esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Further Analysis of Designed Controller Robustness
### Case 2: Stochastic Power Fluctuations--Real Data  
In this case, a realistic real recorded waves and solar dataset, as is depicted in Figure 11, was collected from the international ocean data center and the Keweenaw Research Center [23], taking into consideration the validation of the proposed load-frequency controller approach. This examined case was carried out for the purposes of providing more realistic simulation results. Figure 12 depicts the frequency response comparison of the six different algorithms with the 2 DOF-PIDF controller.  
Figure 11: Real data for energy profiles considered, for a 15 min period (**top** to **bottom**): ship energy demand (load variation), sea wave energy provided, and photovoltaic source energy provided.  
Figure 10: Frequency response of 2 DOF-PIDF controllers according to the profiles of Case 1.  
In this case, the frequency deviation response range lied between [\(-\)0.03 0.03] Hz for controllers optimized, e.g., with the GOA and GWO, while the corresponding range for the JSO-tuned 2 DOF-PIDF was only \(\pm\)0.01 Hz. Additionally, it was observed that there were no \"sharp\" edges in this plot because the changes in the load and the corresponding RES energy contribution were changing more smoothly.  
Figure 12 shows that, when the load presents its maximum value (0.73 p.u.) and the solar energy contributes at 80% of its maximum value (i.e., 0.1 p.u.), while wave energy contributes less than 50% of its maximum value (i.e., 0.06 p.u.) at approximately \(t\) = 420 s, there is a frequency deviation of \(-\)8.14 mHz in the case of the GOA, \(-\)3.02 mHz in the case of the WOA and GWO, and \(-\)2.26 mHz in the case of the JSO. Furthermore, at the time of \(t\) = 825 s, the contribution of renewable sources was almost negligible (0 p.u. and 0.02 p.u. for solar and wave, respectively), while when the load demand was almost 50% of its maximum value (0.55 p.u.), the frequency deviation for the GOA, ALO and JSO were \(-\)25.43 mHz, \(-\)11.7 mHz, \(-\)10.38 mHz, respectively. As demonstrated in Figure 12, all examined bio-inspired algorithm-tuned controllers were capable of balancing demand and generation, even under fast-changing random load conditions, but the 2 DOF-PIDF-JSO controller outperformed.
## 3 Adaptive Time-Frequency Analysis
### Empirical Mode Decomposition and Optimization Models
#### 3.1.2 Ensemble Empirical Mode Decomposition
Second, the algorithm's computational complexity increases significantly, with single decomposition time approximately a hundredfold that of EMD, limiting real-time processing capabilities for massive datasets in long-term monitoring scenarios such as bridges and dams. Although boundary distortion in IMFs caused by end effects can be partially mitigated through mirror extension or local mean prediction (e.g., the OFCEEMDAN algorithm), signal anomalies under extreme operating conditions may still interfere with envelope fitting precision. Additionally, residual noise contamination from repeated noise additions and energy non-conservation in IMFs might affect the reliability of quantitative damage assessment, necessitating post-processing optimization through wavelet threshold denoising or energy compensation algorithms. To address these limitations, current research focuses on improvements from two dimensions: algorithm optimization and engineering implementation. Regarding noise-assisted strategies, complementary ensemble empirical mode decomposition (CEEMD) reduces residual noise while preserving modal separation advantages through symmetric positive-negative noise pair addition, significantly enhancing signal-to-noise ratio. Adaptive parameter adjustment methods dynamically optimize noise amplitude based on signal entropy or energy distribution, reducing manual intervention requirements. The introduction of hardware acceleration technologies provides new pathways for real-time breakthroughs--for instance, GPU-based parallel computing can improve long-sequence signal processing efficiency by 5-8 times, while the development of FPGA-embedded modules meets low-power demands for field monitoring equipment. Multi-sensor fusion strategies further expand EEMD's application boundaries. Multivariate EEMD achieves cross-scale feature extraction in composite damage diagnosis of wind turbine blades through joint analysis of multi-physical field data (vibration, acoustic emission, and fiber optic sensing), with false alarm rates reduced to below 3%.
# Guessing Human Intentions to Avoid Dangerous Situations in Caregiving Robots
## 3 Methodology
### Simulation-Based Approach to Artificial Theory of Mind
#### 3.2.1 Problem Formulation  
\(\mathcal{W}\) contains all current robot beliefs about its environment. The context \(ctx\) is defined as the content of \(\mathcal{W}\), including all the robot's current beliefs about its environment. The internal simulator typically runs synchronously with the context, effectively maintaining a copy of the state. When the \"like-me\" process starts, the simulator is temporarily detached, and people's intentions and the robot's actions can be safely simulated. Upon completion, the simulator goes back into synchronization mode.  
We define an intention as the engagement that a person establishes with an object in the environment, possibly through its affordances. Intentions are enacted by linking them to one or more of the robot's potential actions and then simulated with its internal model.  
Let \(H=\{h_{1},h_{2},\ldots,h_{n}\}\) be the set of humans in the environment and \(O=\{o_{1},o_{2},\ldots,o_{m}\}\) be the set of detectable objects.  
We introduce an intention function \(I:H\times O\times A\times G\rightarrow\{0,1\}\), where \(A\) is the set of possible actions and \(G\) is the set of possible gaze directions. \(I(h_{i},o_{j},a_{k},g_{l})=1\) if the intention is considered risky (i.e., leads to a collision), and 0 otherwise.  
We have implemented two algorithms that read and modify \(\mathcal{W}\). The first agent is activated when people are in \(\mathcal{W}\). It will guess and enact their intentions, marking some of them as dangerous if a collision is detected. The marked intentions activate the second agent, seeking a feasible robot intervention that cancels it. Both agents communicate through annotations in \(\mathcal{W}\). Two predefined lists are provided to simplify the notation: Gaze and Actions, which contain the minimum and maximum values for the vertical gaze of a generic human and the list of the robot's actions, respectively.
## 2 ARD Framework
### Processing Pipelines
#### 2.4.3 Collocation of Sentinel-1 and Sentinel-2 Products  
Similar processing steps are followed with the addition of the collocation step when the framework is configured to produce Sentinel-1 and Sentinel-2 pairs; the processing pipeline for collocated Sentinel-1 and Sentinel-2 products is shown in Figure 6. The collocation with bilinear interpolation is performed within the ESA SNAP toolbox by selecting Sentinel-2 as the master product and retaining the UTM projection to maintain the accurate pixel-level alignment between neighbouring tiles required for mosaicking. Collocated tiles are cropped to the boundary of the ROI.
## 3 Results
### Selected TLRs and NLRs Localization in Healthy Gingival and Periodontitis Biopsies  
The presence and location of selected TLRs and NLRs in healthy gingival and periodontitis tissue sections were detected by immunohistochemistry (Figure 1), and semi-quantitatively, the percentage proportion of positive stain per unit section area are shown in Table 3. TLR1, TLR2, TLR4, TLR5, TLR6, NOD1, NOD2, NLRP1 and NLRP2 were detectable in both healthy gingiva and periodontitis tissues. TLR3/7/8/10 was not detectable on any section of the specimens collected (data not shown), while TLR3 was readily detectable in sections of human hepatocellular carcinoma (data not shown). TLR9 was not tested since no RNA messenger could be detected in HGK/HGF/HGT. TLR1, TLR2, TLR4, TLR5, TLR6, NOD1, NOD2, NLRP1 and NLRP2 appeared detectable by their corresponding antibodies both in the epithelial and connective tissue compartments of the biopsy sections. Positive DAB staining, representing TLR or NLRP detection, were mainly congregated around basal (TLR4/5/6, NOD1/2 or NLRP1/2; Figure 1(C1-C3,D1-D3,E1-E3,F1-F3,G1-G3,H1-H3,I1-I3)) and/or spinous (TLR1/2/4/6; Figure 1(A1-A3,B1-B3,C1-C3,E1-E3)) layers of healthy gingival (oral or H-OE; oral sulcur or OSE; junctional or JE) epithelium. Nevertheless, overall expressions of TLR1, NOD1/2 or NLRP1/2 appeared relatively low in healthy gingival epithelium (Figure 1(A1-A3,F1-F3,G1-G3,H1-H3,I1-I3)). Concerning sections from periodontitis biopsies, all pocket epithelium (PE) were positive with TLR1, TLR2, TLR4, TLR5, TLR6, NOD1, NOD2, NLRP1 and NLRP2 staining. However, TLR2, TLR4, TLR5, TLR6 staining appeared stronger at PE. Regarding detection of respective DAMP sensor at sub-sulcular/pocket epithelial connective tissue, positive DAB staining indicated TLR1, TLR2, TLR4, TLR5, TLR6, NOD1, NOD2, NLRP1 and NLRP2 (Figure 1(A5-I5)) appeared similar and at times, stronger in periodontitis than healthy biopsies. The more intensely stained areas apparently were beneath the pocket epithelium with many infiltrated cells expressing the DAMP sensors of interest.
## 4 Experiments and Analysis
### Comparative Analysis of Average Latency
From Figure 10, the following can be observed: (1) as the task concurrency increases, the average latency of both methods increases with a slight fluctuation; (2) when the concurrency is below 100, the average latency of the proposed method remains more or less consistent with that of the classic token bucket algorithm; (3) as the task concurrency increases and exceeds 100, the average latency of the proposed method is consistently lower than that of the classic token bucket algorithm. At a concurrency level of 130, the difference is the largest, with the classic token bucket algorithm's average latency being 1.08 times that of the proposed method, indicating that the proposed method effectively reduces the average latency for mixed tasks. To further validate the robustness and adaptability of the proposed algorithm for the real scenarios, a daily traffic envelope model is used to simulate dynamic traffic conditions for the experimental evaluation, which is proposed in Section 4.3 (\"System Load\") of [PERSON] et al. 's work [PERSON] et al. (2018). Specifically, this paper adopted Equation (14) from their study to introduce realistic variability into the traffic generation process. This approach can assess  
Figure 10: Average latency for mixed task scenarios. Figure 9: Average latency for complex task scenarios. the performance of both the classic token bucket algorithm and the proposed adaptive method under fluctuating traffic loads, simulating real-world usage patterns. The study calculated the average latency for each algorithm under varying levels of system load (low, medium, and high) each corresponding to different resource utilization. By applying the daily traffic envelope model with various speedup parameters, this study simulated different temporal scales of daily traffic patterns, ensuring that the evaluation encompasses both peak and off-peak periods. From Figure 11, the results indicate that while the classic token bucket algorithm struggles with maintaining consistent performance during high variability scenarios, the adaptive task traffic-shaping algorithm demonstrates superior resilience and efficiency. Specifically, the proposed method reduces the average latency by approximately 9.56% compared to the classic token bucket algorithm, effectively lowering the average latency. These findings underscore the potential of the proposed method to enhance the QoS in GIS platforms, particularly under resource-constrained and high-concurrency environments. To avoid the misleading effects that outliers or skewed distributions may have on the mean values, this study also adopts percentile-based metrics (e.g., the 90 th percentile latency) as a latency indicator to investigate the comparison between the two methods under dynamic traffic conditions. From Figure 12, the proposed method reduces the latency by approximately 10.03% compared to that of the classic token bucket algorithm. The result is largely consistent with that of the average delay indicator, which fully demonstrates the effectiveness of the proposed method in reducing latency.
## 4 Experimental Setup and Realistic Simulation Scenarios  
The experimental evaluation of the impact of signal jammers on the commercial GNSS receiver was verified using semi-real data. Figure 2 shows the experimental setup used in this research. The Spirent GSS6700 simulator controlled by SimGEN(tm) software, which can provide a controlled simulation environment and realistic simulation scenarios, is utilized to generate the RF GPS L1 and GLONASS L1 signals. The Keysight N5172B-502 vector signal generator is used to generate the RF jamming signal. Both the RF GNSS signal and the jamming signals are combined using the Spirent GSS8366 interference combiner unit. The output RF signal was logged using a commercial NovAtel SPAN SE GNSS receiver.  
The trajectory time was set to 2 August 2017, at 12:00 am and lasts for around 15 min. The traveled distance was about 15 km in Kingston, Ontario, Canada with an initial latitude of 44\({}^{\circ}\)13'43.56'' N and longitude of 76\({}^{\circ}\)27\({}^{\circ}\)56.88'' W. The vehicle was stationary for 1 min, after which it accelerated until its speed reached 60 km/h as shown in Figure 3. The trip started at the red circle indicated in the figure.  
The velocity changed between zero and 100 km/h with several turns until the vehicle stopped at the same starting point. Ten GPS satellites and seven GLONASS satellites were available at the time of the experiment. The sky plot of the available satellites is shown in Figure 4.  
Figure 3: The full experiment trajectory.  
Figure 2: The complete experimental setup.  
The jamming signal was operated after three minutes from the start of the trajectory and remained \"ON\" for 74.6% of the time of the whole trajectory. The jamming profile is shown in Figure 5. Two jamming signals are considered in this paper, which are narrowband and wideband interference. The narrowband interference is chosen to be in the form of a continuous wave (CW) signal, one time targeting the GPS frequency band and another time targeting the GLONASS frequency band. Similarly, the wideband interference is generated using swept continuous wave (SCW) signals that span the whole band of either GPS or GLONASS as depicted in Table 1.  
Figure 4: Sky-plot of the available satellites.  
Figure 5: Jamming profile.  
The trajectory, as described above, is repeated six times for the purpose of comparing the effect of the selected jamming signals on the performance of a NovAtel SpanSE receiver. The interference signal targets one constellation at a time, i.e., either GPS L1 band or GLONASS L1 band as shown in Table 2.
## 4 Results and Discussion
### The Influence of Nozzle Geometry on Internal Steam Condensation
#### 4.1.3 Length of the Nozzle's Divergent Section  
To investigate the effect of a nozzle's divergent section length on steam condensation, we selected six different lengths (0.2, 0.3, 0.4, 0.5, 0.6, and 0.7 m). As can be seen in Figure 8, all of the steam parametric curves were completely overlapped. The only difference was found in those parameter values that were positioned at the nozzle's outlet. Figure 8a demonstrates that the steam condensation released the same amount of latent heat (i.e., the same temperature step) under the different nozzles' divergent section lengths. All of the parametric curves had the same condensation shock position and shock intensity, as illustrated in Figure 8b,c. As the nozzle's divergent section was lengthened, the pressure value at the nozzle's outlet gradually decreased. Compared with the pressure value, the nozzle outlet's Mach number showed the opposite result.  
Figure 7: The steam parametric curves along the axis at different expansion angles of the nozzle’s divergent section: (**a**) temperature, (**b**) pressure, (**c**) Mach number, (**d**) saturation ratio, (**e**) droplet nucleation rate, (**f**) droplets per unit volume, (**g**) liquid mass fraction, (**h**) droplet average radius.  
Figure 8: The steam parametric curves along the axis under different lengths of the nozzle’s divergent section: (**a**) temperature, (**b**) pressure, (**c**) Mach number, (**d**) saturation ratio, (**e**) droplet nucleation rate, (**f**) droplets per unit volume, (**g**) liquid mass fraction, (**h**) droplet average radius.  
Due to the release of the same amount of latent heat from the steam condensation, the same saturation ratio (Figure 8d) and droplets' nucleation ratio (Figure 8e) were formed under the different nozzles' divergent section lengths. The same curves of these two parameters will inevitably lead to the same curve for the droplets per unit volume, as shown in Figure 8f. Figure 8g illustrates that the liquid mass fraction of the nozzle's outlet increased with the increase in the nozzle's divergent section length, which can be attributed to the increase in the droplet number in the nozzle. This is the product of the volume of the nozzle's divergent section and the number of droplets per unit volume.
## 1 Introduction  
Neoadjuvant chemoradiation (nCRT) is an established procedure for locally advanced rectal carcinomas stage union internationale contre le cancer (UICC) II and lymph node positive rectal carcinomas stage UICC III [1; 2; 3; 4; 5]. Local tumor control is improved by nCRT. The percentage of sphincter preservation rate is increased and local recurrence rates after complete surgical removal of the tumor (R0) is decreased [4; 6]. No improvement on overall patients' survival is noted after nCRT [4]. The response to nCRT varies considerably with the patient cohorts. Applying the histopathological regression grading described by Dworak, poor regression was observed in 15%, intermediate regression in 70% and complete regression in 15% of the carcinomas [7]. The regression rate seems to improve prognosis significantly and there may be a correlation between tumor response, distant metastasis and patients' survival [8]. However, in 6% of complete tumor response to nCRT (ypT0) lymph node metastases can be detected in the resected specimens. Lymph node metastases are considered as crucial prognostic indicators [8].  
Neoadjuvant therapy comes with costs, not only is it associated with toxic side effects but it also represents an economic burden. Because the therapeutic effect of nCRT is variable and around 15% of the treated patients do not experience a therapeutic benefit at all, a case selection prior to therapy is desirable [9]. But currently no predictive indicators for nCRT response exist for routine clinical use. Today selection for nCRT is based on imaging techniques. These imaging techniques have limitations in detecting tumor-affected lymph nodes. Molecular markers of the tumor would be ideal to select patients, because they can be analysed prior to therapy in endoscopically harvested tissues. As of today no such markers exist.  
To identify molecular markers predictive for nCRT therapy response, we analyzed the proteome of laser captured micro dissected rectal carcinoma biopsies prior to nCRT with isotope coded protein label (ICPL), a recent quantitative technique for protein analysis [10; 11]. These results were postoperatively correlated with histopathological tumor response grade (Dworak) of the resected specimens.
# The Debris Flow Risk Prediction Model Based on PCA-Elman
## 3 Data Analysis and Processing
### Data Processing

Table 2: Principal component eigenvalues and variance contribution rates. indicators, demonstrating that the selection of these indicators is consistent with the actual conditions and general patterns of debris flow formation. By linearly combining the scoring coefficients of the evaluation indicators from (Table 4) with the evaluation indicators, the principal components F1, F2, F3, and F4 are obtained, as shown below:  
\[\left\{ \begin{array}{l}\text{F1}=0.33\text{S1}+0.28\text{S2}-0.15\text{S3}-0. 09\text{S4}-0.06\text{S5}+0.11\text{S6}+0.34\text{S7}+0.05\text{S8}-0.05\text{ S9}-0.05\text{S10}\\ \text{F2}=-0.05\text{S1}-0.12\text{S2}+0.42\text{S3}+0.45\text{S4}+0.29\text{ S5}+0.04\text{S6}-0.11\text{S7}+0.09\text{S8}-0.04\text{S9}-0.13\text{S10}\\ \text{F3}=-0.02\text{S1}-0.16\text{S2}-0.09\text{S3}-0.24\text{S4}+0.08\text{ S5}+0.26\text{S6}+0.07\text{S7}+0.09\text{S8}-0.04\text{S9}+0.84\text{S10}\\ \text{F4}=0.08\text{S1}-0.06\text{S2}+0.04\text{S3}+0.12\text{S3}+0.01\text{ S5}-0.07\text{S6}+0.06\text{S7}+0.81\text{S8}+0.42\text{S9}+0.08\text{S10}\\ \end{array} \right.\\\]  
Subsequently, these four linearly independent principal components are used as input variables for the Elman Neural Network model, reducing the dimensionality of the input variables in order to enhance the accuracy of the predictive results.
# Cost-Effective Satellite Ground Stations in Real-World Development for Space Classrooms
## 6 Reception Results
### Reception from L-Band Antenna System for GEO Satellites  
#### 6.1.1 Fengyun-2H (Fy-2h)  
Fengyun satellites are a series of meteorological satellites from China, including FY-1 through FY-4. FY-2 is a geostationary full-disk meteorological satellite covering 1/3 of the Earth, with eight satellites in total [27]. The ground station in this research can receive signals from FY-2G and FY-2H. Sample images and data from FY-2H are shown in Figures 12 and 13. Table 6 shows the parameters for the reception of the FY-2H signal.
# Fault-Tolerant Model Predictive Control Applied to a Sewer Network
## 2 Theoretical Framework
### MPC Formulation  
Model Predictive Control (MPC) is a control technique that calculates the control law solving an online optimal problem [5]. Control objectives are formulated as an objective function \(J\) to be minimized over a time-finite horizon \(N\), considering constraints like a system mathematical model, actuator, or sensor limits and/or disturbances. The problem can be stated at a certain time instant \(t\) as:  
\[\begin{gathered}\underset{\mathbf{U}(t)}{\min}J\big{(}\mathbf{X} (t),\mathbf{U}(t),\mathbf{D}(t)\big{)},\text{ subject to :}\\ \widetilde{x}\left(t\right)=x_{0}\\ \widetilde{x}\left(k+1+t\right)=g_{1}\Big{(}\widetilde{x}\left(k+ t\right),u(k+t),d(k+t)\Big{)},\text{ }k=0,1,\ldots,N-1\\ g_{2}\Big{(}\widetilde{x}\left(k+t\right),u(k+t),d(k+t)\Big{)}\leq 0, \text{ }k=0,1,\ldots,N-1\\ x_{\min}\leq\widetilde{x}\left(k+t\right)\leq x_{\max},\text{ }k=0,1,\ldots,N-1\\ u_{\min}\leq u(k+t)\leq u_{\max},\text{ }k=0,1,\ldots,N-1\end{gathered} \tag{1}\]  
where \(N\) is the time optimization horizon; \(g_{1}\) and \(g_{2}\) represent the system model; \(x_{0}\) is the values of the system states at instant \(t\) and \(\mathbf{X}(t)\) is the sequence of the states, \(\mathbf{U}(t)\) is the sequence of control signals and \(\mathbf{D}(t)\) is the sequence of disturbances. These sequences are calculated or estimated at instant \(t\) and extend to \(N\).  
This formulation is suitable for adding fault-tolerant properties to the controller because the MPC problem can be updated with the information provided by the FDD module [35].