esa-sceva/satcom-chunk-collection huggingface dataset:
# Channel Correlation-Based Adaptive Power Transmission for Free-Space Optical Communications
## 1 Introduction
The degree of similarity in the phase and amplitude of the signal between different transmission paths is reflected by the value of the channel correlation coefficient. A high channel correlation coefficient means that the channel characteristics are similar between the different links, and the opposite indicates a large difference in channel characteristics. Therefore, the channel correlation coefficient is used as a reference metric to adjust the power of the reverse transmission signal to simplify the system design and increase the transmission efficiency in the proposed technique. In this study, we propose a channel correlation-based adaptive power transmission for FSO communications, which aims to achieve the compensation of the turbulence scintillation effect. The signal power at the transmitting end of the reverse transmission is dynamically adjusted based on the fluctuation strength of the received uplink or downlink transmission signal and the channel correlation coefficient between the uplink and downlink. The uplink and downlink channels with different link distances and turbulence intensities are established. To validate the effectiveness of the proposed technique, the proposed adaptive power transmission technique was compared to the conventional FTD and ATD techniques. The simulation results illustrated that the bit error rate (BER) performance of the proposed technique was significantly enhanced compared to the FTD technique, and it was close to the ATD technique in different transmission conditions. The turbulence scintillation effect was effectively compensated for.
# LoRa, Zigbee and 5G Propagation and Transmission Performance in an Indoor Environment at 868 MHz
## 5 Measurement Results and Discussion
### BER Measurements and Relation with the FI Path Loss Model
#### 5.2.1 LoRa BER  
Each measured LoRa BER value, previously shown in Figure 14, is grouped with the power estimation made by each loss model at each particular point, including the NLOS-1 and NLOS-2 zones. Thus, four vectors are obtained where the abscissa axis is the received power predicted by the model for a specific point, and the ordinate axis is the empirical LoRa BER measured at that point. This relationship between the predicted power for each model and the measured LoRa BER is shown in Figure 15. From this figure, we can extract that the LoRa BER does not increase to a value greater than \(5\%\) until a measured power of \(-88\) dBm or a LoRa RSSI of \(-119\) dBm is reached, while for Zigbee RSSI and SS-RSRP, it remains below \(5\%\) over the entire measurement range. The first row of Table 4 shows these results.  
Figure 14: LoRa BER, Zigbee BER and 5G SS-RSRQ.  
Figure 13: Comparison of narrowband transmission received power and LoRa RSSI.
## 3 Results
### Systematic Content Review Results for Sensors Monitoring Mine Tailings Storage Facilities
#### 3.3.1 Content-Based Data Perspective Analysis
Considering their relatively recent usage in TSFs, of the options presented in Table 6, the non-invasive instrumentation sensor tools are presented below, including satellite images and interferometry, among others. The modern technologies applied in recent years include UAVs or drones to collect aerial photos or conduct analyses with photogrammetry or LiDAR systems, satellite and radar technologies (InSAR) to analyze the subsidence or displacement of soils or dams, and satellite images, which are visual representations captured by sensors mounted on artificial satellites in space. Satellite images may be purchased or higher-resolution images may be generated with drones. There are several alternatives for purchasing these images; for example, an online platform to acquire images allows images with resolutions of up to 0.30 m per pixel to be obtained. Additional \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Sensor Name** & **Sensor Typology** & **Data Obtained/ Variable Measured** & **Monitoring Results** & **Monitoring Frequency** & **Real-Time Monitoring** \\ \hline Light detection and ranging (LiDAR) & 3D displacement, beach crest height & Exterior, feature, high accuracy, point cloud data, high data processing cost & 1/day & Yes \\ \hline Unmanned aerial vehicles (UAVs)/drones & Mine tailings storage facility panoramic view, dry beach length & Interior/exterior, point, high reliability, medium cost & 1/month & Yes \\ \hline Unmanned survey vessel (USV) & Mine tailings water pond bathymetry & Interior/exterior, point, high reliability, medium cost & 1/month & Yes \\ \hline Aerial imaging analysis & 2D/3D displacement & Exterior, feature, stereographic need for vertical displacement & 1/day & Yes \\ \hline Satellite data analysis & 2D/3D displacement & Exterior, feature, measurement limited by satellite return time and atmospheric conditions & 1/day & Yes \\ \hline Interferometric synthetic aperture radar (InSAR) & 3D displacement & Exterior, feature, ground-based or satellite-borne, sub-centimeter accuracy & 1/day & Yes \\ \hline \hline \end{tabular}
\end{table} 
Table 6: _Cont._  
Figure 23: Example of the implementation of an invasive sensor instrumentation system in a mine TSF. modern technologies include GNSS, a global satellite navigation system that is used to support any satellite navigation system with global coverage, which transmits highly precise geolocation information to GNSS devices and receivers to determine an object's current location and movements in 3D, and USVs to conduct bathymetry in the process water ponds of TSFs [102].
# Adaptive Cruise Control for Intelligent City Bus Based on Vehicle Mass and Road Slope Estimation
## 3 Problem Formulation
### MPC Optimizing Algorithm  
The nonlinear MPC algorithm is proposed to optimize the tracking capability, driving safety, driving comfort, and energy consumption. In Section 3.1, the state vector has been known and coordinating with the vehicle dynamics model, the discretization of system predictive model can be written as follows:  
\[s_{veh}(k+1)=s_{veh}(k)+v_{veh}\cdot T_{s}+\frac{1}{2}a_{veh}(k)\cdot T_{s}^{2} \tag{26}\]  
\[v_{veh}(k+1)=v_{veh}(k)+a_{veh}(k)\cdot T_{s} \tag{27}\]  
\[SoC(k+1)=SoC(k)-\frac{U_{oc}-\sqrt{U_{oc}^{2}-4 RT_{m}(k)\omega_{m}(k)\eta_{m} ^{a}(k)}}{2 RQ_{b}}\cdot T_{s} \tag{28}\]  
Therefore, Equation (7) can be expressed as follows:  
\[x(k+1)=f(x(k),\mathbf{u}(k),\mathbf{d}(k)). \tag{29}\]  
Considering the nonlinearity of the system, it is hard to know the analytical solution by receding horizon optimization. In this paper, the optimal solution of the system can be obtained by enumerating the control vectors: Firstly, enumerating the \(a_{veh}\) which is restricted from \(a_{\min}\) to \(a_{\max}\) and the optimization indexes can be calculated. Then, it can be determined if the loop should continue by judging whether the indexes are the most optimal indexes.  
In this paper, the sample time is set as 0.1 s, predictive horizon \(P\) equals 8 to avoid the whole prediction time exceeds 1 s, and the control horizon is set as 1. In the ACCprocess, the bus mass and road slope are changing with time and the EFK method is used to estimate the two variables. The MPC + EKF combining algorithm is described in Figure 6.
## 4 Establishment of Finite Element Model
### Selection of Model Materials and Parameters  
The yield criteria are based on the Drucker-Prager criterion, which assumes that all soil layers are ideal elastic-plastic constitutive models. Each soil layer's material properties are established using the \"Test Report of Physical and Mechanical Parameters of Artificial Frozen Soil at Gongbei Port on the Zhuhai side of the Hong Kong-Zhuhai-Macao Bridge\". The thermophysical parameters are listed in Table 2. Table 3 shows the mechanical properties of frozen soil, where the elastic modulus E of frozen soil is calculated using the interpolation procedure in the table, the freezing temperature \(T\) (\({}^{\circ}\)C) is negative, and the jacked pipe and concrete are considered elastic materials.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Soil Layer**} & **Density** & **Moisture** & **Thermal** & **Specific Heat** & **Latent Heat of** & **Freezing** \\  & **(kg\(\cdot\)m\({}^{-3}\))** & **Moisture** & **Conductivity** & **Capacity** & **Phase Transition** & **Point** \\  & & & & & & (\(\times\)kg\({}^{-1}\).K\({}^{-1}\)) & **(k\(\cdot\)m\({}^{-3}\))** & **(\(\times\)**C)** \\ \hline Artificial & Unfrozen & 1660 & 16.05 & 1.398 & 1420 & 36.32 & \(-0.5\) \\ backfill & Frozen & 2000 & 13.54 & 1.217 & 1410 & & \\ \hline Gravelly & Unfrozen & 2000 & 13.54 & 1.758 & 1340 & 31.48 & \(-0.4\) \\ sand & Frozen & 1820 & 47.6 & 1.485 & 1510 & & \\ \hline \multirow{2}{*}{Mucky soil} & Unfrozen & 1820 & 47.6 & 1.772 & 1450 & 41.26 & \(-1.1\) \\  & Frozen & 2010 & 26.37 & 1.537 & 1560 & & \\ \hline \multirow{2}{*}{Silty clay} & Unfrozen & 2020 & 26.37 & 1.824 & 1470 & 56.24 & \(-1.6\) \\  & Frozen & 2020 & 17.92 & 1.439 & 1430 & & \\ \hline \multirow{2}{*}{Medium sand} & Unfrozen & 2020 & 17.92 & 1.756 & 1360 & 32.87 & \(-1.5\) \\  & Frozen & 1890 & 31.98 & 1.442 & 1580 & & \\ \hline \multirow{2}{*}{Gravel clay} & Unfrozen & 1890 & 31.98 & 1.790 & 1470 & 48.25 & \(-1.5\) \\  & Frozen & 2040 & 17.31 & 1.887 & 1380 & 33.06 & \(-1.7\) \\ \hline \multicolumn{2}{c}{Concrete} & 2450 & — & 1.74 & 920 & & — \\ \hline \multicolumn{2}{c}{Jacked pipe} & 7800 & — & 44.5 & 318 & & — \\ \hline \hline \end{tabular}
\end{table}
Table 2: Thermophysical parameters of soil layer materials.  
Figure 4: Numerical calculation model: (**a**) Model size and soil layer distribution; (**b**) Computational domain grid.
## 2 Preliminaries and Related Works
### Protocols Exploiting Link Correlation
Moreover, the measurement itself incurs considerable overhead. Our proposed LACE approach can be employed by all of these works, providing more accurate and lightweight link correlation estimation, further improving the protocol performances.
# Digital Twins Generated by Artificial Intelligence in Personalized Healthcare
## 3 Results
### AI-Based DT Building

Table 2: Stages of building AI-generated DTs in personalized healthcare (own version based on [33, 34, 35, 36, 37, 38, 39]). Figure 9: Example of pharmaceutical workflow for AI-based DTs for personalized drugs (own version based on [22, 23, 24, 25, 26, 27]). Artificial intelligence ensures model convergence by optimising algorithms that adapt and improve based on a variety of biomedical data, ensuring high quality and accurate simulations. Machine learning models dynamically adapt as new data are introduced, providing a personalised profile that becomes increasingly accurate over time. To systematically populate the model, artificial intelligence integrates structured (clinical data, lab results) and unstructured data (doctors' notes, imaging) using NLP and other AI techniques. The model predicts individual responses to treatment and medical conditions by simulating complex interactions in the patient's biological systems. Using patient-specific data helps doctors make more informed decisions about diagnosis, treatment plans, and disease prevention. The AI system continuously monitors convergence rates, ensuring that as the digital twin develops, its simulations are consistent with the patient's actual physiological responses. As more patients submit data, the AI learns from the larger dataset, further refining predictions and personalised care recommendations. Finally, the convergence of the model relies on a feedback loop between data collection, AI-based insights, and actual clinical outcomes, ensuring both the precision and accuracy of personalised healthcare. The structure of such an integrated digital twin model in personalised healthcare consists of several interconnected layers: data acquisition, data processing, AI-based modelling, simulation, and feedback. Data acquisition includes the continuous collection of biomedical data from wearables, EHRs, and genetic databases, ensuring the capture of both real-time and historical data. The data processing layer cleans, organises, and standardises the incoming data, enabling consistent input for AI algorithms. AI-based modelling uses ML and deep learning techniques to interpret the data and generate personalised health models that adapt as more information becomes available. Layers of simulation then predict patient-specific outcomes, such as disease progression, response to treatment, or physiological changes.
# Design Criteria for High-Gradient Radio-Frequency Linacs
## 3 RF Breakdown
### Material Studies
#### 3.6.2 Clad Structures  
Based on experience with DC vacuum breakdown, where molybdenum and stainless steel have better breakdown characteristics than copper, several cavities were fabricated where iris tips, the location of the highest surface electric fields and rf power flow, were made of these materials (Figure 8). These so-called clad cavities were manufactured by various institutions and then tested at a high power. The anticipated difficulty in manufacturing such clad structures is associated with damage to the bimetallic joint during high-temperature bonding or brazing.To solve this problem and eliminate the need for high-temperature brazing, cells with molybdenum iris tips made at KEK [79] were clamped together. I reported high-power test results in [90]; the structures did not surpass standard cavities, probably due to damage to the bimetallic joint by pulsed heating.  
Figure 7: (a) Breakdown rate for cavities of the same geometry made from the soft and hard copper vs. accelerating gradient. The cavities are fed with a shaped rf pulse with flat top of 200 ns. The aperture radius of the cavities is \(a/\lambda=0.105\); (b) breakdown rate for the soft copper, hard copper, and two hard CuAg cavities vs. accelerating gradient. All these cavities are fed with a shaped rf pulse with flat top of 150 ns.  
The clad structures that were brazed were developed separately by the Haimson Research Corporation (HRC). Unlike KEK, which built short SW cavities, HRC developed full-scale TW accelerating structures assembled from clad cells with stainless steel and molybdenum iris tips. HRS has gone to great lengths to minimize damage to the tip-cell junction. High-power tests of copper and stainless steel clad TW structures were conducted at MIT. The stainless steel structure showed a better breakdown performance than the copper structure during initial conditioning [91]. Note that the breakdown rate map was not obtained during this experiment. Another HRS project, a TW structure with molybdenum iris tips, has been completed [92], but its high-power test has not been finished.  
To summarize, the clad structure approach has not produced major improvements in high-gradient performance; the effort that went into this complex technological problem was relatively small, but it should not be ignored, especially for high-power TW structures and klystrons.
## 4 Experimental Results
### General Setting
#### 4.1.3 Compared Methods  
We introduced several classic Transformer-based methods for comparative experiments to verify the effectiveness of our model APTrans. The following mainly introduces some representative pre-training model methods. In specific experiments, we also referenced models with better performance on corresponding datasets, so as to enrich experimental data, ensure the fairness of comparison experiments, and prove the scientificity and effectiveness of our method.  
**[PERSON]**[46]: [PERSON] et al. proposed a special text generation model. Specifically, this model combines the characteristics of BERT and GPT-2 [36], adds random noise to the text to destroy the text, and allows the model to self-learn how to restore the original text, building a powerful generative architecture that efficiently performs in NLU.  
**[PERSON]**[47]: A simple but effective network architecture, this model greatly reduces the number of parameters required by proposing two mechanisms, factorized embedding parameterization and crosslayer parameter sharing. Using a self-built self-supervised loss function that focuses on the consistency between sentences allows the model to have better downstream task performance.  
**[PERSON]**[11]: This model achieves better downstream task performance by improving the pre-training research of BERT, removing the next prediction loss, applying a dynamic mask mechanism, and increasing the training time and sequences.  
**ERNIE**[48]: [PERSON] et al. integrated AR and AE networks, used phrase-based and entity-based masking mechanisms, adopted incremental training and continual learning mechanisms, and expanded the training scale. Therefore, this model achieves better semantic understanding and efficiently performs text classification on Chinese datasets.  
**ELECTRA**[49]: [PERSON] et al. proposed a replacement token detection study. Specifically, the input is destroyed by replacing the text with the generator, and the discriminative model is trained to determine whether a token has been replaced in the text, thereby achieving better results than the masking language modeling (MLM) pre-training method.  
**MPNet**[50]: They proposed a novel method for training, which combines the advantages of BERT and XLNet, utilized the dependency between the predicted tags through permutation language modeling, and introduced auxiliary location information as an input, which reduces position difference during the model training process. The effect is better than MLM and PLM.
# Practical Adaptive Sliding-Mode Control Approach for Precise Tracking of Robot Manipulators
## 3 Simulation  
### Simulation Setup  
To illustrate the proposed SS-ASMC approach, a 2-link planar robot manipulator model (Figure 2) is adopted in these simulations, which is given in the Appendix D. The parameters of the proposed SS-ASMC approach are chosen as \(\bar{M}_{1}=4\times 10^{-3}\), \(\bar{M}_{2}=2\times 10^{-3}\), \(\bar{R}_{s,1}=1\), \(\bar{K}_{s,2}=1\), \(\bar{R}_{g,1}=6\times 10^{2}\), \(\bar{K}_{g,2}=8\times 10^{2}\), \(\bar{\delta}_{1}=7\), \(\bar{\delta}_{2}=22\), \(\bar{\epsilon}=9\times 10^{-1}\), \(\bar{\gamma}_{1}=5\times 10^{4}\), \(\bar{\gamma}_{2}=1\times 10^{4}\), \(a_{1}=2.5\), \(a_{2}=1.5\), \(\bar{\eta}_{1}^{*}=\bar{\eta}_{2}^{*}=1\times 10^{5}\), \(\eta_{1}^{*}=\eta_{2}^{*}=1\times 10^{3}\), and \(\bar{\mu}=1\times 10^{4}\).  
Figure 1: A block diagram of the proposed strong and stable adaptive sliding-mode control (SS-ASMC) approach.
## 4 Attitude Control Experiments
### Communication Speed Experiment  
The underwater optical communication speed between the AUV and the ground was set to a maximum of 1 Mbps in the operation program and was found to be satisfactory. Camera image information in the AUV could be checked in real-time by setting the resolution at VGA (640 \(\times\) 480) with the frames per second (FPS) at 20. The change in the camera FPS could be examined with the presence and absence of indoor fluorescent lights and dimming. The camera FPS increased to a maximum of 21.2 FPS, and the camera image was streamed without any interruption. Figure 18 shows the total number of received data packets matches the count of CRC-verified packets, indicating successful data integrity verification.
## 2 Materials and Methods
### Data Process  
In one cycle, approximately one-hundred parameters are queried. Therefore, it was necessary to clean the logged data to extract relevant information from the raw data set. Data from the diagnostic port were logged using a computer and Matlab software. Data cleaning aims to extract relevant information from the raw data set. ETL (Extract, transform, load) tools essentially allow data to be loaded from multiple data sources, combined, and converted into a format that can be loaded into a database for further query. Communication and data recording with the e-Golf BMS system is performed with Simulink, and the resulting log file with the *.txt format is processed. The KNIME software was applied for data analysis, a free and open-source data analysis, reporting, and integration platform. The data processing structure using the KNIME is shown in Figure 3.  
First, unnecessary data are removed from the raw data file. Then the resulting format is sorted according to a defined system (e.g., cell values with the same ID are put into a single column). At the end of the process, the extracted data are saved in an Excel file format. The next step in processing data is to check the time of data saving.  
Figure 3: The structure of data processing.  
Figure 2: Data query schematic.  
It is important to note that during the readout, all cell values, continuously mounted, along with current and temperature information, were approximately one-hundred data per readout cycle. Figure 4 shows the average readout time for a cell with a given ID.  
Figure 4 shows the readout time and ID of all 88 cells. The results show that the readout value per cell was approximately the same. Thus, the same amount of data was received from all battery cells, the only difference being the readout speed. Cell information was obtained in an average of 1.5 s for constant measurements and 1.2 s for dynamic measurements. Fewer data would be sufficient for the constant load cases, but a more significant amount is needed for the dynamic tests to analyze the transient values better. Therefore, the information retrieval was set to 1.2 s, as this does not involve data dropout. From a cell diagnostic point of view, the measurement response time was also set to a sufficient speed.  
In Section 3, the cleaned data are analyzed in detail, and the battery cells considered to be the worst are selected based on the results.
## 3 Proposed Protocol
### Hole Determination and Hole Information Dissemination  
All the nodes use TENT rule [24] to identify whether they are the stuck nodes (i.e., the nodes that may incur the local minimum phenomenon). Then, every stuck node initiates an HBA packet and sends this packet around its hole by using the RIGHT-HAND rule [24]. The HBA packets collect the coordinate of all the nodes it has traversed. Moreover, the boundary nodes also compute the core polygon covering the hole. Accordingly, when an HBA packet comes back to its initiator, it has the location of all nodes on the hole boundary as well as the coordinates of vertices of the core polygon. Please note that for the same hole, there may be multiple HBA created, thus to reduce the control overhead, we use an election algorithm to drop redundant HBA packet.  
After the first phase, the initiator of the HBA packet has coordinates of all nodes on the hole boundary. This initiator creates a packet named HCI packet that conveys information of all hole boundary nodes and broadcast the HCI packets to the neighbors. When a node receives an HCI packet, it performs the following procedure:  
* If the HCI packet contains information of a hole, then the node checks whether it already stored information of the hole in the local memory. If \"yes\", it simply drops the HCI packet. Otherwise, it stores information about the hole into the local memory. Moreover, if the node has already stored information on the hole's core polygon in the memory, then it removes the core polygon's information because now it already has information on the hole. The node then checks whether it stay inside the hole's vicinity. If \"yes\", it broadcast the HCI packet. If \"no\", if replace the information of the hole by the information of the core polygon before broadcast the HCI packet.
* If the HCI packet contains information of a core polygon, then the node checks whether it already stored information of the same core polygon or a hole whose core polygon is the core polygon in the HCI packet. If \"yes\", the node simply drops the packet. Otherwise, the node stores information of the core polygon into the local memory and broadcast the HCI packet.