esa-sceva/satcom-chunk-collection huggingface dataset:
# 100 Picosecond/Sub-10\({}^{-17}\) Level GPS Differential Precise Time and Frequency Transfer
## 7 Conclusions  
In this paper, a differential precise time transfer (DPT) with a clustering constraint method was employed to estimate the time difference between two clocks, aiming to achieve accurate and precise time and frequency transfer. For the experiment of common clock time transfer, the DPT time transfer results showed good consistency across different days, with an intra-day accuracy of within 20 ps. Furthermore, we evaluated the self-consistency of the DPT technique using closure among three stations. The baseline DPT closure values of the three stations were always within 10 ps, with a peak-to-peak value of closure of about 25 ps. The closure did not change over time, indicating the self-consistency of the DPT processing in time transfer. Using the station clock solutions provided by IGS as a reference, the STDs of the four baselines were all less than 100 ps within 30 days. It  
Figure 11: Time stability results of the four baselines from MJD 59,764 to 59,793.  
was found that the single-frequency DPT achieved a \(10^{-17}\) level of MDEV at an averaging time of about 1 day and a \(10^{-18}\) level at an averaging time of 7 days. The IF DPT achieved a \(10^{-16}\) level of MDEV at an averaging time of about 1 day and of \(10^{-17}\) at an averaging time of 7 days. The TDEV of the IF DPT was less than 20 ps for an averaging time of 1 day, especially less than 10 ps within 7 days. As for the single-frequency DPT, the time stability was less than 4 ps for an averaging time of 1 day and approached 1 ps within 7 days. Finally, it can be used for high-accuracy and high-precision time and frequency transfer applications.  
Conceptualization, W.S. and F.Z.; methodology, W.S. and F.Z.; software, W.S. and H.W.; validation, H.W. and W.S.; formal analysis, W.S.; investigation, H.W.; resources, C.S. and F.Z.; data curation, H.W.; writing--original draft preparation, W.S.; writing--review and editing, W.S. and F.Z.; supervision, C.S. and F.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Plan, grant number 2022 YFB3904601; the National Nature Science Foundation of China, grant number 42227802; and the Fundamental Research Funds for the Central Universities, grant number YWF-23-JC-12.  
Not applicable.  
The data used to support the findings of this study are available from the authors upon request.  
We would like to acknowledge the efforts of the IGS in providing final station clock products and all the institutes operating the involved IGS tracking sites for providing the GNSS RINEX files. We also would like to acknowledge the efforts of the BIPM for providing GPS calibration results from a calibration trip.  
The authors declare no conflict of interest.
# Watermarking Protocols: A Short Guide for Beginners
## 2 Requirements
### Usability Requirements  
The studies conducted in the past few years show that usability requirements must be given priority over security requirements in designing modern watermarking protocols. In fact, if a protocol makes the involvement of buyers in the interaction scheme difficult, or if it does not meet the actual service needs of content providers, the distribution of protected contents on the Internet can be seriously hampered. This harms the business interests of content providers, thus making the security capacities of the protocol useless [9].
# PRG: A Distance Measurement Algorithm Based on Phase Regeneration
## Principle of PRG  
### Phase Regeneration Ranging Algorithm  
The PRG algorithm range finding model is shown in Figure 1. The detailed process of the PRG algorithm is as follows: Firstly, the pseudo-range measurement \(d_{p}\) between the master node M and the slave node S can be obtained from the coarse range finding method. Secondly, the master node M continuously transmits the range finding signal to slave node S. After the slave node S receives the range finding signal and synchronizes the phase and frequency through the carrier tracking loop, then the slave node S continuously transmits the replication range finding signal to master node M. Thirdly, the master node M receives its replication range finding signal from previous slave node S and then obtains the phase difference measurement \(d_{\phi}\) between the transmitted and the received replication carrier. Finally, the distance \(d\) between the master and slave node can be expressed by:  
\[d=\lambda\cdot\left(\frac{1}{2}d\phi+\left|\frac{d_{p}}{2\lambda}\right|_{floor}\right) \tag{1}\]  
where \(\lambda\) is the wavelength of range finding signal, \(\left\lfloor\cdot\right\rfloor_{floor}\) represents round down function. In the ideal case, it can be seen from the above Equation (1) that high precision range finding can be achieved without coarse range finding phase when the distance between master and slave node is less than \(\frac{\lambda}{2}\). In addition, the PRG method is unavailable due to carrier phase integer ambiguity in Equation (1) when the error of coarse range finding is greater than \(\frac{\lambda}{2}\). A conservative estimation method for carrier-tracking loop error is to ensure that the \(1\sigma\) tracking error is no more than \(\frac{\lambda}{2\lambda}\). We assume that carrier-tracking loop error of the master and slave node is independent and the condition of no carrier phase integer ambiguity is that the precision of coarse range finding is \(d_{p}\leq\frac{5\lambda}{12}\).  
Figure 1: PRG range finding model.
# Data Exchange in Cluster Structure for Longevity of IoT
## 3 Proposed Property-Based Data Transfer
### System Operation Process
#### 3.3.3 Data-Exchange Phase  
This phase performs the data exchange between things in different clusters. In a cluster, any nodes can request the information they do not possess. In \"data exchange phase\", to obtain the desired information, the data exchange occurs directly between clusters. After this phase is completed, the data exchange between nodes is suspended temporarily. All the nodes begin to undergo the advertisement phase to configure new clusters.  
Figure 4 shows that a member node of cluster A requests the information of \"weight\" property, which is produced in the member node of cluster B. This request is handled according to the following steps.  
1) A member of cluster A transmits a query to the header to obtain the weight property information.  
2) The header of cluster A receives the query from the member, and sends the query to the headers of other clusters that include the member nodes capable of obtaining the weight information. At this  
Figure 3: Advertisement phase sequence diagram setup-cluster phase sequence diagram.  
time, since the header of cluster A utilizes the stored information of weight property, the query is sent to only the headers of clusters relevant to this property.  
3) The header of cluster B sends a query to the member node relevant to the weight property. The header requires weight information.  
4) The member node receiving the query obtains the weight information through measurement, calculation, etc. Finally, this member node transmits the weight information to the header node according to its own slot time in data collection, which is assigned in the setup cluster phase.  
5) The header of cluster B sends the weight information to the header of cluster A.  
6) The header of cluster A transmits the weight property information to the corresponding member node that issued the query originally.
## 2 Study Area and Engineering Situation
### Exavation Method and Characteristics of the Surrounding Rocks  
Owing to the complexity of the tunnel site, the drill-and-blast excavation method was utilized. Since the main rock type encountered in the tunnel alignment is broken and weakened, the blasting and the vibration velocity were scrupulously controlled. Throughout the entire line of the tunnel, based on a geological survey, the surroundings rocks are mainly categorized as class IV (soft rocks) and class V (broken soft rocks). Figure 3 illustrates the excavated tunnel subjected to a virgin in situ stress \(P_{0}\), and a view of the tunnel portal.  
Any excavation method provokes increased damage to the surrounding rocks of deep-buried tunnels. Nevertheless, drill-and-blast excavation has the reputation for causing significant damage to the rocks hosting the tunnels [18]. In fact, such an excavation method is typically characterized by a large unloading rate and short unloading time [19; 20; 21], and therefore considerably affects the relevant properties of the host rocks. Based on relevant research outcomes [13; 22; 23; 24], the average basic values of the physical-mechanical parameters of the host rocks are presented in Table 1 in dry states.  
Figure 3: Details of excavation: (**a**) excavated section; (**b**) a view of the tunnel portal.  
Figure 2: The main lithology along the tunnel route.  
It is important to relate that the rock parameters are affected by the presence of groundwater in the regional environment of the tunnel. As already explained, due to the hydrological conditions of the tunnel site, groundwater effects cannot be neglected. Indeed, most rock parameters are diminished due to the effects of water [25]. Under wet conditions, uniaxial compressive strength, elastic modulus, internal friction, and cohesion are generally reduced, while density and Poisson's ratio can be enhanced [26]. For wet conditions, the average values adopted for the physical-mechanical parameters of the host rocks are presented in Table 2.
# An Energy-Efficient Coverage Algorithm for Macrocell--Small Cell Network Systems
## 1 Introduction
The remaining sections of this paper are organized as follows. An overview of the related previous research is provided in Section 2. Section 3 consists of an extended consideration of a two-layer cellular network system model, while Section 4 presents the study's proposed energy-efficient coverage algorithm for use in such systems. The results of simulations run to test the algorithm's performance are then presented in Section 5, while the key conclusions of the study are presented in Section 6.
# The Smart Buildings Revolution: A Comprehensive Review of the Smart Readiness Indicator Literature
## 4 Results and Discussion
### Dominant Themes in the Smart Readiness Literature
#### 4.1.4 The SRI as a Tool for Smart Retrofitting  
The SRI provides a practical measure of the technical smartness of building systems, highlighting the environmental and sustainability benefits of increasing building intelligence [18].  
[PERSON] et al. [62] examined the retrofitting costs associated with enhancing building smartness and improving SRI scores by evaluating various residential building renovation scenarios across five EU countries. They found that retrofitting older buildings, which typically fall into the lowest SRI class (0-20%), can improve the scores to 65-80%by enhancing automation and control, particularly in areas like comfort and well-being. Retrofitting strategies focusing on automation and control can raise these buildings to higher SRI classes (SRI score of 65-80%) [62].  
The majority of homeowners and tenants prioritize energy efficiency, heating sources, and comfort when purchasing or renting property. Additionally, smart technology is deemed important by 34% of homeowners and tenants [18]. Smart technologies present an ideal solution for retrofitting existing buildings [46].  
Similarly, [PERSON] et al. [63] analyzed eight smart building typologies representing the Italian residential building stock. The SRI was calculated for each typology using three scenarios, focusing on energy retrofitting. From a smart perspective, the analysis revealed national average SRI values of 5.0%, 15.7%, and 27.5%. In the \"energy\" scenario, which reflects the current trend of retrofitting existing buildings, the SRI was relatively low, ranging from 15% to 23%. This is primarily attributed to the limited level of automation and control in the systems commonly employed for retrofitting existing residential buildings. Notably, the installation of highly energy-efficient systems or renewable energy production systems does not impact the SRI; rather, it is the degree of automation and control that influences the indicator.  
[PERSON] et al. [61] evaluated the energy savings resulting from adjustments made to regulate the operational parameters of heating system components in buildings. Their study provides valuable insights into establishing minimum requirements for the SRI. Notably, their analysis covers six of the nine service domains outlined in the ISO 52120 standard.  
As smart technologies gain prominence, they offer an effective path for retrofitting existing buildings to align with modern energy and comfort demands.
## 3 Experimental Results
### Image Histogram Analysis  
Image histogram analysis is a classic method of statistically analyzing and visualizing the pixel values of an image. It presents the image's brightness, contrast, and color distribution in a histogram by counting the frequency of occurrence of each pixel value. By observing the shape and features of the histogram, we can conduct a preliminary analysis of the characteristics of the image, such as whether the brightness distribution is uniform and whether the contrast is sufficient. It shows the distribution of pixel values of each pixel in the range [0, 255] in the image. The histogram of the original image may exhibit a certain regularity, making the image more susceptible to statistical analysis attacks and thereby jeopardizing the security of the entire encryption scheme. Therefore, to enhance the security of the encryption scheme, a method must be sought to balance the histogram of the encrypted image.  
When evaluating the image encryption scheme, we observed the histograms of the original images 'Pepper', 'Deer', 'Dance', and 'City', as well as their corresponding encrypted images, as shown in Figures 8 and 9. We found that the histogram of the encrypted image shows a more uniform distribution of grayscale pixel values in the range [0, 255], with no discernible patterns. This means that the information of the original image cannot be recovered from the histogram of the encrypted image. Therefore, this encryption scheme demonstrates strong resistance to histogram analysis attacks. Through such observation and analysis, we can be confident that this encryption scheme performs well in protecting image security. By balancing the histogram of the encrypted image, we effectively increase  
Figure 7: The pixel correlation of the encrypted image ‘Peppers’: (**a**) horizontal direction; (**b**) vertical direction; (**c**) diagonal direction; (**d**) anti-diagonal direction.
## 1 Introduction
The comprehensive weighted analysis algorithms mainly include Analytic Hierarchy Process [18], Entropy Weight Method [19], and the Delphi Method [20]. Among them, the Analytic Hierarchy Process and Delphi Method have strong subjectivity in setting indicator weights, while the Entropy Weight Method analyzes data dispersion and sets indicator weights objectively. In addition to the weighted analysis method by establishing an evaluation index system mentioned above, some of the literature also uses non-weighted analysis methods such as data envelopment analysis [21] and the TOPSIS method [22]. [PERSON] [23] proposed a cost minimization problem to intelligently schedule energy production for microgrids equipped with unstable renewable energy sources and combined heat and power (CHP) generators. [PERSON] [24] proposes a novel robust framework for the day-ahead energy scheduling of a residential microgrid comprising interconnected smart users. The above methods have significantly improved results; however, they do not utilize correlation analysis algorithms with weighting, making it difficult to find the optimal solutions for the weakly correlated initial investment cost, environmental management cost, and the total operating cost of the microgrid. The non-weighted analysis method can directly analyze the data methods of the indicator system, but the modelling and calculation are relatively complex and can only achieve qualitative evaluation. Based on the above analysis, this paper proposes the new energy scheduling potential evaluation method based on grey correlation theory. This method can more accurately analyze the correlation between initial investment costs, environmental governance costs, and the total operating costs of microgrids, thereby effectively reducing the cost of the integrated construction of renewable energy. The specific contributions are as follows:  
1. In order to obtain the economic operation indicators of a microgrid under different levels of fluctuation, this paper establishes a two-stage robust optimization model for the collaborative optimization of renewable energy, energy storage devices, and micro-gas turbine units, with the objective of minimizing the microgrid scheduling and operation costs. The constraints are linearized using the big-M method, and the column and constraint generation algorithm is adopted to solve the model. Additionally, the robustness indicators of wind turbines and distributed photovoltaic power stations are adjusted to simulate the fluctuation of renewable energy output. 2. In order to identify the key renewable energy sources that influence microgrid planning, this paper proposes an improved grey relational analysis method combined with the entropy weight method to analyze multi-scenario data. This approach determines the correlation between renewable energy sources and various operating costs, as well as the total operating costs of the microgrid. By ranking these correlation values, a quantitative analysis of the impact of renewable energy sources on grid scheduling is achieved.
###### Abstract  
The objective of this study was to test higher-order statistical (HOS) parameters for the classification of young and elderly voice signals and identify gender- and age-related differences through HOS analysis. This study was based on data from 116 subjects (58 females and 58 males) extracted from the Saarbruecken voice database. In the gender analysis, the same number of voice samples were analyzed for each sex. Further, we conducted experiments on the voices of elderly people using gender analysis. Finally, we reviewed the standards and reference models to reduce sex and gender bias. The acoustic parameters were extracted from young and elderly voice signals using Praat and a time-frequency analysis program (TF32). Additionally, we investigated the gender- and age-related differences in HOS parameters. Young and elderly voice signals significantly differed in normalized skewness (\(p=0.005\)) in women and normalized kurtosis (\(p=0.011\)) in men. Therefore, normalized skewness is a useful parameter for distinguishing between young and elderly female voices, and normalized kurtosis is essential for distinguishing between young and elderly male voices. We will continue to investigate parameters that represent important information in elderly voice signals.  
vocal aging; Praat; time-frequency analysis program; higher-order statistics; elderly voice analysis +
Footnote †: journal: 29 April 2021  
Accepted: 22 July 2021  
Subject: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
# Real-Time Fall Monitoring for Seniors via YOLO and Voice Interaction
## 6 Discussion and Conclusions
### Future Directions  
We propose several enhancements to address the system's limitations and improve its practical deployment. Expanding the dataset to include elderly users and individuals with mobility impairments, along with recordings from varied home environments, would significantly improve the model's generalization and adaptability. Incorporating multi-modal sensing, such as IMUs from wearable devices, can enhance detection accuracy in situations involving occlusion or poor visibility. We are also planning enhancements to the speech recognition and natural language processing components. Future work will focus on improving noise reduction capabilities, handling multiple speakers, supporting users with speech impairments, and expanding multilingual interaction to create a more intuitive and accessible user interface. Furthermore, we aim to advance the voice assistant module to enable dynamically generated responses, moving beyond predefined outputs to support more context-aware and user-specific interactions. Additionally, implementing adaptive sensitivity thresholds and a tiered alert hierarchy could personalize system responses based on user behavior and health status.  
To further increase trust and usability, future work will explore integrating explainable AI components that provide visual or verbal justifications for alerts. Long-term, in-home testing with elderly participants will evaluate the system's stability under real-life conditions, including nighttime monitoring and variable lighting. We aim to collect data from elderly and mobility-impaired participants to better capture these variations and adapt the model accordingly, thereby improving its robustness and applicability in the target population. Future research will also focus on investigating the system's performance when utilizing lower-resolution or thermal imaging cameras. We will explore data augmentation techniques, such as image flipping, brightness adjustment, or motion blur simulation, to enhance model robustness. We will conduct user-centered evaluations to assess social acceptability and ethical impact, ensuring alignment with the values and preferences of elderly individuals. These developments aim to transform the current system into a fully intelligent, context-sensitive assistant for fall prevention and response.
# Short-Term Power Load Forecasting Using an Improved Model Integrating GCN and Transformer
## 4 Analysis of Experimental Results
### Experimental Setup  
#### 4.3.1 Baseline  
To verify the superiority and effectiveness of the proposed method, we set up a comparative experiment and selected four methods, namely, ARIMA, GRU [39], TCN [40], GRU-Attention [41], TCN-Attention [42], MSGNet [31], and MrCAN [43]. GRU is a multivariate time series prediction method proposed by [PERSON] in 2020. It is a variant of RNNs and makes predictions by extracting the time features of time series. The TCN is a temporal convolutional network. The dilated causal convolution in its structure can deal with the scale changes of the sequence while extracting the temporal features of the time series. The GRU-Attention model is a deep learning framework that integrates GRU and the  
Figure 3: Sequences left after random forest feature selection processing based on the ETTh1 and ETTm1 datasets, listed as (**a**) ETTh1 and (**b**) ETTm1.  
attention mechanism (attention). Compared to the traditional GRU model, GRU-Attention can not only capture local temporal patterns but also establish global dependencies. The TCN-Attention model integrates TCNs and the attention mechanism. It captures the temporal characteristics of sequence data through TCNs and then dynamically focuses on the key information using the attention mechanism. MSGNet is a multi-scale prediction method. It uses FFT to process sequence data to extract frequency and determine scale \(k\) and constructs spatio-temporal graph each scale to extract multi-scale spatio-temporal features. MrCAN utilizes the small sample learning module and the spatio-temporal relationship learning module to learn the relationships among sample data and the temporal and spatial relationships within the same sample.