esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This paper focuses on the joint estimation of parameters and time-delays of the multiple-input single-output output-error systems. Since the time-delays are unknown, an effective identification model with a high dimensional and sparse parameter vector is established based on overparameterization. Then, the identification problem is converted to a sparse optimization problem. Based on the basis pursuit de-noising criterion and the auxiliary model identification idea, an auxiliary model based basis pursuit de-noising iterative algorithm is presented. The parameters are estimated by solving a quadratic program, and the unavailable terms in the information vector are updated by the auxiliary model outputs iteratively. The time-delays are estimated according to the sparse structure of the parameter vector. The proposed method can obtain effective estimates of the parameters and time-delays from few sampled data. The simulation results illustrate the effectiveness of the proposed algorithm.  
multivariable system; parameter identification; time-delay estimation; basis pursuit de-noising; auxiliary model; quadratic program +
Footnote †: journal:
## 2 Related Work  
Adaptive video streaming has seen significant advancements in recent years, resulting in the introduction of various approaches. These approaches can be classified into four primary categories based on the adaptation technology employed:  
* Traditional ABR-based approaches: This category encompasses methods that rely on bandwidth measurements, buffer occupancy, or a combination of both to guide streaming decisions. These approaches typically use fixed rules for adaptation throughout the streaming process. While they have found widespread use and implementation, their efficacy may be constrained, particularly in highly dynamic and diverse network conditions.
* Deep-learning-based approaches: Within this category, deep learning techniques, specifically neural network models, play a pivotal role. By training these models on extensive datasets, they can discern intricate patterns and make informed decisions regarding adaptation. Deep-learning-based approaches have demonstrated enhanced performance in adapting to a range of network conditions and user preferences. However, they often necessitate substantial amounts of training data and computational resources for effective model training.
* Reinforcement-learning-based approaches: In this category, adaptation decisions are orchestrated by an agent within an interactive environment, refining its choices through trial and error, guided by rewards. Reinforcement learning empowers the agent to learn and optimize its decisions based on the feedback received. These approaches offer the advantage of adaptability and the capability to navigate dynamic and uncertain network conditions. However, it is worth nothing that training rein forcement learning models can be a time-consuming process, and their performance heavily depends on the design of rewards and exploration strategies.
* Deep-reinforcement-learning-based approaches: This category harnesses the synergy of deep neural networks and reinforcement learning techniques. Deep reinforcement learning approaches utilize deep neural networks to approximate various elements of the reinforcement learning process, equipping them to handle intricate streaming environments and make sound adaptation decisions. By leveraging the representation learning capabilities of deep neural networks, these approaches have exhibited promising results in delivering high-quality video streaming experiences.
## 2 Applications of HREM
### HREM in Embryo Research
#### 2.1.1 Mouse Embryos
DMDD independent stand-alone studies researching genetic regulation of mouse development include the analysis of: normal and abnormal cardiovascular development [49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61]; the development of the limbs, cloaca and pancreas [62; 63; 64; 42]; ciliopathies [65]; and the characterisation of cardiac defects in Down syndrome mouse models [66; 67]. Despite its value for analysing such processes in embryos between embryonic day E8.5 and E14.5, when organogenesis is already finished, HREM also proved its value for analysing developmental processes in very early embryos immediately after implantation [68]. HREM has proved an excellent fit for pure descriptive but also metric analysis, which will have a great impact on researching the influence of biomechanical forces on prenatal tissue and organ formation and remodelling. Simple descriptive studies allowed, for example, the visualisation of pharyngeal arch artery development, proved the transitory existence of a 5 th pharyngeal arch artery in the mouse and defined the dimensions of large arteries in mouse embryos bred on various genetic backgrounds [69; 70; 71; 72; 73; 74]. Precise visualisation of septation and cardiac cushion, the outflow tract and valve development were used as a basis for learning the concepts underlying associated malformations [75; 76; 77; 78; 79; 80; 81; 82; 83; 84; 85; 86]. Besides its importance for research, digital 3D models are of course eagerly anticipated teaching aids, as they can exemplify complex developmental remodelling processes. Simple metric data were produced for defining the dimensions of the great intrathoracic arteries of embryos bred on various genetic backgrounds. However, based on HREM data, more sophisticated models could be calculated, which could mathematically define the description of the heart looping process [52] or use 3D fractal analysis for defining the complexity of trabeculation during ventricular development of the heart [87; 88; 89].
## 3 Materials and Methods
### Design of an IoT-Based Solar PV Data Monitoring System
A comparison between the proposed IoT-supported data monitoring system and some existing data monitoring systems is provided in Table 1.  
In this study, following the integration of IoT-based electronic hardware, the ThingSpeak platform, and the mobile application, experimental measurements were conducted. The data from the experimental measurements were recorded and monitored through internet and mobile platforms. The prototype system, which was purposefully designed and cost-effective, utilized a NodeMCU v2 microcontroller board, a Sparkfun WeatherShield weather measurement board, an ADS1115 ADC module, an ACS712-30A current sensor, an LCD display, an LM35 temperature sensor, and a Sparkfun (Sparkfun Electronics, Boulder, CO, USA) GP-735 GPS receiver. The approximate budget spent on creating this prototype system was around 120 \(\upxi\). Examining the results obtained from the testing process, it was observed that the developed system accurately measured and transmitted data. Electrical and meteorological measurements were corroborated through comparison with diverse sources. The mentioned measurements in the study were repeated and recorded at five-minute intervals. Daily variation graphs for the output voltage of the monitored PV panel, battery voltage, daily irradiance, and ambient temperature are provided in Figure 3.  
Figure 4 illustrates the visualization of the data transferred to the Firebase real-time database and sent to the cloud platform, where they are displayed via the developed mobile application. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Author** & **Network** & **Hardware** & **Software** & **Cost (\(\upxi\))** \\ \hline [PERSON] and [PERSON] [6] & Wired & NI PCI-6024E DAQ & LabView 6.1 & 749 \(\upxi\) \\ [PERSON] et al. [31] & Wired & Agilent 34970A DAQ & LabView 2011 & 1420 \(\upxi\) \\ [PERSON] and [PERSON] [32] & Wireless & Raspberry Pi and Arduino UNO & Arduino IDE 1.0 & 60 \(\upxi\) \\ [PERSON] et al. [33] & Wired & NI USB-6009 DAQ & LabView 2016 & 120 \(\upxi\) \\ Proposed IoT-Based System & Wireless & ESP8266 & Arduino IDE 2.2 & 5 \(\upxi\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison between the proposed solar PV monitoring system and existing solar PV monitoring systems. Figure 2: (a) IoT-based data acquisition board; (b) Experimental setup. Figure 4: Visualization of solar data using (**a**) the ThingSpeak server and (**b**) the developed mobile application. Figure 3: Voltage, illuminance, and ambient temperature measured in (**a,b**) Karabuk (9 November 2021) and (**c,d**) Karabuk (16 November 2021). The subsequent section addresses the analysis of the collected data with the aim of utilizing machine learning methods to predict the future performance of the solar PV system.
## 4 Results
### Analytical Results
Additionally, it can be mentioned that when the interfering device transmission power increases the required transmission power for the smart monitor and the gateway must increase to overcome the effect of the interference and at the same time reach the required system QoS. Figure 14 demonstrates the required smart monitor devices and gateway transmission power to reach the required system QoS. Figure 14a,b show that for the uplink and downlink  
Figure 12: Required transmission power for the UP/DL communication (dBm) versus interfering devices’ transmission power (\(P_{I}\)) (dBm). Figure 13: Overall energy efficiency for the UP/DL communication (EE) (bit/J) versus interfering devices’ transmission power (\(P_{I}\)) (dBm). Subfigures (**a–e**) correspond to distances between source S1 and destination D1 and between destinations D1 and D2 of 50 m, 100 m, 150 m, 200 m, and 250 m, respectively. transmission when the required QoS increases, the transmission power for both smart monitor devices and gateway must increase to satisfy the required QoS. Moreover, it can be noted when comparing Figure 14a,b that the required gateway transmission power is always greater than the required smart monitor device transmission power, whether the interfering transmission device is 17 dBm or 23 dBm, this is because the gateway is always facing interference greater than any other receiving device as the gateway is considered the main source that can easily communicate with all its surrounding devices and coordinate the communication among them.
# Energy-Efficient De-Duplication Mechanism for Healthcare Data Aggregation in IoT
## 5 Result and Analysis
### Impact of the Number of Attributes on Control Overhead  
The control overhead involves the exchange of additional messages that are essential for maintaining network performance. These messages are transmitted to facilitate the selection of transmission slots, ensuring a smooth data transmission process. The process of choosing transmission slots involves a sequence of requests, replies, and transmissions. When selecting additional slots, multiple series of these messages are exchanged.  
The control overhead is directly related to energy consumption. Higher control overhead leads to high energy utilization. In the proposed scheme, only critical data is transmitted, decreasing the data size and subsequently demanding fewer slots. This reduction helps alleviate the control overhead. The exchange of minimal control messages for slot selection lessens network congestion, thereby reducing the likelihood of data loss. In the FDAS, slot and parent selection happens simultaneously, efficiently reducing control overhead. Furthermore, the EHDA approach compresses data before transmission, further reducing the required number of slots. Yet, schemes like DICA and DICA\(\_\)EXTENSION do not employ any strategy to mitigate this problem. Simulation results, depicted in Figure 5a, show that EE-FDAS performs well, with an overhead of 600. The EHDA, FDAS, DICA, and DICA\(\_\)EXTENSION have overhead values of 950, 1500, 4050, and 4030, respectively, in cases where the number of attributes is 2.
# Handover Management for Drones in Future Mobile Networks--A Survey
## 5 Handover Challenges in Drone Networks
### Security and Privacy  
Drones' networks are also at risk to various types of the privacy and security threats. Therefore, it is essential to protect upright sky networks from any related privacy and security threats. Various survey and research studies have been conducted and provided a vital basis for considering the drone threats that need to be addressed [88; 89; 90; 91]. However, no study has provided an optimal solution for the existing issues. More studies related to the privacy, security, security level, privacy threats, secured architecture, types of attacks, and more efficient attack mitigation techniques still need to be conducted.
## 6 Discussion
### Key Findings  
Methods based on static analysis use techniques such as signature matching and symbolic execution which do not require the program to be executed. This allows them to impose a low overhead but can also result in a lower accuracy. For example, MINOS has the fastest detection time at under one second but also has the lowest F\({}_{1}\)-score of all cryptojacking detection methods. Methods that rely solely on signature or keyword matching can be easily bypassed through the use of obfuscation techniques [46; 76]. Additionally, methods that rely on semantic execution may be limited by the path explosion problem.  
Methods based on dynamic analysis execute the program in a controlled environment to extract behavioral features that can be used for further analysis. To do this, various techniques such as taint tracking, binary instrumentation, fuzzing, and monitoring system resources have been proposed. This typically results in higher overhead but also a better performance than static-based methods. For example, Wasabi is able to perform heavy-weight dynamic analysis through binary instrumentation, but it might also incur a high overhead. Since dynamic analysis is based on behavioral features, it is less susceptible to evasion through obfuscation techniques. However, it can still be bypassed in some cases, such as by throttling processor usage.  
Static and dynamic techniques are not mutually exclusive but complementary techniques. A hybrid approach can leverage the low overhead of static techniques and the high accuracy of dynamic techniques. In such an approach, static techniques can be used to  
\begin{table}
\begin{tabular}{l l l l l l l l l l l l} \hline \hline  & & & & \multicolumn{4}{c}{**Vulnerability Detection**} & \multicolumn{4}{c}{**Performance**} \\ \cline{3-11}
**Type** & **Scheme** & **Technique** & **FE *** & **FN *** & **BD *** & **RB *** & **MAV *** & **Precision** & **Recall** & **F\({}_{1}\)** & **DT *** \\ \hline Static & EVulHunter [62] & CFG & ✓ & ✓ & ✗ & ✗ & ✗ & 89\% & 100\% & 93\% & 1–3 s \\  & WANA [63] & Symbolic & & ✓ & ✓ & ✗ & ✗ & 100\% & 100\% & 100\% & 0.21 s \\  & EOSAFE [37] & Symbolic & & ✓ & ✓ & ✗ & ✓ & 100\% & 96\% & 98\% & - \\  & execution & & & & & & & & & & \\  & EOSIOAnalyzer [65] & ICFG & ✓ & ✓ & ✓ & ✗ & ✗ & 93\% & 100\% & 96\% & 7.6 s \\ \hline Dynamic & EOSFuzzer [18] & Fuzzing & ✓ & ✓ & ✓ & ✗ & ✗ & 88\% & 88\% & 88\% & - \\  & WASAI [64] & Concolic & & & & & & & & & \\  & (2022) & fuzzing & ✓ & ✓ & ✓ & ✓ & 100\% & 98\% & 99\% & - \\ \hline \hline \end{tabular}  
* Abbreviations: fake EOS (FE), fake notification (FN), Blockinfo dependency (BD), rollback (RB), missing authorization verification (MAV), and detection time (DT). ✓: Vulnerability is patched. ✗: Vulnerability is not patched.  
\end{table}
Table 5: Data for detecting vulnerabilities in WebAssembly smart contracts.  
identify candidate functions and dynamic techniques can then be employed to analyze them accurately. Currently, only WASP\({}_{2}\) employs such a hybrid approach.
## 3 Results and Analysis
### Relationship between Peak Stress, JRC and Joint Inclination  
Figure 7 demonstrates the relationship between peak stress, JRC, and joint inclination.  
Figure 6: Failure modes of jointed specimens with various JRCs when the joint inclination angle is \(45^{\circ}\). (**a**) JRC = 0, (**b**) JRC = 5, (**c**) JRC = 10, (**d**) JRC = 15, (**e**) JRC = 20.  
As shown in Figure 7, under similar impact loading, both JRC and joint inclination angle significantly affect the peak stress of the jointed specimens. The peak stress exhibits a trend of initially decreasing and then increasing as the joint inclination angle increases, namely an approximate V-shape variation. On the other hand, the peak stress increases with JRC increasing. For instance, when the JRC is 0, 5, 10, 15, and 20, the average peak stress of jointed specimens with a joint inclination angle of 45\({}^{\circ}\) are 8.68 MPa, 16.33 MPa, 27.04 MPa, 33.95 MPa, and 45.26 MPa, respectively. Compared to the complete specimen, the average peak stress has decreased by 86.3%, 74.2%, 57.2%, 46.3%, and 28.4%, respectively. It can be observed that the 90\({}^{\circ}\) joint inclination has the least impact on the peak stress, whereas the 45\({}^{\circ}\) joint inclination has the greatest effect. With the same JRC, the peak stress tends to decrease nonlinearly with the joint inclination angle ranging from 0\({}^{\circ}\) to 45\({}^{\circ}\), while the peak stress demonstrates an overall increasing trend with the joint inclination angle ranging from 45\({}^{\circ}\) to 90\({}^{\circ}\). Furthermore, when joint inclination angle is 0\({}^{\circ}\) or 90\({}^{\circ}\), the peak stress of jointed specimens is closer to each other regardless of the JRC. This can be attributed to the fact that when the joint surface is parallel or perpendicular to the direction of the loading stress wave, the JRC has a diminished effect on the peak stress, resulting in a weak stress wave reflection.
# Heterogeneously Integrated Multicore Fibers for Smart Oilfield Applications
## 3 Heterogeneously Integrated Multicore Fibers
### Transmission System Demonstration  
To further investigate the performance of the multicore fiber in actual applicable conditions, we model the signal transmission process in VPItransmissionMaker Optical Systems. A 100 Gb/s quadrature phase-shift keying (QPSK) signal is generated at 1550 nm, and the input powers of the digital signal and the analog signal are 0 dBm and 13 dBm, respectively. The core pitch is set as 50 \(\upmu\)m. The number of splices is 2 and the splice loss is 0.4 dB. The loss of LP\({}_{01}\) and LP\({}_{11}\) is 0.20 dB/km and 0.22 dB/km, respectively. The mode crosstalk is \(-60\) dB/km. In the simulation, the fan-in and fan-out devices and (de)multiplexers are somewhat ideal devices. The crosstalk of these two types of devices is \(-50\) dB, and the insertion loss is 1 dB and 3 dB, respectively. Taking into account the processing capacity of widely used chips, the number of taps is set as 32 to maintain the possibility of real-time transmission. The DSP algorithm is referred to in [38], which could adjust the distribution of filter taps and enhance the utilization to efficiently recover signal. The SI-TA fiber is set as the core for analog transmission. The Q\({}^{2}\)-factor performances versus different transmission distances with the digital signal transmission cores of the SI fiber, the GI-TA fiber, and the fabricated GI-TA fiber are shown in Figure 7. Because the designed fiber would be applied in smart oilfields, the maximum transmission distance is set as 50 km. The Q\({}^{2}\)-factor threshold of forward error correction (FEC) is 6.5 dB, which is recommended by IEEE Standard 802.3 [39]. We could see that when the core for digital transmission is the optimized low-DMGD three-mode TA-GI fiber, the Q\({}^{2}\)-factors of two guided modes decrease slightly as the length increases and those are over 15 dB within 50 km. Compared with this, if the SI fiber is set as the core for digital transmission, the Q\({}^{2}\)-factor of each guided mode decreases more sharply from approximately 15 dB to 6 dB. The effective transmission distance is approximately 43 km, which is smaller than 50 km. It shows that the performance of the TA-GI fiber is more available and could be applied in this environment. As for analog transmission performance, due to the 13 dBm input power of analog signal transmission, it is almost 20 times larger than the input power of digital signal transmission. For such an asymmetry channel, the performance of the analog single transmission is almost the same as that in a single-core transmission system.  
Figure 6: The relationship between the crosstalk of guided modes and the core pitches. In each row, the cores for digital transmission are the SI fiber, the GI fiber, and the GI-TA fiber. In each column, the cores for analog transmission are the SI fiber, the GI fiber, and the SI-TA fiber, respectively.  
Figure 7: Q\({}^{2}\) factor performances versus transmission distances with different types of fibers for digital transmission.
## 5 Conclusions  
This paper presented the virtual reality application SoundFields, a serious game developed as an intervention tool to address auditory hypersensitivities in individuals with autism spectrum disorder. The system is designed to expose participants to common environmental sounds which often cause distress in everyday scenarios within a safe, controlled and engaging virtual environment using head-tracked binaural based spatial audio. Pilot study experiments observed a decrease in participants' self reported levels of anxiety towards target stimuli after just four 30 min sessions playing the game. In addition, despite the increase of exposure levels as the intervention progressed, the time each participant exposed themselves to target stimuli also increased. Although the engagement with the game by participants and the experimental results are positive, future studies are required with larger intervention groups to replicate these findings and to test the validity of binaural based spatial audio as a rendering technique for graduated exposure therapy.  
Conceptualisation, [PERSON] and G.K.; Methodology, D.J.; Software, D.J.; Validation, D.J., H.E. and G.K.; Formal Analysis, D.J., H.E.; Investigation, D.J.; Resources, D.J.; Data Curation, D.J.; Writing--Original Draft Preparation, D.J.; Writing--Review & Editing, D.J., H.E. and G.K.; Visualisation, D.J.; Supervision, H.E. and G.K.; Project Administration, D.J.; Funding Acquisition, G.K. All authors have read and agreed to the published version of the manuscript. Funding was provided by a UK Engineering and Physical Sciences Research Council (EPSRC) Doctoral Training Award, via the Department of Electronic Engineering at the University of York, EPSRC Grant Number: EP/N509802/1. The authors would like to thank Springwater School (Harrogate, UK) for their participation and support of this research. The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data, in the writing of the manuscript, or in the decision to publish the results.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} ASD & Autism Spectrum Disorders \\ HMD & Head Mounted Display \\ NPC & Non-Playable Character \\ SG & Serious Game \\ VE & Virtual Environment \\ VR & Virtual Reality \\ VRET & Virtual Reality Exposure Therapy \\ \end{tabular}