esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Analysis
### Effect of Water Content on Rock Chip Volume  
The rock chip volume can directly reflect the quantity of the rocks broken by the cutter after indentation. By collecting the rock chips generated by the cutter after indentation, the mass of the rock  
Figure 12: Characteristics of rock breaking with different water contents: (**a**) peak penetration force, (**b**) consumed energy, (**c**) chip volume, (**d**) specific energy.  
chips can be weighed by an electronic scale, and the rock chip volume can be calculated according to the following formula:  
\[V=m/\rho \tag{8}\]  
where \(m\) is the mass of the rock chips (g), and \(\rho\) is the rock density (g/cm\({}^{3}\)).  
It can be seen from Figure 12c that the higher the water content, the greater the volume of the rock chips and that the volume of the rock chips generated in the second indentation is always greater than that generated in the first indentation. The main reason is that when the cutter is sequentially loaded, the specimen shows cracks to different extents inside and on the surface in the first indentation, and a second indentation at this time can facilitate the coalescence of the cracks.
# Recent Advances in Plasmonic Sensor-Based Fiber Optic Probes for Biological Applications
## 5 FOPS Performance Evaluation
### Sensitivity  
The sensing sensitivity of FOPS can be determined by the degree of discrimination of resonance wavelength shift or intensity change caused by the variation of RI of the analyte, namely wavelength interrogation and amplitude interrogation. The sensitivity of wavelength interrogation can be expressed as \(S_{A}(nm/RIU)=\Delta\lambda_{peak}/\Delta n_{a}\). Here, \(\Delta n_{a}\) is the analyte RI variation for an RI-based FOPS system (this can also be defined in terms of other variables, for example, variation in temperature for the detection of temperature sensor or variation in liquid concentration as a chemical sensor) and \(\Delta\lambda_{peak}\) is the resonance wavelength peak shift [48]. In comparison to wavelength interrogation, amplitude interrogation has the advantages of simple technical requirements and low cost. It can be defined as \(S_{A}(\lambda)\big{(}RIU^{-1}\big{)}=-(\partial a(\lambda,n_{a})/\partial n_{ a})/a(\lambda,n_{a})\), where \(a(\lambda,n_{a})\) is the lost energy of the core mode for a particular wavelength [49]. However, this method suffers from the disadvantages of low sensing capability and a small operational range.
## 3 Design of the System with Base Generation (Proposed Study)  
### Improvements in Approach  
Because the system design method proposed in the referenced study is highly dependent on PV power generation with a nonuniform amount of daily power generation, an improved system design method was proposed to stably supply power to a minimum customer load. Figure 2 is a flowchart of the system design method proposed in this study. The design method presented in the flow chart is as follows.  
1. Select the target area where the power generation system will be installed.
2. Collect the basic data, such as the amount of sunlight and average daily load (\(P_{day}\)) in the target area.
3. Calculate the annual minimum load (\(P_{min}\)) as the base load using the collected load data.
4. Calculate the capacity of the base power source (\(F_{0}\)) to supply power to the annual minimum load (\(P_{min}\)) and the daily base power production (\(P_{base}\)). At this time, the capacity is determined larger than the annual minimum load (\(P_{min}\)), and with the minimum capacity in the rated power product. Since the fuel cell, which is the base power source, can be operated with the same output all day, the daily base power production (\(P_{base}\)) can be obtained by multiplying the capacity of the base power source (\(F_{0}\)) by 24 h.
5. Calculate the middle and peak loads (\(P_{remain}\)) by excluding as much as possible the daily base power production (\(P_{base}\)) from the average daily load (\(P_{day}\)). Middle and peak loads are powered by photovoltaic and energy storage devices.
6. Collect details of the system components (inverter, cable, electrical energy storage, etc.).
7. Calculate the daily photovoltaic power production (\(PV_{0}\)) by using the median and peak loads (\(P_{remain}\)) and the previously collected details of the system components.
8. Calculate the photovoltaic generation capacity (\(PV_{E}\)) and the energy storage capacity (\(ESS_{0}\)).  
The design method is generally similar to that of the referenced study but differs in whether or not the base load (\(P_{min}\)) is considered in the design process. Processes (3)-(5) in the red box of the flow chart is a newly proposed process in this study; the equation of Case (1) in Process (7) is used because the base power production exists. However, the design process in the referenced study jumps directly from Process (2) to Process (6) because the base load (\(P_{min}\)) is not considered. The equation of Case (2) in Process (7) is used in the referenced study.
## 5 Results and Analysis
### Key Performance Indicators
#### 5.5.3 Time Indicator  
Only in a limited time, the load reached 4 kW on the four chosen days. The peak demand lasted 0.09, 0.03, 0.05, and 0.25 h on these days in the spring day, the summer day, the autumn day, and the winter day, respectively. The rest of time in these days saw a low demand. The fuel cell module provided electricity to the load for 14.6, 10.3, 9.2, and 7.3 h, respectively. The wind turbine continuously exported electricity to the system while the electrolysis period was 9.4, 12.5, 13.6 and 16.7 h, respectively. The operating time of both the fuel cells and hydrogen generation reached 24 h in each single day. The discrepancy between the generation and the supply was met by the energy storage system so as to improve the dynamic response of the whole system.
###### Abstract  
Objectives: This study aimed to develop and validate a sensitive and reliable liquid chromatography-tandem mass spectrometry (LC-MS/MS) method for the quantification of flualprazolam and isotonitazone in serum samples to address critical gaps in forensic and clinical toxicology. Materials and Methods: A single-center validation study was conducted using serum samples spiked with analyte standards. Analytical parameters, including linearity, precision, recovery, the limit of detection (LOD), and the limit of quantification (LOQ), were evaluated in accordance with international guidelines. The LC-MS/MS method employed a Shimadzu Triple Quadrupole(tm) MS 8045 system with solid-phase extraction (SPE) for sample preparation. Results: The method exhibited high linearity for flualprazolam (r\({}^{2}\) = 0.997) and isotonitazone (r\({}^{2}\) = 0.999) over a concentration range of 1-100 ng/mL. The LODs were determined as 0.608 ng/mL and 0.192 ng/mL, and the LOQs were 1.842 ng/mL and 0.584 ng/mL for flualprazolam and isotonitazone, respectively. Recovery tests yielded results within the acceptable range of 70-120%. Flualprazolam demonstrated recovery rates of 98.0% and 97.0% at theoretical concentrations of 10 ng/mL and 50 ng/mL, respectively. In contrast, the isotonitazone recovery rates were slightly lower, measuring 75.5% at 10 ng/mL and 71.9% at 50 ng/mL, suggesting minor matrix effects that could influence its quantification. Precision analysis, including both repeatability and reproducibility, highlighted the reliability of the method. The %RSD values for flualprazolam were consistently below 7.07%, with mean concentrations closely aligning with theoretical values across fortification levels. For isotonitazone, the %RSD values remained below 6.24%, although recoveries at higher concentrations indicated potential challenges in matrix interaction. Conclusions: This validated LC-MS/MS method offers high sensitivity, precision, and recovery for detecting flualprazolam and isotonitazone in serum, filling a critical need in toxicological investigations. Further validation in other biological matrices is recommended to broaden its applicability.  
fullprazolam; isotonitazone; LC-MS/MS; method validation; serum level; solid-phase extraction 2025
# The Quanta Image Sensor: Every Photon Counts
## 3 Imaging Characteristics
### Flux Capacity  
An important figure of merit for QIS devices is _flux capacity_. Flux capacity \(\phi_{w}\) is defined as the nominal maximum photon flux that results in \(H=1\). It is dependent on the density of jets \(j\) in the image sensor (jots/cm\({}^{2}\)), the readout field rate \(f_{r}\), the shutter duty cycle \(\delta\) and the effective quantum efficiency \(\overline{\gamma}\) according to [14]:  
\[\phi_{w}=jf_{r}/\delta\overline{\gamma} \tag{3}\]  
For photography and cinematography, high flux capacity is required so that the QIS does not saturate under normal imaging conditions. Note that the QIS can handle exposures for \(H>1\) without saturating, typically \(5\times\) higher due to its overexposure latitude, but \(H=1\) is taken for convenience. For example, with a jet pitch of 500 nm, 1000 fps field rate, unity duty cycle and 50% avg. QE, the flux capacity is \(8\times 10^{11}\) photons/cm\({}^{2}\)/s which at F/2.8, QE = 50%, lens T = 80%, scene R = 20%, corresponds to \(\sim\)400 lux at the scene (yielding \(H=1\) at the sensor). It can be seen that high jet density and high field readout rate are driven by flux capacity and not necessarily by improved spatial nor temporal resolution of the final image, although these are additional benefits. The sub-diffraction jet pitch requires use of advanced-node processes that are expensive and difficult to access today. The high readout rate creates challenges in controlling power dissipation in the readout circuit.
## 5 Conclusions
The advantage is that the calculation of tracking error can be simplified without the calculation of high precision SPA, thus can decrease the computational complexity, and for most SPAs, the accuracy is quite good. However, for a few empirical algorithms with large errors, they not only introduce large annual average errors, but also present larger errors for the calculation of the instantaneous efficiency when they are used for the trough system, which shows they are not suitable for solar trough system, in particular, the Cooper algorithm, Stine algorithm and Wang algorithm. Whether this evaluation method is applicable to other solar energy systems requires further study. Conceptualization, B.L. and W.H. ; methodology, B.L. and W.H. ; software, B.L. ; validation, B.L. and W.H. ; formal analysis, B.L. and W.H. ; resources, W.H. ; data curation, B.L. ; writing--original draft preparation, B.L. ; writing--review and editing, B.L. ; supervision, W.H. and C.Z. ; project administration, W.H. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The part of numerical calculations in this paper has been done on the supercomputing system in the Supercomputing Center of University of Science and Technology of China. The authors declare no conflict of interest. The energy distribution function of reflected ray in radial direction (W/m\({}^{2}\)/rad)  
\(B_{linear}(\theta)\)  
\(DNI\)  
\(E\)  
total DNI energy in a year (J/m\({}^{2}\))  
\(f_{0}\)  
focus length (m)  
\(f_{p}\)  
distance from the reflection point to the focus point (m)  
\(L\)  
length of the parabolic mirror (m)  
\(q_{loss}\)  
heat loss of receiver (W/m\({}^{2}\))  
\(q_{net}\)  
net energy power in any time (W/m\({}^{2}\))  
\(R\)  
radius of envelope for vacuum tube receiver (m)  
\(r_{0}\)  
radius of the tube receiver (m)  
\(T\)  
temperature of receiver (K)  
time (day)  
\(w\)  
half width of the trough mirror (m)
## 1 Introduction
### Benefits  
Maritime transport is an ideal solution for those who have to transport large quantities of goods (raw materials, products of various kinds, energy resources) over long distances, without urgency and in an economical way. The logistic path of goods, which must be shipped by sea, is multimodal and involves sea and land transport. Generally, it can be summarized in four steps:  
1. The goods are packed according to their nature, shape and size and then loaded into containers;
2. The containers are moved via land (by road or rail transport) from the loading hub to the port, passing through the customs of the point of origin;
3. After the Customs Agency, the containers are loaded on merchant ships;
4. Once they reach the port of destination, the containers pass through the Customs Agency again and are then released and delivered to the unloading hub. The goods are ready to be delivered to the recipients in a last-mile transport logic.  
There are, however, some weaknesses. An example is the control and integrity of the goods; the containers are not protected from tampering or theft, so the recipient checks directly upon delivery whether their goods are intact. Risk is also a factor to consider. Ships can suffer the effects of fires onboard, adverse weather events, loss of containers, etc. Furthermore, when cargo is travelling by sea, it can be very difficult to monitor its progress and condition.  
In this context, the 5 GT System, combining GEO satellite technologies and the 5G cellular technology for the massive IoT, makes it possible to connect devices on a centralized cloud network by acquiring and sharing data in real time, with the advantage of obtaining constant and continuous monitoring along the entire container supply chain, involving various means of transport, maritime terminals and intermodal hubs.  
Existing solutions are not capable of real-time communication for all dry containers carried by ships, which is why applied research in this field is still very active (see Related Work section). The approach proposed by the 5 GT System outperforms the existing solutions because it introduces the 5G technology for the massive IoT that can effectively provide communication to a large number of devices. In addition, the architecture of the system avoids technology lock-in and ensures interoperability in the global supply chain marketplace. This is achieved by adopting standard reference technologies for machine-to-machine (M2M) communication, such as 3 GPP for NB-IoT cellular networks and OneM2M for the maritime service platform. Thus, a horizontal layer with open and standard API is available for the further development of third-party applications. The tiered architectural approach and its related standards are shown in Figure 1.
# Bacteriotherapy in Breast Cancer
## 3 Anticancer Substances Released by Bacteria
### Bacteriocins in Cancer Therapy
#### 3.1.4 Nisin A  
Nisin A belongs to the class I bacteriocins and is a polycyclic antibacterial peptide containing 34 amino acids derived from _Lactococcus lactis_ subsp. This peptide has a broad-spectrum inhibitory effect on gram-negative bacteria and is extensively used as a food preservative. Moreover, nisin is able to inhibit and prevent the growth of multiple cancer cell lines by changing the integrity of the cell membrane and forming pores, and therefore changes the potential of the membrane [49]. Nisin A inhibits and prevents the local tumor invasion and metastasis of human cell lines of breast adenocarcinoma such as MCF-7 [48].
## 1 Introduction  
### Traditional Printing Quality  
Thermal barcode printing technology is an economical, fast, and environmentally friendly printing method that has been widely used in various fields, such as logistics, retail, and healthcare, in recent years. Its working principle involves heating thermal paper with a thermal print head, causing a chemical reaction in the thermal coating on the paper to form images or text. However, the quality of thermal printing is influenced by various factors, such as the temperature control of the print head, printing speed, and paper material [1]. Small changes in these factors can lead to fluctuations in printing quality, which, in turn, affect the readability of the barcode and the overall printing effect. In practical applications, the readability of barcodes is directly related to the accurate transmission of logistics information and the efficiency of inventory management, while fluctuations in print quality may result in barcode scanners failing to correctly identify the barcodes, which, in turn, can trigger a series of chain reactions, such as logistics delays and inventory errors. Therefore, the instability of thermal barcode print quality is an urgent issue that needs to be addressed, and how to accurately predict and control the print quality of thermal barcodes has become a current research hotspot and challenge.  
Conventional quality control methods in printing predominantly depend on empirical parameter adjustments and manual inspections [2]. These approaches are characterized by inefficiency and struggle to adapt to the complexities and variability in printing environments [3]. Consequently, the integration of contemporary artificial intelligence and machine learning technologies, particularly neural network-based predictive models, presents a promising avenue for addressing these challenges [4]. Neural networks possess robust nonlinear mapping capabilities and can analyze extensive datasets to predict and optimize printing parameters, thereby enhancing the stability and precision of printing quality [5].
# Joint Luminance Adjustment and Color Correction for Low-Light Image Enhancement Network
## 4 Experiments
### Ablation Experiments
#### 4.5.3 Effectiveness Analysis of Color Loss Function  
In addition to content loss and perceptual loss, this paper also designed a color loss function to correct the color distortion during low-light image enhancement. In order to verify the effectiveness of the color loss function, this chapter designed two sets of experiments: (a) LACCNet (w/o color loss), in which only content loss and perceptual loss are used, without adding the color loss function; and (b) LACCNet, in which three loss functions are used: content loss, perceptual loss, and color loss. Figure 9 shows the test sample images of these two sets of experiments in the LOL-Blur dataset. It can be seen that the image color generated by network (b) with color loss is closer to the ground truth color.  
Figure 8: Comparison chart of ablation results of the Color Correction Module on the LOL-V1 dataset.  
Figure 7: Comparison chart of ablation experiment results of Luminance Adjustment Module on the LOL-V2-real dataset.  
truth, the color is brighter, and it has a specific improvement effect on the color distortion problem. Therefore, the addition of a color loss function is practical.  
In order to further objectively analyze the effectiveness of each functional module designed, the four indicators PSNR, SSIM, LPIPS, and NIQE were used for objective evaluation on the LOL-Blur test set. Table 4 shows the comparison of the objective evaluation results. As can be seen from Table 4, in the ablation experiment results of the Luminance Adjustment Module, the network without the Luminance Adjustment Module performed the worst in terms of PSNR, SSIM, LPIPS, and NIQE. The network structure without the Color Correction Module has three lower indicators: SSIM, LPIPS, and NIQE. The network without the color loss function has three lower indicators: SSIM, LPIPS, and NIQE. In contrast, the network that added the complete Luminance Adjustment Module, the Color Correction Module, and the color loss function performs best on all four metrics.
# A Review of the Synthesis and Applications of Polymer-Nanoclay Composites
## 3 Application
### Food Packaging
The LBL coating was prepared using a polyanion, deposited on a hydrophobic styrene copolymer. This material reduced permeability to oxygen and CO\({}_{2}\) substantially. [PERSON] et al. [169] investigated the effect of a PE/closite 20A nanoclay composite film on the quality-related parameters of mature peaches. The film had a selectable barrier property that allowed fruit respiration and inhibited bacterial growth. Peaches protected by the film exhibited lower polyphenol oxidase and higher peroxidase and catalase activities than the others. [PERSON] et al. [170] developed a multilayer packaging film coated with polyvinyl alcohol (PVA)/vermiculite nanoclay composites, and investigated the potential implications of the film for food with high moisture contents. The study showed that the oxygen permeability increased when the humidity was over 60%. This effect was reversible without any deterioration of the oxygen barrier property. [PERSON] et al. [171] developed a biodegradable packaging film using PP/MMT nanoclays with a pro-degradant additive. The advanced film delivered a high oxo-biodegradation rate and increasing MMT content (one to three parts per hundred), and improved the mechanical and thermal properties of the film, but was detrimental to the degradation process. [PERSON] et al. [172] prepared pectin/nanoclay bioccomposite films filled with two different types of halloysite nanoclays. In this study, halloysite nanoclays significantly improved the thermal, tensile, and contact angle properties of the bioccomposite films. Moreover, the synthesized pectin/nanoclay bioccomposite films exhibited an antimicrobial property against four different strains of Gram-positive and Gram-negative bacteria, which indicated its promising potential in the food packaging industry. The use of polymer/nanoclay composites in food packaging is still a developing area of research. A variety of newly-developed, low-cost materials are continuously being introduced, providing significant enhancements and relatively simple processability.
## 3 Experimental Setup and Results  
### Experimental Setup  
The proof-of-concept experiment of the novel active transmitter detection scheme in SM that provides SSD was conducted using the two-transmitter-single-receiver setup in Figure 2(a), where the SM implementation is illustrated in the inset. The digital signals were generated offline using the proposed approach in Figure 0(a). Then, instead of directly routing the pre-distorted CAP signals to each transmitter, zeros were inserted into these CAP signals at the time slots when that transmitter was inactive, representing null signal transmission. The length of zeros at each SM time slot is equal to the CAP oversampling factor. After that, these new packed digital signals were fed into an arbitrary waveform generator (AWG), with two channels of 10 GS/s AWG outputs routed to modulate two 12 GHz directly modulated lasers (DMLs) using wavelengths of 1549.6 nm and 1550.3 nm, respectively. After each DML, an optical attenuator was placed to differentiate optical power between two transmitters, thus, enabling SM transmission. Additionally, an optical delay line was utilized in one of the channels for the fine-tuning of channel synchronization (coarse-tuning is achieved by digitally delaying 180 samples in the \"fast\" channel). Through such optical fiber links, the optical signals were then emitted into the FSO channels respectively, with a 16\({}^{\circ}\) beam divergence and a maximum power of 3.5 dBm for each signal, which is below the laser safety regulation [4]. After a 0.5 m FSO transmission, partial optical signal power from two transmitters was collected and coupled into the photodetector using a lens system. The digital photodetector (PD) output was finally input into an oscilloscope for offline signal processing. A photo of the experimental setup is shown in Figure 2(b). The physical parameters of the experimental setup are summarized in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Device Name** & **Model** & **Parameters** \\ \hline \hline \multirow{4}{*}{Waveform generator} & \multirow{4}{*}{Tektronix AWG 7102} & 10 GS/s 4-CAP (oversampling factor = 4) \\  & & Vpp = 1 V \\  & & Sample length = 20,000 \\ \cline{1-1}  & & 1549.6 nm and 1550.3 nm \\ \cline{1-1}  & & General Photonics Corp. \\ \cline{1-1}  & & VariDelayTM \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Physical parameters of the experimental setup.