esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Error Modeling
### Driving Errors  
From Equation (9), the three input angles \(\theta_{11}\), \(\theta_{21}\) and \(\theta_{31}\) are the functions of coordinate value (\(x\), \(y\), \(z\)) of the origin \(P\) in the end-effector coordinate system \(\left(xyz\right)_{5}\) at any time respectively. The functions \(\theta_{11}\)= \(f_{1}(x,y,z)\), \(\theta_{21}\)= \(f_{2}(x,y,z)\) and \(\theta_{31}\)= \(f_{3}(x,y,z)\), are expanded using the first-order Taylor approximation at their designed values respectively:  
\[\Delta\theta_{i1}=\frac{\partial f_{i}}{\partial x}\Delta x+\frac{\partial f _{i}}{\partial y}\Delta y+\frac{\partial f_{i}}{\partial z}\Delta z\ (i=1,\ 2,\ 3) \tag{14}\]  
Therefore,  
\[\left[\begin{array}{l}\Delta\theta_{11}\\ \Delta\theta_{21}\\ \Delta\theta_{31}\end{array}\right]=\left[\begin{array}{l}\partial f_{i}\\ \partial q\end{array}\right]\left[\begin{array}{l}\Delta x\\ \Delta y\\ \Delta z\end{array}\right]\ (q=x,y,z) \tag{15}\]  
where: \(\left[\begin{array}{l}\partial f_{i}\\ \partial q\end{array}\right]=\left[\begin{array}{l}\frac{\partial f_{1}}{ \partial x}&\frac{\partial f_{1}}{\partial y}&\frac{\partial f_{1}}{\partial z }\\ \frac{\partial f_{2}}{\partial x}&\frac{\partial f_{2}}{\partial y}&\frac{ \partial f_{2}}{\partial z}\\ \frac{\partial f_{3}}{\partial x}&\frac{\partial f_{3}}{\partial y}&\frac{ \partial f_{3}}{\partial z}\end{array}\right]\)-Jacobian Matrix of the robot;  
\(\left[\Delta\theta_{11},\Delta\theta_{21},\Delta\theta_{31}\right]^{T}\) are the error vector of three input angles; \(\left[\Delta x,\Delta y,\Delta z\right]^{T}\) are the position error vector of the end-effector.  
The two sides of Equation (15) are simultaneously left multiplied by the inverse of the Jacobian matrix \(\left[\begin{array}{l}\partial f_{i}\\ \partial q\end{array}\right]^{-1}\) of the robot:  
\[\left[\begin{array}{l}\Delta x\\ \Delta y\\ \Delta z\end{array}\right]=\left[\begin{array}{l}\partial f_{i}\\ \partial q\end{array}\right]^{-1}\left[\begin{array}{l}\Delta\theta_{11}\\ \Delta\theta_{21}\\ \Delta\theta_{31}\end{array}\right] \tag{16}\]The position errors of the end-effector caused by the driving errors are:  
\[\Delta q^{D}=\sum_{i=1}^{3}Kq_{i}\Delta\theta_{i1}\;(i=1,2,3,\;q=x,y,z) \tag{17}\]  
where: \(Kq_{i}\) are the corresponding elements in the inverse matrix of Jacobian matrix.  
Suppose that the total errors of the end-effector in the \(x\), \(y\), \(z\) directions are the linear addition of the three kinds of position errors caused by geometric parameters errors, clearance errors of revolute joints, and driving errors:  
\[\Delta q=\Delta q^{O}+\Delta q^{R}+\Delta q^{D}=\sum Kq_{s}\Delta s\;(q=x,y,z) \tag{18}\]  
where: \(Kq_{s}\) is the error transfer coefficient of the corresponding original error \(\Delta s\), whose numerical values are generally determined by the configurations of the mechanism as well as the nominal dimensions of the structural parameters such as \(a\), \(c\), \(r_{1}\) etc.
## 2 LiCSAR and LiCSBAS
### Prepare Stack of Unwrapped Data
#### 2.3.2 Step 0-2: Convert GeoTIFF (and Downsample)  
The downloaded GeoTIFF files are converted to a single-precision floating-point format without header information for the following processing steps. Downsampling (i.e., multilooking) can also be applied if the full resolution of the LiCSAR products (~100 m) is not needed. Downsampling is achieved by boxcar averaging and returns null if more than half of the pixels in the boxcar window are null. Downsampling results in faster overall processing, a reduction in file size, and is also useful for quickly testing processing parameters and making adjustments prior to running the processing at a full or higher resolution.  
Figure 1: Workflow of LiCSBAS comprising the preparation of unwrapped (UNW) interferometric phases and coherence (COH) data (Steps 0-1 to 0-5) prior to the time series analysis (Steps 1-1 to 1-6). Optional steps of incorporating atmospheric corrections (Generic Atmospheric Correction Online Service, GACOS); masking; and clipping are denoted by the dashed lines. Checks are performed for poorly unwrapped interferograms prior to the small baseline (SB) inversion to generate velocities and time series. A range of noise indices are calculated to mask poorly constrained data points. Further masking and filtering occur to generate the final products.
# A Dual-Adaptive Approach Based on Discrete Cosine Transform for Removal of ECG Baseline Wander
## 3 Results
### Experiments with Simulated ECG and BW
Therefore, for all retrieved signals, the first and last 500 samples were not considered when calculating CC in order to make a fair comparison. The comparison results are plotted in Figure 9, in which each point represents an average of 30 experiments. Two major aspects were revealed in Figure 9. First, it is striking that the performances of algorithms I and II were stable and far superior to those of the other three algorithms. Second, the CC curves of algorithms III, IV, and V fluctuated a lot from point to point, which indicates that their performances were sensitive to the morphological features and heart rate of the simulated ECG. Figure 9: Comparison of five algorithms using the correlation coefficient between ’retrieved’ and ’clean’ ECG.
## 3 Hardware Architecture of the Proposed STFT Processor
### Hardware Architecture of the Proposed STFT Processor
#### 3.2.2 Data Reordering Module (DRM)  
The DRM makes the four-channel data after the FFT operation match the predefined overlap ratio, as shown in Figures 6, and then outputs it. In this paper, the DRM is implemented using the architecture proposed in [26], and Figure 9 shows the hardware architecture of the proposed DRM. The pre-commutator reconstructs four-channel input data for the dual-port RAM (DPRAM). Because we need to support 16/64/256/1024-point variable lengths, we use multiplexers to enforce the necessary data alignment pattern for the 16/64/256/1024-point FFTs, respectively. The output of DPRAM is reconstructed and used as the input of the post-commutator, which uses multiplexers to reconstruct the data so that it matches the MDC data structure. The parallel-to-serial (P/S) converter selects data from the four-channel output of the post-commutator in the order that matches the predefined overlap ratio and outputs it as a single stream.
# Investigation on Vibration Influence Law of Double-Shield TBM Tunnel Construction
## 3 Monitoring Program
### Installation of Vibration Sensors  
At first, the positions of sampling holes Z-1 to Z-9 on the ground are determined by using the total station. In order to prevent the collapse of the sampling holes, the air drill is used to drill the sampling holes with an iron pipe. Then, a smaller-diameter PVC pipe is put into the sampling hole to replace the iron pipe after drilling to the fixed depth. Subsequently, the anti-collapse iron pipe is pulled out. It should be pointed out that sampling hole Z-1 cannot be drilled due to site restrictions. Next, the sensors are installed sequentially from bottom to top through a proprietary unloading rack. Before installing, the performance of the sensors should be tested. Moreover, the three sensors in the same sampling hole are fixedly connected by an aluminum rod. When all three sensors reach their predetermined position, the compass is used to calibrate the sensor orientation. It can be confirmed that the X-direction is parallel to the tunnel axis in this way. The sampling holes are plugged with cement slurry after all sensors are installed. Finally, the cables connecting the sensors and dynamic signal acquisition instrument are buried in the trench to prevent damage during ground construction. The installation process of the sensors is shown in Figure 6.  
Figure 5: Measuring point layout of vibration monitoring.  
HXHC dynamic signal monitoring test analysis software (Version 11.7.0), which was developed by Anhui Hengxin Hongce Electronic Technology Co., Ltd. (Hefei, China), is installed on the computer connected to the dynamic signal acquisition instrument. By using the software, the initial sensitivities of the sensors are set. The sampling frequency is set to 128 Hz at the same time. What is more, the signal acquisition method is continuous acquisition, and the data are saved hourly. This vibration monitoring uses the signal acquisition instrument with 32 signal channels. According to the vibration monitoring scheme, there are 72 vibration signals to be acquired in the entire monitoring section. Thus, three signal acquisition instruments are required. For instance, signal channel 1-10 only means that the vibration signal of this measuring point is collected by the tenth channel of the first signal acquisition instrument. The signal channels corresponding to monitoring points are shown in Table 2 below.
## 2 Construction of the Spatiotemporal Network for Subway Vehicle Maintenance and Scheduling
### The Details of the Designed Network
#### 3.2.2 Constraints  
Each train is allowed to follow only one path at a given time, ensuring that every scheduled operation in the timetable is assigned to a corresponding train. This requirement is expressed as the timetable coverage constraint:  
\[\sum_{ij\in N_{p},i<j}x_{ij}^{r};train=1,2,\ldots,N_{train};r=1,2,\ldots,N_{R}, \tag{1}\]  
\begin{table}
\begin{tabular}{c c} \hline
**Variable** & **Illustration** \\ \hline \(Day=\left\{N_{day}\middle|day=1,2,\ldots,N_{day}\right\}\) & Set of daily schedules \\ \hline \(Train=\left\{train\middle|train=1,2,\ldots,N_{train}\right\}\) & Set of subway vehicles \\ \hline \(M=\left\{m|m=1,2,\ldots,N_{m}\right\}\) & Set of maintenance operations \\ \hline \(R=\left\{r|r=1,2,\ldots,N_{R}\right\}\) & Set of scheduling operations \\ \hline \(C_{m}\) & Cost associated with maintenance operation \(m\) \\ \hline \(l_{train}\) & Travel distance of vehicle \\ \hline \(t_{train}\) & Operating time of vehicle \\ \hline \(t_{m}l_{m}\) & Time interval and mileage required for maintenance operation \(m\) \\ \hline \(N_{m}\) & Maximum number of vehicles that can undergo maintenance simultaneously at the depot \\ \hline \(N_{p}\) & Total number of scheduling operations \\ \hline \end{tabular}
\end{table}
Table 2: The key variable description.  
where \(x_{ij}^{r}\) represents the relationship between the vehicle's path \(r\) and scheduling operations \(i\) and \(j\), and \(x_{ij}^{r}=1\) represents that the vehicle can only continue scheduling operation \(j\) after completing scheduling operation \(i\).  
Considering that the turnout state matching requirements need to be met during the train entry and exit process, it is necessary to ensure that there is a minimum time interval between the trains that switch the turnout. The constraint condition is  
\[T_{train}<60\;\text{s};Train-num\in[2,3,\ldots,n], \tag{2}\]  
where \(T_{train}\) represents the time interval between two trains, and \(Train-num\) represents the train number that needs to switch the turnout.  
In the vehicle depot, the number of hot standby vehicles in the depot is limited. The constraint condition of the number of hot standby vehicles in the depot is expressed as  
\[\sum_{train\in N_{train}}\sum_{r\in R}\sum_{i,j\in N_{z}i<j}\left[1-x_{ij}^{r} \right]\leq N_{B}, \tag{3}\]  
The vehicle cannot be overdue for maintenance, and the advance maintenance should be less than 10% of the maintenance cycle. The constraints are expressed as  
\[l_{train}\leq 1.1L_{m};\;i,j\in N_{b};\;m\in M;\;train\in N_{train},r\in R, \tag{4}\]  
\[t_{train}\leq 1.1T_{m};\;i,j\in N_{b};\;m\in M;\;train\in N_{train},r\in R. \tag{5}\]
# Development of Multi-Channel Seismic-Electrical Combined Rolling Coverage Measurement System
## 4 Conclusions  
This paper presents a multi-functional, high-power, multi-channel, rolling fast measurement system that integrates electrical and seismic methods, with a comprehensive description of its hardware and software design. The system supports both electrical and seismic measurements, offering equipment for integrated seismic-electrical exploration. Its high-power capability enables deeper electrical exploration. Additionally, the system introduces a new coverage cable that reduces exploration wiring time and saves manpower. The test results demonstrate that the system provides complete data, maintains normal communication with the host computer, and meets the practical requirements of field exploration.  
Although the developed system is designed for multi-method geophysical surveys, including AC resistivity, induced polarization (IP), and integrated seismic-electrical acquisition, the current field validation has been limited to DC resistivity surveys. This constitutes a limitation in the present work. Due to time and logistical constraints during the field campaign, no seismic data acquisition was performed. We recognize that the absence of demonstrative seismic validation, particularly seismic refraction, limits the reader's ability to evaluate the effectiveness of the integrated cable design. In future work, we plan to conduct comprehensive field experiments covering AC resistivity, IP, and seismic data acquisition to fully verify the system's multi-functional capabilities. Preliminary seismic validation is scheduled for the upcoming field season (Q3 2025). These tests  
Figure 23: Sampling results of borehole at 400 m on \(L1\) measurement line.  
will include seismic waveform quality evaluation, signal-to-noise ratio analysis, and joint inversion feasibility.  
This research was designed, tested, and implemented by all authors. The text was written by [PERSON] [PERSON] worked on the hardware design. [PERSON] worked on the software design. [PERSON] provided revisions and corrections during the creation of the article and performed the tests. All authors have read and agreed to the published version of the manuscript.  
This study is supported by the National Science and Technology Major Project for Deep Earth Probe and Mineral Resources Exploration (grant No. 2024 ZD1002700), the National Key R&D Program of China (grant Nos. 2022 YFF0706202 and 2021 YFC2801404), the National Natural Science Foundation of China (grant No. 42074155), and the Key Research Program of the Chinese Academy of Sciences (grant No. KGFZD-145-22-06-02).  
Not applicable.  
Our research is supported by national projects; thus, the data are not publicly accessible due to a confidentiality agreement.  
We thank the China University of Geosciences (Beijing) for providing an excellent testing environment.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 4 Machine Learning Algorithms
### Data Preprocessing
#### 4.1.2 Data Calibration  
Conventional logging curves typically have a sampling interval of 0.125 m. However, experimental analysis reveals that the depths of TOC after repositioning do not fully align with the sampling points of logging data. When using logging software (Explorer2024) to batch read logging curve values corresponding to the repositioned TOC depths, the software typically defaults to selecting the nearest logging point value to the repositioned TOC depth, which may introduce artificial errors [24]. To address the core-logging depth deviation issue, this study innovatively proposes a DWC Method. Utilizing high-resolution Gamma Ray (GR) curves (\(\pm\)0.1 m) as a reference, multi-well depth alignment is achieved through the Dynamic Time Warping (DTW) algorithm. Logging curve values corresponding to the repositioned TOC depths were extracted (see Figure 6). Comparison with manual calibration results demonstrates that this method reduces depth matching errors from \(\pm\)0.18 m to \(\pm\)0.06 m (a 66.7% reduction).  
\[L_{A}=\frac{D-X_{2}}{0.125}\times L_{1}+\frac{X_{1}-D}{0.125}\times L_{2} \tag{3}\]  
where \(L_{A}\) is Logging curve value at depth A. \(D\) represents the depth corresponding to point \(A\). \(X_{1}\), \(X_{2}\) are upper and lower logging depths adjacent to point \(A\), and \(L_{1}\), \(L_{2}\) are logging curve values at X\({}_{1}\) and X\({}_{2}\), respectively.
# 6G Use Cases and Scenarios: A Comparison Analysis Between ITU and Other Initiatives
## 7 6G Initiatives from Single Vendors  
The main vendors from the telecommunication sector have published documents and reports on the 6G use cases and scenarios. In [22], Ericsson defined the following four use case categories for the 6G system:  
* The Internet of Senses: data from sensors, images, audio and video, and haptics enable new digital sensory experiences;
* Connected intelligent machines: AI-based intelligent devices (e.g., Cobots);
* Digitized and programmable physical world: digital copy of a real object using AI-based software (e.g., DT);
* Connected sustainable world: ICT contribution to energy efficiency strategies.  
In [23], Huawei underlines the important role of 6G as a game changer for the economy and society and as an enabler for the new paradigm of the future Intelligence of Everything (IoE). The sustainability characteristic has been also considered. Nokia sees the 6G system as the link between three worlds, physical, digital, and human, and refers to the same 6G vertical market sectors indicated by NGA [24]. In [25] ZTE identifies three main 6G scenarios characterized by the implementation of AI-based technologies within the communication networks:  
* Supporting the enhancement of 5G-advanced mobile technology to improve network efficiency;
* Value-added applications based on AI (e.g., metaverse, immersive XR, Digital Twins, and autonomous vehicles);
* Sustainable communication networks thanks to AI-native technologies (e.g., resource awareness and allocation, dynamic control in case of 6G multiple operators, and multiple frequency bands).
## Data and Methodology
### National Land Cover Database  
The USGS National Land Cover Database (NLCD) [47] was used to assess the LC changes that occurred in the six investigated MAs during the twenty years under consideration. The NLCD is a 30-m Landsat-based land cover database spanning four periods (1992, 2001, 2006 and 2011). It was directly retrieved from GEE on the ROIs corresponding to the six MAs for the years 1992 and 2011.  
Specifically, the database provides an LC classification for functional classes whose identification codes suffered some modifications during the considered interval 1992-2011: indeed, the NLCD classification scheme used for the NLCD1992 product differs slightly from that adopted in the more recent products (i.e., NLCD2001, NLCD2006, NLCD2011). In order to overcome this problem and to compare LC homogeneous data, the various LC classes were grouped into main three classes related to urbanized, cultivated and forest/shrubland areas as follows:  
Figure 4: Constant (left panel) and rate (right panel) parameters of the LST linear model of Equation (1) for the Atlanta MA (pixel size of 300 m).  
Figure 3: Maps of the LST annual median for 1994 (**left**) and 2010 (**right**), computed through CE over all the Landsat images available for the considered year within the ROI of Atlanta MA. The pixel size is 30 m, as the raw Landsat images from which they derive.  
* the _Urbanized_ class includes the classes _Low Intensity Residential_, _High Intensity Residential_ and _Commercial/Industrial/Transportation_ of the NLCD1992 and the classes _Developed Open Space_, _Developed Low Intensity_, _Developed Medium Intensity_ and _Developed High Intensity_ of the NLCD2011;
* the _Cultivated_ class includes the classes _Pasture/Hay_, _Row Crops_, _Small Grains_ and _Fallow_ of the NLCD1992 and the classes _Pasture/Hay_ and _Cultivated Crops_ of the NLCD2011;
* the _Forest/Strubland_ classes include the classes _Deciduous Forest_, _Evergreen Forest_ and _Mixed Forest_ for both the considered years and the classes _Shrubland_ of the NLCD1992 and _Dwarf Scrub_, _Shrub/Scrub_ of the NLCD2011.  
Moreover, after grouping into the chosen three main classes, an undersampling with the same factor of 10 as before was performed on the LC database; in this way, the spatial resolution of the derived LC database for the investigated MAs was equal (300 m GSD) to the one of the derived parameter maps, allowing a one-to-one pixel correspondence. This is a simplification, but it is convenient to highlight the main influences of the long-term relevant LC changes (from rural-cultivated and forest/shrubland to urban areas and vice versa) on the variations experienced by the LST over the considered period.
## 3 Urban Water System Issues and Potential Smart Solutions
### Water Losses  
Water losses occur from leaks, unmetered consumption (legal or illegal), and meter inaccuracies [15]. A multi-city study done by Mayer and DeOreo found that 13.7% of indoor residential water use is due to leaking water fixtures in the U.S. [4]. At the level of the municipality and distribution system, the percent of water lost varies by location, from 3 to 8 percent in newer cities and 25 to 30 percent in older cities [5].  
Smart meters and end-use sensing devices can help with leak detection. For example, continuous data from a residential smart meter could reveal a leak by showing a positive water flow when all fixtures are off. Finding leaks in municipal water supply could also be accomplished through smart step testing. Traditional step testing involves manually monitoring the flow rate on a section of pipe, while manually turning off valves in order to pinpoint the section of pipe a leak is located in--a water flow in an isolated pipe means there is a leak. With a smart step testing system, smart valves and smart meters can replace workers out in the field and would require only one person at a computer terminal. The process could even be automated, only requiring human attention if something goes wrong.