esa-sceva/satcom-chunk-collection huggingface dataset:
# Error Model of a Measurement Chain Containing the Discrete Wavelet Transform Algorithm
## 4 Application of the Proposed Analysis Method
### Random Errors Sources
Assuming that there were no non-random sources of error during the experiment (constant ambient conditions, constant input voltage for the analyzed series of measurements), the random error signal \(e_{x,r}(i)\) related to quantity \(x(i)\) can be described as:  
\[e_{x,r}(i)=\bar{x}(i)-\bar{s}(iT_{p}), \tag{59}\]  
while the random error signal \(e_{c,r}(i)\) of quantity \(c(i)\) can be described in the form:  
\[e_{c,r}(i)=c(i)-f_{c}\big{(}f_{y}\big{(}\bar{s}(iT_{p})\big{)}\big{)}. \tag{60}\]  
Hence, based on the subsequent values of the error signal \(e_{c,r}(i)\) and Equations (57) and (58), the parameters of the error signal \(e_{x,r}(i)\) can be estimated. Figure 4a shows a graph of the characteristics given by Equation (57) obtained for the measurements, while Figure 4b shows a histogram of the error signal realization values \(e_{x,r}(i)\) calculated in accordance with Equation (59). Based on the indicated histogram, it is possible to estimate the variance in this signal equal to \(\sigma_{x,r}^{2}=0.13\)\(\,\mathrm{\SIUnitSymbolMicro V}\) and the associated standard uncertainty \(\sigma_{x,r}=0.36\)\(\,\mathrm{mV}\). Based on the shape of the histogram, it can be concluded that the distribution of the error signal in question is a normal distribution with an expected value of zero.
## 3 Results and Discussion
### Fracture Modes and Morphologies  
Figure 8 shows the cross section of the fracture of lock bottom welds. It was found that there were three fracture modes. Firstly, when 0 Hz \(\leq\)\(f\) < 50 Hz, as shown in Figure 8a, there was a large pore with a diameter of 0.97 mm in the weld, and the weld was completely broken in the EQZ. Secondly, when 50 Hz \(\leq\)\(f\)\(\leq\) 200 Hz, as shown in Figure 8b,c, there were small pores with a diameter of 0.39 mm in the weld. The inclination angle of the fracture line gradually decreased from nearly 90\({}^{\circ}\), and the fracture position transferred from EQZ to CGZ. Finally, when \(f\) > 200 Hz, as shown in Figure 8d, the pores were fully eliminated in the lock bottom weld. After the crack generated, it rapidly propagated to the EQZ, and then it passed directly through the CGZ to the HAZ under the maximum shear stress in the 45\({}^{\circ}\) direction, showing a typical pure shear fracture characteristic.  
As shown in Figure 9, all samples were characterized by ductile fracture with many dimples. In detail, as shown in Figure 9a,b, a large number of process pores appeared on the fracture surfaces of the weld without beam oscillation. However, as shown in Figure 9c-h, except for small pores at the fracture of weld fabricated with 50 Hz, other fractures had no obvious pores and their morphologies were flatter. According to fracture theories, the toughness was proportional to the dimple size. Similar results were obtained in this study, that is, the average dimple size of fracture at 350 Hz was 6.7 \(\upmu\)m, which was larger than that of fracture without oscillation, showing a higher elongation.
# Recent Research Progress in Surface Ligand Exchange of PbS Quantum Dots for Solar Cell Application
## 3 Solution-Phase Ligand Exchange
### Acid-Assisted Ligand Exchange for Infrared Quantum Dots  
Large-size (small bandgap) PbS CQD can provide a chance to harvest an infrared (IR) region solar spectrum. The light absorption onset of well-known photoactive materials including metal halide  
Figure 7: Photovoltaic performance depending on ratio of used ligand species (ammonium acetate (AA) and tetrabutylammonium acetate (TBAA)) in solution-phase ligand exchange. (**a**) Trends of V\({}_{\text{OC}}\) and E\({}_{\text{loss}}\) observed in PbS CQD photovoltaics according to ligand ratio. (**b**) J–V curves of PbS CQD PVs with various ligand blend ratios. Reprinted from reference [50]. Copyright 2017 Advanced Materials.  
perovskite, organic dye, crystalline silicon, is limited under 1100 nm. Therefore, the large-size PbS CQD solids can be a promising platform to realize multijunction or IR optoelectronics. Recent advance in solution ligand exchange suggest that the CQD polydispersity can be minimized via engineering of ligand reactivity [34; 46]. However, these approaches are undesired for a large-size IR CQD film where the charge carrier transport become more important. Undesirable organic residues on the CQD surface can lead to a loosely-packed CQD film morphology resulting in poor charge transport and device performance [61; 62; 63].  
To address this issue, an acid-assisted solution-phase ligand exchange was suggested to promote the release of the long-chain original ligands, leading to the attainment of compact PbS CQD thin films and enhanced mobility. [PERSON] et al. found that the employment of hydroiodic acid (HI) can facilitate dense PbS CQD film formation aid by proton and iodide donation resulting in full removal of organic residues and better CQD passivation [63]. It is demonstrated that HI-treated dense IR PbS CQD films show higher carrier mobility than that of convention ligand-exchanged CQD film (Figure 8a,b). In the IR CQD PV, the device shows a Si-filtered _PCE_ of \(\sim\)0.9% and a high external quantum efficiency (EQE) of 50% at 1550 nm, whereas the conventional device (without HI treatment) exhibits less than a Si-filtered _PCE_ of \(\sim\)0.6% with a lower EQE of 30% [63]. This study suggests a new way to produce high-quality IR PbS CQD films leading to efficient charge transport, which is one of the essential factors for realizing IR optoelectronic devices.
## 4 Poster Presentations
### P-09. Classical PKU with Unusual Neonatal Presentation  
[PERSON] 1,2,*, [PERSON] 3, [PERSON] 3, [PERSON] 3, [PERSON] 1,2, [PERSON] 3, [PERSON] 2,3,4 and [PERSON] 2,4  
1 Swiss Newborn Screening Laboratory, University Children's Hospital, Zurich, Switzerland  
2 Children's Research Center, Zurich, Switzerland  
3 University Children's Hospital, Basel, Switzerland  
4 Division of Metabolism, University Children's Hospital, Zurich, Switzerland  
*Correspondence: [PERSON]; Tel.: +41-44-266-7732; Fax: +41-44-266-8110  
Newborn screening (NBS) for phenylketonuria (PKU) started in Switzerland in 1965. Since 2005, NBS for PKU, using tandem mass spectrometry, has been performed at the University Children's Hospital in Zurich. Phenylalanine (Phe) and the phenylalanine/tyrosine (Phe/Tyr) ratio are sensitive and specific tests to detect phenylketonuria (PKU). The main clinical features of untreated PKU are developmental delay and intellectual disability, which become evident from the first weeks of life.  
We describe a boy born at term to non-consanguineous Swiss parents with tetrahydroobiopterine (BH\({}_{4}\)) sensitive PKU with unusual neonatal presentation. The child presented with floppiness, irritability, recurrent bilious vomiting and failure to pass meconium until 32 h after birth, resulting in the clinical suspicion of an intoxication type metabolic disease, such as maple syrup urine disease (MSUD) rather than PKU. Ketonuria or hyperammonaemia were not observed.  
Newborn screening performed on a dried blood sample from the 4 th day of life revealed elevated Phe of 650 umol/L, low Tyr of 30 umol/L, and a Phe/Tyr ratio of 22. However, slightly elevated branched-chain amino acids (leucine/isoleucine 300 umol/L; valine 299 umol/L) initially supported the clinical suspicion of MSUD, but alloisoleucine was not detectable. Morbus Hirschsprung was suspected due to dilated intestinal loops and lack of intestinal gas in the anorectal region. On day 8, branched-chain amino acids had normalised; A BH\({}_{4}\)-test resulted in a significant decrease of Phe from 1011 to 437 umol/L within 24 h with normal pterin measured in urine; supporting the diagnosis of BH\({}_{4}\) sensitive PKU. Dietary restriction of Phe was initiated immediately. However, oral feeding turned out difficult due to the intestinal motility disorder.
## 3 Results and Discussion
### Water Activity  
As part of the research activities, water activity was examined for currant powders. Water activity is a measure of the availability of water in the raw material. It is an important parameter that significantly affects microbiological stability and safety during prolonged storage of food products. The higher the level of water activity, the greater the development of microorganisms in the food product [30; 31; 32]. Low-temperature spray drying resulted in the lowest water activity in currant powder samples [19; 33; 34]. Based on previous studies involving inulin, the lowest water activity was demonstrated in raspberry powders [23]. In the current study involving currant powders, low water activity levels were also obtained for inulin (IN), milk whey protein (W), microcrystalline cellulose (C) and fiber (F) (Figure 3). Thus, in order to extend shelf life and maintain high product quality, in addition to inulin, milk whey proteins, microcrystalline cellulose and fiber appear to be alternative carriers. In the aspect of the water activity value criterion addressed for raspberry powders [23] was also maintained in the current study, where low-temperature spray drying was used. Virtually all currant powders obtained low water activity, below 0.2 except for maltodextrin (0.25). This low activity criterion significantly affects the microbiological quality and shelf-life extension of food products (Figure 3).  
Figure 2: Moisture content of powders containing 50% and 70% of blackcurrant concentrate solids obtained with gum arabic (GA), inulin (IN), maltodextrin (MD), microcrystalline cellulose (C), fiber (F) and whey milk proteins (W) by dehumidified air-assisted spray drying.
## 4 Results and Discussion
### Loop 3 Fuel Cost  
The Loop 3 \"Fuel cost\" (Figure 4) highlights a negative feedback mechanism driven by the operating costs of traditional transportation (Balancing Loop). An increase in the use of traditional trucks raises the \"N. of traditional shipments \" and the \"N. of kilometers traveled by traditional vehicles\" leading to greater \"Fossil consumption\" and, consequently, an increase in the \"Refueling cost\". This rise in refueling costs translates into higher \"Traditional transport costs\" which, with a time delay, can incentivize a larger \"Portion of alternative vehicles\" by making lower-fuel-consumption or alternative-powered options more economically advantageous. However, the strength of this balancing loop is closely tied to the \"Cost of fuel\" and the \"Efficiency of traditional vehicles\". High fuel prices amplify the impact of increased consumption on costs, potentially accelerating the transition to alternatives. Conversely, improvements in the efficiency of traditional vehicles can weaken this loop by reducing the sensitivity of operating costs to increased utilization. Furthermore, the \"Capacity of traditional vehicles\" plays a crucial role, as greater capacity can reduce the number of trips required, mitigating the increase in fuel consumption and total costs. Therefore, the effectiveness of this loop in promoting the adoption of alternative vehicles depends on a complex interplay between economic, technological, and logistical factors.  
The analysis of CLD highlights the complex interrelationships influencing air and noise pollution stemming from transportation, and the quality of urban life. Transport emissions, directly correlated with the fossil fuel consumption of traditional vehicles, represent a primary source of air pollution.  
A key strategy to mitigate these negative impacts lies in increasing the proportion of alternative vehicles, supported by UVAR, and incentives, which can significantly reduce the use of internal combustion engine vehicles and, consequently, air emissions. As explicitly indicated in the diagram, the number of kilometers traveled by traditional vehicles directly contributes to noise pollution due to the inherently higher noise levels of combustion engines. Although noise pollution is also influenced by traffic and congestion, the reduction  
Figure 4: Loop 3 Fuel cost (Balancing Loop).  
in the use of traditional vehicles and the promotion of quieter alternatives represent key strategies to mitigate its impact.  
Ultimately, the quality of urban life emerges as a synthetic variable, negatively influenced by both air and noise pollution, but potentially improvable through policies that promote sustainable mobility and the optimization of urban spaces.
# Bioreactors: Applications and Innovations for a Sustainable and Healthy Future--A Critical Review
## 6 Recent Advances in Bioreactor Applications
### Bioremediation  
Bioreactors may also be used for bioremediation, providing a controlled environment for the breakdown of pollutants by microorganisms [33]. Pollutants are converted into less toxic or environmentally friendly compounds by microorganisms. Microbial remediation can be applied in ex situ or in situ conditions. However, in situ bioremediation is a slow process that is difficult to optimize and control. bioreactors provide optimum conditions for the growth of microorganisms and microbial biodegradation mechanisms. [83]. Several wastes may be treated, such as dye pollutants [33], aquaponics waste [84], and even petroleum hydrocarbons [85]. Usually, bioreactors are used in bioremediation, such as the fluidized-bed bioreactor [86], fixed-bed bioreactor [87], stirred-tank bioreactor [88], airlift bioreactor [89] and packed-bed bioreactor [85]. Promising alternatives are being studied, such as microbioreactors to treat wastewater from several processes, reducing operational costs and carbon footprint, enhancing process control, and allowing high-throughput screening [32; 33]. Microbioreactors may involve complex structures, such as a lab-on-a-chip device, containing microchannels that allow a controlled flow of liquids [34], or be as simple as polyurethane foam impregnated with oil-eating bacteria [35]. The treatment of open spaces, such as petroleum leaks on seawater, is usually performed by recovering the oil, transporting it to a place onshore, and processing that oil with physical and chemical processes [85; 90]. An alternative suggested by [85] is to use floating oil spill containment booms as a bioreactor basin, allowing the bioremediation to be performed on site, reducing costs associated with transportation.
## 3 Results  
### Evaluation of Multi-Source Model on External Datasets  
The primary objective of our work was to train a model using multi-source data (including data from all training datasets: NIH, CheXpert, MIMIC-CXR, PadChest, SIIM-ACR, and VinDR-CXR), and evaluate the model performance on unseen data using external test sets (BRAX, CANDID-PTX, CRADI, PTX-498, and Indiana). The results are shown in Table 4, and are calculated both per-dataset and aggregated in two ways:  
* Mean \(\pm\) SD (macro-averaged): Taking the arithmetic mean and standard deviation of each metric across individual datasets, each dataset is treated equally (irrespective of the number of samples), which is useful for evaluating model robustness to different dataset sources. * Overall (micro-averaged): By combining all test samples into a single dataset and calculating their metrics, so that each image has equal weight irrespective of its source, this is useful for reflecting real-world performance but can hide biases in smaller test datasets. While we trained a multi-label model (_'pneumothorax'_, _'support devices'_, and _'no finding'_ labels), the evaluation focuses on pneumothorax, as it is the primary target of the study. The performance on the remaining labels will be only shown in the www.github.com/santibacat (accessed on 26 June 2025). The PTX-498 dataset lacks any negative samples (only contains pneumothorax images), and, therefore, some metrics cannot be calculated. Those metrics will be excluded, and the ROC and PR curves will not be shown for this dataset. The results for the overall external test set were as follows: ROC-AUC 0.961 (0.957-0.964), PR-AUC 0.825 (0.814-0.834), sensitivity 0.862 (0.852-0.872), specificity 0.945 (0.943-0.947), PPV (precision) 0.566 (0.554-0.577), and NPV 0.988 (0.987-0.989). The metrics are similar across individual datasets except for the BRAX dataset, which shows a much worse performance than the other datasets. The Indiana dataset also shows a lower performance but that may be secondary to the low number of simples and the pneumothorax label coming directly from reports (which includes confounders like 'hydropneumothorax'). Figure 3 shows the receiver operating characteristic (ROC) and precision-recall curves, with their respective areas under the curve (AUROC or ROC-AUC, and AP or PR-AUC). They contain information both from the overall performance (micro-average, thick line) and from the individual datasets.
# Analysis and Optimization of Output Low-Pass Filter
## 3 Frequency and Filter Component Normalization
### Practical Limits  
Although Figure 6 shows that depending on a desired cutoff frequency or characteristic impedance, a low-pass filter can be readily designed, it should be mentioned that components could run into practical limitations, including cost, weight, size, etc. For instance, a 1 kW GCI's inductance of 0.6 pu roughly equals to 110 mH. The current rating of the inverter, specified at 4.2 A, requires an inductor that is big, bulky, and costly. Consequently, selecting a reduced value of \(L_{n}\) will decrease the cost, weight, and dimensions of the filter and the inverter. To achieve the same cutoff frequency, a higher capacitance would be necessary.  
A higher cutoff frequency is achieved by selecting smaller values for the capacitance and inductance of the filter. In order to effectively filter out the PWM harmonics, the inverter requires a higher switching frequency, \(f_{sw}\). Nevertheless, the inverter switching losses, \(P_{SW}\), are directly proportional to \(f_{sw}\). Consequently, an increase in the switching frequency yields some losses. Maximum limits are required for both switching and cutoff frequencies to reduce losses and have a decent efficiency.  
For a 50 Hz GCI, a maximum switching frequency of 10 kHz (200 pu) was selected. The upper cutoff frequency depends on a number of factors. These are the filter topology, harmonic attenuation level, and inverter switching frequency. If these variables are analyzed carefully, the filter design can be optimized for efficient and effective harmonic reduction.
# Lycopene: A Natural Arsenal in the War against Oxidative Stress and Cardiovascular Diseases
## 4 Lycopene in Human Health and Diseases  
Humans benefit from lycopene in a number of ways [26]. A lycopene-rich diet may help to prevent or lower the risk of cardiovascular disease and some malignancies [118]. According to study [119], 5 to 7 mg of lycopene per day may be sufficient to gain the benefits. Higher doses of lycopene (35-75 mg/day) may be provided in the occurrence of cancer or cardiovascular disease [120]. When combined with prostaglandandins and phospholipids in cell membranes, lycopene can improve skin defense mechanisms [121]. Lycopene has been linked to the prevention and treatment of a wide range of ailments (Table 1).  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Diseases** & **Issue** & **Dose of Lycopene** & **Subjected Time** & **Role/Activity of Lycopene** & **References** \\ \hline Loss of gap junctional communication & 4–8 mg & 3–12 months & Suppression of carcinogen formation. & [92] \\ Communication (GJC) & 60 mg & 9 weeks & GJC is boosted by a lycopene oxidation product. & [66; 122] \\  & 50 mg/kg & 5 to 7 days & Metabolite of lycopene, can enhance connexin 43, which is linked to GJC. & [116; 123] \\ Apoptosis & 10, 40, 120 mg/kg & 9 weeks & Lycopene triggers apoptosis in cells. & [124] \\ Melanoma & 10 mg/kg 15 mg/day & 5 weeks 12 weeks & Lycopene inhibits melanoma development. & [121; 125] \\  & Mammary and endometrial cancer & 76 to 154 mg & 14 days & Lysoline-like growth factor 1 (IGF-1R) signaling pathway. & [125; 126; 127] \\  & Prostate and colon cancer & 10–30 mg & 3 to 5 days & Inhibit Ras signaling. & [26; 128] \\  & ovarian cancer & 20–40 mg/kg & 18 weeks & Downregulation of STAT3 & [129] \\  & Lung cancer & 5 mg/kg & 16 weeks & Inhibits lung cancer. & [87] \\  & Oral cancer & 5 mg/kg & 16 weeks & ROS scavenger. & [87] \\ Vitamin A & & 10–2400 mg & 90 days & Upregulate signaling pathways & [130] \\ deficiency & & & & & Lycopene boosts antioxidant \\  & & 50, 100, 150 mg/kg & 24 weeks & gene expression, inflammatory mediators. & [17; 119] \\ Inflammatory Diseases & Brain tissue inflammation & & & Lycopene helps to prevent inflammation by lowering the levels of plasma interleukin (IL)-6 and TNF. & [131; 132] \\  & Photodamage by UV-B & 10 mg/kg & 5 weeks & Lycopene inhibits epidermal ornithine decarboxylase. & [121; 133] \\ Skin Diseases & Atopic dermatitis (AD) & 100 mg & 7 days & The activation of nuclear hormone receptor signaling pathways. & [134]. \\  & Photo aging & 2.5–10 \(\mu\)M & 24 h & Lycopene gel provided superior photogaing protection. & [135] \\ Cardiovascular & & 2 mg/day & 12–20 weeks & Lycopene reduces atherosclerotic plaques. & [136; 137] \\ diseases & & 50, 100, 150 mg/kg & 24 weeks & Lycopene stimulates immune response. & [119; 138] \\ Bacterial infection & & & & & \\ \end{tabular}
\end{table}
Table 1: An illustration of the nutraceutical impact of lycopene against diseases.
## 3 Methods
### Center Self-Distillation  
Class centers contain comprehensive information regarding all features, and ref. [16] shows that learning knowledge from multiple samples of the same class simultaneously facilitates network generalization. Therefore, in the proposed center self-distillation (CSD), class centers are considered as an alternative to same-class samples. The purposes of designing CSD are twofold: (1) to further verify whether using the same soft target for each class can eliminate intra-class variance and improve network generalization, and (2) to eliminate the dependence of the inter-sample self-distillation method on additional sampling. As shown in Figure 1, the core of CSD is to employ class predictions generated by class centers as soft targets for knowledge distillation. Specifically, let \(\{f_{k}\}_{k=1}^{k}\) be the class centers. Each of them will be initialized from zero. Then, the \(c\)-th class center \(f_{c}\) (\(\mathbf{x}\) belongs to the \(c\)-th class) can be updated as follows:  
\[f_{c}\gets f_{c}+\beta(\mathbf{x}-f_{c}), \tag{3}\]  
where \(\beta\) controls the update rate of \(f_{c}\). The class centers are updated simultaneously with the network parameters, so we can obtain dynamic soft targets for the distillation of different samples. Here, the soft target \(\mathbf{q}^{c}\) of the \(c\)-th class is calculated according to the \(c\)-th class center as follows:  
\[q_{k}^{c}=\frac{e^{\mathbf{u}_{k}{f_{c}}^{T}/\tau}}{\sum_{i=1}^{K}e^{\mathbf{u}_{i}{f_ {c}}^{T}/\tau}}, \tag{4}\]  
where \(\mathbf{q}_{k}^{c}\) denotes the \(k\)-th element in \(\mathbf{q}^{c}\). Setting up soft targets in this way allows features with the same label \(\mathbf{f}\) to have exactly the same target position in the feature space. But, complete elimination of intra-class variation can lead to over-fitting. Therefore, we offset the soft target to the corresponding feature (here, the feature is \(\mathbf{x}\)) to preserve a certain intra-class variation:  
\[\mathbf{\hat{q}}=\lambda\mathbf{q}^{c}+(1-\lambda)\mathbf{\hat{p}}, \tag{5}\]where \(\lambda\) is the offset factor. This process encourages different features to maintain a certain individuality and prevents features from aggregating to a single point. The final overall loss of the method is as follows:  
\[\mathcal{L}_{csd}=(1-\alpha)H(\mathbf{y},\mathbf{p})+\alpha\tau^{2}D_{KL}(\hat{\mathbf{q}} \|\hat{\mathbf{p}}). \tag{6}\]  
To observe the effectiveness of the method, we visualize it in Figure 3. It can be seen that it not only drives the features towards their respective class centers but also allows for some differences between features. This is the reason why it can provide better generalization on various tasks.