esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Defect States in Perovskite Materials
However, it is still not completely understood what defect states are the most predominant and play the most crucial role in non-radiative recombination. Based on current literature we can conclude that iodine vacancies are a predominant defect and could lead to iodine transport, but that trapping of holes and electrons is maybe more likely to occur at iodine interstitials. However, electron trapping with long lifetimes can also have positive influences on the \(\text{V}_{\text{OC}}\). Furthermore, the role of environmental factors, such as atmosphere and light, are beyond the scope here but are an important topic for further understanding of defect behavior. For example, light soaking has been reported to decrease trap densities, but is also recently reported to increase hole trap states, which can be deactivated using \begin{table}
\begin{tabular}{l c} \hline \hline \multicolumn{2}{c}{**Recombination Type**} & \multicolumn{1}{c}{**V\({}_{\text{OC}}\) (Theory)**} \\
**Effective Carrier Lifetimes** & \(\tau_{e}\)**, \(\tau_{h}\)** & 1.30 \\ \hline bimolecular (no traps) & 1.30 \\ \(\tau_{e}=100\) ns, \(\tau_{h}=10\) μs & 1.26 \\ \(\tau_{e}=\tau_{h}=100\) ns & 1.14 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Calculated open-circuit voltage (\(\text{V}_{\text{OC}}\)) for the case of pure bimolecular recombination, short-lived electrons and long-lived holes, as well as for short lived charge carriers. Reproduced with permission from _Energy Environ. Sci._**2016**, \(9\), 3472–3481. Reference [8]. oxygen [26; 27]. Defect chemistry, its nature, and its dynamics will continue to be important ongoing topic. There is a growing body of literature in which non-radiative recombination is reduced, entailing distinct routes toward defect-free perovskite materials. These include the following: chemical surface treatments, light soaking, low temperature, and compositional engineering [27]. Recently, the latter has gained more attention because of the following two reasons: (1) compositional engineering can aid high-quality film formation; (2) using compositional engineering not only the crystal quality can be enhanced but also optoelectronic properties can be tailored.
# A Practical Neighbor Discovery Framework for Wireless Sensor Networks
## 6 Methods of Reducing Collisions
### Discovery Probability under Collision  
Considering two neighboring nodes \(u_{i},u_{j}\), denote the sets of each node's neighbors as \(N_{i},N_{j}\) respectively (\(u_{i}\in N_{j},u_{j}\in N_{i}\)). Suppose that nodes \(u_{i},u_{j}\) turn on their radio at time \(t\), and denote the sets of neighbors that also turn on the radio as \(\tilde{N}_{i}\subseteq N_{i}\), \(\tilde{N}_{j}\subseteq N_{j}\). Since \(u_{j}\in\tilde{N}_{i},u_{i}\in\tilde{N}_{j}\), \(u_{i}\) and \(u_{j}\) can discover each other only when:  
\[|\tilde{N}_{i}|=1,|\tilde{N}_{j}|=1.\]  
Denote the average duty cycle for node \(u_{k}\) as \(\theta_{k}\). We consider the scenario where node \(u_{k}\) turns on the radio with probability \(\theta_{k}\) independently in each time slot (expected situation). Then, on the basis of the event that nodes \(u_{i},u_{j}\) turn on the radio at time \(t\), the probability for successful discovery is derived as:  
\[\begin{array}{ll}Pr(|\tilde{N}_{i}|=1,|\tilde{N}_{j}|=1)&\leq\min\{Pr(| \tilde{N}_{i}|=1),Pr(|\tilde{N}_{j}|=1)\}\\ &=\min\{\prod_{u_{k}\in N_{j},k\
eq j}(1-\theta_{k}),\prod_{u_{k}\in N_{j},k \
eq i}(1-\theta_{k})\}.\end{array}\]  
If \(\theta_{k}=1\%\) and \(\max\{|N_{i}|,|N_{j}|\}\geq 69\) (or \(\theta_{k}=10\%\) and \(\max\{|N_{i}|,|N_{j}|\}\geq 7\)), the probability of successful discovery is less than \(1/2\). Therefore, the existing algorithms cannot be applied to multiple nodes directly. We adopt the idea of the probabilistic protocols to reduce the communication collisions; two efficient methods are proposed.
###### Abstract  
Concentrated solar power (CSP) plants, in the context of thermal energy storage (TES) upgrades, need to provide a timely and effective response to the corrosion process that occurs due to the effect of high temperatures, where one of the main challenges is to control its effect, and thus the costs related to the materials used. Electrochemical impedance spectroscopy (EIS) and linear polarization resistance (LPR) were applied in this study as a corrosion monitoring technique. The electrochemical tests were carried out on the materials AISI304, AISI430, and HR-224 immersed in a mixture of ternary salt composed of 57 wt.% KNO\({}_{3}\) + 13 wt.% NaNO\({}_{3}\) + 30 wt.% LiNO\({}_{3}\) at 550 \({}^{\circ}\)C during 100 h of exposure and subsequently compared with solar salt. The test was also carried out on the VM12 alloy in the ternary salt with lithium content at 100 and 1000 h of exposure at 550 \({}^{\circ}\)C. The corrosion tests show that the materials conform to a model of protective layer in which the same results were contrasted with the chemical corrosion mechanism of nitrate mixture. According to the results obtained in this research, electrochemical techniques could be an interesting option to control corrosion in CSP plants and reduce operational risks during operation.  
Keywords:thermal energy storage; electrochemical impedance spectroscopy; lithium nitrate; concentrated solar power; corrosion mechanisms +
Footnote †: journal: Applied Soft. 2020, 10, 3160; doi:10.3390/app10093160
###### Abstract  
This study aimed to 3D print a patient-specific chest phantom simulating multiple lung nodules to optimise low-dose Computed Tomography (CT) protocols for lung cancer screening. The chest phantom, which was developed from a single patient's chest CT images, was fabricated using a variety of materials, including polylactic acid (PLA), Glow-PLA, acrylonitrile butadiene styrene (ABS), and polyurethane resin. The phantom was scanned under different low-dose (LDCT) and ultra-low-dose CT (ULDCT) protocols by varying the kilovoltage peak (kVp) and milliampere-seconds (mAs). Subjective image quality of each scan (656 images) was evaluated by three radiologists using a five-point Likert scale, while objective image quality was assessed using signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). Anatomical conformance was assessed by comparing tissue diameters of the phantom and patient scans using Bland-Altman analysis. The phantom's lung tissue, lung nodules, and diaphragm demonstrated radiation attenuation comparable to patient tissue, as measured in Hounsfield Units (HU). However, significant variations in HU were observed for the skin, subcutaneous fat, muscle, bone, heart, lung vessels, and blood vessels compared to patient tissues, with values ranging from 93.9 HU to \(-\)196 HU (\(p<0.05\)). Both SNR and CNR decreased as the effective dose was reduced, with a strong positive linear correlation (r = 0.927 and r = 0.931, respectively, \(p<0.001\), [PERSON], version 2.3.28). The median subjective image quality score from radiologists was 4, indicating good diagnostic confidence across all CT protocols (\(\kappa=-0.398\), 95% CI [\(-0.644\) to \(-0.152\)], \(p<0.002\), SPSS Statistics, version 30). An optimal protocol of 80 kVp and 30 mAs was identified for lung nodule detection, delivering a dose of only 0.23 mSv, which represents a 96% reduction compared to standard CT protocols. The measurement error between patient and phantom scans was \(-0.03\)\(\pm\) 0.14 cm. These findings highlight the potential for significant dose reductions in lung cancer screening programs. Further studies are recommended to improve the phantom by selecting more tissue-equivalent materials.  
3D-print; chest model; low-dose CT; ultra-low-dose CT; lung cancer screening; lung nodules; pulmonary nodules +
Footnote †: journal: Applied sciences
# Current Status and Future Directions for Combined Tillage and Sowing Technology and Equipment
## 5 Analysis of Equipment and Technology Characteristics  
### Characteristics Analysis of Combined Tillage and Sowing Equipment  
Combined tillage and sowing machines currently incorporate diverse functions to streamline agricultural processes. These machines offer stable performance and can complete multiple tasks, reducing the time needed for tractor hook-ups and shortening the production cycle. In addition, they also avoid the impact of human factors, such as equipment that is not in accordance with the planned path of travel, resulting in miss plowing and leakage sowing; equipment that enters into the ground several times; compaction damage to the sowing belt; and other issues, effectively improving production quality and promoting crop yield. Table 5 presents the characteristics of diverse types of combined tillage and sowing machines.  
In terms of the energy and consumption reduction of combined tillage and sowing machines, it is necessary to summarize and implement their mechanical structure, innovatively design new structures, and thus achieve a simplified and compact version of these machines. New high-performance materials can also be combined with advanced processing techniques and methods to further improve the reliability and durability of the machine, reduce its overall weight, achieve the lightweight design of the whole machine, and thus meet the requirements of low energy consumption. In terms of enhancing the coordinated cooperation among operating components, it is necessary to further explore the operating mechanism of key components; combine simulation analysis, production experience and theoretical laws; overcome the obstacles of existing technical problems; optimize the key structures and operating parameters of the device; and strengthen the integrity and coordination of the machine. By creating new mechanisms according to operating characteristics, changing the connecting devices between adjacent devices, and other means, we are able to ensure the stability of operations and improve operation quality and efficiency. In terms of flexible versatility in multiple scenarios, it is necessary to analyze the characteristics of combined tillage and sowing machines of different types and functions, summarize their respective usage scenarios and ranges, and realize the use of specific relevant machines in specific scenarios to achieve the best operation effect. The usage range of the same type of machinery should also be expanded so that each machine can meet more operation requirements, thereby reducing production costs and increasing economic benefits.  
Figure 16: Mesh for seed guide tubes. (**a**) Deviation of preliminary modeling and after adjustment. (**b**).
# A Compact Low-Power LoRa IoT Sensor Node with Extended Dynamic Range for Channel Measurements
## 2 Hardware Design
### Peripherals  
To diversify the potential applications of the wireless node, some peripherals are added to the microcontroller section of the design. First, a 32 Mbit flash memory IC is added to significantly enhance storage space on the node, thus allowing prolonged measurement campaigns to take place. Second, an inertial measurement unit (IMU) is integrated into the system. This sensor encompasses a 3D accelerometer, a 3D gyroscope and a 3D magnetometer, adding applications such as indoor navigation and motion monitoring to the list of possible use cases. Both the flash memory and the IMU can be accessed by means of the microcontroller's SPI interface. The third addition to the node is a real-time clock (RTC) with its own separate 32.768 kHz quartz crystal for keeping time, which is exploited to set timestamps and to wake up the system out of low-power mode using interrupts. Especially this last feature may considerably reduce the total energy consumption of the node, which is very beneficial when using a battery as power source. The microcontroller can communicate with the RTC using its PC interface and offers a dedicated connection for interrupts. Finally, through a flat flexible cable  
Figure 4: Flowchart of the attenuation adjustment algorithm.  
(FFC) and flat printed circuit cable (FPC) connector, a full 8-lead microcontroller I/O port, the SPI bus and a 3.3 V power line are made available to a potential peripheral sensor or sensor array that can be placed on a different printed circuit board (PCB).
# Fusion of Bilateral 2 DPCA Information for Image Reconstruction and Recognition
## 6 Discussion
### Future Directions  
From a certain perspective, 2 DPCA-based algorithms outperform PCA because they preserve the image spatial structure by treating the images as 2D matrices rather than reshaping them into 1D vectors. To further exploit the image spatial structure, we can introduce a spatial structure regularization [66] into the 2 DPCA-based algorithms. This is expected to improve their performances in image analysis.  
Most of the algorithms in Table 1 are built on traditional unilateral 2 DPCA, and can be improved by implementing the direct bilateral idea. Intuitively, this idea can make full use of bilateral information of images for unilateral 2D feature extraction methods, unilateral 2D manifold learning methods and their variants [67,68], thus improving their performances.
# Model Predictive Control and Its Role in Biomedical Therapeutic Automation: A Brief Review
## 3 MPC in Biomedical Applications
### Type-1 Diabetes
The algorithm's goal is to minimise a quadratic function of blood glucose deviation from the normal range and insulin dosage. It has been demonstrated that the performance of the NMPC controller is relatively robust against random minor disturbances, such as exercise, stress and fatigue, among other conditions [63].
## 4 Experimental Results
For providing rated output power (voltage/current), the voltage gain of the LC series resonant tank will decrease when the utility-line voltages increase, resulting in an increase in the switching frequency of the power switches. Thus, the switching losses of power switches and conduction losses of power diodes will increase, resulting in lowered circuit efficiency. Figure 18 \begin{table}
\begin{tabular}{c c c} \hline \hline
**Input Voltage (rms)** & **Voltage Ripple (\%)** & **Current Ripple (\%)** \\ \hline
210 V & 0.75 & 9.14 \\
215 V & 0.75 & 9.73 \\
220 V & 0.79 & 9.28 \\
225 V & 0.75 & 9 \\
230 V & 0.83 & 8.99 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Measured output voltage ripple and current ripple in the presented LED streetlight driver. Figure 15: Measured input current harmonics compared with the IEC 61000-3-2 Class C standards. presents a photograph of supply of the experimental LED streetlight module using the presented driver at a utility-line voltage of 220 V.  
Besides, Table 4 shows some measurements of the relationship between output voltage, efficiency and output load current under an input utility-line voltage of 220 V by altering the equivalent load resistor to represent the specific load current. In addition, the measured minimum efficiency is 86.51%, in a minimum load current of 0.3 A. Moreover, Table 5 shows comparisons between the existing single-stage LED driver for streetlight applications in [18] and the proposed one. From Table 5, it can be seen that the proposed LED streetlight driver has better current THD and efficiency than the existing one. Figure 16: Measured power factor and current total harmonic distortion (THD) under utility-line voltages ranging from 210 to 230 V.  
Figure 17: Measured circuit efficiency under utility-line voltages ranging from 210 to 230 V. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Output Load Current** & **0.7 A** & **0.6 A** & **0.5 A** & **0.4 A** & **0.3 A** \\ \hline Equivalent Load Resistor & 336 \(\Omega\) & 392 \(\Omega\) & 470 \(\Omega\) & 588 \(\Omega\) & 783 \(\Omega\) \\ Measured Output Voltage & 235.31 V & 234.91 V & 235.31 V & 234.86 V & 235.34 V \\ Measured Efficiency & 90.22\% & 89.39\% & 87.82\% & 86.59\% & 86.51\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Measured output voltage and efficiency versus output load current under an input utility-line voltage of 220 V.  
Figure 18: Photograph of supply of the experimental LED streetlight module using the presented driver at a utility-line voltage of 220 V, AC: Alternating-Current.
# Robust Detection of Background Acoustic Scene in the Presence of Foreground Speech
## 3 DNN-Based ASC
### Model Architecture  
In previous work, we proposed [22] a residual network (ResNet) architecture. The architecture of the system is shown in Figures 1 and 2. Except for the input layer, all the pre-pooling layers are Double Conv Blocks which incorporate two convolutional layers, each with a kernel size of \(3\times 3\). The Double Conv Block is detailed in Figure 2. Squeeze-and-Excitation blocks are integrated to leverage global information as well in the frame-level layers. The inclusion of a pooling layer enables the model to handle audio input of varying lengths by computing the temporal mean and standard deviation of input features, creating a fixed-length representation. A final linear layer then maps this fixed-length representation to 10 outputs, representing 10 acoustic scene classes. The activations of these output nodes undergo a softmax layer to calculate the probabilities of different acoustic scenes for each input recording. To implement the softmax layer, we adopt the test configuration of the DCASE2021 development set to train a multinomial logistic regression classifier [24; 25] based on the output scores. Note that the ResNet system [22] previously submitted to the DCASE 2021 challenge is either quantized or pruned to meet low-complexity requirements. The model size (non-zero parameters) is 114.0 kibibyte. We consider different model sizes: both small-footprint and large-footprint models by changing the channel sizes (C1 and C2 in Figure 1) in the model.
## 4 Analysis of Field Test Results  
Data collection for both model training and testing was carried out at Sejong University, Seoul, South Korea. Residual modeling and inflation factor computations were performed using the training dataset, and the created monitoring architecture was validated using the test data. The analysis for the model implementation was conducted using four parameters for each satellite: normalized satellite ENU direction components and the satellite C/No. As this model training is based on SBDD, a total of eight input variable parameters from a pair of satellites were employed for modeling the residuals. In order to simplify the visualization of the trained and modeled data, all the input parameters from the two satellites were set as identical.
# OBU for Accurate Navigation through Sensor Fusion in the Framework of the EMERGE Project
## 3 Sensor Fusion: Loosely Coupled Algorithm Implementation
In this way, the integrated navigation solution of the navigation system is the inertial navigation solution itself, and is obtained, respectively, for the orientation, \(\hat{\mathbf{C}}_{b}^{n}\), the velocity, \(\hat{\mathbf{v}}_{eb}^{n}\), and the position, as follows [17]:  
\[\hat{\mathbf{C}}_{b}^{n+} =\big{(}\mathbf{I}-[\delta\hat{\mathbf{v}}_{nb}^{n}]_{\mathrm{x} }\big{)}\hat{\mathbf{C}}_{b}^{n-} \tag{1}\] \[\hat{\mathbf{v}}_{eb}^{n+} =\hat{\mathbf{v}}_{eb}^{n-}-\delta\hat{\mathbf{v}}_{eb}^{n}\] (2) \[\hat{L}_{b}^{+} =\hat{L}_{b}^{-}-\delta\hat{L}_{b}\] (3) \[\hat{\lambda}_{b}^{+} =\hat{\lambda}_{b}^{-}-\delta\hat{\lambda}_{b}\] (4) \[\hat{h}_{b}^{+} =\hat{h}_{b}^{-}-\delta\hat{h}_{b} \tag{5}\]  
where superscripts \(-\) and \(+\) are used to indicate the solution before and after correction, \(\delta\hat{\mathbf{v}}_{nb}^{n}\) is the vector of Euler angles of the correction (i.e., yaw, pitch, and roll), and \([\delta\hat{\mathbf{v}}_{nb}^{n}]_{\mathrm{x}}\) represents the antisymmetric matrix  
\[[\delta\hat{\mathbf{v}}_{nb}^{n}]_{\mathrm{x}}= \begin{bmatrix}0&-\delta\hat{ \mathbf{v}}_{nb,z}^{n}&\delta\hat{\mathbf{v}}_{nb,y}^{n}\\ \delta\hat{\mathbf{v}}_{nb,z}^{n}&0&-\delta\hat{\mathbf{v}}_{nb,x}^{n}\\ -\delta\hat{\mathbf{v}}_{nb,y}^{n}&\delta\hat{\mathbf{v}}_{nb,x}^{n}&0\end{bmatrix} .\]  
The pseudocode outlined in Algorithm 1 presents the procedural steps of the implemented INS-GNSS loosely coupled algorithm. Initially, it sets crucial parameters to account for IMU errors. The algorithm then begins with an initialisation loop which is used to estimate the initial pose of the vehicle and to configure Kalman filter parameters. After initialisation, it uses IMU data to update the vehicle's pose, incorporating corrections from the Kalman filter when new GNSS measurements are available and providing the integrated navigation solution as output. A Zero Velocity Update (ZUPT) is also employed to mitigate the position drift introduced when the vehicle is stationary. All these steps are further described in the following.
## 4 Discussion
### Future Directions  
Regarding future developments, it is worth mentioning that in CNAO simulation, CT and treatment rooms are equipped with an optical tracking system (OTS), featuring three infrared cameras able to detect the position of reflective markers placed on the patient surface [21]. Our group has demonstrated that, after OTS-based positioning, corrections applied by the X-ray-based system were reduced to sub-millimetric and sub-gradual [19]. This supports the hypothesis that a pre-correction strategy, regardless of the specific method used, could be useful in reducing the magnitude of imaging-based CVs and thus the frequency of re-imaging and treatment time. In fact, according to our institutional protocols, if corrections exceed a certain linear and angular threshold, then the patient needs to be repositioned, and the entire setup verification workflow must be repeated.  
The availability of an optical system also paves the way to synergistic approaches: OTS-based pre-positioning could be successfully coupled with the described methodology using a decision-based rule to make pre-correction even more robust and reliable.  
As stated before, with only 32 treatment courses, more than 850 fractions and 5000 coordinates have been collected. By enlarging the cohort of patients considered, data can increase exponentially. The potential availability of such a large amount of data (i.e., big data), as well as the underlying concept herein adopted to find hidden patterns in data and learn from experience, could pave the way to the implementation of knowledge-based--e.g., artificial intelligence (AI)-based--solutions in substitution for the analytical algorithms herein used. Accordingly, future developments could also foresee the employment of AI to gain valuable insights into patients' data and identify trends and proper strategies to improve setup. Finally, the study is retrospective and only simulates the impact of the described procedure on dampening systematic errors. Future developments could include the integration of such an approach prospectively in the clinical pipeline to assess the true advantage in terms of clinical throughput optimization and the consequent improvement of patients' experience.