esa-sceva/satcom-chunk-collection huggingface dataset:
# State Estimation of Over-Sensored Systems Applied to a Low-Cost Robotic Manipulator
## 3 Test Setup
### Software  
The control loop of the robotic manipulator is separated into two parts. The Arduino is used for low level tasks such as reading measurements from the sensors and controlling the motors. Serving as a central processing unit, a PC application is used, connected directly to the Arduino Uno by a USB cable. This application is coded in the Free Pascal programming language, specifically using the Lazarus Integrated Development Environment (IDE). It is responsible for high level tasks such as state estimation and user interaction.  
More specifically, the control loop starts in the Arduino, where raw measurements from all sensors are collected. The measurements are then transmitted to the PC via serial communication. The application receives these data and displays them in a Graphical User Interface (GUI). The GUI can also display graphically the evolution of any variable of interest and gives the user the ability to interact with the system. For example, the user can move sliders to set the position or velocity of any motor and can set the PID parameters for the control of the motors. Besides interacting with the user through the GUI,  
\begin{table}
\begin{tabular}{c l l} \hline \hline
**Reference** & **Description** & **Price Range** \\ \hline TAL 220 & 10 kg Load Cell & 10–12 € \\ \hline A000066 & ARDUINO UNO REV3 & 20–25 € \\ \hline HY711 & Load Cell Amplifier & 12–15 € \\ \hline ADA-3463 & Adafruit Precision NXP 9-DOF Breakout Board & 15–18 € \\ \hline MPU-9250 & GY-91 10 DOF MPU-9250 and BMP280 Multi-Sensor Module & 10–15 € \\ \hline JGY-371 & 12V DC Motor with worm gearbox and incremental encoder & 25–30 € \\ \hline ADA-1438 & Adafruit Motor/Stepper/Servo Shield for Arduino v2 Kit—v2.3 & 20–25 € \\ \hline TOTAL & & 184–227 € \\ \hline \hline \end{tabular}
\end{table}
Table 1: Bill of materials of the robotic manipulator.  
Figure 2: Designed robotic manipulator.  
the application pre-processes measurements according to its calibration parameters and then uses them in state estimation algorithms. The algorithms for calibration are also run in this application. The references for the motors are then sent to the Arduino, again using serial communication, which controls the motors using a PID controller. Figure 3 shows the GUI with which the user can interact.
## 4 Result and Discussion
This reveals that when the input signal power is high enough, no additional bit error will be introduced for the ONN without the electro-optic nonlinear activation function. However, the introduction of activation function increases the robustness of the proposed ONN. We analyze the impact of the phase shift error on MZI ONN performance. To simulate the phase error of phase shift, we introduce a random normal distribution \(N(0,\sigma^{2})\) and add it to the final training results of the phase shift value for each phase shifter in the MZIs. Here, \(\sigma\) is the standard deviation of the phase error. Therefore, the \(\theta\) and \(\varphi\) in Equation (1) are now written as \(\hat{\theta}\) and \(\hat{\varphi}\) as shown in Equations (15) and (16). \[\hat{\theta}=\theta+N\Big{(}0,\sigma^{2}\Big{)} \tag{15}\]  
Figure 6.0  
Then, we analyze the impact of the phase error on the ONN. Figure 13 shows the BER performance under various standard deviation phase errors at a data rate of 160 Gbit/s. Here, each BER point is obtained by averaging 1000 BER calculations to ensure the randomness. By analyzing phase errors from 0\({}^{\circ}\) to 1.5\({}^{\circ}\), we can observe that the BER performance remains within the SD-FEC threshold when the standard deviation of phase errors is up to 1\({}^{\circ}\). In Figure 13, we also compare the BER performance of the ONN model with and without electro-optic nonlinear activation function under different standard deviation phase errors. Under 1\({}^{\circ}\) phase error, the ONN model with electro-optic nonlinear activation function achieves a slightly lower Bit Error Rate (BER) compared to the standard deviation phase errors. This shows the ONN model with the electro-optic nonlinear activation function possesses a higher tolerance for phase errors, providing a more stable and reliable performance under 1\({}^{\circ}\) of phase error. Figure 6.1 Accuracy and loss performance of different normalized input signal amplitudes with an activation function.
## 5 Conclusions  
Through the experimental study and numerical simulation of laminated reinforced concrete shear walls, the following conclusions can be drawn:  
1. The failure mode, the hysteretic curve and the skeleton curve, the stiffness degradation law, the energy dissipation capacity and the bearing capacity of the laminated shear wall are similar to that of the cast-in-place shear wall, indicating that laminated walls have good seismic performance.
2. The seam can effectively transfer the load when it is well-constructed, and whose performance is similar to the whole wall. At the same time, due to the greater deformation caused by the seam, the wall's energy dissipation capacity is slightly better than the whole wall.
3. Whether the napping treatment or sprayed surface retarder is applied on the surface between the prefabricated part and the cast-in-place part, the old and new concrete can connect with each other well, and work together with integrity.
4. The two structural constructions of the concealed column and the shear wall have little effect on the seismic performance of the shear wall; hence, the appropriate method can be selected according to the actual construction needs.
5. The finite element simulation of the laminated reinforced concrete shear wall is in good agreement with the experimental results. The stress cloud is consistent with the final failure phenomena in the experiments. The simulated displacement-the base shear curve is consistent with the experimental results.  
In summary, the laminated concrete shear wall with a vertical seam is similar to the cast-in-place shear wall, and has good seismic performance. Using good construction technology and proper construction measures, the vertical seam can effectively transmit seismic forces. For economic consideration, it is recommended that napping treatment and structural construction in class II can be chosen in actual construction, which will, relatively, reduce workload and cost.  
Financial support from the National Key Research and Development Program of China (2016 YFC0701101) is highly appreciated. This work is also supported by the Fundamental Research Funds for the Central Government Supported Universities. The supports from Shanghai Science and Technology Committee, and Shanghai Vanke Real Estate Co. Ltd. are also appreciated.  
[PERSON] conceived the experiments and wrote the paper; [PERSON] performed the experiments and analyzed the data; [PERSON] proposed the method, conceived and designed the experiments, and revised the paper; and [PERSON] helped to analyze the experimental data and revised the paper.  
Figure 12: Average experimental results and the calculated results of specimens in the two groups.
# Innovative Approaches for Minimizing Disinfection
## 4 DBP Minimization Approaches
### Advanced Water Treatment Technologies
#### 4.5.5 Magnetic Ion Exchange (MIEX) Process  
Before the coagulation-flocculation processes, public water treatment involved the MIEX process as a pretreatment stage to boost the removal of precursors. With unique characteristics like a smaller bead size and a magnetic core, the MIEX resin is a robust base-anion exchange resin with exceptional ion exchange capacity and rapid separation and regeneration properties. MIEX's capability to adsorb both organic and inorganic anions enable it to absorb DOC and bromide, thereby decreasing the formation of DBPs during chlorination. According to [PERSON] et al. [34], the MIEX resin treatment, when applied at a higher dose, effectively removed both DOC and bromide, surpassing the performance of coagulation treatment in terms of total trihalomethanes (TTHMs) and HAN precursors removal. The MIEX resin and coagulation treatments preferred removing humic-like DOM over protein-like DOM, with the MIEX treatment showing a more pronounced effect. This led to treated water being enriched in protein-like DOM compared to raw water. Additionally, the MIEX resin treatment demonstrated superior efficiency in controlling the formation of di-brominated species (DBCM and DBAN) compared to coagulation treatment, highlighting its effectiveness in organic substance and bromide removal. This study reported THM concentrations of 185 \(\mu\)g L\({}^{-1}\) and HAN concentrations of 22.7 \(\mu\)g L\({}^{-1}\) in raw water, with MIEX resin treatment achieving removal efficiencies of 19-51% for THMs and 70-95% for HAN precursors.  
According to [PERSON] et al. [6], the MIEX treatment demonstrated a 38 to 77% reduction in TTHMs formation potential and 44 to 74% in HAAs formation potential, varying based on the water source.
## 4 Results
### Data Overview
CO queries reflected a mix of instructional and chain-of-thought prompting, especially when students asked for step-by-step refactoring suggestions or performance improvements. Code Understanding and Concept Clarification queries were typically structured as zero-shot requests, where students asked for explanations without providing additional context or examples. Best Practices and Documentation queries also aligned with zero-shot or instruction-based modes, as students sought general standards, guidelines, or references without referencing specific code. By mapping these organically occurring student interactions to known prompting strategies, we bridge the gap between controlled prompt design research and real-world LLM usage in educational programming contexts. After the data extraction process and the category identification, descriptive statistics of the measured variables used in the analysis were performed. The data were grouped into two conditions: queries where no tag was present and queries where a tag was present. Table 1 shows an overview of the students' prompts with the mean, standard deviation, and sample size. Table 1 presents the descriptive statistics of the students' queries, highlighting differences in mean values across tagged and untagged queries. While EF and CU were the most frequent queries when no tags were present, their frequency significantly decreased when tagging was introduced. Conversely, DOC queries saw a notable increase in the tagged condition despite having a lower mean when untagged. To further illustrate these patterns, Figure 3 provides a comparative bar chart, visually emphasizing how tagging influenced the frequency of each query type. The chart reinforces that students in the cases where a tag was present tended to ask fewer questions in categories like EF and CU, suggesting that precise tagging may lead to more effective or self-sufficient problem-solving. On the other hand, the increase in DOC queries when tagged indicates that students were more deliberate in seeking structured documentation. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**No Tag Present**} & \multicolumn{3}{c}{**Tag Present**} \\ \cline{2-7}
**Tag** & **Mean** & **SD** & **N** & **Mean** & **SD** & **N** \\ \hline FI & 1.970 & 4.815 & 5 & 0.650 & 3.819 & 32 \\ BP & 0.755 & 4.100 & 33 & 1.438 & 2.075 & 4 \\ DOC & 0.747 & 3.941 & 36 & 3.750 & / & 1 \\ CC & 1.025 & 4.567 & 10 & 0.756 & 3.743 & 27 \\ EF & 3.625 & 5.044 & 6 & 0.287 & 3.508 & 31 \\ CU & 4.688 & 4.033 & 4 & 0.361 & 3.695 & 33 \\ CO & 1.293 & 4.429 & 21 & 0.219 & 3.158 & 16 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Descriptive statistics of measurement.
# A Simple AMC Antenna for Liquid Monitoring in an Infusion Bag
## 1 Introduction
For telemedicine applications, a flexible AMC-based antenna was proposed to reduce the impedance mismatches close to human tissues [31]. A flexible printed dipole antenna with an AMC unit was introduced to optimize the impedance and gain for blood volume detection in lossy environments [32]. Nonetheless, the high cost and complexity of the current AMC sensors for liquid detection limit their use in infusion settings. In this paper, an AMC antenna for liquid-running-out detection in an infusion bag is proposed. Four square loop AMC unit cells are employed to enhance the gain, impedance, and sensitivity of the sensor when placed on the lossy structures of an infusion bag. It also fully reflects the incident electromagnetic waves and mitigates interference from the standing pole, offering a simple and low-cost solution for infusion monitoring. In the without-liquid state, the proposed antenna operates stably within the 2.4 GHz ISM frequency band and leverages existing Wi-Fi resources for WLAN communication, thus facilitating a cost-effective sensing solution. In the with-liquid state, the antenna's operating frequency shifts to 2.0 GHz. The effectiveness of this sensing approach is determined by the antenna's performance variation between the without-liquid and with-liquid states. This paper is organized as follows. Section 2 presents the geometry and configuration of the proposed antenna. Section 3 mainly introduces the working principle and implementation process of the proposed antenna, which includes the analysis and the entire design flow of the proposed antenna and AMC unit cell. The fabricated and measurement results of the proposed antenna are presented in Section 4. Finally, a brief conclusion is given in Section 5.
## 1 Introduction
By integrating optical and radar imagery with a large field dataset, we aimed to produce a current and accurate land cover map of the Masai Mara National Reserve (henceforth, \"the Reserve\") in southwestern Kenya. This work is unique in three important ways. First, we aim to differentiate among grasses of different heights, which represent an important component of habitat suitability for various animals (e.g., large herbivores [26]), among other diverse land cover types in a heterogeneous landscape. Second, our methods apply a novel fusion of sensor imagery to the classification of land cover within a grassland ecosystem: PALSAR-2 radar imagery, Sentinel-1 radar imagery, and Sentinel-2 optical imagery. Third, our resulting land cover map provides a highly accurate, detailed, and novel map of a region that is of utmost conservation priority because it comprises the natural habitat of an enormously diverse fauna.
## 4 Handover and Mobility Process in 5G HetNet
### Advantages of HO in 5G HetNets  
The HO process in 5G-NR is similar to that in 4G/LTE-A, but 5G-NR outperforms 4G/LTE-A in terms of speed, reliability, and efficiency. The use of beamforming technology in 5G-NR enhances the accuracy of signal measurements, resulting in faster HO decisions. In contrast, 5G's edge computing technology efficiently reduces HO latency compared to LTE-A. Furthermore, the use of DC technology in 5G-NR improves HO reliability more than in previous generations (4G LTE). DC enables the UE to maintain a connection with the source and target gNBs during the HO process, reducing data packet loss and improving reliability. In addition, the implementation of SDN and NFV in 5G-NR allows the network to be easily reconfigured to meet challenging requirements.
## 4 Wireless Body Area Networks (WBANs)
### New Sensors Generations for BASNs
#### 4.4.3 Symbiotic Nodes  
The transition from the state of the art, the scientific community is pushing to more aggressively scaled cubic millimeter size devices (called \"smart dust\" [72]), which may support a number of new in-body bio-monitoring applications. The technical problems to be addressed are, first, the implementation of self-powered nodes that can collect energy from the body (temperature gradients, movement, in-body chemical reactions, _etc._). Second, the size limitation imposes demanding requirements on the process of integration and microfabrication: wireless communications, electronic data processing, chemical data processing, microfluidic capabilities should all be packed in a few cubic millimeters. Finally, further problems arise in connection with the safety requirements: nodes must be short and long term bio-compatible. We call these nodes symbiotic because they have a true mutual advantage relationship of the target organism.
# Robust Phase Estimation of Gaussian States in the Presence of Outlier Quantum States
## 3 Robustness of M-estimator
### Asymptotic Breakdown Point  
Intuitively, the value of the breakdown point represents the maximum ratio of outliers in a datum, which can be suppressed by an M-estimator. Beyond this breakdown point, the M-estimator will no longer return a trusted value, which is typically far away from the parameter set \(\Theta\). Consider a one-parameter model \(M=\{f_{\theta}|\theta\in\Theta\}\) for the sake of simplicity and denote \(\partial\Theta\) by the boundary of the open set \(\Theta\). Fix the M-estimator \(\hat{\theta}\) under consideration. A contaminated model with outliers is given by (12).  
The asymptotic breakdown point of the estimator \(\hat{\theta}\) for the model (12) is denoted by \(\varepsilon^{*}\left(\hat{\theta},F_{\theta}\right)\). This is defined by the maximum value of \(\varepsilon\in[0,1)\), in which \(\hat{\theta}\) on the boundary remains finite for arbitrary outlier distributions. Mathematically, this is expressed as:  
\[\forall\varepsilon<\varepsilon^{*},\forall G\in\mathcal{G}_{\text{outlier}}, \ \hat{\theta}_{\infty}\left((1-\varepsilon)F_{\theta}+\varepsilon G\right)\in K, \tag{13}\]  
where \(K\subset\Theta\) is a closed bounded subset satisfying \(K\cap\partial\Theta=\mathcal{O}\) and \(\hat{\theta}_{\infty}(F)\) denotes asymptotic behavior of \(\hat{\theta}\) for the distribution \(F\). For example, the asymptotic breakdown point of the median is 0.5, since it gives values outside of the parameter set \(\Theta\) when half of the sample size is made up of outliers.
# Flexural Performance of Novel Nail-Cross-Laminated Timber Composite Panels
## 3 Experimental Results and Analysis
### Effects of Different Panel Design Parameters
#### 3.3.4 Effect of Number of Nailed Layers  
Specimens KWL30070235240 and KWL50070235480 were used to examine the influence of the number of nailed layers. When the number of nailed layers increased by two, the ultimate bearing capacity of the NCLT panels increased by 55.3%, and the ultimate bending moment increased by 55.2%. In addition, the maximum displacement at mid-span slightly increased by 1.2%. It can be deduced from the aforementioned results that increasing the number of nailed layers can greatly improve the load resistance of NCLT panels. According to Figure 13a, the maximum displacement of the 3-layer NCLT panels was 130.69 mm, while that of the 5-layer NCLT panels was 132.47 mm. The effective bending stiffness also increased when the number of nailed layers increased. Strain of the 5-layer NCLT panels was significantly smaller than that of the 3-layer panels (Figure 13b). This implies that the increase in number of nailed layers improved the overall rigidity of the NCLT panels and decreased the deformation rate in the bottom tension zone.  
Figure 12: Effect of nail number on load-deflection and load-strain relationship. (**a**) Load-deflection curves of specimens with a different number of nails; (**b**) Load-strain curves of mid-span and one-third sections in the specimens with a different number of nails.
# Evaluating Preprocessing Techniques for Unsupervised Mode Detection in Irish Traditional Music
## 4 Results and Discussion  
This section presents the results and discussion of our classification problem. The goals of the experiments were threefold. Firstly, we aimed to demonstrate which PCP-based or embedding-based approach performs best according to our classification problem. Secondly, we aimed to check whether a featured-based approach yields better classification results than a more automatic approach, such as the embedding approach. Finally, we examined how the unsupervised learning approach behaved with musical data when classifying mode, paying attention to the variety of algorithms that can be used in this paradigm.  
MinMax normalization was applied to all PCP-based features to standardize the data and enhance the robustness of the classification process against variations in feature ranges (except PCP Binary because the feature consists of vectors whose elements are one or zero). Additionally, SMOTE (the Synthetic Minority Over-sampling Technique) was used to balance the class distribution, particularly for modes with fewer instances, such as Mixolydian and Dorian. Some internal experiments were carried out without using this technique to decide whether to apply it. The results showed a considerable improvement of the results when using SMOTE. In particular, we used the implementation from the imbalanced-learn Python package [57].
## 2 Materials and Methods
### Materials
#### 2.1.2 Time Series Acquired by Devices Installed In Situ  
In situ water level and air temperature measurements were supplied by the dam managing authority to support the analysis.  
Water levels were measured with a staff head gauge level numbered every decimetre placed on the right bank, while air temperatures were measured via a thermo-hyrometer installed on a service area neighbouring the right bank.  
A time series of GNSS displacements was measured using a permanent station (AGRI) operating 24 h a day at Agrigento (coordinates 4,131,320.31 N, 376,057.91 E; RDN2008 UTM Zone 33 N, EPSG code 6708). Although relatively far away (\(\approx\) 33 km), it was used as a base station to allow a reduction in economic costs. Similar to the setup described in [44], three receivers Geomax GPS Zenith 25 model (by GeoMax AG, Switzerland) were placed on the crest of the dam (Figure 1, panel b, red dots). Receivers were positioned on stainless steel pillars and fixed to the ground through steel plates (coordinates 4,160,249.76 N, 359,692.54 E; 4,160,208.79 N, 359,757.15 E and 4,160,159.71 N, 359,816.90 E). A power supply unit and a smartphone with an internet connection were placed in sealed boxes to transmit data in real time and continuously 24 h a day via File Transfer Protocol (FTP) using the XPAD Survey software (by GeoMax AG, Switzerland). The data transfer protocol allowed verification remotely on the correct functioning of the system. The monitoring campaign was carried out for a whole year with a 30 s postprocessing time step and hourly uploading frequency. According to the setup reported in [44], the Root-Mean-Square Errors of processed baselines are smaller than ~1.2, 2.4, and 1.0 mm (X, Y, and Z components, respectively), while the average percentage of resolved ambiguities is >90%.  
Figure 1: The location of the study area (**a**) is Sicily island-Italy (dark and light grey, respectively), EPSG 4327; the upper left box reports a zoom of the island with the Agrigento permanent station (black triangle) and the Magazzolo reservoir (blue dot), EPSG 6708 and (**b**) hillshade of the dam–reservoir system derived from a digital surface model by setting azimuth and elevation of the incident light at 315 and 45 degrees, respectively). Superimposed the positions of the GNSS stations (red dots).