esa-sceva/satcom-chunk-collection huggingface dataset:
# Gaining Insights into Conceptual Models: A Graph-Theoretic Querying Approach
## 5 Use Cases for OPM Queries
### Business Process Optimization Using Path Finding: The Case of the IT Support Workflow
This state is connected to the **Incident Ticket Creating** process with an instrument condition link, which triggers **Incident Ticket Creating**. With the help of the instrument **Incident Resolving System**, **Incident Ticket** is created in its initial state **open**. The process **Incident Ticket Resolving**, handled by **Level 1 and 2 Support Agents**, changes the state of the informational object **Incident Ticket** from **open** to **closed** and the **Incident** object from **unresolved** to **resolved**. Figure 11: Incident resolving system OPM model—system diagram (SD). Having completed our model design of the system, we can query it. We want to find the shortest path--the path with the minimal number of hops (edges), which involves as few sub-processes as possible and still gets the same result of resolving an incident for the end user. This is also the path that passes through the minimal number of OPM things. In Figure 14, we see the query result of the shortest path query execution as one of the \"Path Finding\" OPM queries. The path starts from the source **Unresolved Incident** and ends at the target **Resolved Incident**. The result shows that the shortest path passes through **IT Portal Contact Method**. This answer to the query clearly indicates that using the IT portal works better (faster) than contacting an IT staff member, even though customers tend to think intuitively that approaching a staff member directly via email or messaging would be faster. Figure 12: SD1 of the **Incident Resolving System** model, in which **Incident Resolving** is in-zoomed. Figure 13: SD1.1 of the **Incident Resolving System** model, in which **Manual IT Skill Classifying** is in-zoomed. The longest path query, which is another query in the collection of \"Path Finding\" OPM queries, generates the result in Figure 15. This solution includes processes and objects that were not part of the shortest path query result. One of these processes is **IT Personnel Contacting**. The insight we can gain from combining both queries is that using the **IT Portal** not only provides better service to the end users, as it contains the minimal number of processes; It also benefits the IT engineering teams who provide Level 3 support, as their productivity can grow thanks to reducing unnecessary overheads and time spent on classifying issues and the distractions caused by unnecessary context switching.
# Junction Properties Interpretation of Textile Geogrids Using Multi-Junction Clamp
## 1 Introduction  
Textile geogrids(referred to as geogrid) are the representative reinforcement materials consisting of connected parallel sets of intersecting ribs with aperture of sufficient size to allow strike-through of surrounding soil, stone, or other geotechnical materials. Thus, geogrids are matrix-like materials with large open spaces called apertures which are typically 10 to 100 mm between ribs that are called longitudinal and transverse, respectively [1; 2; 3]. The network structure of geogrids gives stronger interaction to adjacent soil than other sheet type reinforcement. In general, the interaction between soil and geogrid is divided into three terms; (a) friction of geogrid to adjacent soil, (b) friction soil to soil directly contacted through apertures and (c) passive resistance of cross direction ribs of geogrids. Geogrid imposed loads due to a friction of cross direction rib surface to soil, and passive resistance of cross direction ribs, are transferred through junction to mechanical direction rib. Therefore, sufficient junction strength is needed to carry the induced force [4]. In general, junction efficiency and junction strength per unit width is calculated by a conversion equation that is recommended in GRI GG-2 [5]. The GRI GG-2 test method has an inherent weakness in not being able to take into account the scale effect of geogrid specimens. Most of the geogrwheties being used for civil engineering are used as tensile members, and they are subject to an assessment of their tensile strength by taking a specimen of wide width [6; 7; 8]. Therefore, more reliable test methods which include the scale effect are needed. The current trend in the assessment of the tensile strength of geosynthetics is that tensile strength tests on a specimen with a width as wide as possible. This study is a desirable attempt to provide more accurate and reliable test data, considering the scale effect of a specimen. In this study, the multi-junction test method was suggested for the assessment of the geogrids junction strength to consider the aperture size scale effect.
# A Comparative Analysis of Wi-Fi Offloading and Cooperation in Small-Cell Network
###### Abstract
CoMP implementation requires additional overhead communication due to exchange of channel state information (CSI), scheduling complexity and additional backhaul limitations. JT/JP typically requires additional transmit power to deliver capacity gains, especially for cell-edge UEs [9; 10]. In this paper, we present the comparison of Wi-Fi offloading and cooperative communication in a small-cell network. The proposed techniques can be adopted to enhance the QoS of the network and they can be implemented using network function virtualization (NFV) and software-defined networking (SDN) on top of the standard infrastructure. Due to the QoS enhancement of these strategies, we base our analysis of the two schemes as a QoS maximization problem. We consider sum data rate as our QoS measure and formulate the two problems individually. For Wi-Fi offloading, the data rate maximization relies mainly on the Wi-Fi leftover capacity, whereas, for cooperative communication, the number of associations of a user with small-cell (SC) base stations is the key factor. After analyzing each of the scenario, switching criteria is set for switching scheme. The rest of the paper is structured as follows: Section 2 presents related research work. Section 3 describes the transmission models for both cooperation and Wi-Fi Offloading. Section 4 presents our proposed heuristics i.e., SCRE and SORE for the case of cooperation and offloading. Results and performance analysis is discussed in Section 5. Section 6 provides the future directions and conclusion.
# The Development of a Spaceborne SAR Based on a Reflector Antenna
## 3 Reflector Antenna Configurations in Spaceborne SAR Systems
### Feed Configurations for Reflector Antenna
#### 3.2.1 Single-Beam Feed  
Single-beam feed is the most fundamental and widely used feed type in reflector antennas. Traditional reflector antennas, such as parabolic antennas, typically use a single-beam feed, often implemented as a horn antenna. The primary beam generated by the feed is shaped by the reflector to form a directed beam. The primary objective in single-beam feed design is to produce an initial radiation pattern with optimal characteristics to efficiently illuminate the reflector and maximize overall antenna performance.  
While single-beam feed reflector antennas lack beam-scanning capability, they remain suitable for many basic SAR applications, particularly those with relatively simple operational requirements. For example, Qilu-1, a Chinese SAR satellite launched in 2021, utilizes a single-beam reflector antenna to provide remote sensing services for land monitoring, urban planning, agriculture, forestry, energy, and disaster management in the Shandong region of China. Meanwhile, the Capela Arcadia SAR satellite [19] of Capela Space in the United States also adopted the traditional single-beam feed. As illustrated in Figure 10, the horn antenna in the red box represents a single-beam feed.
# A Combined High and Low Cycle Fatigue Life Prediction Model for Wind Turbine Blades
## 4 Model Validation and Comparison  
### Model Validation Classical CCF Life Prediction Model  
The widely used linear injury model for CCF is Miner's rule [20], a straightforward combination of LCF and HCF injuries:  
\[D=D_{L}+D_{H}=\frac{1}{N_{L}}+\frac{n}{N_{H}} \tag{22}\]  
Since [PERSON]'s rule neglects HCF-LCF interactions, it has limitations. To address this under CCF loading, several nonlinear cumulative damage techniques have been proposed, with the M-H model and T-K model being the most representative. The cumulative damage in the M-H model [21] is computed as follows:  
\[D=\left(\frac{1}{N_{L}}\right)^{\left(\frac{N_{L}}{N_{H}}\right)^{\beta}}+ \frac{n}{N_{H}} \tag{23}\]  
where \(\beta\) denotes the material constant.  
The T-K model [22] cumulative damage is calculated as follows:  
\[N_{f}=(n+1)N_{L}\left(\frac{1}{n}\right)^{r\frac{\sigma_{eff}H}{\sigma_{H,L}}} \tag{24}\]  
where \(r\) denotes the material constant.
# Q-LBR: Q-Learning Based Load Balancing Routing for UAV-Assisted VANET
## 2 Related Studies
### Q-Learning-Based Routing Protocols  
In recent years, artificial intelligence techniques, which include machine learning, have attracted a significant amount of interest from researchers of various fields [8]. Among such techniques, reinforcement learning (RL) is being investigated in wireless systems because it provides a solution to optimize the system parameters by learning the surrounding area in a dynamic and complicated wireless environment [10; 11; 12]. Q-learning is a representative RL, and studies on using this approach to allocate routing policies in a dynamically changing network environment have been conducted. The Q-learning algorithm [13] solves this problem by utilizing the following Q-value update equation:  
\[Q(s_{t+1},\alpha_{t+1})\leftarrow(1-\alpha)Q(s_{t},\alpha_{t})+\alpha\{f_{r}( s_{t},\alpha_{t})+\gamma max_{\alpha^{\prime}}(Q(s_{t},\alpha_{t}),\alpha^{ \prime})\}, \tag{1}\]  
where \(Q(s_{t},\alpha_{t})\) is the Q-value of the current state \(s_{t}\) when action \(\alpha\) is selected at time \(t\), \(f_{r}(s_{t},\alpha_{t})\) represents the reward function when state \(s_{t}\) selects action \(\alpha_{t}\), and \(\max(Q(s_{t},\alpha_{t})\), \(\alpha^{\prime})\) is the maximum possible Q-value in the next state \(s_{t+1}\) when possible action \(\alpha^{\prime}\) is selected. The learning rate \(\alpha\) and discount factor \(\gamma\) have values between zero and one. As an advantage of Q-learning, it can be used to design optimal policy functions even in unknown environments. In general, a wireless network environment is extremely complex and difficult to predict and, therefore, it is considered that reinforcement learning such as Q-learning is more suitable than supervised learning.  
There are several noteworthy studies on Q-learning-based routing protocols. Q-Geo [14] proposed an ad hoc routing method based on geographic information through Q-learning in an unmanned robotic network. This algorithm enables network enhancement using local information without full network knowledge by calculating the packet travel speed. The energy-aware QoS routing protocol (EQR-RL) [15] uses a reinforcement learning algorithm and the reinforcement learning based geographic routing (RLGR) [16] are proposed methods for applying Q-learning in routing decisions for a network lifetime enhancement in a Wireless sensor network (WSN). Q-learning based fuzzy logic [17] for multi-objective routing algorithm is proposed as a method for flying ad hoc networks (FANET).  
Although there have been numerous studies applying Q-learning, results for UAV-assisted VANET are yet to be presented. In addition, the key issue for applying RL to a rapidly changing network environment is solving the convergence speed problem. Specifically, RL is based on the results of experiences acquired through exploration and, thus, it sometimes takes significant trial and error to obtain meaningful results. Likewise, until recently, reinforcement learning in the field of networking has not been considered.
# A Brief Survey on Deep Learning-Based Temporal Knowledge Graph Completion
## 3 Deep Learning-Based TKGC Methods
### Attention-Based Methods  
The attention mechanism [55] is popular in sequence modeling tasks such as machine translation [56] and speech recognition [57]. Formally, the relevant score of the \(i\)-th data in the input sequence and the \(j\)-th data in the output sequence is computed as:  
\[e_{ji}=a(S_{i}^{in},S_{j}^{out}), \tag{5}\]  
where \(a()\) represents the alignment function, which can be implemented using a neural network, \(S_{i}^{in}\) is the \(i\)-th data in the input sequence, and \(S_{j}^{out}\) is the \(j\)-th data in the output sequence. In addition, the softmax function is often used to compress the relevance scores into positive values between 0 and 1.  
[PERSON] et al. [35] restructure TKG into a form of relational multi-chains and propose a relation-oriented attention mechanism to capture the features of relations while considering temporal information. Then, they employ DE-SimpIE [58] to obtain the features of entities. Finally, these features are fed into the scoring function of the static KGC model SimpIE [22] to calculate the plausibility scores of the given facts.  
Inspired by the self-attention mechanism [59] and the diachronic embedding technique [58], [PERSON] et al. [36] propose HSAE, which has an encoder-decoder architecture. The encoder module is composed of two layers--an entity self-attention embedding layer and a relation self-attention embedding layer--which are used to obtain embeddings of entities and relations containing time series information and graph structure information, respectively. The decoder module feeds these embeddings into a scoring function to compute their plausibility scores.  
Methods in this category use the attention mechanism to model the importance of entities and/or relations in TKGs. They have the characteristics of theoretical simplicity but usually need to be combined with other methods to complete the TKGs.
## 3 Inner Wheel Lifting Phenomena
### Understanding Mechanism of Lifting Phenomena
#### 3.1.3 Relation between Steering Angle and Input Frequency  
Lateral displacement _LD_ is expressed using Equation (3). Steering input angle _MA_ proportional to the square of steering input frequency \(f\) gives maintained _LD_, as shown in Figure 9. However, an extreme increase of steering input angle _MA_ means fairly severe conditions for inner wheel lifting phenomena. Therefore, increasing steering input frequency \(f\) results in a sudden turnover.  
Only steering input frequency \(f\) was changed, while maintaining steering input angle _MA_ = \(\pm\)60 deg, without maintaining _LD_, as shown in Figure 10. The minimum residual load of the front left wheel (inner wheel) is almost zero at \(f\) = 0.71 Hz, and the inner wheel lifts clearly at \(f\) = 1.0 Hz. It can be understood that front inner wheel lifting is caused not only by the large steering angle input but also directly by quick steering input.  
Figure 10: Quick steering input directly causes wheel lifting (I = 50).  
Figure 9: Steering angle proportional to square of frequency results in sudden turnover (I = 50).  
Quick and large steering input is actually very difficult for human drivers. The effects of three axis moments of inertia on front inner wheel lifting phenomena by sudden steering input were examined with 0.5 Hz (quick L/C) as the upper limit of standard drivers' ability.
## 2 Research Problem
### Power and Bandwidth Constraints of LEDs  
LEDs working as lighting sources and transmitters are nonlinear and power-limited devices with a bandwidth constraint.  
Since the optical OFDM signals can be modeled as Gaussian random variables, the magnitude can be very large. Due to the peak transmitted power constraint of LEDs, the high-valued signals greater than this power limit must be clipped. As shown in Figure 3, the signals beyond the peak power and below zero must be hard clipped. This clipping distortion is unrecoverable and can severely degrade the OFDM system performance.  
Due to the slow rise-time of the LED, its bandwidth is limited, which impacts the data throughput of UOWC systems. In this paper, we compare DCO-, ACO-, and U-OFDM in UOWC systems with considerations of zero and peak power constraints, as well as the bandwidth limit.
## 4 Experimental Verification
### The Experimental Result with Load Torque Change  
Figures 8 and 9 illustrate the experimental results for the method in [14] and the 2 DOF IMC ADO method when the SRM operates at 400 r/min with 1 N\(\cdot\)m load. When the time is 2.3 s, the load torque varies from 1 N\(\cdot\)m to 4 N\(\cdot\)m. Figure 8 displays the experimental results of \(dq0\)-axes currents, where \(i_{d}^{*},i_{q}^{*},i_{0}^{*}\) are the reference current of \(i_{d},i_{q},i_{0}\). Figure 9 shows the experimental results of the rotor speed, total torque and phase current.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Speed (rpm)** & **Load Torque (N \(\cdot\) m)** & \(T_{r}\) \\ \hline Reference [14] & 200 & 2 & 52.2\% \\  & 500 & 2 & 65.1\% \\  & 750 & 2 & 100.8\% \\  & 200 & 2 & 47.3\% \\  & 500 & 2 & 54.75 \% \\  & 750 & 2 & 94.1 \% \\ \hline \hline \end{tabular}
\end{table}
Table 2: The torque ripple percentages for both methods when the reference speed changes.  
It can be seen from Figure 8b that the \(d\)-axis current fluctuations of both methods increase when the load torque changes at 2.3 s. The method in [14] results in a \(i_{d}\) fluctuation of 1.25 A; in contrast, it is only 0.93 A in the case of the 2 DOF IMC ADO method. Moreover, compared to Figure 8a,b, it is obvious that a significant improvement is obtained in the current tracking performance by using the proposed 2 DOF IMC ADO method. Similar to the above analysis, by considering the SRM characteristics, the proposed method can achieve good control performance and a fast response under a step-torque load case, as shown in Figure 9. In addition, as the ADO can eliminate the disturbance from the variable load torque, the robustness is further enhanced, which can be observed in Figures 8 and 9. With the good current control performance, good torque ripple suppression can be achieved by using the proposed method in Figure 9. Furthermore, it can be seen that when the load torque steps, a shorter regulation time and smaller ripple of the proposed method can be found, as in Figure 9, which is also consistent with the above analysis.  
Table 3 lists the results of the \(T_{r}\) for both methods. Obviously, in terms of torque ripple suppression, the proposed 2 DOF IMC ADO approach outperforms the method in [14], and  
Figure 8: Experimental results of the \(dq0\)-axes currents with the load torque change. (**a**) Method in [14]. (**b**) 2 DOF IMC ADO method.  
Figure 9: Experimental results of the rotor speed, total torque and phase current with the load torque change. (**a**) Method in [14]. (**b**) 2 DOF IMC ADO method.  
the \(T_{r}\) is reduced by 8.25% and 18%. The above-compared results indicate that the proposed method can achieve good tracking and disturbance rejection performance at a step load torque condition.
## 4 Design of the Equalizers
### Linear Equalizer  
A linear equalizer is a finite impulse response (FIR) filter with adjustable coefficients followed by a threshold decision circuit. Figure 4 illustrates the principle of the linear equalizer, whose coefficients can be optimized using one of the equalization rules that will be presented in this paper.  
H(z) and C(z) represent the z-transforms regarding the channel filter and the equalizer filter, respectively.  
Figure 4: Diagram of a linear equalizer [15].  
The output of the equalizer is related to its input using the following equation:  
\[\hat{x}(n)=\sum_{k=-\infty}^{+\infty}c_{k}y(n-k), \tag{7}\]
# Future Industrial Applications: Exploring LPWAN-Driven IoT Protocols
## 3 Wireless Protocols Architecture
### LoRaWAN  
LoRaWAN was developed by the LoRa alliance for applications requiring long-range and low-power wide area networks (WANs) [38]. It enables highly efficient WANs by reducing the necessity of repeaters between nodes, leveraging its very low power consumption. This increases the overall system efficiency by reducing the number of required devices and also increasing the battery life of the nodes. LoraWAN utilizes the Sub-Ghz ISM band [39]. A noteworthy drawback of LoRaWAN is that the data rate is low [40], thus catering to only some specific IoT applications. The ultra-low power consumption also means that only star topology can be implemented, where all the nodes send information to the central gateway, which communicates with LoRa servers [41; 42]. LoRa utilizes the chirp spread spectrum (CSS) modulation scheme [13]. A CSS transmit signal can be shown as follows:  
\[x_{k}[n]=\sqrt{\frac{E_{s}}{N}}exp\bigg{(}j\frac{2\pi}{N}kn\bigg{)}c[n] \tag{1}\]  
\(E_{s}\) is the signal energy, \(c[n]\) is the discrete time chirp signal with a period of \(N\), and \(k\) is the data symbol:  
\[k=\sum_{i=0}^{SF-1}2^{i}[b]_{i} \tag{2}\]  
and \(b\) is the bit-word, as such:  
\[b\in\{0,1\}^{SF} \tag{3}\]  
The data rate of LoRa transmission, \(R_{b}\), depends on the modulation bandwidth \(BW\) and the spreading factor \(SF\):  
\[R_{b}=SF*\frac{BW^{SF}}{2} \tag{4}\]
# A Critical Review of the Modelling Tools for the Reactive Transport of Organic Contaminants
## 3 Modelling
### Stochastic Models
#### 3.2.4 Hips-Proban  
The HPS-PROBAN approach combines the semi-analytical transport model (Horizontal Source Model) with a probability analysis program--PROBAN. The HPS model was proposed by Galya [467]. It is a semi-analytical 3D model that solves the transport equations of advection and dispersion, and can simulate first-order decay and sorption. The numerical output of the model proposed by Galya is the concentration distribution at each point down gradient from the contamination source. PROBAN [468] is a software package developed to perform the complex probability analyses. The link between PROBAN and HPS is established via user-defined FORTRAN 77 subroutines. This approach was validated using data from the Environmental Protection Agency (EPA) [469] and findings from a review in the work of Newell et al. [470]. In this case study, the probability of not reaching target concentrations of o-xylene downstream of the spill was estimated. The random variables included the aquifer, the source, and the chemical parameters. The results, particularly the probability of not reaching the target contaminant concentration, were largely related to the velocity, length of the source, and the kinetic rate of biodegradation. The final results were compared with Monte Carlo simulations, and showed good agreement with relatively low computational effort.
## 3 Results
### Multifunctional Nanoparticles for Therapy and Imaging
#### 3.1.2 Engineered Nanoparticles for Enhanced Diagnostic Imaging
Therefore, QDs represent a powerful platform for cancer diagnosis, offering high sensitivity and specificity while remaining compatible with modern imaging and cellular analysis techniques. Their multiplexing capacity, spectral clarity, and chemical adaptability position them among the most promising fluorescent contrast agents in precision medicine.