esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 A Case Study of Pakistan's National Population Census Bureau
A self-sovereign identity (SII) model would help count populations without official IDs. A suggested consortium blockchain aims to balance openness with security. * The participants outlined needs such as digitization, reliability, accuracy, and upgraded infrastructure and desired features including immutability, accountability, encrypted storage, etc., as listed in Table 3.  
In summary, the interviews revealed gaps in the current processes around traceability, transparency, and population coverage. A permissioned blockchain solution could potentially address these through cryptographic security, smart identity management, and controlled participation, enhancing integrity while retaining privacy. Requirements and Function DecompositionBased on the findings from the structured interviews and the challenges, requirements, and new features for the proposed system, the logical functional units for the \begin{table}
\begin{tabular}{c c c} \hline \hline
**Requirement** & **Function Decomposition** & **Priority** \\ \hline \multirow{3}{*}{Data digitalization} & Website; login portal; digital & \\  & questionnaire; authorized logins; & High \\  & data sharing & \\ \hline Data consistency, immutability, & & \\ reliability, security, & & \\ and encryption & & \\ \hline Eliminate political relevance & & \\ \hline Elimination of data redundancy & & \\ \hline Summarized data accessibility & Easy access to data downloads\({}^{\prime}\) & \\ for everyone & data in the required format & \\ \hline Thumbprints collection & Biometric thumbprint collection & Medium \\ \hline Progress Dashboard/Reports & Visibility of ongoing census count & Low \\ \hline Confirmation notification to the & & \\ interviewee & & \\ \hline \multirow{3}{*}{NCOs/NPOs integration} & Register themselves on the & \\  & website or bureau of census & \\ \cline{1-1}  & & directly, get them registered, and \\ \cline{1-1}  & & provide essential access. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Priorities of the requirement and functional decomposition [49]. solution are identified and presented in Table 3 below. Our primary research objective is to address the issue of missing persons in the population census count. We propose the integration of NGOs and non-profit organizations (NPOs) in the census process, as the census bureau typically holds exclusive data and does not incorporate reliable third-party or open-source information. This integration would distribute responsibility and costs, ensuring comprehensive nation-wide coverage [50]. It is worth noting that comprehending the cultural boundaries of the population and housing census bureau is crucial. Due to their limited familiarity with emerging technologies and a cautious approach to sharing trust and responsibilities, the bureau will involve NGOs selectively rather than implementing an open registration module for all. Initiating from a standpoint where they gradually share responsibility is essential to cultivating a trustworthy culture. The registration process can be streamlined as necessary after successfully piloting this project.
# A Survey on Change Detection and Time Series Analysis with Applications
## 4 Change or Breakpoint Detection within Non-Stationary Time Series  
Abrupt changes may occur in the trend component of the time series due to various reasons. For example, in remotely-sensed satellite image time series, an abrupt change may occur due to wildfire, drought, flood, insect attack, urbanization, etc. To visualize this, a simulated Normalized Difference Vegetation Index (NDVI) time series [64] is illustrated in Figure 5. In environmental applications, accurate estimation of breakpoints, e.g., the abrupt change in panel b, their types and magnitudes are very challenging due to the presence of noise, complexity of trend and seasonal components, and missing values. This section briefly reviews the most recent change detection methods that can detect the location and magnitude of such changes. The methods are also able to detect seasonal changes that, for instance, may be the result of climate change in environmental applications.
# Energy Efficient Routing Protocol for an IoT-Based WSN System to Detect Forest Fires
## 5 Comparative Analysis
### Energy Consumption in LEACH  
The amount of energy used by an ordinary sensor node is expressed by EON_LEACH and is computed as shown in Equation (1).  
\[\text{EON\_LEACH}=\text{ERE\_ADV}+\text{ERE\_JOIN}+\text{ERE\_SCH}+\text{ERE\_DATA} \tag{1}\]  
The energy consumed by the node in receiving advertisement packets is denoted by \(\text{ERE\_ADV}\). The energy spent in sending a join request to the selected CH is denoted by \(\text{ERE\_JOIN}\). The term \(\text{ERE\_SCH}\) denotes the energy spent in receiving the schedule from the CH. \(\text{ERE\_DATA}\) represents the energy consumed by the node in transmitting data to the CH. \(\text{ECH\_LEACH}\) indicates the energy consumed by a CH and is determined by using Equation (2).  
\[\text{ECH\_LEACH}=\text{EBR\_ADV}+\text{ERE\_JOIN}+\text{EBR\_SCH}+\text{ERE\_DATA }+\text{ERE\_DATA} \tag{2}\]  
The energy spent by the CH in broadcasting an advertisement packet is denoted by \(\text{EBR\_ADV}\). \(\text{ERE\_JOIN}\) is the energy spent in receiving join-request packets from ordinary nodes. \(\text{EBR\_SCH}\) represents the energy consumed by the CH in broadcasting the schedule in its cluster. \(\text{ERE\_DATA}\) represents the energy consumed in receiving data from member nodes. \(\text{ERE\_DATA}\) represents the energy consumed by the CH in sending data to the BS. The total energy spent in a cluster in a LEACH frame is denoted as \(\text{ELEACH}\) and is calculated using Equation (3). Here, n is the number of member nodes in the cluster.  
\[\text{E}_{\text{LEACH}}=\text{n}^{*}\,\text{EON\_LEACH}+\text{ECH\_LEACH} \tag{3}\]
## 4 Evaluation
### Evaluation Procedure
For the baseline we use the sensor data from the volunteers in our Drill data with the 17 users who have recorded one session only being used for training and the remaining 11 users with their total of 30 sessions as testing set. For the generation of training data from videos using our regression model we have the 12 YouTube Videos and the videos of the volunteers of our Drill experiments. This way we test our regression model on activities which it has not seen before. In summary we consider three different sources of training data:  
1. The real IMU data from our training users in the Drill dataset. This is the \"gold standard\". Systems trained with this data should perform best. 2. Simulated IMU data generated from the videos of our training users in the Drill dataset. Here the system trained on data generated from video is trained on exactly the same users and activity instances as the baseline system ensuring the \"fairest\" and most consistent comparison. 3. IMU data generated from the 12 YouTube videos. When investigating how well the generated sensor data works for the training of new activity recognition applications we consider four more specific questions:  
1. What is the effect of different pre-processing techniques described in Section 3.1 on the performance difference between a system trained on the real sensor data and one trained on data generated from video using our approach? In this context it is important to also consider the effect of the respective techniques on the absolute performance of the baseline system. Thus, we do not want to consider techniques that may reduce the difference between simulated and real sensor data based systems at the price of making both systems significantly worse. 2. How well does our approach perform for different types of sensor signals (see Section 2)? Again we need to consider not only how the different sensor choices affect the difference between simulated and real sensor data but also how they impact the absolute performance. 3. Can we compensate for potentially inferior quality of the training data generated from videos by providing a larger amount of training data? Given that our approach gives the user access to huge amounts of data contained in online videos (much more than can realistically be collected as labeled data with on-body sensors), it is not necessary to match the performance of real sensor data in tests on the same sized training sets (as done with respect to points 1 and 2 above). Instead we need to investigate what happens when we provide the system, using simulated sensor data, with more training examples than the real sensor data based system. 4. Can the quality of training based on simulated sensor data generated from videos be improved by combining it with a small amount of real sensor data? The question is if small amounts of real sensor data, that can be easily collected, can \"fine-tune\" HAR systems trained on large amounts of simulated data to improve their performance.
## 3 Results
### Mechanical Properties
#### 3.2.5 Shear Strength Curve  
The damage of rocks under natural conditions is mostly compression-shear damage, so it is important to study the shear properties of rocks. Shear strength is one of the important parameters of rock mechanical properties, defined as the maximum tangential stress on the shear surface of a rock specimen when it is sheared. In this paper, the shear strength of limestone is obtained by variable angle shear test. The tangential stress \(\tau\) and the direct stress \(\sigma\) of the specimen are calculated by Equations (10) and (11).  
\[\tau=\text{P}/\text{A}\;(\text{sin}\alpha-\text{f}\cos\alpha) \tag{10}\]  
\[\sigma=\text{P}/\text{A}\;(\text{cos}\alpha+\text{f}\;\text{sin}\alpha) \tag{11}\]  
where A is the projected area of the shear damage surface (mm\({}^{2}\)); \(\alpha\) is the shear angle (\({}^{\circ}\)), f is the rolling friction coefficient between the testing machine indenter and the shear rollers, f = 1/n d, n is the number of rollers, d is the diameter of the rollers (cm). In this test, n = 10; d = 8 cm.  
After obtaining \(\tau\), \(\sigma\) for three angles (45\({}^{\circ}\), 50\({}^{\circ}\), 55\({}^{\circ}\)) through the test, the shear strength curve was obtained by least squares fitting in the direct stress--tangential stress coordinate  
Figure 10: Trends of tensile strength of limestone with temperature after thermal shock.  
system, and the test values were averaged for analysis in this paper. The summarized results are shown in Table 2.  
From Table 2, it can be seen that the characteristics of shear strength curves of limestone after thermal shock at different temperature working conditions are similar, so a set of typical curves were plotted for analysis. As shown in Figure 11A, the shear strength curve of limestone specimens after thermal shock in water of a given temperature increases in inclination and decreases in intercept with the increase in heating temperature, which shows a rotation in the \(\sigma\)-\(\tau\) coordinate system in the counterclockwise direction. From Figure 11B, it can be seen that the shear strength curves of limestone specimens at the same temperature after thermal shock in water at different temperatures decrease in inclination and increase in intercept with the increase in cooling temperature, and rotate in the clockwise direction in the \(\sigma\)-\(\tau\) coordinate system. The change characteristics are opposite to the shear strength curves at different heating temperatures. The shear strength curve obtained from the test can determine whether the limestone remains stable after thermal shock in a high temperature environment: if the stress value at the measured position in the rock lies in the region below the curve, the rock is stable; otherwise, the rock is destabilized. Therefore, the shear strength curve of rocks under thermal shock is important for the analysis of high temperature rock stability.
# Joint Task Offloading and Power Allocation for Satellite Edge Computing Networks
## 5 Performance Analysis
### Delay Performance with Different SSs and Bandwidths  
As shown in Figure 7, we choose different bandwidths from [200 MHz, 400 MHz, 600 MHz] and observe how the delay varies with the number of satellites in different bandwidths. Figure 7 shows that each 300 MHz bandwidth increase reduces data transmission delay by 22.5%.
## 2 System
### Attitude Estimation  
In conventional studies, only the _Yaw_ axis value necessary to calculate the number of steps is obtained; however, we always perform estimation in all axes of _Roll_, _Pitch_, and _Yaw_ using all of the acceleration, angular velocity, and geomagnetism sensors, and always obtain the direction in which the sensor is facing in the world coordinate system. This has the advantage that the acceleration in the \(x,y\) direction can be separated from the acceleration in the \(z\) direction no matter what state the sensor is in, which facilitates the estimation procedure.  
There are several methods for attitude estimation, including methods using the extended Kalman filter [21] and the Madgwick filter [22]. However, the extended Kalman filter is computationally expensive, while the Madgwick filter is slow in convergence. Both of these filters require time to obtain an accurate attitude. Therefore, we use the complementary filter in this study. In addition, because the use of Euler angles in calculating angles results in the appearance of many trigonometric functions and requires a great deal of computation time, calculations are performed using quaternions.
# Edge and Cloud Computing in Smart Cities
## 5 Challenges, Open Issues, and Future Research Directions
### Network Scalability and Latency Optimization  
Edge-cloud architectures rely on high-speed communication networks to facilitate real-time data exchange and deliver low-latency service. However, the scalability of these networks remains a fundamental challenge as the number of connected devices and data-intensive applications continues to rise. The proliferation of latency-sensitive services such as autonomous driving, industrial automation, and remote healthcare necessitates network architectures that can efficiently handle massive data volumes while maintaining ultra-reliable low-latency communications. Traditional cloud-centric networking models struggle to meet these stringent requirements, prompting the need for innovative approaches that leverage edge computing for localized data processing and hierarchical network management [209; 210; 211].  
The dynamic nature of mobile edge computing environments further complicates latency optimization, as user mobility patterns, network congestion, and fluctuating bandwidth availability introduce unpredictable delays. Conventional routing mechanisms are often inadequate for handling time-sensitive data streams, necessitating adaptive transmission protocols that prioritize critical information while mitigating network bottlenecks. The emergence of 5G and beyond-5G technologies presents promising solutions for enhancing network scalability, yet challenges related to spectrum allocation, interference mitigation, and MEC integration persist. These unresolved issues highlight the importance of developing advanced network architectures capable of dynamically provisioning resources, optimizing data paths, and ensuring seamless connectivity in highly distributed edge-cloud ecosystems [213; 214; 215; 216; 217].  
The increasing demand for low-latency applications requires 5G and beyond technologies to be integrated with edge-cloud architectures. AI-driven adaptive routing protocols should be developed to mitigate congestion dynamically. Furthermore, MEC can be leveraged to bring computation closer to users, reducing latency and improving scalability [218; 219; 220].
## 4 Results and Discussion
### Ambient Conditions  
Plasterworks from this case study are exposed to external ambient conditions but are protected by a gallery that prevents direct contact with rainwater and protects them from the prevailing winds of the city, i.e., southwest winds, favoring their preservation.  
The presence of moisture (filtration moisture, capillary moisture, humidity by direct contact with rainwater, etc.) is the main source of the most significant damage to plasterwork and its polychromies, promoting the oxidation of the metal fixing elements; causing detachments, the appearance of surface stains of humidity, and stability problems in the internal structure of the material; and reducing C-Shore surface hardness, compressive strength, and adhesion to the support [80, 81, 82, 83, 84]. Environments with a relative humidity (RH) close to or greater than 90% weaken the structure of the plasterwork and involve the proliferation of microorganisms and biological agents.  
Above 40 \({}^{\circ}\)C, calcium sulfate dihydrate (CaSO\({}_{4}\).2H\({}_{2}\)O) begins to dehydrate in dry environments [85], so the plasterwork can initiate internal destabilization. However, situations of high temperatures combined with high RH do not alter the mineral phases of gypsum [86]. In this regard, [PERSON] and [PERSON] (1970) indicate the relationship that must occur between ambient temperature and RH for gypsum to be transformed into basanite (CaSO\({}_{4}\).0.5H\({}_{2}\)O); the material is likely to lose stability only if unfavorable conditions are maintained over time [86, 87].  
In the case of polychromies in plasterwork--usually tempera--the presence of moisture implies the alteration of binders, and it could also impede the transpiration of the layers--mainly in those cases in which there are repolychromed layers with oils and waves or golds of later times [40]--accelerating the degradation processes.  
Measurements taken in the Courtyard of the Maidens reveal that the temperature never exceeds 40 \({}^{\circ}\)C, and the RH measurements indicate that the RH was close to 90% only during the month of January; thus, the hygroscopic adsorption is very low and the risk of deterioration due to the action of hygroscopic water is eliminated under these ambient conditions [81, 82]. Nevertheless, even in the case of the RH being below 90% and the T exceeding 40 \({}^{\circ}\)C in the summer months, there would not be a problem with gypsum dehydration, as it is a reversible internal phenomenon, requiring a long period of time under these ambient conditions to avoid the irreversibility [85, 52].
## 6 Results and Discussion
### Floating Interleaved Boost Converter  
The semiconductor devices of this converter will have the same thermal behavior due to the symmetric structure (Q1 = Q2, D1 = D2, and D3 = D4). Due to multiple benefits of this converter (e.g., low input current ripple), it is very suitable for fuel cell applications. In addition, by increasing the number of stages in this structure, the overall performance of this converter would be improved; however, adding each floor increases the cost and complexity. The results of the thermal analysis are provided in Figure 13.  
Figure 11: The junction temperature of the power semiconductors within the conventional boost converter.  
Figure 12: The junction temperature of the power semiconductors within the interleaved boost converter.  
In this converter, the most dominant heat source is the diode D3 (or D4), which operates at a temperature of 91.66 \({}^{\circ}\)C. The switches also work at an operating temperature of 69.57 \({}^{\circ}\)C, which is much lower than the conventional boost converter. This obvious temperature reduction is also repeated for the diode, and the anti-parallel diode's temperature is only about 49 \({}^{\circ}\)C.
We will begin this communication by studying the linear equations:  
\[iu_{z}+c_{0}u_{zz}+c_{2}u_{tt}-ic_{3}u_{ttt}=0 \tag{4}\]  
\[iu_{z}+c_{0}u_{zz}+c_{2}u_{tt}+c_{4}u_{4t}=0 \tag{5}\]  
and afterwards we will focus our attention on the nonlinear equations:  
\[iu_{z}+c_{0}u_{zz}+c_{2}u_{tt}+c_{4}u_{4t}+\gamma_{1}|u|^{2}u-\gamma_{2}|u|^{4 }u=0 \tag{6}\]  
\[iu_{z}+c_{0}u_{zz}+c_{2}u_{tt}-ic_{3}u_{ttt}+c_{4}u_{4t}+\gamma_{1}|u|^{2}u- \gamma_{2}|u|^{4}u=0 \tag{7}\]  
\[iu_{z}+c_{0}u_{zz}+c_{2}u_{tt}+c_{4}u_{4t}+\gamma_{1}|u|^{2}u=0 \tag{8}\]  
The study of the linear Equations (4) and (5) will reveal how the interplay between the non-SVE term \(u_{zz}\) and the higher-order dispersive terms generates bands of forbidden frequencies or forbidden wavenumbers. Then, we will show that the nonlinear Equations (6)-(8) have exact solitons of different types, some of them being _embedded solitons_. The structure of this paper is the following. In Section 2 we will show that the linear dispersion relations of Equations (4) and (5) have highly unusual forms. We shall see that the dispersion relation of Equation (4) is an _elliptic curve_, while that of Equation (5) presents bands of forbidden frequencies, or forbidden wavenumbers. As the occurrence of elliptic curves in this context opens the possibility of relating Equation (4)--or its solutions--to other areas where elliptic curves play an important role, in Appendix A we describe in more detail the basic characteristics of these curves, and in Appendix B we briefly discuss the relationship of these curves with modular forms and [PERSON]'s last theorem. In Section 3 we study the linear Equations (4) and (5). We will begin this section by paying attention to the fact that several results indicate that the Cauchy problems for these equations are _ill-posed_, and consequently the specification of initial conditions is a delicate issue. Then we present different solutions of these equations. These solutions will show that narrow pulses that evolve according to Equations (4) and (5) emit radiation in a completely novel and unexpected way, never observed in other models of optical pulses. In Section 4 exact soliton solutions of Equations (6)-(8) are presented. It is shown that Equation (6) has two different types of solitons (_embedded solitons_ and solitons with a nonlinear frequency shift), Equation (7) has _moving_ solitons of different heights and different velocities, and the solitons of Equation (8) are given by _squared_ hyperbolic secants, thus proving that the interplay between the non-SVEA term \(u_{zz}\) and higher-order dispersive and nonlinear terms permits the existence of different types of solitons. Finally Section 5 contains the conclusions of the paper.