esa-sceva/satcom-chunk-collection huggingface dataset:
# Doherty Power Amplifier Design via Differential Combining
## 4 Conclusions  
In conclusion, this work introduces a novel differential combiner that effectively compensates for transistor output capacitances at any chosen frequency, overcoming the limitations of previous designs. By incorporating a capacitor with a value of \(2C_{OUT}\) into the midpoint of the \(\lambda/4\) transmission line, the proposed combiner achieves precise capacitance cancellation, making it suitable for high-frequency applications with high load impedances. This innovation greatly simplifies the design process for DPAs, eliminating the need for complex parasitic compensation and making it applicable not just for millimeter-wave frequencies but for any design frequency. The DPA design leveraging this combiner demonstrates impressive performance, with simulation results showing a 6 dB OBO PAE ranging from 14% to 28%, and up to 37% at saturation, while maintaining a gain above 11 dB and output power exceeding 36.5 dBm. The comprehensive simulation results validate the effectiveness of the proposed design, showing a potential bandwidth of 4 GHz and optimal performance at 32 GHz.  
Conceptualization, J.J.M.R. and A.N.; Methodology, J.J.M.R.; Software, A.N.; Validation, J.J.M.R. and A.N.; Formal analysis, J.J.M.R.; Investigation, J.J.M.R.; Writing--original draft, J.J.M.R.; Visualization, A.N.; Supervision, J.J.M.R. All authors have read and agreed to the published version of the manuscript.  
This work was funded in part by Silicon Austria Labs under project IWS Testbed 709000.  
**Data Availability Statement:** Data are contained within the article.  
Figure 9: Normalized output power spectral density of the designed PA at 32 GHz carrier frequency, with and without DPD.  
**Acknowledgments:** We gratefully acknowledge the support of [PERSON] and [PERSON] from Keysight Technologies for their guidance on using the DPD Explorer in ADS.  
**Conflicts of Interest:** Authors are [PERSON] and [PERSON] were employed by the company Silicon Austria Labs GmbH. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# A Multi-Machine and Multi-Modal Drift Detection (M2D2) Framework for Semiconductor Manufacturing
## 2 M2D2 System Framework
### System Workflow and Visualization Interface  
Once multi-modal data start streaming in, M2D2's preprocessing module merges and cleans them into consistent feature vectors, which are subsequently fed into the baseline model to compute reconstruction errors or latent embedding distances. Short-term detections trigger real-time alerts if these measures exceed the chosen threshold, supported by interpretability cues that help engineers or operators identify potential causes. Meanwhile, long-term analyses are used to periodically compare the evolving data distribution with historical baselines; significant divergence leads to a \"long-term drift\" alert. In both cases, a user-friendly dashboard provides historical trend graphs, root-cause explanations, and maintenance log cross-referencing. False alarms can be labeled to refine future detection thresholds, whereas confirmed new anomalies are archived in the anomaly repository for eventual retraining. Under multi-site or multi-machine setups, each node periodically transmits updated local parameters and drift scores to the central server, which conducts drift-aware parameter aggregation based on data volume and drift severity, preserving consistency and precision across the network.
## 3 Direct CSI SVM Modulator
### Major Simulation Blocks Based on Top-Level Drawing
This guarantees constant parameter values among several modulation methods under the same operational settings, making a proper comparison of the performance characteristics for these methods. Figure 14 shows the implementation of the SVM for Sector 1. Figure 14a illustrates the unfiltered current waveforms for Sector 1 at the top. The red waveform denotes phase 1, the green waveform indicates phase 2, and the blue waveform corresponds to phase 3 of the CSI. The total of the phase currents is equal to zero, as seen in Figure 14b. This validates the pattern for the direct duty ratio CSI SVM modulator. This illustration presents an enlarged depiction of the upper waveform displayed in Figure 14a. Sector 1 SVM pulses are illustrated below the unfiltered current waveform. The lower waveform in Figure 14a demonstrates nearly pure sinusoidal current waveforms of the three-phase CSI inverter. Figure 13: Modulation signals (S\({}_{1}\)–S\({}_{6}\)) for the direct CSI SVM modulator, implementing the vector sequences of Figure 5a. Pulse widths correspond to vector dwell times (\(d_{(ac)},d_{(ab)},d_{(aa)}\)).
## 4 Model Prediction Performance Discussion
### Interpretability of the Prediction Model  
The uplifting behavior of overlying excavations on existing tunnels is a complex civil engineering problem. Explainability is an important criterion for assessing whether or not machine learning models are trustworthy. In other words, a model should not only provide accurate predictions to engineers but also provide a safe and reliable basis for decision-making. The XGBoost model, for example, provided an interface for visualizing decision trees. This feature enhances the interpretability of a model by providing engineers with an intuitive representation of the decision-making process. Figure 17 illustrates the visualization of the last tree by specifying the parameter \"num_trees\". This shows that the model consisted of five layers, including four layers of tree structure and one layer of leaf structure, and the features at the nodes determined the cut of the nodes. This visualization capability of the XGBoost model aided in model analysis and provided valuable insights into the decision-making process. It enhanced the model's explainability, which will allow engineers to understand how a model arrives at its predictions, thereby enabling informed decision-making based on a safe and reliable foundation.  
Feature importance plays a crucial role in elucidating a model's behavior. By utilizing the feature importance module within the XGBoost model, the proportionate significance levels of the top ten features were calculated according to the number of times they were utilized for splitting the tree. Figure 18 shows the feature importance of the prediction model based on the BO-XGBoost model for the maximum displacement of the tunnel vault uplift.  
According to the weight of the feature importance, the three factors SWM, BP, and DW were the most prominent. This indicated that the excavation enclosure structures were the most important factors in this prediction model. Additionally, the remaining features, in descending order of their weight shares, included SM_3, Lc, Sil, Mud, Sof, Gra, and SM_4. The control measure of using uplift-resistant piles (SM_3) in the excavation could effectively control the soil uplift below the excavation bottom. Prior to designing an excavation, equal attention should be given to the actual length (Lc) of a tunnel's under-crossing. Unexpectedly, the four soil types were ranked at the bottom according to their  
Figure 17: The XGBoost model’s tree structure.  
weights, which were far below the construction measures. This confirmed that suitable construction measures can significantly counteract the effects of geological factors resulting from underlying tunnel uplift. As a result, the interpretability of the BO-XGBoost model provided significant guidance to the tunnel engineers in the decision-making process.
## 3 Results and Discussions
### Results and Analysis of SSC Prediction Test
#### 3.2.2 Selecting Characteristic Wavelengths  
We used different methods in MATLAB R2021b to select feature wavelengths: SPA, MCUVE, SPA-MCUVE (based on the feature wavelengths selected by SPA and then using MCUVE to select feature wavelengths), and MCUVE-SPA. The results are as follows:  
1. Used SPA to select characteristic wavelengths and select 32 wavelengths, namely, 393.7, 407.2, 416.2, 420.7, 423.0, 427.5, 429.8, 482.4, 524.3, 557.2, 573.9, 641.2, 690.2, 715.0, 795.5, 798.0, 823.6, 859.6, 875.2, 911.8, 946.1, 951.4, 962.0, 972.6, 986.0, 988.7, 991.4, 999.4, 1002.1, 1004.8, 1007.5, and 1010.2 (unit: nm).
2. Used MCUVE to select characteristic wavelengths and select 35 wavelengths, namely, 391.5, 402.7, 454.8, 468.6, 484.7, 531.3, 536.0, 543.1, 552.5, 557.2, 564.4, 573.9, 617.0, 638.8, 653.4, 680.4, 687.8, 692.7, 707.5, 712.5, 732.4, 742.4, 760.0, 815.9, 841.5, 859.6, 864.8, 875.2, 909.2, 924.9, 943.4, 954.0, 964.7, 986.0, and 1010.2 (unit: nm).
3. Used SPA-MCUVE to select characteristic wavelengths and select 13 wavelengths, namely, 393.7, 423.0, 482.4, 557.2, 573.9, 715.0, 795.5, 859.6, 875.2, 911.8, 962.0, 988.7, and 1010.2 (unit: nm).
4. Used MCUVE-SPA to select characteristic wavelengths and select 15 wavelengths, namely, 391.5, 557.2, 573.9, 638.8, 680.4, 687.8, 712.5, 742.4, 760.0, 859.6, 909.2, 943.4, 964.7, 986.0, and 1010.2 (unit: nm).  
In addition, in order to investigate the effects of spectral pretreatment and the selection of feature wavelengths, a control group was set up: no feature selection, and first derivative pretreatment without feature selection.
# Anti-Biofilm Agents to Overcome _Pseudomonas aeruginosa Antibiotic Resistance_
## 2 The Biofilm Life Cycle
### Biofilm Dispersion and Bacterial Dissemination  
The dispersion of the biofilm is an essential part of its life cycle. Several factors such as a lack of nutrients or oxygen, strong competition or an outgrown population can induce a partial or complete dispersal. Released bacteria quickly revert to a planktonic phenotype and colonize another surface that is richer in nutrients and more supportive for growth [41]. It is noteworthy that the biofilm can also undergo a mechanical erosion induced by environmental physical forces such as flow velocity. Those cells are then more likely to retain a sessile state. Spontaneous dispersal is mainly triggered by the production and detection of different signal molecules. For instance, nitrous oxide (NO) and glutamate diminish the c-di-GMP levels, thus increasing flagellar expression and promoting bacterial motility. On top of that, QS stimulates the secretion of rhamnoid biosurfactants which create cavities in the extracellular matrix to facilitate the detachment of microcolonies [42]. This dispersion promotes the dissemination of the infection, in particular on abiotic devices in which bacterial biofilms rapidly develop. Ventilation-associated pneumonia therefore significantly increases mortality among hospitalized patients. In such a situation, the removal of the device is the most effective way to eliminate the source of the infection.  
Considering the increased bacterial pathogenicity and resistance in biofilm-related infections, the development of anti-biofilm agents appears to be a relevant therapeutic strategy.
# Orbit Decomposition Method for Rotordynamic Coefficients Identification of Annular Seals
## 6 Comparison with Experimental Data  
To further validate the orbit decomposition method, its prediction results are compared with the experimental data tested on the high-pressure seal test rig owned by GE Oil & Gas [17]. The test object is the labyrinth seal. It has 14 teeth on the stator. The nominal diameter is 220 mm, radial clearance is 0.3 mm, tooth pitch is 5 mm, tooth height is 4 mm, tooth width at the tip is 0.2 mm, and tooth angle is 30 deg. At the seal upstream, the total pressure is 77.9 bar, static pressure is 72.3 bar, temperature is 12.5 \({}^{\circ}\)C, and presswirl ratio is 0.87. At the seal downstream, the static pressure is 50.6 bar. Five whirl frequencies are measured with a 10,000 rpm running speed. The detailed test conditions can be found in [17].  
Figure 18 presents the rotordynamic coefficient comparisons between the present method and experimental data. Figure 18a-c show the whirl frequency effect on the cross-coupled stiffness, direct stiffness, and direct damping, respectively. The rotordynamic coefficients are predicted well. The prediction results capture the correct frequency dependence of rotordynamic coefficients. The cross-coupled stiffness and direct damping increase with the whirl frequency. The direct stiffness decreases with the whirl frequency. The cross-coupled stiffness and direct damping are two key parameters that affect the rotor  
Figure 17: Variations in rotordynamic coefficients with ellipticity.  
stability. The positive cross-coupled stiffness tends to destabilize the rotor by adding energy to the forward whirl motion of the rotor. The positive direct damping tends to stabilize the rotor because it acts to remove energy from the forward whirl motion of the rotor. Considering these two coefficients comprehensively, the effective damping is defined as:  
\[C_{eff}=C(\Omega)-\frac{k(\Omega)}{\Omega} \tag{20}\]  
Figure 18 depicts the effective damping versus whirl frequency. The predicted and measured results are in good agreement. The effective damping increases with the whirl frequency.  
In general, the present method has good accuracy to predict the frequency-dependent rotordynamic coefficients for annular seals.
## 2 Method  
### Borehole Laser Scanning System  
To access the surface morphology of boreholes for rock-socketed piles in situ, a LiDAR-based roughness measurement system was developed. The system utilizes the flight time of the laser to range, enabling the rapid acquisition of the surface morphology of the measured borehole. The system hardware comprises a laser probe, depth gauge, stabilizer, computer, CNC winch, and the necessary power supply and data transmission cables. A photograph of the main components of the laser system is shown in Figure 2. The system is capable of automatically measuring dry boreholes of varying diameters and depths, with a minimum diameter of 0.1 m, and can complete the sizing process with high precision and efficiency.  
In the field, the devices are positioned as illustrated in Figure 3, with a computer utilized to oversee the entirety of the measurement process. The CNC winch is employed to lower and elevate the LiDAR in a vertical trajectory, and the sampling density of the drilling point cloud can be regulated by modulating the lowering velocity. A depth gauge is affixed to the winch and linked to the LiDAR via a cable, enabling real-time recording of the depth at which the LiDAR was situated. The LiDAR continuously emits laser beams and receives reflected signals from the borehole wall while descending at a constant speed. It also continuously rotates to scan the borehole wall step by step, recording and transmitting the time difference and rotation angle in real time. A corrector is employed to mitigate the impact of laser vibration on the measurement accuracy.  
The above raw data were summarized in a computer for processing to obtain the 3D point cloud data of the borehole. First, the ranging and angular values recorded by the laser probe were used to calculate the 2D polar coordinate positions of the points on the rock surface within the borehole, and this polar coordinate system, with the center of the laser sensor at the time of the measurement as the origin, represents the cross-section of the borehole at different depths. The depth data were subsequently introduced to transform the 2D coordinates of the points into the 3D coordinate system in which the entire borehole is located. This conversion process is shown in Equation (1),  
\[\mathrm{P}_{(x_{i}g_{i})}=\left(\frac{1}{2}ct\cdot\cos\alpha,\frac{1}{2}ct\cdot \sin\alpha\right)\xrightarrow{+D}P_{(x_{i}g_{i}z_{i})}=\left(\frac{1}{2}ct \cdot\cos\alpha,\frac{1}{2}ct\cdot\sin\alpha,d\right) \tag{1}\]  
where \(c\) is the speed of light, \(t\) and \(\alpha\) is the time and angular values recorded by the laser probe, respectively.
# A Computation Offloading Strategy in LEO Constellation Edge Cloud Network
## 5 Numerical Simulation
### Performance Metrics  
The comparative performance metrics include satellite resource utilization, task response rate and joint optimization cost. Specifically, satellite resource utilization is defined as the ratio of allocated satellite resources to the total computing capacity of the entire constellation, which is calculated as (17).  
\[\sum_{m\in\mathcal{M}}\sum_{n\in\mathcal{N}}C_{n}x_{n,m}/\sum_{m\in\mathcal{M}}F _{m} \tag{17}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Satellite terminal uplink power & 0.2 W \\ Satellite terminal transmit rate & 2 Mbps \\ Inter-satellite transmit power & 3 W \\ Inter-satellite transmit rate & 30 Mbps \\ Boltzmann’s constraint \(K_{b}\) & 228.6 dB \\ System noise temperature \(T_{s}\) & 25 dB \\ \hline \hline \end{tabular}
\end{table}
Table 4: Communication parameters.  
Figure 6: Constellation 2 snapshot.  
Figure 7: Constellation 3 snapshot.  
The task response rate is defined as the ratio of the number of successfully offloaded tasks to the number of all current task offloading requests, which can reflect the ratio that user requests can be accessed. In addition, considering there is little research on the whole constellation related offloading strategy, we adopted the ground commonly used two kinds of offloading strategy in edge computing as a benchmark.  
(1) Random strategy: When the task offloading request arrives, the orchestrator randomly selects service satellites for the task. If the service satellite has enough computing resources, the task will be accepted; otherwise, the task will be rejected.  
(2) First fit strategy: The orchestrator will select service satellites from all the satellites that can be offloaded for the task. The selection is generally randomized. Here we use a greedy strategy to select the satellite with the most resources available.  
In the whole constellation offloading scenario, we compared and evaluated the joint optimization strategy with the benchmark strategy with different optimizing options, including energy cost, load balancing cost and joint optimization cost.
# A Comparative Study of Signal Processing Methods for Contactless Geodetic Monitoring
## 4 Discussion  
The measurements we performed at the bridging objects enabled us to analyze the induced vibrations, which were performed taking into account the results of representative measurements with different measuring equipment. The presented monitoring of the dynamic response of bridging structures using geodetic and physical measuring instruments represents a unique way that provides a comprehensive insight into the behavior of such structures. The use of algorithms such as FFT and LS gives very good results of the frequency range of the oscillation of the structure. Our measurements have shown that even with geodetic instruments, if the appropriate sampling frequency is enabled, we can very accurately measure the dynamic response of the bridging structure. The comparison  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **RTS TS50** & **RTS TS30** & **MinimatePlus** \\ \hline Vertical displacement (mm) & 5.9329 & 5.7000 & 0.893 \\ Dominant frequency (Hz) & 2.5555 & 3.3116 & 2.781 \\ Vertical amplitude (mm) & 0.5200 & 0.4046 & 2.222 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Analysis—comparison of results.  
Figure 9: FFT analysis (Blastware Software 10.74- Advanced Module) for vibration measurements.  
of frequency spectra obtained with the described measuring equipment confirms the consistency of the source signals and the matching of the frequency spectra. The practical use value of such monitoring is also confirmed by the comparable accuracy of the results.  
Based on the comparative analysis of individual measurements with RTS TS50 measuring and geodetic equipment and on the basis of the described available literature, we found that the highest accuracy of measurements was achieved at frequencies lower than 20 Hz. The highest achieved oscillation frequency was 30 Hz, which according to [PERSON]'s sampling theorem, would mean that we would need measuring instruments with a minimum frequency of 60 Hz. However, currently available geodetic equipment does not allow such sampling. Said measuring equipment can be measured in the frequency range between 20 and 30 Hz.
# Copper- and Iron-Based Nanoflowers in Cancer Theranostics
## 5 Limitations and Future Perspectives  
The mechanisms underlying the interactions between biomolecules and metal ions are not yet fully understood. Various metal ions can affect the activity of proteins or enzymes involved in the formation of protein-inorganic HNFs; for instance, copper ions may inhibit \(\beta\)-Galactosidase (\(\beta\)-Gal) enzyme activity while enhancing horseradish peroxidase (HRP) enzyme activity. Gaining a deeper insight into these interactions could facilitate the development of protein-inorganic HNFs with optimal biological activity. Additionally, the properties of HNFs, such as morphology and size, significantly influence their performance. Studies suggest that the morphology of organic-inorganic nanohybrids can impact enzymatic activity, with NFs that have a higher surface-to-volume ratio experiencing fewer mass transfer limitations and thus increasing the likelihood of substrate interaction. The size of HNFs is also critical for their performance in various applications; producing nanoflowers with a narrow size distribution is essential for nanoflower-based immunosensors, as size variability can reduce analysis accuracy [35].  
Although NFs show a great variety of applications, currently, there is not a fulfilling number of studies or clinical trials investigating their impact towards living organisms or the environment and/or the ecological balance.  
Also, ever since the invention of nanomaterials, nanotoxicity has been a rising concern in the scientific community [126; 127]. According to research, NFs tend to be recognized as foreign substances in the body and concentrate in the liver and the spleen [111; 116]. Although NFs are non-hemolytic, partial deformations of red blood cell (RBC) morphology is possible [83].  
Another limitation of NFs that should be taken into consideration is their variable effect as a therapy modality. According to the literature, the anticancerous action of NFs is not universal to all types of cancer. The viability of cancer cells can vary; for example, breast cancer cells have shown a viability of around 20% [111; 112] and 50-70% [110] in different studies, while lung adenocarcinoma cells survived at 70-80% [83]. At the same time, aggressive types of cancer, such as brain glioblastoma, showed 75% viability, even after a high concentration of Fe NFs was applied [113]. On the contrary, NFs acting as a contrast agent in imaging modalities for cancer diagnosis show excellent potential. The future utilization of NFs as a contrast agent could be applied for the diagnosis of other diseases. More specifically, these NPs could especially be useful for the diagnosis of the \"silent\" diseases, such as atherosclerosis [128], as there is a need for urgent treatment. Further research is imperative for NFs if they are to be used as the main modality in cancer theranostics.