esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel and Adaptive Angle Diversity-Based Receiver for 6G Underground Mining VLC Systems
## 3 Adaptive Orientation Receiver  
In an effort to improve the performance of VLC systems applied to underground mines and inspired by the characteristics of strong light directionality required in VLC, we propose a solution based on a adaptive orientation receiver.
# A Survey of Handover Management in Mobile HetNets: Current Challenges and Future Directions
## 3 Background
### Handover Challenges
#### 3.5.4 Contradiction among Handover Parameters  
In 4G/5G Hetnets, MRO and LBO are the key handover optimization functions that aim to improve handover performance and network capacity. However, there can be a situation when the objectives of MRO and LBO may conflict with each other during the handover process, leading to trade-offs and potential issues. For example, in some situations, LBO may want to perform a handover to a cell that is farther away but has less load, while MRO may want to perform a handover to a cell that is closer but has more load in order to minimize the RLFs. Similarly, MRO and LBO can have different handover parameters and decision algorithms, leading to different handover decisions and potential inconsistencies. To mitigate these issues, coordination and standardization of handover parameters across different networks and network elements are necessary [38; 84; 91].
# An Explainable Artificial Intelligence Approach for Detecting Empathy in Textual Communication
## 2 Preliminaries
### Pattern-Based Classification  
Pattern-based classification is an approach of classification that makes use of \"patterns\" for deciding the label of each instance. It is related to rule-based classification, as it relies on the extraction of characteristics from a database to classify unseen instances [15; 16; 24]. In pattern-based classification, instead of extracting rules, a series of features that apply to various instances in a class are obtained. These feature combinations are what we call patterns, and they describe a collection of objects [25]. Patterns can be used as discriminators in classification tasks [24], as they can describe a large number of instances in one class and not in another. How often a pattern is present in a class is described as its _frequency_. After they are extracted, they are usually used as new features, which can then be input into the classification model [16].  
The key challenges for pattern-based models classification models are [16]:  
Figure 1: Simple model of empathy presented by [PERSON] and [PERSON] [18].  
Pattern finding: Mining patterns from a given dataset can be tackled through various algorithms. However, different approaches to ensure the quality of these patterns must also be considered. For example, a large number of patterns can be post-processed in order to reduce its size, or the number of patterns can be reduced through an iterative algorithm.
2. Pattern application: Independent patterns can be mined using an algorithm, which can then be used as features for any classifier. However, it is also possible to mine patterns that explicitly take into account the type of model which will use them for classification.  
The value found in this classification approach is that it can contribute other characteristics besides just the class predictions [16; 26]. Pattern-based classification can also contribute information regarding the instances that can be easily interpreted by experts in the topic [26]. The frequency of patterns can be identified and analyzed by users in order to obtain additional information that can contribute to the understanding of the problem. This has been useful in previous research [27]. Additionally, this advantage can contribute insight in class imbalance problems, specifically [28].  
Due to the advantages of pattern-based classification, we consider this approach as especially useful for our research. Because of the challenge in defining empathy, we believe finding patterns can help us obtain insight into the nature of empathetic textual communication. Additionally, this approach has been previously used in NLP tasks and has provided findings regarding language structure [29].
## 1 Introduction
To date, numerous researchers have implemented studies using shear tests, numerical simulations, model box tests, field jacking force monitoring, and lubrication records [9]. Yet, research in regard to an automated identification system for pipe jacking grouting that is applicable to different formations is lacking. In Ref. [20], the future of intelligent tunnel construction technology was envisioned in China: on-site intelligent assembly combined with digital management will become the trend for intelligent tunnel construction. It has been found that human-machine collaboration is an important research trend for the interaction between humans and intelligent construction machinery [21]. Digital construction technology for urban underground spaces has been analyzed, and construction methods (auxiliary methods) with intelligent pre-control have been proposed for construction safety [22]. Although the aforementioned studies mentioned the application of intelligent technology to tunnel excavation, specific designs or research on intelligent technologies in pipe jacking are still lacking. This paper develops an automatic pipe jacking friction resistance identification and warning system for pipe jacking construction. In Section 2, we comprehensively analyze the influence of the pipe jacking-soil pressure-mud relationship on the friction force in concrete pipe jacking construction, describe friction resistance models, and provide the formula for friction resistance calculations. In Section 3, we describe the automated pipe jacking friction resistance identification and warning system along with identification sensors for pipe jacking construction. Furthermore, we explain the construction method in Section 4. Finally, we demonstrate the practical application of this system in pipe jacking construction using a specific engineering case.
# Towards Contactless Learning Activities during Pandemics Using Autonomous Service Robots
## 3 Discussion  
### Human-Robot Voice Interaction Results  
To test our Human-Robot Interaction subsystem, we tested the wake word detection, the intent accuracy, and the webhook response and audio clarity. In each test, we measured the detection accuracy in a different tone. Our system can detect the wake word \"[PERSON]\" at least 90% of the time regardless of the user's tone and accent. We also tested whether the right intent gets triggered based on the audio command provided by the user and concluded that at least 80% of the intents were classified correctly. Some words such as \"Fourier\" were misclassified. To achieve better results, more training phrases need to be provided. Our proposed robot is able to display the correct content on the user interface along with clear and proper audio. The user interface views at each stage are depicted in Figure 13.
# A New Intra-Cluster Scheduling Scheme for Real-Time Flows in Wireless Sensor Networks
## 2 Proposed Scheme
### Procedure of the Proposed Scheme
Similarly, cluster \(H3\) schedule transmission \(H3\)\(\rightarrow\)\(k\) in the IntraRecv time slot whereas transmission \(k\)\(\rightarrow\)\(l\) in the IntraSend time slots. Next cluster \(H4\) schedule transmission \(H4\)\(\rightarrow\)\(r\) in the IntraRecv time time-slot while transmission \(r\)\(\rightarrow\)\(o\) in the IntraSend time slots so utilized both the IntraSend and IntraRecv time slots as shown below in Table 6.  
To compare the previous and proposed system, we consider the flows arrived at their destination node. In the previous systems, \(F1\) arrived at destination node at time slot #12 while in the proposed system \(F1\) arrived at destination node at time slot #9. The reason is \(F1\) in the proposed system utilized the empty time slots #4 of IntraRecv time slots while \(F1\) in the previous systems did not utilize the empty time slots of IntraRecv scheduling. F2 arrived at time slot #16 to the destination node in the previous scheme while in the proposed scheme due to utilization of IntraRecv time slot #4, \(F2\) reached to the destination node at slot #13. In the previous scheme, \(F3\) arrived at the destination node in slot #8 whereas flow \(F3\) in the proposed scheme arrived at the destination node at time slot #5. Because \(F3\) in the proposed scheme utilized the empty IntraRecv time slot #4, by the IntraSend scheduling, an empty time slot of IntraSend is utilized by IntraRecv scheduling. Similarly, \(F4\) in the proposed scheme arrived at the destination node at time slot#12 while in the previous scheme F4 arrived at time slot #16 due to the non-utilization of the IntraRecv time slot. Finally, \(F5\) in the previous scheme arrived at the destination node at time slot #24 while in the proposed scheme \(F5\) arrived at the destination node at time slot #13 because the IntraSend scheduling utilized the empty time slots of IntraRecv. Similarly, the empty time slots of IntraSend is utilized by the IntraRecv scheduling. The flow diagram describing different states of the proposed scheme is shown in Figure 3. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & \multicolumn{8}{c}{**IntraRecv Scheduling**} \\ \hline
**Cluster** & **Flow\#** & \(a_{i}^{DH}\) & **4** & **5** & **8** & **9** & **12** & **13** & \(a_{i}\) \\ \hline \multirow{2}{*}{H5} & F1 & 7 & & H5\(\rightarrow\)m & m\(\rightarrow\)q & & & & 9 \\ \cline{2-8}  & F5 & 11 & & & & H5\(\rightarrow\)n & n\(\rightarrow\)t & 13 \\ \hline \multirow{2}{*}{H3} & F2 & 11 & & & & H3\(\rightarrow\)k & k\(\rightarrow\)l & 13 \\ \cline{2-8}  & F4 & 10 & & & & H3\(\rightarrow\)d & & 12 \\ \hline \multirow{2}{*}{H4} & F3 & 3 & H4\(\rightarrow\)r & r\(\rightarrow\)o & & & & 5 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results of proposed scheme IntraRecv scheduling of Figure 2.
# An Accurate Jacobian Matrix with Exact Zoeppritz for Elastic Moduli of Dry Rock
## 4 Calculation of Jacobian Matrix Elements Using Exact Zeoppritz and Biot-Gassmann Equations
### The Calculation of \(\frac{\partial A}{\partial m_{j}}\)
#### 4.1.2 Partial Derivative of Matrix A with Respect to \(K_{d2}\)  
Since \(\alpha\) and \(\beta\) are independent of \(K_{d2}\), the partial derivatives of the trigonometric functions of angles \(\alpha\) and \(\beta\) with respect to \(K_{d2}\) are zero. The detailed derivations of all partial derivatives also are shown in Appendix A.  
Accordingly, the partial derivative of matrix A with respect to \(K_{d2}\) is:  
\[\frac{\partial A}{\partial K_{d2}}=\left[\begin{array}{cccc}0&0&-\tau_{kd2}^ {vp2}\sin\alpha^{\prime}&-\tau_{kd2}^{v2}\tan\beta^{\prime}\sin\beta^{ \prime}\\ 0&0&-\tau_{kd2}^{vp2}\tan\alpha^{\prime}\sin\alpha^{\prime}&\tau_{kd2}^{v2}\sin \beta^{\prime}\\ 0&0&\tau_{kd2}^{vp2}a_{33}+2\tau_{kd2}^{vv2}\Big{(}a_{33}+\frac{\rho_{2}}{\rho_ {1}}\frac{v_{p2}}{v_{p1}}\Big{)}&\tau_{kk2}^{v2}a_{34}(2-\tan^{2}\beta^{\prime})\\ 0&0&a_{43}\big{(}2\tau_{kd2}^{v2}-\tau_{kd2}^{v2}\tan^{2}\alpha^{\prime}\big{)}& (3a_{44}+2\frac{\rho_{2}}{\rho_{1}}v_{s2})\tau_{kd2}^{v2}\\ \end{array}\right] \tag{21}\]where \(\tau_{kd2}^{vp2}=\frac{1}{v_{p2}}\frac{\partial v_{p2}}{\partial K_{d2}}\) and \(\tau_{kd2}^{v\approx 2}=\frac{1}{v_{s2}}\frac{\partial v_{s2}}{\partial K_{d2}}\).
## 3 Results and Discussion
### Thermodynamic Equilibrium Conditions  
The gas hydrates formed in different cells that are set at various pressure conditions can dissociate by applying a gradual heating process. The data captured for the entire procedure of gas hydrate formation and dissociation are used to estimate the thermodynamic equilibrium point for the prescribed system. The T-cycle method is used for the evaluation of the thermodynamic equilibrium point. A sample pattern that is developed from the captured data is shown in Figure 5. All the thermodynamic points are premeditated by analyzing the data logged into the system during the experimental procedure and then plotting the pressure-temperature profile. All these points are used for developing the HLVE curve based on the multiphase system to understand the phase behavior. The plot of the thermodynamic equilibrium curve is presented in Figure 6.  
Figure 4: Gas chromatography and mass spectroscopy (GCMS) analysis of the Gasoline sample.  
Figure 5: Pressure–Temperature Profile generated during experiments (T-Cycle).  
Figure 6: Hydrate Liquid Vapor Equilibrium (HLVE) curve of Deionized Water, Gasoline, and Crude oil systems.  
The plot suggests that there is a definite shift in the thermodynamic equilibrium curve when the thermodynamic gas hydrate data is analyzed, which is produced by using a multiphase system that consists of fuel oils.  
For the curve developed from the experimental data generated by the gasoline system, it clearly shows a chosen pressure, and the thermodynamic temperature is below the actual deionized water temperature. This result reflects that the product pipelines have an inhibition effect. This is because of the additives that are added into the processed fuel, which should be more stable towards any fluctuations in temperature and pressure conditions. For the curve developed by the experimental data generated by the crude oil system, at a set pressure condition, the thermodynamic equilibrium temperature is above the water condition. This reflects that the system is promoting gas hydrate formation. This can be because of the high possibility of accumulation of gas hydrates, due to the availability of longer carbon chains available in the system. The presence of longer carbon chains impacts the system, as the higher carbon chain compounds act as surfactants [21]. Moreover, crude oil is an unprocessed fuel which contains a lot of impurities. These impurities can be referred to as COS, SO\({}_{2}\), NH\({}_{3}\) and H\({}_{2}\)S, and reflect on the difference in phase behavior. This can also reflect the terms of promotion, because of its highly reactive nature at high-pressure and low-temperature conditions [22].  
The thermodynamic equilibrium conditions for the simple system, and the multiphase systems consisting of gasoline and crude oil, are presented in Table 1.
## 4 Applications in Optimization
### Particle Swarm Optimization (PSO)
#### 4.1.1 Thermal Vacuum System
In order to reproduce these challenging environmental conditions that satellites experience during their operational life as closely as possible, it is possible to use thermal vacuum chambers that consist of a chamber, a _'shroud'_ (set of pipes) which heats or cools off the environment, and auxiliary equipment. In the thermal vacuum system used by the Brazilian National Institute for Space Research (INPE), the original controller was designed to control the temperature on the shroud [61]. The three proposed approaches shown here have been tested thoroughly. The authors succeeded in eliciting a TS fuzzy model for this nonlinear and time-delay application, with the best algorithm obtained using COSP3.
## Materials and Methods
### Selection Criteria  
Initially, the identified record titles and abstracts were screened to determine relevancy. The records that passed the preliminary screen were read in their entirety to classify the inclusion and exclusion criteria (Table 1). Briefly, inclusion criteria were as follows: diagnosis of idiopathic sudden sensorineural hearing loss, published in the English language, published on or after 1990, published in peer-reviewed sources, utilized OAEs (DPOAEs, TEOAEs, and/or SOAEs) as the primary physiologic outcome measure, and pure tone audiometry as the primary behavioral outcome measure.  
\begin{table}
\begin{tabular}{c l l} \hline \hline
**Variable** & **Inclusion** & **Exclusion** \\ \hline Population & 10-86 years (mean 47.2) & \\ \hline Language & English & All other languages \\ \hline Publication Date & 1991–2018 & \\ \hline Diagnostic Methods & Evoked toacoustic emissions & Did not include evoked otoacoustic emissions and pure tone audiometry \\ \hline Report Type & Published in peer-reviewed sources, meta-analyses, randomized controlled trials, cohort studies, case control, cross-sectional studies, retrospective studies, and prospective studies & Theoretical papers, opinion-based editorials, reviews, qualitative studies, case studies, records with no statistical data reported, theses, and dissertations \\ \hline Diagnosis & _Idipathic_ sudden sensorineural hearing loss & Sudden sensorineural hearing loss (SSNHL) with a known cause \\ \hline Physiologic Measurements & Otoacoustic emissions (DPOAEs, TEOAEs, and SOAEs), Auditory Brainstem Response (ABR, and Vestibular Evoked Myogenic Potential (VEMP( \\ \hline Behavioral Measurements & Pure tone audiometry thresholds; pure tone average (PTA) & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Study inclusion and exclusion criteria. DPOAE, distortion product otoacoustic emissions; TEOAE, transient evoked otoacoustic emission.  
Figure 1: Comprehensive search process to identify included studies.
# On the Feasibility of Wireless Multimedia Sensor Networks over IEEE 802.15.5 Mesh Topologies
## 2 Related Work
Consequently, it is necessary that each node is aware of its location. [PERSON] _et al._[22] developed another multipath routing scheme for video transmission that combines proactive routing, which provides fast response to events of interest, with reactive routing. In this way, upon the detection of an event, the transmission of data packets to the sink starts immediately and, simultaneously, a route discovery phase begins where new paths to the sink are discovered. These paths are selected according to a metric defined by the authors that estimates their amount of interference. Simulation results demonstrate that selecting the less interfering path can increase throughput and decrease packet loss. Table 1 shows the main contributions of the abovementioned proposals, as well as other features. As it can be observed, many of the proposals use either the IEEE 802.11 standard or the non-beacon mode of the IEEE 802.15.4 standard, which does not define how duty-cycling should be carried out. The 802.11 standard allows higher data transmission bit rates, therefore allowing for higher data throughput. However, it is not well suited for WSN because of its power consumption requirements. Similarly, 802.15.4 without a sleep/wake-up schedule, _i.e._, duty-cycle, draws a considerably higher amount of power, albeit not as high as with 802.11. In the case of short-range transmissions, such as  
the typically used in WSNs, transmission power is lower than the power spent in reception mode [23], therefore the most effective measure to limit power consumption is to make devices remain as much time as possible in low power (idle) states. For this same reason, battery-powered ZigBee-based solutions, where the non-beacon enabled mode is used, are known to have shorter network lifetimes simply because it forces all nodes with routing capability to be always on, with the subsequent battery drain. Finally, other routing schemes used in the discussed proposals exhibit other concerns. Hierarchical routing tend to overload nodes in the top layers of the hierarchy, which are closer to the sink, and, a priori, have more complex route repair procedures (_i.e._, it takes some time to adapt to node failures, provided that the protocol is designed to perform route repair). Multipath schemes may overload some nodes if the path selection criteria lead to always choosing the same paths, potentially affecting network lifetime. As stated before, the purpose of this work is to demonstrate the feasibility of video transmission using the IEEE 802.15.5 mesh standard, which is designed to create highly robust and reliable communication links. This standard allows for a very low power operation by defining how nodes should alternate periods of activity and inactivity to save energy and how these same nodes should coordinate with one another for a reliable transmission of data. This work is also intended to provide the scientific community with simulation tools for further evaluation of new protocols and improvements for WMSNs.
# Protecting Private Communications in Cyber-Physical Systems through Physical Unclonable Functions
## 3 Proposed Encryption Scheme
### Key Generator  
As said before, in order to guarantee a total protection level, the proposed key generator must generate a random key stream. Figure 3 shows the proposed design to reach that objective. In order to create a number sequence meeting the three basic requirements described in Section 3.2, in this key generator a simple lightweight pseudorandom number generator (PRNG) is considered to ensure a behavior as random as possible at low computational cost.  
In particular, we propose the use of the Trifork PRNG [15; 40]. This PRNG is based on three perturbed lagged Fibonacci generators (PLFGs), interconnected in such a manner that only using shift registers and OR logic gates make it possible to create a number sequence with a very long period (very random) and totally protected against cyberattacks (as proved by the NIST tests [40]). Then REQ#1 is met.  
In order to create the key stream, Trifork must be configured with a secret seed \(SEED[n]\). This seed includes three N-bit length values (9), \(x_{0}[n]\), \(y_{0}[n]\), and \(z_{0}[n]\). This collection is employed by a seeding module to create the initial sequence (not included in the secret key stream) to trigger the key generation process.  
\[SEED[n]=\ \{x_{0}[n],\ y_{0}[n],\ z_{0}[n]\} \tag{9}\]  
This secret seed \(SEED[n]\) also changes with time, in order to guarantee the same secret key stream is not used indefinitely. However, as the period of the sequences generated by Trifork is very long, the secret seed may change quite slowly.  
Figure 3: PUF-based key generator.  
This secret seed is usually stored in a ROM memory, calculated through a certain algorithm or received from remote servers, but all these options have been proven to be unsecure. Thus, in this work this secret seed is calculated using PUF. Figure 4 shows the proposed PUF for seed generation.  
As can be seen, the proposed PUF is based on the parallel connection of \(N_{d}\) exchange-bias magnetic elemental devices; and one additional non-EB magnetic device. All these elemental devices are challenged using the same analog excitation. In order to get all magnetic materials (devices) to respond to the excitation (challenge) with an unclonable signal changing with time, we propose the material is activated to suffer the exchange-bias effect (EB).