esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Case Study
### Multiple-Criteria Assessment
The main component in this decision-making system works as a Value Map (see Figure 10) and presents the main goals for sustainable renovation in a visual form. The focus is on multiple-criteria analysis and this component can be used in conjunction with the services of sustainability consultants. Data must also be checked to make sure their collection has been statistically and methodologically sound. The results can be used to audit, monitor, and evaluate renovation effectiveness and assist with decision making throughout the life cycle of a project. This can be useful for identifying and establishing the key performance indicators and criteria in early steps of the design stage. In addition, the results can be used to guide stakeholder engagement and decision making. The advantages and disadvantages of each renovation alternative can also be compared to determine their specific value on a case-by-case basis. Table 12 presents the performance evaluation system for the criteria of the sustainable development decision support system. A graded rating system is used with points between 1 to 5, where 1 indicates substandard quality, while 5 is the highest score in this framework marking what can currently be considered a roughly \"fully sustainable retrofitting\", e.g., almost zero-energy renewable project. \begin{table}
\begin{tabular}{c c c c c} \hline \multicolumn{2}{c}{**Thermal Transmission**} & & & \\ \multicolumn{2}{c}{**Coefficient of a**} & & & \\ \multicolumn{2}{c}{**Double-Glazed Window**} & & & \\ \hline \hline \multirow{4}{*}{**Double-Glazed Window**} & 1 & Sub-standard & Low & 0.65 \\ \cline{2-5}  & 2 & Minimum standard & Reasonable & 0.77 \\ \cline{2-5}  & 3 & Good practice & Moderately & 0.82 \\ \cline{2-5}  & 4 & Best practice & High & 1.12 \\ \cline{2-5}  & 5 & Exemplary & Very high & 1.15 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: A system for the performance evaluation in a sustainable development decision support system. The indicator ‘Thermal transmission coefficient of a double-glazed window’ is used as an example. Figure 10: Emotional and economic criteria. A Value Map related to the same researched area in the developed decision-making framework (windows replacement is taken as an example. The criteria used in this study can be of two types: either qualitative or quantitative. The factors that correspond to quantitative indicators in the Value Map are mostly quantifiable, such as service life or investment costs, duration of work or durability; their values can be determined in a sufficiently objective way. Quality factors mean that their evaluation has to be qualitative. The drawing stage is the step when these factors should be met and designed.
## 2 Materials and Methods
### In Situ Chl-a Concentration Data  
In situ matching data on Chl-_a_ were extracted from the three databases identified in Table 2. (Note: the hyperlinks to these databases are provided in the Code and Data availability Statement at the end of the paper). These databases have varying data availability periods and Chl-_a_ concentration measurement methods (Table 2). The datasets were filtered based on two criteria: (i) a maximum time window of \(\pm\)4 days between the in situ measurements and satellite image acquisition dates and (ii) a sampling water depth ranging from near surface to a maximum of 1 m. Because only a small portion of the datasets contained _pheophytin_-corrected records, the matchup analysis was conducted based on uncorrected Chl-_a_ concentrations. In the few cases where uncorrected Chl-_a_ concentration measurements were not available, _pheophytin_-corrected Chl-_a_ data were utilized. This process resulted in 600 near-synchronous, co-located in situ matchups for WLO and HH, covering the period from 2000 up to 2022.  
Of the total matchup data, 22% were collected during autumn/winter (September to February) and 78% in spring/summer (March to August). To categorize the data based on the Chl-_a_ concentration data, we used Carlson's Trophic State Index [TSI] [46] calculated as TSI = 9.81 \(\times\) In(Chl-_a_) + 30.6. Eutrophic to hypereutrophic conditions are defined by TSI \(\geq\) 50, which is equivalent to Chl-_a_ concentrations greater than 7.2 \(\upmu\)g/L, while oligog-mesotrophic conditions correspond to TSI < 50 or Chl-_a_ concentrations below 7.2 \(\upmu\)g/L [47]. With this definition, 51% of the matchups fell in the oligotrophic/mesotrophic category, and the other 49% in the eutrophic/hypereutrophic category. The mean Chl-_a_ concentration across all matchups was 10.5 \(\upmu\)g/L, with a standard deviation of 11.3 \(\upmu\)g/L. See Table S1 in Supplementary Materials for further details on the in situ Chl-_a_ matchup data.  
Figure 1: Map of the study area showing in situ measurement locations of matchup data. Diamond and square markers represent Hamilton Harbour (HH) and Western Lake Ontario (WLO) measurements, respectively. Each marker is colour-coded according to the respective trophic state.  
In this study, in situ Chl-_a_ data were classified into four categories (Figure 2): the 'all' category includes all the data, the location category separates data between WLO and HH, the seasonality category has 'Autumn/Winter (AW)' and 'Spring/Summer (SS)' subcategories, and the TSI category divides the data among the 'Oligotrophic/Mesotrophic (OM)' and 'Eutrophic/Hypereutrophic (EH)' subcategories. The categorization helps in evaluating the performance of each scheme and to better understand the factors that influence the accuracy of satellite-derived Chl-_a_ concentration estimations. For instance, a chi-squared test from in situ measurements revealed a significant association between location and the TSI categories (\(\chi^{2}=235\), \(p<0.001\), \(V=0.63\)), which reflects the typically more eutrophic conditions encountered in HH than WLO.
# Missing Data Imputation in the Internet of Things Sensor Networks
## 4 Experiments
### Methodology
#### 4.2.4 Regression Imputation  
Inspired by the research in [44], we also performed missing data imputation based on the traditional simple linear regression model. Generally, we generated a regression model using the variables that presented high correlation results with the imputation variable based on Equation (2) below. These variables were used to predict and replace the missing values on the target variable.  
\[\hat{Y}=a+b_{1}x_{1}+\cdots+b_{q}x_{q,i} \tag{2}\]  
where \(\hat{Y}\) is the dependent variable with missing values, \(x_{1}\) is the independent variable and \(b_{1}\) represents unknown parameters.
# QuietPlace: An Ultrasound-Based Proof of Location Protocol with Strong Identities
## 5 Technical Challenges
### Geocoding  
Geocoding has a double meaning. On the one hand, it refers to the computational process of transforming a physical address description (i.e., a street number in a city) to a spatial representation on the Earth's surface (i.e., numerical coordinates) [26,27]. On the other hand, it refers to a geographic coordinate system, which assigns numbers, names, or other identifiers (i.e., alphanumerical sequences) to locations on Earth [28]. People mostly use addresses, while computers use coordinates to refer to locations. In our work, we use the second meaning of the term, which is also known as location encoding.  
There are many geocoding systems available [23]. Their common feature is that they use identifiers to refer to either a point or an area on the earth's surface. Others, such as geographical coordinates, also support referring to the height above surface. In the context of proofs of locations, we seek a geocode system that will use short, easy to remember identifiers that refer to areas and not points. In this way, it will be easier for nodes to deduct that they are close to each other, without the need of calculating the distance between them. We also require that the system supports gradation of the areas, with a corresponding change to the identifier, in a way that the subareas within an area have the same prefix as the area. In this way, it will be trivial to support location granularity levels by beginning from a small area and gradually removing the last characters until we reach a bigger one.  
We decided to use Plus Codes [19], a free, open-source geocoding system that supports location granularity levels. Plus Codes have a full length of 10 or 11 characters. They are universally unique and refer to areas. First, the Earth is divided to a grid of 9 rows and 18 columns. Each area is 20 degrees by20 degrees long and is further divided to a \(20\times 20\) grid up to 4 times, until areas become approximately \(14\) m \(\times\) 14 m long. At this stage, the code has a length of 10 characters. A last division to a \(5\times 4\) grid results in areas of approximately \(3\) m \(\times\) 3 m that are referred to with an 11-character code. Beginning with two characters in the first division, each time a subarea is chosen, two characters are added to the end of the code, except from the last stage where only the 11 th character is added. In this way, we use the different grid levels as our granularity levels.
# Water Pipeline Leakage Detection Based on Coherent \(\varphi\)-OTDR and Deep Learning Technology
## 5 Conclusions  
In response to the issue of water pipeline leaks in long-distance transportation, we analyzed, in this paper, the fundamental principles of the phase-sensitive optical time-domain reflectometry system for locating and deciphering disturbance signals. Furthermore, the \(\varphi\)-OTDR system was combined with a deep learning technique to enhance the detection rate of leakage events. By utilizing Mel spectrograms as inputs to a convolutional neural network (CNN) and ResNet18, recognition accuracies of 99.7% and 99.5% were achieved on the validation dataset. In addition, in the multi-leakage point detection experiment, the ResNet18 neural network had better generalization ability compared to the CNN model and successfully located three pre-set leakage points. This has demonstrated that it has good application prospects in the field of monitoring the leakage of long-distance water pipelines.  
Conceptualization, S.W. and S.H.; methodology, S.W. and S.H.; software, S.Z., B.J. and S.W.; validation, S.W. and S.H.; formal analysis, S.Z., Z.Y. and S.W.; investigation, S.Z., Z.X., N.L. and S.W.; resources, S.H.; data curation, S.Z., Z.X. and N.L.; writing--original draft preparation, S.Z.; writing--review and editing, S.W. and S.H.; visualization, S.Z.; supervision, S.W. and S.H.; project administration, S.H.; funding acquisition, S.H. All authors have read and agreed to the published version of the manuscript.  
This work was supported partially by the Ningbo Science and Technology Project (2021Z030, 2023Z179), the National Key Research and Development Program of China (2022 YFC3601002), the Key Research and Development Program of Zhejiang Province (2021C03178, 2022C03051, 2023C03135), and the Scientific Research Foundation for Talent Introduction of Zhejiang University's Ningbo Campus (20201203Z0180).  
Not applicable.  
Data Availability Statement: Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.  
The authors are grateful to [PERSON] of Zhejiang University and [PERSON] of Ningbo Water Meter (Group) Company Ltd. for valuable discussions and also grateful to Ningbo Water Meter (Group) Company Ltd. for providing the water leakage test environment.  
The authors declare no conflicts of interest.
# Improving the Design of MEMS INS-Aided PLLs for GNSS Carrier Phase Measurement under High Dynamics
## 5 Conclusions  
To obtain accurate carrier phase for GNSS precision positioning under dynamics, we focused on improving the design of MEMS INS-aided PLLs. Typical MEMS INS-aided PLLs were implemented in our software GNSS/INS deeply-coupled system, and tracking errors caused by aiding information were tested and analyzed under a jerk of 2000 m/s\({}^{3}\) using hardware simulator. Results showed that there was a step change in the aiding information at each integer second, which deteriorated the carrier phase accuracy, even leading to the unlocked PLL. The improved structure of MEMS INS-aided PLLs is proposed to eliminate the step change impact on tracking error. Results showed that even when the jerk was 2000 m/s\({}^{3}\), the tracking error caused by the optimized aiding information was no more than 3\({}^{\circ}\). Finally, the tracking and navigation performances of stand-alone PLLs and INS-aided PLLs were tested using a spinning platform under the field condition. The stand-alone PLLs could not lengthen integration time and reduce bandwidth to suppress noise, and the carrier phases significantly increased under dynamics. The improved MEMS INS-aided PLLs can suppress tracking errors caused by thermal noise and dynamics simultaneously, and improve carrier phase measurement accuracy under dynamic environments.  
This work was supported in part by the National High Technology Research and Develop Program of China (No. 2015 AA124002), the National Key Research and Development Program of China (No. 2016 YFB0501803), and the National Natural Science Foundations of China (No. 41404029). The colleagues of the Navigation Group from the GNSS Research Center of Wuhan University who participated in the deeply-coupled system research and testing are thanked for their cooperation. Thanks to [PERSON] for his contributions to the implementation of the GNSS/IMU raw data recorder. Furthermore, thanks to [PERSON] and [PERSON] for their contributions to the testing.  
[PERSON]: the GNSS/INS deeply-coupled system and the INS-aided PLLs were proposed by him, and the writing of the paper was primarily carried out by him. [PERSON]: the error analysis of the PLLs, the tests and data process were all carried out by him. [PERSON]: he proposed the improved idea, guided the detailed analysis of tracking error, and also guided the writing of the paper. [PERSON]: he guided the INS-aided PLLs testing base on simulator. [PERSON]: the reported research direction was assigned by him, and the structure of this paper was also designed by him.  
The authors declare no conflict of interest.
## 4 Discussion
### Limitations and Further Directions  
In this study, the applicability of SIF obtained from field experiments in drought monitoring was explored, and good results were obtained. However, to upscale SIF for satellite remote sensing, a lot of disturbance factors need to be considered, such as the soil background. With the development of sensor technology and the continuous improvement of fluorescence extraction algorithm, several new instruments on geosynchronous satellites, including OCO-3 and Sentinel-4, have been launched or are planned to be launched in the next few years [46]. At that time, SIF products with higher spatial resolution and data quality will become a reality. This will greatly promote the research of drought monitoring, vegetation productivity and terrestrial carbon cycle.  
At present, the resolution of satellite SIF products used is relatively coarse. Researchers have used downscaling methods to obtain continuous high spatial and temporal resolution SIF data [42]. In addition, the deep learning method has been widely used [47]. In the future, the deep learning method will be used to downscale the SIF data and to establish a drought index incorporating SIF to improve the accuracy of drought monitoring.  
SIF is the most direct signal used to detect photosynthesis in a terrestrial vegetation ecosystem, which can accurately capture the response of the vegetation physiological state to environmental changes. The coarse spatial and temporal resolution of SIF limits its ability to detect photosynthetic activity in terrestrial vegetation. The relationship between SIF and photosynthesis differs under the influence of environmental changes, e.g., when water deficit conditions occur. The nonlinear relationship between SIF and photochemical reactions exists not only at the leaf scale but also at the canopy scale. The complex relationship between SIF and photosynthesis needs further investigation.
## 2 System Description
### Sensitivity Analysis
#### 2.3.2 Electric Field Sensor Measurement Sensitivity Analysis  
The electric field sensor in this design uses the parallel-plate capacitor principle to measure electric fields, consisting of a pair of parallel plate electrodes and the associated amplification circuitry. The amplification circuit is implemented using a two-stage amplification method, where the first stage is a voltage follower and the second stage is a differential amplifier with a gain of 37.5 times. While the amplification circuit enhances the electric field signal and improves the sensor's frequency characteristics, it also introduces noise, thereby limiting the sensor's electric field detection sensitivity.  
The primary source of antenna noise is thermal noise, defined by \(\epsilon_{d}=\sqrt{4\mathcal{K}TR_{a}}\), where \(k\) is [PERSON]'s constant, \(T\) is the ambient temperature in Kelvin, and \(R_{a}\) is the antenna's ohmic resistance. However, in this frequency band, noise from voltage-following and back-amplification circuits dominates the antenna noise [24; 26].  
The test results from the shielded room are shown in Figure 9, with the test method being similar to that used for magnetic sensors.  
It can be seen that the output voltage noise of the electric field sensor (including the amplifier circuit) reaches \(3.2\,\mathrm{\SIUnitSymbolMicro V}/\mathrm{m}\sqrt{\mathrm{Hz}}@10 kHz\).
## 3 Results
#### 3.2.2 Technology Used for Companies That Market Devices  
For commercial prototypes, the hardware and software employed are significantly more advanced to meet the higher standards of precision and durability. High-precision sensors, such as high-resolution cameras and advanced biometric sensors, replace the lower-cost sensors used in previous prototypes (see Figures 5 and 6). Similarly, device enclosures available in the formal market are of higher quality and made from materials that enhance the devices' appeal to end-users. Furthermore, commercial devices feature a more robust software infrastructure with user-friendly interfaces designed to simplify user interaction.  
Companies that market IoT products to support pet care are increasingly common. However, within the specifications, most of the products do not provide technical details about the sensors and tools integrated into the devices. Marketing focuses on describing their features and capabilities; for example, a device marketed for tracking pet activity only specifies that it assists in monitoring the pet's activity and sleep, allowing the owner to track objectives and rankings according to the established plan. The devices sold by these companies are typically compatible with Wi-Fi, RFID, or Bluetooth. However, depending on the manufacturer, integrating them with other devices in an IoT network implemented in a connected smart home can be complex.
# Effects of Wave-Induced Processes in a Coupled Wave-Ocean Model on Particle Transport Simulations
## 2 Methods
### Models and Set-Up
#### 2.1.2 The Wave Model WAM  
The wave model WAM [39; 40] is a third-generation wave model, which solves the action balance equation without any a priori restriction on the evolution of spectrum. It is based on the spectral description of the wave conditions in frequency and directional space at each of the active model sea grid points of a certain model area. The version used in this study is the WAM Cycle 4.7, which is described in [41; 42; 43]. The source function integration scheme is made by [44], and the updated source terms of [45] are incorporated (Appendix A). The new version considers the wave-induced processes needed for coupling and are described in the next section. The spectrum in WAM is discretized with 24 directions and 25 frequencies. The wave model boundary information used at the open boundaries is taken from the regional wave model EWAM for Europe, which is run twice daily in operational routine at DWD.
## 4 Experimental Results and Discussions
### Experimental Setup
#### 4.1.2 Evaluation Metric  
The Adjusted Rand Score (\(ARI\)) metric is the Rand Index (\(RI\)) metric adjusted for chance. The ARI metric measures the similarity between the actual labels and the labels obtained through the algorithm so that the permutations present in the clusters' labels are ignored. That is, it is possible to rename labels 0 and 1 to 2 and 3 and obtain the same score, where the score 1.0 represents a perfect label and the lower its value (0.0 or negative numbers) the more incorrect the labels are [38,39].  
The following equation defines the \(RI\) metric:  
\[RI=\frac{a+b}{C_{2}^{n_{sample}}}, \tag{9}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Data** & **N° of Samples** & **N° of Clusters** \\ \hline Horseshoe & 3247 & 2 \\ T & 3744 & 1 \\ Moon & 5000 & 2 \\ Three Spirals & 621 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The three sets of synthetic data used.  
Figure 8: The Three Spirals synthetic dataset. (**a**) The first 261 samples from the Three Spirals dataset. (**b**) The Three Spirals dataset.  
Figure 7: The Moon synthetic dataset. (**a**) The first 3500 samples from the Moon dataset. (**b**) The Moon dataset.  
where \(a\) represents the number of pairs of elements that are in the same set in the actual labels and labels obtained, \(b\) is the number of pairs of elements that are in different groups in the set of actual labels and labels obtained, and \(C_{2}^{n_{samples}}\) is the total number of possible combinations in the dataset, disregarding the order.  
Then, the \(ARI\) metric is defined as:  
\[ARI=\frac{RI-E[RI]}{max(RI)-E[RI]}, \tag{10}\]  
where \(E[RI]\) param is the expected \(RI\), and \(max(RI)\) is the maximum \(RI\), where an upper limit usually replaces it [38].
## 2 Construction of New Dynamic Rock-Cutting Model and Analysis of Cutting Periodicity
### Construction of a New Dynamic Cutting Model
The key operational steps are as follows:  
Firstly, the cutting parameters are set, the cutting test is conducted, and the time history signal of the tangential cutting force \(\mathbf{F_{c}}^{t}\) is monitored and collected. Next, the tangential force signal is processed using FFT, and the tangential force spectrum is extracted. Finally, based on the spectral pattern of the tangential force, the dominant frequency corresponding to the peak is extracted as the cutting frequency. The cutting frequency obtained by this method is denoted as \(\mathbf{f_{c}}\)-\(\mathbf{F}\). The test system of rock cutting consists of two parts: cutting system, test, and post-processing system. The cutting system refers to the mechanical platform for rock cutting, which is mainly used to complete the fixation of rock samples, as well as control of the tool moving speed and cutting depth. In the actual experiment, these settings are completed by the automatic control platform, and in the numerical simulation experiment, they are set by the relative position change, boundary conditions, and constraints. The test and post-processing system are mainly used to collect and process the operating state parameters of the cutting system. In the indoor experiment, the tangential cutting force signal is collected by the force sensor, and the cutting force change is obtained directly through the monitoring function in the numerical simulation experiment. After that, by using the MATLAB software R2017b to program, the cutting force data are fast-Fourier-transformed to obtain the main frequency. In addition, the Drucker-Prager criterion (D-P criterion) is commonly used in the numerical simulation of single-tool rock cutting. In order to verify the above theoretical analysis of cutting dynamic characteristics, appropriate single-tooth cutting experiments should be carried out. At the same time, through the combination of tool inclination, cutting speed and cutting depth, the influence of these three important parameters on the dynamic characteristics of single-tooth cutting is further explored.