esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results  
### Results Using Loopback Waveguide Test  
According to the initial experimental results using the loopback waveguide, phase instability was observed specifically during the first 192 chirps and the first 5 bursts at the start of the measurement. Figure 9 shows the chirp and burst framework at the experiential measurement and stable phase data to be picked up. This instability is attributed to the MMIC instability caused by the self-heating of the circuit. Figure 10 shows the standard deviation (STDEV) of the raw phase data from a single measurement, which consists of 62 bursts and 2048 chirps. The data indicates that instability occurs during the first 5 bursts out of the total 62 bursts and the first 192 chirps out of the total 2048 chirps, while the subsequent data remain highly stable. Figure 11 illustrates the data after the removal of the unstable segments, resulting in 1856 chirps and 57 bursts, which demonstrate very stable phase data.  
Next, Figure 12 presents a histogram of the measurement data, focusing exclusively on the stable phase data described earlier. Table 7 summarizes the statistical analysis of sensor accuracy and precision. The maximum phase offset of +0.359 degrees, corresponding to a displacement of +1.907 \(\upmu\)m, represents the accuracy error of the sensor. Similarly, the maximum \(\pm\)1.180 degrees, corresponding to \(\pm\)1.180 \(\upmu\)m displacement, reflects the sensor's precision. All discussions are based on a 3-sigma scenario for statistical reliability because this work aims to prove the capability for commercial use cases where a 3-sigma scenario has always been adopted.  
\begin{table}
\begin{tabular}{c c} \hline
**Target Displacement by Shaker [\(\upmu\)m]** & **Captured Total Raw Phase Data Count** \\ \hline
2.91 & 378,880 \\ \hline
1.94 & 368,640 \\ \hline
1.55 & 376,832 \\ \hline
1.16 & 372,736 \\ \hline
0.78 & 374,784 \\ \hline
0.39 & 372,736 \\ \hline
0.19 & 368,640 \\ \hline
0.17 & 376,832 \\ \hline
0.16 & 378,880 \\ \hline
0.14 & 376,832 \\ \hline
0.12 & 389,120 \\ \hline
0.10 & 380,928 \\ \hline
0.08 & 378,880 \\ \hline
0.06 & 372,736 \\ \hline
0.04 & 391,168 \\ \hline
0.02 & 374,784 \\ \hline \end{tabular}
\end{table}
Table 6: Captured phase data samples by 1600 Hz/Long PRT.  
Figure 11: Standard deviation of phase in the stability zone.  
Figure 10: Standard deviation of phase in one measurement.  
Figure 9: Chirp and burst framework in experiment.  
According to Figure 12, the means are shifted to the positive side, even though only the stable phase data are plotted. Since the waveguide is entirely rigid, this offset can be attributed to the inherent characteristics of the MMIC itself. Through this experiment, the fundamental accuracy and precision of the radar MMIC sensor were successfully validated using the rigid loopback waveguide method.
# Analysis and Experimental Research of a Multilayer Linear Piezoelectric Actuator
## 4 Results and Discussion  
### Harmonic Response Characteristics  
The research of harmonic response is used to determine the steady-state response under a sine load whose frequency changes in a given range of linear structure. The harmonic response characteristic of the piezo-vibrator was gained using a scanning laser Doppler vibrometer (PSV-400-M2, Polytec Inc., Waldbronn, Germany). The result is shown in Figure 7. Part of the parameters of the vibrometer and the experimental setting were as follows:  
(1) The scanning laser Doppler vibrometer can generate a laser spot size of \(\Phi\) 1 mm at the surface of the measured piezo-vibrator.  
(2) The resolution of the measured velocity amplitude through this equipment is 1 mm/s.  
(3) The result was obtained in the condition where the piezo-vibrator was mechanically free, a condition which is the same with the boundary condition of FEA modal analysis.  
The experimental result indicates that the resonant frequencies of the first longitudinal and second bending vibration modes are 56.671 and 53.1875 kHz, respectively. The frequencies are not the same as the FEA results, mainly because: firstly, the analysis ignored the influence of the inner electrodes; secondly, the actual material parameters are not totally identical to what is used in FEA due to the limitations of the measuring apparatus.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Zone** & **1** & **2** & **3** & **4** \\ \hline d\({}_{33}\) (pC/N) & 2336 & 2338 & 2252 & 2236 \\ C\({}_{0}\) (nF) & 28.5 & 28.7 & 27.9 & 28.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The measured d\({}_{33}\) and the C\({}_{0}\) of the multilayer piezoelectric ceramic.  
Figure 6: SEM photo of the cross-section of the multilayer piezoelectric ceramic. (**a**) Cross-section 800\(\times\); (**b**) Cross-section 6000\(\times\).  
The mechanical quality factor \(Q_{\rm m}\) can also be calculated from the result shown in Figure 7, with Equations (10) [22]:  
\[Q_{\rm m}=f_{\rm r}/\Delta f \tag{10}\]  
According to Equations (10), \(Q_{\rm mL1}=f_{\rm rL1}/\Delta f_{2}=20\), where \(f_{\rm rL1}=56.671\) kHz, \(\Delta f_{2}=2.89\) kHz; \(Q_{\rm mB2}=f_{\rm rB2}/\Delta f_{1}=50\), where \(f_{\rm rB2}=53.1875\) kHz, \(\Delta f_{1}=1.062\) kHz. It is known that the vibration magnitude at resonance is proportional to \(Q_{\rm m}\)[23]. The mechanical quality factor \(Q_{\rm m}\) of the two vibration modes is not equal, and \(Q_{\rm mB2}\) is bigger. It means that the second bending mode vibration will be more drastic, and the piezoelectric ceramic of the piezo-vibrator tends to fracture at the crest of the second bending mode shape. Figure 8 shows a fracture form of the piezo-vibrator at 53 kHz, 140 V\({}_{\rm pp}\). Adopting the piezoelectric ceramic with high \(Q_{\rm m}\) could enhance the vibration amplitude and reduce the heat production, but would make the piezoelectric ceramic easier to fracture.
# Fully Printed Flexible Single-Chip RFID Tag with Light Detection Capabilities
## 4 Conclusions  
A smart RFID tag fully printed on flexible substrate has been designed, fabricated and tested. The device is comprised of, in an area of 25 cm\({}^{2}\), a dipole antenna, an SMD inductor, an RFID chip integrating one temperature sensor and a printed photodiode. The RFID tag is able to provide two different environmental parameters: light intensity and temperature. Moreover, sensor information is processed and digitalized on the RFID chip ensuring reliability. Sensor information can be acquired by a commercial EPC Gen 2 RFID reader or be stored on RFID chip EEPROM memory for future access. Therefore, this device is quite interesting for application as supply chain control or environmental light monitoring. The key point of this work is to successfully combine the sprayed photodetector on a flexible substrate, using as the bottom electrode an inkjetted silver line. To succeed in the fabrication process, it was necessary to spray a PEI thin layer on top of the inkjetted silver to obtain a working photodiode.  
Regarding sensor performance, the range of detected current can be chosen in the user application. In the present work, we have tested two different ranges of illuminance intensity: 0-100 mW/cm\({}^{2}\) and 0.02-0.06 mW/cm\({}^{2}\). In both modes, the results are satisfactory. The device is able to detect currents from hundreds of nA to several \(\mu\)A. In the worst case, the error has been of 10 Least Significant Bits (LSB) that is equivalent to a standard deviation of \(\pm\)20 nA.  
The presented architecture can be applied to other printed sensors and is able to integrate more printed sensors on the same tag. Moreover, single/multiple RFID tags could be attached to different objects or placed in environments where sensor information is necessary. These characteristics, combined with the low production cost of the device and the high possible production throughput, represent a major step towards the realization of low power wireless sensor networks and the growth of the Internet of Things.  
Figure 8: Optical power density vs. photocurrent.  
**Acknowledgments:** This work was partially supported by the European Union through FP7-PEOPLE-2012-ITN 316832-OLIMPIA and FP7-PEOPLE-2013 607896-OrgBIO. In addition, this work was supported by the German Research Foundation (DFG) and the Technical University of Munich (TUM) in the framework of the Open Access Publishing Program. We thank the TUM Institute for Cognitive Systems for the use of the inkjet printer.  
**Author Contributions:** [PERSON], [PERSON] and [PERSON] conceived and designed the experiments; [PERSON], [PERSON] and [PERSON] performed the experiments; [PERSON], [PERSON] and [PERSON] analyzed the data; [PERSON], [PERSON] and [PERSON] wrote the paper. All the authors contributed to review and proofread the manuscript before submission.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Problem Space for DL Applications
### IoMT Architecture Layers
#### 4.1.1 Smart Device Layer  
The smart device layer forms the foundation of the IoMT by capturing real-time medical data from diverse sources, enabling continuous monitoring, early disease detection, and timely interventions. It includes a wide range of sensing devices, such as wearable sensors, implantable medical devices, smart hospital equipment, remote monitoring systems, and environmental sensors, all designed to collect vital physiological parameters and behavioral data. Wearable devices like smartwatches, ECG monitors, and glucose sensors continuously track health related status like heart rate, blood pressure, oxygen saturation, and glucose levels, offering insights into a patient's health trends. Implantable medical devices, such as pacemakers and neurostimulators, monitor and regulate critical bodily functions,  
Figure 2: Problem space for DL-empowered IoMT.  
enhancing the quality of life for patients with cardiovascular and neurological disorders. Remote monitoring systems, including blood pressure monitors, spirometers, and respiratory sensors, enable patients with chronic conditions to be monitored outside hospital settings, reducing the need for frequent hospital visits and minimizing healthcare costs.  
Also, smart hospital equipment, including automated infusion pumps and AI-powered drug dispensers, ensures precise medication administration while enhancing patient safety and operational efficiency. Environmental sensors that track temperature, humidity, and air quality play a crucial role in preventing hospital-acquired infections (HAIs) and ensuring a safe clinical environment, especially for ICU and immunocompromised patients. Interaction within the IoMT ecosystem: Once the smart device layer collects data, secure communication protocols (e.g., Bluetooth Low Energy (BLE), Zigbee, Wi-Fi, 5G, LoRaWAN) facilitate real-time transmission to network and computing layers for further analysis.  
To minimize latency and bandwidth overload, edge AI models deployed on local devices can perform preliminary anomaly detection, noise reduction, and redundancy filtering before sending critical insights to central servers. For example, an AI-enabled ECG monitor can detect abnormal heart rhythms, such as atrial fibrillation, and trigger an alert for immediate intervention. Additionally, federated learning can be used to train AI models across multiple IoMT devices without transferring raw patient data, enhancing privacy and security while improving diagnostic accuracy. By integrating IoMT sensing devices with AI-driven edge computing, the smart device layer ensures high-quality medical data acquisition, paving the way for personalized, real time, and proactive healthcare solutions. Table 4 lists some studies focused on sensors attached to different devices with DL to satisfy specific requirements.
## 3 Methods
### Dual-Channel Deep Counterfactual Prediction Model for OD Matrix Prediction
#### 3.3.1 Shared Contrastive Learning-Enhanced URT Station Semantic Extraction  
In the proposed framework, contrastive learning is employed to learn robust URT station embeddings that can capture spatiotemporal interactions between stations. Given that this module serves as a shared layer, its embeddings are used as inputs for both the factual and counterfactual task-specific towers, making it essential for generating consistent, high-quality features for downstream tasks. The semantic attribute vector \(\mathbf{S}_{i}\) for each station \(i\) is defined by the POI features and location semantics \(\mathbf{S}=[\mathbf{S}_{1},\mathbf{S}_{2},\ldots,\mathbf{S}_{l_{p}}]\), where \(l_{p}\) denotes the largest semantics for URT station. The contrastive learning method is then used to project these semantic attributes into a higher-dimensional embedding space \(\boldsymbol{\mathcal{S}}_{\text{embed}}\), where each station is represented by its embedding vector \(\mathbf{s}_{i}\). The model then identifies positive and negative station pairs based on the daily outflows sequence, denoted as \(\mathbf{O}_{t}=[\mathbf{O}_{1},\mathbf{O}_{2},\ldots,\mathbf{O}_{l_{p}}]\), where \(\mathbf{O}_{i}\) represents the outflow at station \(i\) during time interval \(t\). The cosine similarity between the outflow sequences of two stations \(i\) and \(j\) is defined as Equation (6):  
\[\text{Sim}\big{(}O_{i},O_{j}\big{)}=\frac{O_{i}\cdot O_{j}}{\|O_{i}\|\big{\|}O_ {j}\|} \tag{6}\]  
Based on the pre-defined similarity thresholds \(\theta^{+}\) and \(\theta^{-}\), station pairs are classified into positive or negative pairs. The contrastive loss is defined as Equation (7):  
\[\mathcal{L}_{\text{contractive}}=-\sum_{i\in\mathbf{S}}\ln\frac{e^{\mathbf{ s}_{i}\cdot\mathbf{s}_{\text{pos}}}}{e^{\mathbf{s}_{i}\cdot\mathbf{s}_{\text{pos}}}+ \sum_{j\in\mathbf{S}_{\text{neg}}}e^{\mathbf{s}_{i}\cdot\mathbf{s}_{j}}} \tag{7}\]  
where \(\mathbf{s}_{\text{pos}}\) is the embedding of the positive station, \(\mathbf{s}_{j}\) are the embeddings of the negative stations, and \(\mathbf{S}_{\text{neg}}\) is the negative set for each anchor-positive pair. After training, the high-dimensional semantic embeddings \(\boldsymbol{E}\) for each station \(\mathbf{s}_{i}\) are obtained, which serve as the station-level input features for downstream station-level and OD-level passenger flow prediction tasks.  
The semantic embedding vector used in the shared contrastive learning-enhanced URT station semantic extraction process primarily includes static features, such as land use characteristics (e.g., the proportion of different POI types within a 500 m radius around each station) and the presence of transport hubs, central business districts, universities, and public transport stations. Dynamic features such as incident type (dummy variable), peak hours, workday status, and adverse weather conditions, are also considered but are not station-specific and are not part of the shared contrastive learning process.
## 4 PD Recognition System
### Feature Extraction
#### 4.3.1 Fractal Dimension  
The definition of fractal dimension by self-similarity is straightforward, where self-similar means they are the same from near as from far, it is often difficult to be either estimated or computed for a given image data. However, a relevant measure of fractal dimension, the box dimension, can be more efficiently computed instead. In this work, suggested by [PERSON], the method has been followed for the computation of fractal dimension D from the image data. Define \(p(m,L)\) as the probability that there are m points within a box of size L, i.e., a cube of side L, centered about a point on the image surface. \(p(m,L)\) is normalized for all \(L\), expressed as [21]  
\[\sum_{m=1}^{N_{b}}p(m,L)=1 \tag{18}\]  
where \(N_{b}\) represents the number of possible points within the box. Letting \(S\) be the number of image points (i.e., pixels in an image), there are (_S/m_) \(p(m,L)\) number of boxes with \(m\) points inside the box in case the image is overlaid with boxes of side \(L\). Therefore, the expected number of boxes in total required to cover the whole image [22] is given as  
\[N(L)=\sum_{m=1}^{N_{b}}\ \frac{S}{m}\ p(m,L)=S\sum_{m=1}^{N_{b}}\ \frac{1}{m}\ p(m,L) \tag{19}\]  
This value is found to be proportional to \(L^{-D}\), according to which the box dimension can be estimated by calculating \(p(m,L)\) and \(N(L)\) for various values of \(L\), and subsequently by performing a least square fitting on \(\{\log(L),\text{-}\log(N(L))\}\). In an attempt to evaluate \(p(m,L)\), the cube of size \(L\) must  
Figure 5: Block diagram of partial discharge (PD) measurement experiment.  
be centered around an image point, and the neighboring \(m\) points, that fall within the cube, must be counted. The occurrences of each number of neighboring points over the image are accumulated in the determination of the occurrence frequency of \(m\), which is normalized to obtain \(p(m,L)\).
# Comparison of Microbial Gene Diversity in Grassland Topsoil Depending on Soil Quality
## 3 Results
### Genomic Characteristics of Prokaryotes and Eukaryotes According to Soil Quality
#### 3.2.2 Characteristics of Eukaryotes
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Soil Quality** & **Rank** & **Division** & **Identification (Genus or Species-Level)** & **High** & **Low** & **High-Low** \\ \hline \multirow{8}{*}{High} & 1 & Eu & _Sorsophareula veronica_ & 5.30 & 0.40 & 4.90 \\  & 2 & Eu & _Pythium violae_ & 1.90 & 0.12 & 1.78 \\  & 3 & Pro & Nitrospira & 1.85 & 0.40 & 1.45 \\  & 4 & Eu & _Tribonchida_ & 1.50 & 0.23 & 1.27 \\  & 5 & Eu & _Heterocyclmaleria_ & 1.25 & 0.31 & 0.94 \\  & 6 & Pro & _Polycyclocorans_ & 1.00 & 0.16 & 0.84 \\  & 7 & Pro & _Geobacter_ & 1.20 & 0.37 & 0.83 \\  & 8 & Eu & _Klebsormidium flaccidum_ & 0.65 & 0.02 & 0.63 \\  & 9 & Eu & _Trinema_ & 0.85 & 0.23 & 0.62 \\  & 10 & Pro & MND1 & 1.70 & 1.09 & 0.61 \\  & 11 & Pro & RB41 & 3.00 & 2.42 & 0.58 \\  & 12 & Eu & _Rhizopus_ & 0.55 & - & 0.55 \\  & 13 & Eu & _Sorsphareula veronica_ & 0.55 & 0.04 & 0.51 \\  & 14 & Eu & _Cercoza_ & 0.50 & 0.08 & 0.42 \\  & 15 & Eu & _Acanthamoeba_ & 0.55 & 0.15 & 0.40 \\  & 16 & Pro & _Candidatus_ & 0.50 & 0.14 & 0.36 \\  & 17 & Pro & _Geottrix_ & 0.40 & 0.05 & 0.35 \\  & 18 & Pro & _Bacillus_ & 0.50 & 0.16 & 0.34 \\  & 19 & Pro & _Candidatus_ & 0.75 & 0.44 & 0.31 \\  & 20 & Eu & _Chaetonotida_ & 0.40 & 0.10 & 0.30 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: List of organisms at the genus or species level, predominant depending on the quality of the grassland topsoil. 2.3 Characteristics of Prokaryotes and Eukaryotes at the Genus and Species Levels according to Soil Quality  
At the genus and species level, the predominant microorganisms in the high-quality soil were _Penicillium_ (4.90% difference), _Pythium violae_ (1.78% difference), _Nitrospira_ ( 1.45% difference), _Triplonchida_ (1.27% difference), _Heterocphaleria_, _Polycyclolovorans_, _Geobacter_, _Klebsormidium flacidum_, _Trirema_, MND1, RB41, _Rhizopus delemar_, and _Sorosphaerula veronicae_ (difference between 0.5% and 1.00%), and _Cercoza_, _Acanthamoeba_, _Candidatus_ Nitrososphaera_, _Goethrix_, _Bacillus_, _Candidatus_ Nitrosotalea_, and _Chaetonida_ (difference between 0.1% and 0.5%). On the contrary, the predominant microorganisms in the low-quality soil were _Artemisia_ (4.43% difference), _Acari_ (2.21% difference), _Burkholderia-Caballeronia-Parburkholderia_ (1.65% difference), _Candidatus_ Udaeobacter (1.65% difference), _Pseudomonas brassicacarum_ subsp. _brassicacarum_ (1.22% difference), _Pseudarthrobacter_, _Vigna_, _Steeroidobacter_, _Crenobacter_, _Pyrerochenta_, _Bradyrizobium_, _Plantatinospora_, _Polaromonas_, and _Rhizobacter_ (difference between 0.1% and 0.5%), and _Rhodanobacter_, _Spunella_, _Bryobacter_, _Triplonchida_, _Rhynchoboda_, and _Archacorhizomyces_ (difference between 0.1% and 0.5%).
# Advances in Tissue Engineering for Disc Repair
## 3 Tissue Engineering for IVD Regeneration
### Biomaterials
Although a variety of biomaterials have been studied to investigate the effects of biomaterials on cell delivery and stem cell differentiation, few materials are currently available for clinical application due to the limitations of mechanical properties, immunogenicity, and uncontrollable deviations in inducing stem cells differentiation. In addition to the mechanical properties and biocompatibility of biomaterials, maintaining stem cells activity in a local niche and enhancing the ability of stem cells to differentiate into NP and AF cells facilitates the application of biomaterials in clinical practice [74; 75; 76; 77; 78; 79; 80; 81; 82; 83]. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Author** & **Materials** & **Effect** \\ \hline
2020, [PERSON] et al. [74] & Decellularized Wharton’s jelly matrix from human umbilical cord as ECM-based scaffold & Promoted cell differentiation toward a disogenic phenotype, positively affected the expression of regulators of IVD homeostasis \\ \hline
2019, [PERSON] et al. [75] & AD-MSC-Tissue engineered construct Rat & Regenerative efficacy was investigated structurally and biomechanically up to 6 months after implantation \\ \hline
2018, [PERSON] et al. [76] & Type II collagen/chondroitin sulfate (CS) composite hydrogel-like adipose-derived stem cell delivery system & Minimally invasive approach to promote the regeneration of degenerated NP \\ \hline
2018, [PERSON] et al. [77] & Injectable decellularized NP-based cell delivery system (NPCS) & The mechanical properties of the NPCS system were similar to those of fresh NP; Biocompatible; It induced NP-like differentiation and ECM synthesis \\ \hline
2015, [PERSON] et al. [78] & Collagen-glycosaminoglycan (GAG) co-precipitate and multiple lamellae of a photo-crosslinked collagen membrane & A biphasic scaffold comprising 10 AF-like lamellae had the best mechanical performance and elastic compliance \\ \hline
2015, [PERSON] et al. [79] & Collagen-GAG coprecipitate MSC and contracted collagen gel, MSC & Spinal motion segment tissue engineering. Provided a 3D model for studying tissue maturation and functional remodeling \\ \hline
2014, [PERSON] et al. [80] & Electrospun poly scaffold with cell-seeded hydrogels and disc-like angle-ply structure & Optimized the design of functional disc replacement in vivo \\ \hline
2014, [PERSON] et al. [81] & Biomimetic GAG analogue based on sulphonate-containing polymer & Provided intrinsic swelling pressure which could maintain disc hydration and height \\ \hline
2014, [PERSON] et al. [82] & Hyaluronic acid-poly(ethylene glycol) composite hydrogel & Highest number of NP and AF cells on HA-PEG hydrogels from lower molecular weight HA \\ \hline \hline \end{tabular} AD-MSC, adipose tissue-derived mesenchymal stem cell; GAG, glycosaminoglycan; HA, hyaluronic acid.  
\end{table} 
Table 2: Biomaterials for IVD regeneration.
## 2 Materials and Methods
### IT-SPME Conditions  
For IT-SPME coupled to Cap-LC, segments of different commercially available GC columns of 30 cm length and 320 \(\upmu\)m i. d. were used as extractive capillaries for IT-SPME, namely TRB 35, ZB-FFAP and PS-DVB, and the results were compared with those obtained with the synthesized TEOS-MTEOS reinforced with SiO\({}_{2}\) or TiO\({}_{2}\) NPs capillaries. The TRB 35 capillaries, coated with a 35% diphenyl-65% PDMS, 3 \(\upmu\)m coating thickness, were purchased from Teknokroma (Barcelona, Spain). The ZB-FFAP(nitroterephthalatic acid modified PEG), 1 \(\upmu\)m film thickness, was supplied from Phenomenex (Torrance, CA, USA). The PS-DBV, 20 \(\upmu\)m coating thickness, capillary was obtained from Agilent Technologies.  
The extractive capillaries were used as the loop of the six-port injection valves. Samples were manually loaded into capillaries using a 500 \(\upmu\)L precision syringe; then, the valve was changed to the injection position, so the analytes retained in the capillary were desorbed with the mobile-phase and transferred to the analytical column for separation and detection. For connecting the extractive capillaries to the valve 2.5 cm sleeve of 1/6 i. n. (340-380 \(\upmu\)m i. d.) polyether ether ketone (PEEK) tubing, 1/6 i. n. PEEK nuts and ferrules were used. All assays were made by triplicate at ambient temperature.