledge graph (\(\mathcal{G}\) removes all subgraphs of task relations) \\ \(\varepsilon\),\(\mathcal{R}\) & entity and relation of a knowledge graph \\ \(\mathcal{S}_{r}\) & reference set corresponding to relation \(r\) \\ \(\mathcal{Q}_{r}\) & query set corresponding to relation \(r\) \\ \(\mathcal{Q}_{r}^{-}\) & negative query set corresponding to relation \(r\) \\ \(\mathcal{R}_{br}\) & the relation set of background knowledge graph \(\mathcal{G}^{\prime}\) \\ \(\mathcal{R}_{task}\) & task relation set \\ \(\mathcal{C}_{h_{j},r}\) & candidate set for the potential tail entity of \((h_{j},r,?)\) \\ \(T_{mr}\) & set of meta train \\ \(T_{mte}\) & set of meta test \\ \(h_{r},r,t\) & embedding features of fact triplets \(h\),\(r\),\(t\) \\ \(\mathcal{N}_{h}^{\prime}\) & the higher-order neighbor set of entity \(h\) \\ \(\varepsilon\) & entity representation updated by neighborhood entity encoder \\ \(r^{\prime}\) & task relation representation updated by relation encoder \\ \(z_{i}^{k}\) & representation of triples corresponding to relation \(r\) \\ \(\mathcal{L}\) & loss function \\ \hline \hline \end{tabular}
\end{table}
Table 1: Symbol explanation table.  
few-shot knowledge graph completion problems are performed with a limited number of reference samples.  
In summary, we provide the definition of a few-shot KGC problem as follows.  
**Definition 1**.: _Given a problem relation \(r\) and its reference set \(\mathcal{S}_{r}=\{\,(h_{i},t_{i})|(h_{i},r,t_{i})\in\varepsilon\times\mathcal{R} \times\varepsilon\}_{i}\), the tail entity \(t_{i}\) is predicted based on the information provided by the knowledge graph \(\mathcal{G}\) and \(\mathcal{S}_{r}\), as well as the connection between the head entity \(h_{j}\) and the problem relation \(r\) in the query triple \(\left(h_{j},r,?\right)\). In this case, \(|\mathcal{S}_{r}|=K\), where \(K\) is typically a small value, hence the term few-shot knowledge graph completion._
## 1 Introduction
Due to the very low frequency spacing between sub-carriers, Fast-OFDM signals suffer more from inter-carrier interference compared to the conventional O-OFDM [3; 15], consequently, the importance of realizing an equalizer to mitigate nonlinear impairments in optical Fast-OFDM is much higher. An attempt to tackle nonlinearities in optical Fast-OFDM systems was made via a Wiener-Hammerstein electrical equalizer, with however marginal performance improvement [20]. Unfortunately, the use of ANN nor IVSTF has not been reported yet, therefore in this work, for the first time, a low-complex ANN-based machine learning NLE is numerically demonstrated in low-cost intensity-modulated and directed-detected optical Fast-OFDM links using a standard single-mode fiber (SMF) as a transmission medium. A comparison is also made with the deterministic IVSTF which is also implemented for the first time in optical Fast-OFDM. The paper is organized as follows: Section 2 shows the linear equalization performance of the direct-detected-based optical Fast-OFDM system over additive white Gaussian noise (AWGN). In Section 3, there are details on the adopted ANN algorithm and IVSTF for optical Fast-OFDM signals. In Section 4, ANN is tested over an SMF-link, while in Section 5, a comparison between ANN and IVSTF is conducted for Fast-OFDM signals over SMF, considering the impact of key DAC/ADC parameters when compared to linear equalization. Thereafter in Section 6, a detailed computational complexity analysis of the ANN-NLE and IVSTF-NLE is conducted in terms of number of subcarriers for different system parameter is discussed. Finally, the paper is concluded in Section 7.