esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Controller Design  
### PID Controller Design  
A PID control equation is an equation that mathematically expresses the behavior of a proportional-integral-derivative controller [45]. The control equation is generally calculated as expressed in Equation (44). In Equation (44), \(u(t)\) represents the control signal. The expression \(e(t)\) is used as an error representation. The difference between the set point and the output of the actual process, that is, \(r(t)\) is the reference desired value, \(y(t)\) is the output of the system, and \(e(t)=r(t)-y(t)\).  
\[u(t)=k_{j}\overset{t}{0}e(t)dt+k_{p}e(t)+k_{d}\frac{de(t)}{dt}, \tag{44}\]  
The equation involves three coefficients: \(k_{p}\), \(k_{i}\), and \(k_{d}\). The proportional coefficient, \(k_{p}\), directly influences the output based on the error. The integral coefficient, \(k_{i}\), determines the cumulative effect of the error over time. Lastly, the derivative coefficient, \(k_{d}\), governs the impact of changes in the error on the output. The integral \(\int_{0}^{t}e(t)dt\) represents the accumulation of mistakes over a period of time. This equation governs the output of the PID controller. To expedite the attainment of the desired set point and minimize error, this equation incorporates the immediate error value, its cumulative total, and its rate of change. The parameters \(k_{p}\), \(k_{i}\), and \(k_{d}\) are used to fine-tune the performance of the controller.
## 3 Results
### Longitudinal Analysis of Respiratory and Echocardiographic Changes  
Upon examining the change in respiratory parameters and echocardiographic parameters at all three echocardiogram time points--prior to course, immediately at steroid completion, and 7-14 days post steroid course completion--we found that, for each additional echocardiogram, the MAP significantly decreased, on average by 0.78 units ((95% CI = \(-\)1.39--0.16), \(p\) = 0.015); the SiO\({}_{2}\) significantly decreased, on average by 4.54 units ((95% CI = \(-\)8.36--0.71), \(p\) = 0.021); and the PIP significantly decreased, on average by 2.50 units ((95% CI = \(-\)4.77--0.22), \(p\) = 0.032). The PEEP did not show a significant change over time (\(p\) = 0.496). Figure 2.  
The echocardiographic parameters for each additional echocardiograph RV FAC significantly increased, on average by 0.02 units ((95% CI = 0.00-0.04), \(p\) = 0.037). In addition, the TAPSE significantly increased, on average by 0.09 cm ((95% CI = 0.06-0.13), \(p\) < 0.001); The eccentricity index significantly decreased, on average by 0.07 units (95% CI = \(-\)0.14--0.01), \(p\) = 0.030. These findings are represented in Figure 3. The PAAT and PAAT:RVET ratio did not show significant change over time. From the LV perspective, the LV S' significantly increased on average for each additional echocardiography by 0.56 units ((95% CI = 0.18-0.94), \(p\) = 0.005). The septal S' increased by an average of 0.61 units per additional echocardiography ((95% CI = 0.23-1.00), \(p\) = 0.003). The RVS' significantly increased on average per echocardiography by 0.97 units (95% CI = 0.11-1.84), \(p\) = 0.028. No significant changes were noted in the posterior wall thickness, relative wall thickness, LV chamber dimensions, or LV EF, or in the presence or absence of PDA.  
Figure 2: Changes in respiratory parameters at three time points: prior to dexamethasone, immediately after dexamethasone completion and 7–14 days after dexamethasone completion.  
Figure 3: Changes in echocardiographic parameters at three time points; prior to dexamethasone (referred to as the “Prior to Dex” timepoint), immediately after dexamethasone completion (referred to as the “At Dex Completion” timepoint), and 7–14 days after dexamethasone completion (referred to as the “1–2 Weeks Post Dex” timepoint. Parameters include **(a)** PAAT:RVET (ratio, mean), **(b)** TAPSE (cm, mean), **(c)** eccentricity index (%, mean), and **(d)** RV FAC (%, mean).
# ZSM Framework for Autonomous Security Service Level Agreement Life-Cycle Management in B5G Networks
## 6 Performance Evaluation
### Comparison with Other Works  
According to the results we obtained, the research outcomes are promising. In Table 3, we compare our findings with those from other works in the scientific community. For instance, in [49], the OSM slice deployment time was measured using vCache and vDPI, where a 4G Evolved Packet Core (EPC) with Network Services was deployed in a single domain, averaging 68 s for full slice deployment. This result falls within the standard deviation range of our single-domain deployment times.  
Similarly, in [50], OSM-based slice deployment in a single domain was evaluated, where the network slice included a VNF with two Virtual Deployment Units (VDUs) deployed on OpenStack as virtual machines. The deployment time was approximately 58 s, which also aligns with our single-domain standard deviation. Furthermore, Ref. [51] validated network slice deployments using OSM and OpenStack in a single domain. Their work, which included the configuration time for a network slice composed of a mobile core and RAN, reported slice deployment times of around 40 s in the worst-case scenario, with configuration times extending up to 125 s--figures consistent with our configuration times. Regarding STA, further insights into the ML model's performance compared to other approaches can be found in [34].  
From these comparisons, we can conclude that, with current technology, approximately 52 s per domain are typically required for each sub-slice instantiation via OSM. The remaining time is accounted for by the E2E orchestration process, the time needed for services to become operational, and configuration tasks. As noted, these times can vary significantly depending on the software and hardware used (e.g., external controllers) and the load on the services. Additionally, real E2E coordination across multiple domains has been scarcely tested, making our work particularly innovative in this area.  
Figure 12: False positives/negatives and Mean Time to Resolve. (a) False positives/negatives; (b) Mean Time To Resolve.
## 5 Discussion
### Limitations and Threats to Validity  
This study has several limitations. The participant sample consisted primarily of Master's students in informatics and data technologies, which limits the generalizability of the findings to other user groups, such as secondary school students, PhD candidates, or professional staff. These groups may have different levels of familiarity with tools like ChatGPT, Gemini, and Taguette, potentially affecting their experiences and evaluations. Additionally, the use of self-reported measures may not fully reflect actual behavior or cognitive processes. Future research should include a more diverse user base and incorporate objective data, such as task completion times, to provide a more comprehensive understanding of QDA tools.  
The study's findings are subject to several potential threats to validity. Using the short version of UEQ (UEQ-S) allows for only a rough measurement of higher-level meta-dimensions, and can impact the robustness and precision of the results. Consequently, comparisons made to other studies that employed the full UEQ may be less accurate, and the data interpretation should be cautiously approached. This study did not establish a baseline comparison of the task difficulty without the use of tools, which could provide a critical context for evaluating the effectiveness of the tools. As described in Section 3.3, all dimensions analyzed in this study were measured as perceived by users through self-reported scales. Perceptions may not fully align with users' actual behavior or cognitive processes. Reported results reflect subjective user perception and should be interpreted within the context of these limitations. User responses were only partly validated, raising concerns about the reliability and accuracy of the feedback received. This study compared two AI-based chatbots and one non-AI tool; therefore, the generalization from AI chatbots to other AI-enhanced tools is limited. There is a possibility that more specialized tools for UX evaluation tasks exist though they were not included in this study. This limitation could affect the generalization and applicability of the findings. Finally, the study involved Master's students studying informatics and data technologies, which means the results could differ for less tech-savvy users and other design-oriented IT practitioners.
## 5 Performance Evaluation  
The proposed MOFT is evaluated by comparing it with the prevailing OFT-based group key management approaches in terms of computation and communication cost.  
From Tables 2 and 3, it is evident that the proposed MOFT approach attains the least communication cost, and the cost is even less than the OFT approach itself. The proposed MOFT reduces the communication cost by 39% than the earliest OFT. Although the computation cost is higher than the OFT scheme, it is comparatively less than the existing collusion-resistant OFT schemes. Figure 11 depicts the evaluation of the proposed approach with the existing approaches regarding total communication cost. The schemes OFT and ROFT share the same communication cost. [PERSON] and [PERSON]'s and [PERSON]'s schemes share the same communication cost, whereas the proposed work's communication cost is less with \((2\log_{2}\text{n}+3)\times\text{L}\) messages, which is lesser than the original OFT. The number of messages transferred is equal to the number of keys needed to be distributed.  
Figure 10: Collusion attack.
## 6 Simulations
### EAR Performance Compared with No Power Adaptation and No Fire Situations  
We evaluate EAR routing when using 3 power levels adaptation and no power adaptation situations. Figure 9 illustrates the end-to-end delay with power adaptation and without power adaptation situations. We get the results with 1 sink and 3 sinks respectively. It is obviously the end-to-end delay decreases a lot when we use power adaptation. By the benefit of power level adaptation, we could increase the network connectivity in fire and also help to find lower delay route path to guarantee a real-time packet delivery under the given delay bound.  
Figure 8: Average node energy when delay bound = 70 ms.  
Figure 9: End-to-end delay with/without power adaptation.  
Figure 10 shows the miss ratio with/without power adaptation. The miss ratio increases greatly if we adapt power level in the network to increase the probability of real-time packet delivery.  
We then evaluate energy efficiency of EAR routing when fire happens and no fire happens situations. Figure 11 illustrates the average node energy in the simulation time when delay bound is set to 50 ms. From the results, it is obvious that average node energy decreases in fire situation. But until 250 s of simulation, the average node energy is larger than 0. For delay bound chosen as 50 ms, the network is still effective until close to the end of the simulation in building fire situations.  
Figure 11: Average node energy when delay bound = 50 m.  
Figure 10: Miss ratio with/without power adaptation.
# NFT Technology for Enhanced Global Digital Registers: A Novel Approach to Tokenization
## 9 Practical Applications and Use Cases
### Identity Management and Intellectual Property Considerations  
Our system implements a robust identity management framework that aligns with the following intellectual property requirements:1. Decentralized Identifiers (DIDs): We utilize W3C-compliant Decentralized Identifiers to create persistent, verifiable, and decentralized digital identities for both users and digital assets;
2. Verifiable Credentials: The system supports the issuance and verification of claims about identities, such as copyright ownership or licensing rights, using the W3C Verifiable Credentials data model;
3. Multi-Signature Ownership: For complex IP scenarios involving multiple stakeholders, our system supports multi-signature ownership of NFTs, ensuring that all relevant parties must approve transactions;
4. Timelock and Escrow Mechanisms: Smart contracts can implement timelock and escrow functionalities to manage time-bound rights, such as licensing agreements or embargo periods for academic publications;
5. Hierarchical Rights Management: The system allows for the creation of hierarchical structures within NFTs, enabling the representation of complex IP scenarios, such as derivative works or collective authorship.
## 3 IVR-Based SDO Streaming Distance Education System
### Network Configuration for IVR-SDO Streaming Education System  
Because the IVR-SDO distance education system requires a high bandwidth, it uses the Korea Research Environment Open Network (KREONET) ([[https://www.kreonet.net/eng/](https://www.kreonet.net/eng/)]([https://www.kreonet.net/eng/](https://www.kreonet.net/eng/)), accessed on 1 September 2021) as the backbone. KREONET supports high-performance network infrastructure to provide diverse science technology information resources, supercomputing, GRID, and e-science applications to approximately 200 research and development institutes. As shown in Figure 5, SDO images that are periodically transmitted from NASA connected to KREONET are stored in the network attached storage (NAS) of the Korea Astronomy and Space Science Institute (KASI), considered the Asian data hub. The SDO image server transmits user-requested SDO images stored in the NAS to the relay server. The SDO client receives SDO images via streaming by connecting to the relay server. Unlike SDO images, the voice and hand/head pose data are relayed through independent channels and transmitted to the SDO client.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Data Type** & **Data Specification** & **Data Size/s** \\ \hline \multirow{4}{*}{SDO image} & * 4096 \(\times\) 4096 resolution & \\  & * 1 MB/frame & 300 MB/s \\  & * 10 types & \\  & * 30 fps & \\ \hline \multirow{4}{*}{Voice} & * 44.1 KHz sample rate & \\  & * 2 channels & \\  & * 2 bytes/sample & 368.64 KB/s \\  & * 2048 samples & \\  & * 45 fps & \\ \hline \multirow{4}{*}{Hand/head poses} & * Hand pose (22 joints \(\times\) 2 hands \(\times\) 4 \(\times\) 4 pose matrix) & \\  & Head pose (4 \(\times\) 4 pose matrix) & 172.8 KB/s \\ \cline{1-1}  & * 60 fps & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Data specification for IVR-SDO streaming education system.  
Figure 4: Examples of 10 types of SDO AIA ultraviolet wavelength images and HMI illuminance/magnetism measurement images.
## 3 Results and Discussion
In our half wavelength spacing of 775-nm, the SMSR and FWHM can demonstrate 11 dB and 4.6 degrees, respectively, for a full steering angle of 140 degrees. Figure 2: (**a**) The grating lobe from the different modes (**b**) the grating lob-free from the same output mode. In order to get the adiabatic connection from two different pitches between the phase shifter and emitter, optical path equalization is implemented and shown in Figure 4a. The phase compensation and mode equalization on the two index-mismatched silicon wire-based emitter array can be found in Figure 4b and the total length of the emitter is 847.1 \(\upmu\)m.  
A schematic structure of optical phase array with 64 waveguides is demonstrated for binary splitters, phase shifter array, and emitter array. The operating wavelength is set at 1.55 \(\upmu\)m and waveguide construction is assumed as 220-nm for height. For the half wavelength spacing, the main lobe from 64 emitters using phase-compensated two index-mismatched silicon wires-based emitters demonstrates the grating lob-free at the field of view of \(-25^{\circ},0^{\circ}\) and \(25^{\circ}\), as shown in Figure 5.  
Since the emitters are close to each other and share the same wavelength, a test pattern for the two index-mismatched silicon wire emitters--other than using silicon wires integration--can be implemented for monitoring the phase and mode variation. Then an optical low-coherence interferometry (OLCI) including a fiber-optic Mach-Zehnder interferometer and a continuous-wave broadband source could be utilized to gauge the optical path difference [15]. After the characterization on two emitters with 400-nm and 500-nm in width, the phase difference can be illustrated. Figure 4: (**a**) The optical path equalization in OPA; (**b**) Two index-mismatched silicon wires-based emitter array with the phase compensation and mode equalization. Figure 3: (**a**) SMSR (**b**) FWHM versus the full steering angles at different pitches.
## 3 Results and Discussion
The spatial resolution has been determined from power spectrum as the half of critical wavelength (Figure 4a), which is inverse to the critical spatial frequency \(k_{\rm x}\) at which the signal turns to noise level [2, 3, 4, 5]. In a Figure 4b the estimated resolutions are shown against the carrier frequency \(f_{c}\). The resolutions are about 10-12 nm, which is similar to the previous results obtained at low frequency of magnetic field. They also have the same value in the whole frequency range 0.1-1000 MHz. So, A-MFM demonstrates the imaging possibility of magnetic field energy in a broad frequency range without losses in image quality. We believe that the frequency limits could be extended further above 1 GHz up to the values determined by magnetic resonance of PMR head materials.
# Coexistence in Wireless Networks: Challenges and Opportunities
## 3 Types of Wireless Communication
### Wi-Fi Networks  
Wi-Fi networks, often referred to as Wireless Fidelity networks, are a type of wireless local area network (LAN) technology that enables devices to connect to the internet and communicate with one another wirelessly inside a constrained space [32; 33; 34]. Wi-Fi networks are secured through protocols such as WPA, WPA2, and WPA3, ensuring  
Figure 1: Types of wireless networks.  
Figure 2: General architecture for cellular networks [31].  
data encryption and connection safety. Wi-Fi is frequently used to wirelessly connect devices like smartphones, computers, tablets, and smart home appliances to the internet in homes, offices, public spaces, and various other locations, as shown in Figure 3[35; 36]. Recent advancements, such as mesh networks for eliminating dead zones, highlight the pivotal role of Wi-Fi in enabling modern communication and supporting the growth of smart technologies.  
Bluetooth and Bluetooth Low Energy (BLE): Bluetooth is a short-range wireless technology that enables devices to connect and exchange data over short distances, typically up to 100 m [38]. It was initially created for voice communication and information exchange between electronic devices like PCs, headsets, and cellphones. Bluetooth has developed over time to offer a number of profiles and services, such as audio streaming, file sharing, and device pairing [39]. A Bluetooth extension known as Bluetooth Low Energy was created for applications that only need sporadic data exchanges, optimized for minimal battery consumption [40]. Fitness trackers, smartwatches, and other IoT gadgets that frequently run on batteries are excellent candidates for BLE [41]. Like conventional Bluetooth, it uses the 2.4 GHz radio spectrum to operate [42]. Recent advancements, such as Bluetooth 5.0 and beyond, have enhanced BLE's range, speed, and support for mesh networking, further solidifying its role in modern wireless ecosystems. Both Bluetooth and BLE play complementary roles, enabling seamless connectivity across diverse applications.
# Toward Personal Identification Using Multi-Angle-Captured Ear Images: A Feasibility Study
## 1 Introduction
2. Valuable feature points for personal identification exist and are independent of the shooting direction. To test the hypotheses, a machine learning method capable of image identification and interpretation is required. In this study, we conducted identification experiments based on multidirectional ear images using CNN and Gradient-weighted Class Activation Mapping (Grad-CAM). [PERSON] et al. [29] and [PERSON] et al. [28] have demonstrated the potential robustness of CNN to slight orientational changes in ear images [32]. Therefore, we conducted a CNN-based personal-identification experiment on ear images captured from multiple directions and used Grad-CAM to visualize the feature regions that were independent of the shooting direction. This study contributes the following to the extant literature:To demonstrate the feasibility of performing ear-image-based personal identification under realistic conditions, thereby facilitating system development regarding the capability to handle diverse image-capture directions, which has not been addressed. 2. To visualize the ear regions that are crucial for identification regardless of the shooting direction, thus providing a foundation for further studies on ear-image-based biometric identification. The remainder of this paper is structured as follows: Section 2 describes the methodology for collecting ear images and constructing the identification model, Section 3 presents the experimental setting and evaluation framework, Section 8 reports the results of the individual identification performances and visualizations of discriminative ear features, Section 9 discusses the implications of the findings and directions for future research, and Section 10 concludes the study, stating the limitations and outlining the directions for further investigation.
###### Abstract  
Surface solar irradiance (SSI) is influenced by important factors such as total cloud cover and aerosols. However, the influence of vegetation-runoff coupled system on SSI has not been investigated yet. The present study aims to study this issue in tropical Africa within the framework of the regional climate model (RegCM4) using two runoff schemes: (TOPMODEL; TOP and Variable Infiltration Capacity; VIC). For the vegetation-runoff systems to be coupled, the Carbon-Nitrogen (CN) module was enabled. The two simulations were designated as: CN-TOP and CN-VIC. Furthermore, the RegCM4 was downscaled by NCEP/NCAR2 reanalysis for a duration of 13 years and evaluated with respect to a reliable reanalysis product. The results showed that the CN-VIC outperforms the CN-TOP in all seasons particularly during the summer season. For instance, the CN-VIC underestimates the SSI over the Northern Savannah region by 15 W m\({}^{-2}\); while the CN-TOP underestimates the SSI by 30-50 W\(\cdot\)m\({}^{-2}\). Over different sub-regions, the CN-VIC performs better than the CN-TOP over Northern Savanna during the summer season and over the Congo basin during the winter season. Moreover, the four parameters of the VIC surface dataset need to be recalibrated with respect in-situ observation of tropical Africa, and additional sensitivity experiments are needed to ensure a better performance of the CN-VIC.  
Keywords:regional climate model; surface solar irradiance; tropical Africa; vegetation-runoff +
Footnote †: journal: _Engineering proceedings_