esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of Elastic Nonlinearity Using Continuous Waves: Validation and Applications
## 3 Validation
### Feasibility of the Separation of Modulus and Damping Contributions
#### 3.1.1 Low Linear Damping  
When damping is low (high Q-factor materials), Equation (13) and table reported in Figure 1 suggest that the velocity dependence on amplitude is due only to the modulus nonlinearity (being the damping contribution to velocity very small). Furthermore, if damping nonlinearity is strong, contribution of modulus nonlinearity to the amplitude dependence of attenuation coefficient is slightly more significant but still almost negligible.  
To validate our approach under these conditions, numerical simulations were performed for the four combinations of nonlinear parameters reported in Figure 1. More specifically, we consider here a quadratic nonlinearity for damping parameter (Case 1), quadratic nonlinearity for elastic modulus (Case 2), quadratic nonlinearity in both modulus and damping (Case 3) and quartic and quadratic nonlinearity in damping (Case 4).  
Results of the analysis of the data are reported in Figure 3, where the relative variations of velocity and attenuation coefficient (respect to their corresponding linear values) are reported vs. amplitude \(A\). The analysis of the data demonstrates that:  
* The method allows separation of modulus and damping contributions. Indeed, in Cases 1 and 2, in which only one physical parameter is nonlinear (either \(S\) or \(\lambda\)), only the corresponding measurable quantity (either \(c\) or \(\alpha\)) manifests nonlinearity.
* The method allows determining accurately the functional dependence on \(A\) of velocity and attenuation coefficient relative variations. Curves were fitted with a polynomial function in the form: \[y=q_{1}A^{2}+q_{2}A^{4}\] (17) While in Cases 1, 2 and 3 the fitting coefficient \(q_{2}\) was negligible (for the velocity dependence on \(A\)), in Case 4 the coefficient plays a crucial role (more details in comments on Figure 4).
* In this analysis, there is no quantitative prediction of the nonlinear parameters. Indeed, numerical solutions were based on a dependence of velocity and attenuation on strain, and not on displacement amplitude. Hence, as expected, the fitting coefficients \(q_{1}\) and \(q_{2}\), both for the relative variations of velocity and attenuation, do not correspond to the nonlinear parameters used as input in the numerical code. A quantitative analysis, which is discussed in Section 3.2, is possible but it implies further considerations.  
Figure 3: Validation of the method: behaviour of \(\Delta c/c_{0}\) and \(\Delta\alpha/a_{0}\) vs. displacement amplitude for different input values of nonlinear parameters, as shown in Figure 1. Solid lines correspond to the best fit obtained using the function defined in Equation (17). The same colours were used in the two subplots and are related to the cases reported in the legend.
# IoT OS Platform: Software Infrastructure for Next-Gen Industrial IoT
## 3 Theoretical Framework of the IoT OS Platform
### Structured Organization Mechanism for IIoT
#### 3.1.2 Transport Layer  
The transport layer describes the various protocols involved in data transmission within IIoT scenarios. With the advancement of communication technologies, some protocols can no longer be categorized into a single layer of the network protocol stack but span multiple layers or coexist with peer layer protocols [17]. To describe data transmission in IIoT scenarios, the transport layer in this architecture is divided according to different business types, thus better expressing the flexible orchestration and combination of protocols under limited transmission conditions in IIoT scenarios. Application protocols, such as CoAP, MQTT, HTTP, AMQP, OPC UA, Modbus, etc., are a category of protocols that describe data transmission businesses, including different degrees of implementation for establishing connections, transmitting data, and parsing data. Service discovery protocols are crucial for achieving interoperability among IIoT devices, similar to DNS in the traditional internet. Supporting service discovery protocols enables self-organizing networks and interoperability of IIoT devices based on the IoT OS platform. Infrastructure protocols [17] manage communication and data exchange between different components of the network infrastructure, ensuring reliable and efficient data transmission. Security protocols are a focal point in IIoT scenarios [17], constructing an efficient and reliable security protocol stack with the support of the IoT operating system platform to address data transmission and application among a massive number of heterogeneous devices in IIoT scenarios.
## 2 Experimental Data
### Data Collection  
Currently, the center for orbit determination in Europe (CODE), GFZ, GNSS research center of Wuhan University (WHU) (participating in the multi-GNSS experiment (MGEX)), and the International GNSS monitoring and assessment system (iGMAS) are the main research institutions of BDS precision clock offset, which obtain experimental data through Orbit Determination and Time Synchronization (ODTS) and publish it publicly [22]. Thus, GFZ precision clock offset data were selected mainly because of their high efficiency. As a result, BDS precision clock offset data of 620 days from 1 July 2020 to 12 March 2022 were collected; the data efficiency of every satellite clock is shown in Figure 1.  
From Figure 1, the effective rate of BDS satellite atomic clock data released earlier and later is lower than the average value, which is caused by the fact that the operation is at the late or unstable stage. Specifically, the longest running retired satellite, C01, has a data efficiency of 93.09%; the four GEO clocks C02-C05 have a data efficiency of around 95%; and the MEO satellites C19-C37 of BDS-3 have a data efficiency of over 99.4%. The data efficiency of the C41-C60 satellites decreased because they have not been in operation long enough or are still in the on-orbit commissioning phase. Overall, the BDS-3 stable operation satellites (PRN C19-C40) have an average data efficiency of 99.45%, which is higher than the 97.43% of BDS-2 (PRN C01-C16).
## 2 Materials and Methods
### Meta-Analysis  
Meta-analysis was performed to estimate the pooled effect size of CNT on COD performance. The standardized time of the COD task completion served as the effect size, calculated as Hedges' g. This metric was specifically chosen to correct for the potential overestimation bias of [PERSON]'s d in studies with small sample sizes, a key consideration as many of the included studies were based on a small number of participants (e.g., \(N<20\)). When comparing the performance of CNT with other training methods, we first calculated the difference in effect size between the CNT group and other training groups (i.e., PT, ST, DT, AT, and control) within each study and then estimated the pooled differences, along with their 95% confidence intervals. All analyses were conducted using Stata 17.0 (StataCorp LLC, College Station, TX, USA). Study heterogeneity was quantified using the I\({}^{2}\) index and the \(\tau^{2}\) statistic. A fixed-effect model (FE) was estimated when I\({}^{2}\leq 50\%\), and a random-effect model (RE) was estimated when I\({}^{2}>50\%\). Publication bias was assessed by and [PERSON]'s tests [27]. Statistical significance was determined using two-sided tests, with a significance level set at \(p<0.05\). The standardized effect size (ES) was categorized according to [PERSON] guidelines: trivial (0.0-0.19), small (0.2-0.59), moderate (0.6-1.19), large (1.2-2.0), and very large (>2.0) [28]. If substantial heterogeneity was detected, sensitivity analyses were performed to assess the robustness of the results, and funnel plot analyses were conducted to investigate the presence of publication bias. In addition, subgroup analyses were conducted to determine the potential moderating factors that may affect the effectiveness of CNT intervention.  
Subgroup analysis was performed based on population characteristics and training methods using dichotomous variables (female vs. male; amateur vs. elite) and continuous variables (age: <18 years or \(\geq\)18 years; training intensity: \(\geq\)85% 1 RM or <85% 1 RM; frequency: <3 week\({}^{-1}\) or \(\geq\)3 week\({}^{-1}\); duration: >6 weeks or \(\leq\)6 weeks) that might influence the outcomes following CNT and other interventions. Additionally, subgroup analyses were conducted for the primary sports (basketball, soccer, or handball) and the methods of COD measures (turn: \(\leq\)3 or >3). The cutoff was determined empirically based on prior literature [29; 30; 31]. The COD measurement methods were divided into two categories based on the number of turns: \(\leq\)3 turns (e.g., Shuttle Test, 505 Test, Arrowhead Test) and >3 turns (e.g., T-Test, Illinois Test, S4 \(\times\) 5 Test). To address the issue of intensity, when two different intensities were employed in a training intervention, the mean value was calculated for the subgroup analysis. For training frequency, studies were categorized into two groups based on the weekly training frequency: low frequency (<3 times per week) and high frequency (\(\geq\)3 times per week). Two authors independently processed all data, with one responsible for extraction and the other for verification.
# RF Energy Harvesting Wireless Communications:
## 4 Power Management in RF-EHWC
### Practical Issues in Power Management
#### 4.2.3 Circuit Power  
In the non-ideal circuit, its consumed power is the sum of transmission power and circuit power, which includes the energy dissipated by the microprocessor, AD/DC converter, amplifier, and filter. The non-negligible circuit overhead affects the profile of energy consumption, \(\int_{0}^{t}p(x)\). As a consequence, the energy-rate function is no longer monotonic but first increases and then decreases with the transmission rate [68].  
It is verified that the continuous transmission becomes inefficient and on-off transmission is advocated as an alternative [69; 70; 71]. As proposed in [69], if many packets are reaching their transmission deadline, the system transmits with a high power to catch the deadline. In other cases, the device sends the data with the rate \(r_{m}\) to maximize energy efficiency. Once a transmission is completed, the system enters a sleep mode immediately. A two-phase transmission scheduling policy is provided in [70]: the first phase is to maximize the energy efficiency through an on-off power allocation method; and the spectrum efficiency is optimized in the second phase with a non-decreasing power allocation strategy. The authors of [71] introduced a directional glue-pouring algorithm to solve a similar problem. Due to the constant power consumption over time, a threshold of the transmission power in the directional glue-pouring algorithm needs to be calculated first, and then the process of glue-pouring is performed so that the power level is always higher than the threshold.
## Appendix A Background
### Crossover Methods  
GAs exploit crossover methods to generate offspring from a set chromosome ranked as best. Several crossover strategies are applicable to mix the genetic content of pairs of chromosomes. In the following, we describe four of these techniques.  
* Single-Point Crossover This method selects pairs of chromosomes with \(m\) genes according to their ranking order and samples from a uniform distribution with range \([1,m]\) the index of mutation \(i_{m}\). Then, the genes from \(i_{m}\) to the end are switched between the pair of chromosomes. An illustration of this crossover approach is given in the Figure 11a.
* Double-Point Crossover Similar to the Single-Point Crossover, this method also selects a pair of chromosomes according to their ranking, but differs in that it samples two mutation indices. The first, \(i_{m1}\) is sampled from a uniform distribution with range \([1,m]\); the second, \(i_{m2}\) is also sampled from a uniform distribution but with range \([i_{m1},m]\), so that \(i_{m2}>i_{m1}\). Then, the genes in the range \([i_{m1},i_{m2}]\) are switched between the pair of chromosomes. Figure 11b shows an example of this crossover method.
* Uniform Crossover This method generates a random array with dimension \(m\) (as the number of genes in each chromosomes) sampling from an uniform distribution with range \([0,1]\). The pair of chromosomes doing crossover, which are selected according to their ranking position, switch their genes where the value of the random array is higher or equal than a probability \(p\). A visual example is provided in the Figure 11c, where \(p=0.5\).
* Single-Random Crossover Working almost as the Single-Point Crossover, this method differs only in how the pair of chromosomes are selected. In fact, differently form the previous approaches, which are based on the ranking order, in this one the chromosomes are randomly paired up. Figure 11d shows an example of this approach, where the randomly generated pairs are 3 rd-9 th, 4 th-6 th, and 5 th-10 th.  
Figure A1: Crossover approaches. (**a**) Single-Point Crossover; (**b**) Double-Point Crossover; (**c**) Uniform Crossover; (**d**) Single-Random Crossover.
## 4 Discussion
### Practical Implications and Ethical Considerations  
The findings of this study present significant implications for educational stakeholders, including university administrators, curriculum developers, and policymakers. The aspect-based sentiment analysis model enables a deeper understanding of student perceptions across key areas such as teaching quality, curriculum, infrastructure, and administrative services. This granular insight allows stakeholders to make data-driven decisions to enhance the quality of education. For example, feedback on teaching quality can help identify professional development needs for lecturers. At the same time, curriculum-related insights can guide updates to course content to better align with academic and industry standards. Similarly, infrastructure and service-related feedback can assist institutions in prioritizing resource allocation, such as upgrading laboratory facilities or improving administrative processes. These targeted improvements are expected to create a more student-centered and practical learning environment, fostering satisfaction and better educational outcomes.  
Ethical considerations were a priority in conducting this research to ensure responsible data usage. The data were collected exclusively from publicly accessible sources, ensuring no breach of confidentiality or institutional policies. To protect privacy, all personally identifiable information was anonymized during data preprocessing. Moreover, the feedback analyzed was voluntarily provided on platforms where students were aware their reviews might be used for analysis and research purposes. The study adhered to institutional guidelines and national data protection regulations, maintaining transparency and compliance throughout the research process. These measures ensure that the analysis was conducted ethically, respecting the privacy and consent of the individuals whose data were analyzed.  
By addressing practical applications and ethical concerns, this study demonstrates a responsible and impactful approach to utilizing student feedback data to enhance educational quality in higher education institutions.
# Long-Term Forecasting of Electrical Loads in Kuwait Using Prophet and Holt-Winters Models
## 3 Results and Discussion
### Future Peak Loads Forecasting
It offers a clear vision for the expected maximum consumption of power by proposing the monthly maximum load peaks, which is essential for planning activity in powerplants, such as maintenance scheduling. Optimum planning for the maintenance schedule is vital for unit availability and avoiding any unexpected failures or shutdowns. Preventive maintenance is a cost-effective option that can be implemented when planning is accurate. The monthly maximum load peaks indicate that the best months for scheduling maintenance are from November 2020 until March 2021 for both models. Even with 99% confidence interval, the maximum peaks do not exceed 10,000 MW and 10,500 MW for the Prophet and Holt-Winters models. Additional months can be alternatives, such as October 2020 and April 2021 but with some low risk.
## 7 Performance Analysis
### Property Comparison  
A functional analysis of Refs. [18; 29; 30; 31] is presented in Table 2. To achieve decentralisation, these systems are designed without a central CA entity. Instead, the Joint-Shamir Random Secret Sharing method is used to issue \(PK\) and each group's \(MK_{i}\). Each CR's attribute key is generated by the AA of its respective group, thereby reducing both the computational load and the complexity involved in key generation and management.  
In our proposed scheme, the absence of a CA enables message source verification through the Diffie-Hellman key exchange. If \(AA_{t}\) cannot compute \(C^{\prime}=B^{1/n_{t}}\), then this indicates that the message did not originate from \(AA_{t}\).  
Before transmission, the message is symmetrically encrypted with a key, which is then encapsulated by an access policy. During decryption, a proxy key is generated by the CR to prevent the exposure of the attribute key \(SK_{CR_{j}}\) to other devices, thereby reducing the security risks associated with key delegation. The \(IDM\) returned by \(CN_{r}\) includes the random number \(t\), which is computationally challenging to invert because of the discrete logarithm problem. This ensures that only the CR responsible for generating the proxy key can reconstruct it, effectively minimising the risk of information leakage.
# Evaluating GNSS Receiver Resilience: A Study on Simulation Environment Repeatability
## Appendix A
### Data Acquisition and Preprocessing  
Consider a dataset comprising \(N\) independent measurement trials for a specific combination of GNSS module, jammer type, and power level. Each trial \(i\in\{1,\ 2,\ \ldots,\ N\}\) yields a time series of \(T_{i}\) location samples. For each sample \(j\in\{1,\ 2,\ \ldots,\ T_{i}\}\) within a trial, the recorded data include latitude (\(Lat_{i,j}\)), longitude (\(Con_{i,j}\)), and a calculated location deviation (\(Dev_{i,j}\)). Erroneous or unavailable location fixes, indicated by \(Lat_{i,j}=0\) or \(Con_{i,j}=0\), result in the corresponding deviation value being treated as a missing value.  
To mitigate potential initial offsets in deviation measurements, a correction factor \(CF_{i}\) is calculated for each trial using the first 100 valid deviation samples, under the prerequisite that these initial measurements are acquired in the absence of interference:  
\[CF_{i}=\frac{\sum_{j=1}^{100}Dev_{i,j}\cdot I\big{(}Lat_{i,j}\
eq 0\wedge Lon_{i, j}\
eq 0\big{)}}{\sum_{j=1}^{100}I\big{(}Lat_{i,j}\
eq 0\wedge Lon_{i,j}\
eq 0 \big{)}}, \tag{1}\]  
where \(I(\cdot)\) is an indicator function that returns 1 if the condition is true and 0 otherwise. This correction factor is then subtracted from all subsequent valid deviation values within the same trial to obtain the corrected deviation \(Devt_{i,j}\):  
\[Devt_{i,j}=Dev_{i,j}-CF\ \big{(}if\ Lat_{i,j}\
eq 0\wedge Lon_{i,j}\
eq 0 \big{)}. \tag{2}\]
# Fast Segmentation Algorithm for Cytoid Macular Edema Based on Omnidirectional Wave Operator
## 2 Methods
### Image Segmentation
#### 2.3.2 Contour Extraction by Omnidirectional Wave Operator
It is insensitive to randomly distributed speckle noise by using gradual gray values to identify boundaries, so it can avoid false segmentation caused by noise or local mutation. In addition, compared with the segmentation methods, such as the active contour model and level set, which iterate many times to seek energy minimization, the pixel-based method has outstanding advantages in computing speed. Furthermore, the omnidirectional wave operator has directional characteristics and can extract arbitrary targets with closed curves. Among the segmentation algorithms with the ability to distinguish directions, the omnidirectional wave operator still has advantages. For example, the segmentation method based on the directional graph method [22] can only extract targets with a large number of curves in the same direction, and it depends on the reliability of the points' directions, so the extraction of single-line items such as CME is not good.
## 4 Harmonic Emission Profiles
### Grouping of Emission Profiles
#### 4.2.2 Definition of Profile Classes  
The resulting groups of the clustering process were assigned to one of the classes, D0 to D4, which are displayed in Figure 5. The displayed profiles are used to illustrate the definition of the five classes that were used for the classification process.  
Figure 4: Example for typical emission profiles of the 3 rd harmonic order for different consumer configurations: residential (RES), commercial (COM), office (OFF), and mixed (MIX) areas; min-max scaled 50 th percentile time series for different day types: working days (G1), Saturdays (G2), and Sundays/holidays (G3); phase L1; aggregation interval: 10 min.  
_Class D0:_ Daily time series without distinctive daily cycles, almost non-existent or no harmonic emission at all (c.f. Figure 5a) were categorized as class D0. Examples are the emission profiles of office areas (c.f. Figure 2a) and the electronic markets (c.f. Figure 3a) for non-working or closed days.  
_Class D1:_ The daily time series in class D1 (c.f. Figure 5b) have increasing emission levels of harmonic currents until the evening hours (around 8 o'clock) and decreasing levels during nighttime. The shape of the emission profiles is typical for consumer configurations in residential areas. Differences between single- or multi-family houses were not present for the analyzed LV networks.  
_Class D2:_ The emission profiles of class D2 can be distinguished into two time intervals. For the example of the electronic market (c.f. Figure 5c) the two intervals were within the opening hours (high emission levels) and the closing hours (low emission levels). The transition between the two time intervals is usually within a short time frame, of less than one hour, and is strictly related to the opening hours.  
_Class D3:_ The class D3 (c.f. Figure 5d) represents emission profiles that are typical for consumer configurations like office areas. The emission levels can be divided into two time intervals. In comparison to class D2 the transition between the two intervals has a longer duration of multiple hours. This might occur due to different working hours within different offices.  
_Class D4:_ The emission profiles of class D4 (c.f. Figure 5e) are usually represented by measurements in mixed areas which are a combination of, for example, residential and office areas. This results in the typical \"evening peak\" of emission levels, as in class D1, and the time interval of distinctive emission levels during working hours, as in class D3.