esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
1. Both the quenched and cold pressed residual stress show a lamination distributional type with compressive stress at its surfaces and tensile stress at the center layers. The distribution of residual stress at the stiffened area is asymmetrical, and it is symmetrical at the smooth area.
2. The integral and five-pass stepwise cold pressing results in heterogeneous distribution of residual stress and strain at the stiffened area. Meanwhile, the stepwise cold pressing also induces inhomogeneous distribution of residual stress and strain at the overlap regions.
3. There is no difference between the longitudinal and transverse stress at the smooth area for the quenched and cold pressed components; meanwhile, there is a significant distinction at the stiffened area.
4. The whole distribution of residual stress for the T-type rib is measured using X-ray diffraction and the contour method and they agree with the simulated results. The cold pressing followed by the aging process contribute to a stress reduction of 50% on the surfaces of the T-type rib.
5. The smooth area shows a more significant reduction of maximum tensile residual stress in comparison with that of the stiffened area. The reduction of the maximum tensile stress at the stiffened area is 42% and 50% for the cold pressing and aging, which increases to 54% and 60% at the smooth area, respectively.
6. The mechanism of the stress reduction during the cold pressing and the aging processes is discussed.  
Conceptualization, writing, Y.L.; supervision, writing, T.Z.; methodology, Y.W.; review and editing, H.G. All authors have read and agreed to the published version of the manuscript.  
This project is supported by National Natural Science Foundation of China (Grant No.51705248, No. 51327902); State Key Laboratory for High Performance Complex Manufacturing, Central South University (ZZY)KT2021-05).  
Not applicable.  
Data are contained within the article.  
The authors declare no conflict of interest.
## 5 Methods for Identification and Quantification of Coumarins
### Analytical Trends  
The growing demand for more sensitive, selective, and efficient methodologies has propelled advancements in analytical techniques for the structural elucidation of organic molecules. Some of the cutting-edge analytical methodologies that will undoubtedly have an impact on the determination of coumarins in wood extracts and beverages are those that are currently being used for other organic substrates. High-resolution mass spectrometry (HRMS), for example, enables exact determination of molecular weights, resulting in enhanced accuracy for identifying chemical formulas and assisting in the understanding of molecular structures [179; 180]. Tandem mass spectrometry (MS/MS or MS\({}^{n}\)) procedures yield fragmentation patterns that can be employed to infer the structural characteristics of the compound [173; 177]. Employing multiple stages of fragmentation (MS\({}^{n}\)) can unveil further intricate details. On the other hand, nuclear magnetic resonance spectroscopy (NMR), including 2D-NMR (two-dimensional NMR) and 2D homonuclear and heteronuclear correlation investigations, offers improved resolution and detailed information for determining molecular structure [181; 182]. Ion mobility spectrometry (IMS) is a technique that separates ions in a gas phase depending on their mobility, which helps to distinguish between structural isomers, and might be very useful for chiral coumarin derivatives [183; 184]. Another cutting-edge methodology is the integration of liquid chromatography with nuclear magnetic resonance (LC-NMR), which enables the concurrent separation and determination of the molecular structure of substances [185]. Computational techniques might be very useful as well, such as density functional theory (DFT) and molecular dynamics simulations, which are progressively employed to forecast and explain spectroscopic data, facilitating the determination of molecular structure [186]. The use of advanced data processing techniques such as data mining and cheminformatics, along with machine learning algorithms, is employed and allows the interpretation of extensive analytical data [187]. These methodologies can accelerate the process of determining the structure of compounds, which can aid in identifying new coumarins in spirit beverages and determining their metabolism. The employment of miniaturised analytical techniquesand lab-on-a-chip equipment also might enable quicker analyses with smaller sample volumes, contributing to enhanced efficiency in structural elucidation [188, 189].  
These trends, taken together, can contribute to a more thorough and efficient approach to the advancement of knowledge of organic compounds, including coumarins.
# Widely-Tunable Ring-Resonator Semiconductor Lasers
## 3 Comparison of Classical Widely-Tunable Architectures
Figure 5: Synthesized spectra of all the considered ring structures: (**a**) Ring-bus-ring (RBR) resonator structure; (**b**) Single Coupled-Ring-Resonator (CRR 1\(\times\)) structure; (**c**) Vernier racetrack resonator structure and (**d**) Double Coupled-Ring-Resonator (CRR 2\(\times\)) structure. Figure 6: Optical images of fabricated lasers: (**a**) Ring-bus-ring laser (RBR); (**b**) Single Coupled-Ring-Resonator laser (CRR 1\(\times\)); (**c**) Vernier racetrack resonator and (**d**) Double Coupled-Ring-Resonator laser (CRR 2\(\times\)). The laser linewidths were measured using the delayed self-heterodyne method, with 10 km of fiber in one arm, and an acousto-optic modulator with 100 MHz of frequency shift in the other arm. We quote the 3 dB Lorentzian linewidth calculated from \(-20\) dB points. We show the measurements in Figure 8. All the measured configurations have linewidths in the 200 kHz range, with best results at 150 kHz. For the case of CRR 2\(\times\) laser, in some certain lasing wavelengths, its measured linewidth can be as narrow as 196 kHz, however, the stable single-mode lasing performance cannot be sustained across the whole theoretical tuning range.
# A Review of Machine Learning and Deep Learning Techniques for Anomaly Detection in IoT Data
## 3 Taxonomy of Anomaly Detection Techniques
### Anomaly Types
#### 3.3.2 Contextual Anomaly  
Another type of anomaly is observed for any data pattern that occurs as usual and anomalous in one scenario. The detection of spatial anomalies includes comprehension of the meaning [19], which typically occurs inside time-series data sources. A common example is heavy traffic jams during rush hour, which may be a contextually anomalous traffic activity after midnight because of a crash, poor visibility, or other causes related to foggy conditions. An example of contextual anomaly is shown in Figure 4.
# Slotted ALOHA on LoRaWAN-Design, Analysis, and Deployment
## 6 S-ALOHA Implementation over LoRaWAN
S-LoRaWAN is implemented at the application layer through the block diagram presented in Figure 9, using two timers and the LoRa transmission libraries. The S-ALOHA state machine has five states: (i) Init: in this state, the timers used during firmware execution are initialized. There are three timers: _TxLoraSlotted_ for the calculation of the time to wait to transmit in the first available slot; _TimerBackOff_ for the choice of a random slot for retransmission, and _TimerAck_ at the expiration of which packet acknowledgment receipt is checked. Following the initializations, the system transitions into the Wait state, in which a transmission request is expected. In this state, the MCU is in sleep. (ii) Wait: _TimeToSend_ variable is periodically checked, when it assumes logical value true it is necessary to transmit a new packet to the LoRaWAN network. The state transition between Wait and Send occurs with the triggering of the _TxLoraSlotted_ timer in correspondence of the first time slot in which to transmit (as foreseen by the Slotted Aloha protocol). (iii) Send: in this state, the transmission function is executed, which is responsible for sending the data and calling the _PrepareTxFrame_ and _SendFrame_ functions. These functions deal respectively with the preparation of the payload and the communication with the LoRa transceiver. Following transmission, the system automatically transits to the WaitForAck state. (iv) WaitForAck: the system remains in this state until the timer _TimerAck_ is triggered or the acknowledgement is received. If the ACK is received, the system returns to the Wait state until the next transmission request, otherwise the system transitions to the Rand state where the backoff algorithm is executed. (v) Rand: as a result of non-acknowledgment reception, the MCU programs the transmission in a randomly selected slot. The _TimerBackOff_ is programmed and the device remains in the Rand state until it expires. When it expires, the system automatically transits to the Send state where the message is retransmitted. Figure 9: S-LoRaWAN block diagram.
## 4 Evaluating the Performance of WSN Routing Strategies with Respect to DSA
### Reactive Strategy  
In the reactive strategy, instead of securing a path before data transmission, the path search is conducted in an online fashion alongside the data transfer. Examples of this include the real-time load distribution routing protocol (RTLD) [22], flooded piggyback ant routing (FFT) [27] and scan ant routing (SCA) [27].
# Optimizing CNN-LSTM for the Localization of False Data Injection Attacks in Power Systems
## 2 Power System State Estimation and False Data Injection Attack
### False Data Injection Attack  
Assuming the attacker constructs an attack vector \(\mathrm{a}=(\mathrm{a}_{1},\mathrm{a}_{2},\ldots,\mathrm{a}_{m})^{\mathrm{T}}\) that has the same dimension as the original measurement vector \(z\), then the tampered measurement data are represented as \(\mathrm{z}_{\mathrm{a}}=\mathrm{z}+\mathrm{a}\). At this point, the system state estimated from the false data is \(\hat{\mathrm{x}}_{\mathrm{a}}=\hat{\mathrm{x}}+\mathrm{c}\), where \(c\) is the deviation from the normal state.  
The purpose of an FDIA is to mislead the control center to regard the attacked state estimate \(\hat{\mathrm{x}}_{\mathrm{a}}=\hat{\mathrm{x}}+\mathrm{c}\) as a valid estimate. To achieve this, the attacker needs that the residual \(\mathrm{r}\) after the attack remains unchanged or is smaller than the threshold \(\mathrm{\tau}\), thereby bypassing BDD. In the DC model, the attack vector \(\mathrm{a}\) is usually constructed as a linear combination of the column vectors of the Jacobian \(\mathrm{H}\) matrix, that is, \(\mathrm{a}=\mathrm{H}\mathrm{c}\), where \(\mathrm{c}\) is any \(\mathrm{n}\times 1\) non-zero vector. The tampered measurement vector can be expressed as \(\mathrm{z}_{\mathrm{a}}=\mathrm{z}+\mathrm{a}\). The L2-norm of the measurement residual is shown in Equation (3).  
\[\begin{array}{ll}\mathrm{r}_{\mathrm{a}}&=\left\|\mathrm{z}_{\mathrm{a}}- \mathrm{H}\hat{\mathrm{x}}_{\mathrm{a}}\right\|_{2}\\ &=\left\|\mathrm{z}+\mathrm{a}-\mathrm{H}(\hat{\mathrm{x}}+\mathrm{c})\right\| _{2}\\ &=\left\|\mathrm{z}-\mathrm{H}\hat{\mathrm{x}}+\mathrm{(a}-\mathrm{H}\mathrm{c} )\right\|_{2}\\ &=\left\|\mathrm{z}-\mathrm{H}\hat{\mathrm{x}}\right\|_{2}\\ &=\mathrm{r}\end{array} \tag{3}\]  
At this juncture, the compromised measurement vector \(\mathrm{z}_{\mathrm{a}}\) generates residuals identical to those of the actual measurement vector \(\mathrm{z}\), rendering the BDD based on residuals incapable of detecting the falsified data intermingled within the measurement data.
## 5 Sensitivity Analysis for the Smart PV Inverters with Control Ability _pf_ = 0.95  
To handle project uncertainty, a sensitivity analysis is performed with respect to four key parameters: economic life, discount rate, FIT rate, and PV cost per kWp. The de-rating rate of PV output and the percentage of PV cost for O&M according to field experience are not performed. The system with a PV capacity of 2500 kWp and control ability _pf_ = 0.95 is selected as the _base case_ for sensitivity analysis.  
The NPV and BCR sensitivity graphs as functions of percent deviation from the _base case_ parameters are illustrated in Figures 11 and 12. As shown, based on the PV economic life of 20 years, the 3% discount rate, the FIT of NT$ 4.8845/kWh, and the PV cost of NT$ 56,350/kWp, the NPV and BCR sensitivities between \(-\)50% and +50% variations from each base parameter are demonstrated. For example, the NPVs are increased to NT$ 77 million and NT$ 36 million and decreased to NT$ \(-\)16 million and NT$ \(-\)74.75 million for a 50% increase of FIT rate, economic life, discount rate, and PV cost, respectively. The BCRs are increased by 50% and 22.7% for a 50% increase of FIT rate and economic life, respectively, and are decreased by 11.2% and 33.3% for a 50% increase of discount rate and PV cost, respectively. It shows that the most sensitive parameter is the FIT rate, followed by the PV cost; the third is the economic life and the least sensitive parameter is the discount rate. Moreover, increase in the FIT rate and reduction in the PV cost will raise the values of NPV and BCR.  
Figure 11: NPV sensitivity graph for four key parameters.
# Xanthone Derivatives in the Fight against Glioblastoma and Other Cancers
## 2 Antineoplastic Activity of Xanthone Derivatives
### Xanthone Glucosides
#### 2.5.2 3,6-Bis(2,3,4,6-Tetra-O-Acetyl-\(\beta\)-Glucopyranosyl)Xanthone  
3,6-dihydroxydxyanthone was tested on glioma, lung, and breast cell lines, but demonstrated only weak growth inhibitory activity [66]. New acetylated xanthonoside 3,6-Bis(2,3,4,6-tetra-O-acetyl-\(\beta\)-glucopyranosyl)xanthone (**11**, Figure 6C) and its deacetylated derivative 3,6-Bis(O-\(\beta\)-D-glucopyranosyl)xanthone (**12**, Figure 6D) were tested on glioblastoma cell lines: **U251**, **U373**, and **U87 MG** cell lines, as well as **A375-C5** (IL-1 insensitive malignant melanoma), **MCF-7** (breast adenocarcinoma), and **NCI-H460** (non-small-cell lung cancer) with \(\text{GI}_{50}\) values for the acetylated compound (**12**, Figure 7C): 0.55 (U251), 0.42 (U373) 0.42 (U87 MG), 135 (A375-C5), 0.46 (MCF-7), and 0.19 \(\upmu\)M (NCI-H460), respectively. A deacetylated derivative has not been tested on glioblastoma cell lines, while in the case of other cell types, the \(\text{GI}_{50}\) values were above 150 \(\upmu\)M. Active compounds still have to pass the blood-brain barrier (bbb) without being hydrolyzed by enzymes known as esterases. To solve this problem, the acetylated, glycosidic derivatives of xanthone were encapsulated into liposomes. However, there is one well-known problem with liposomes concerning their becoming unstable after a short time. Therefore, compound **11** (Figure 6C), besides other glycosidic derivatives of xanthone, has been packaged into both liposomes and prolisosomes. Depending on the cell line, the effects of encapsulation in the carrier varied. In the case of liposomes, cytotoxic activity was retained in two out of three tested cell lines (U251 and U373), but it was less marked than that observed with the free compound. In the case of prolisosomes, an unexpected, strong cytotoxic effect was observed in all tested cell lines U87 MG, U251, and U373 (particularly in the highest tested concentration 100 \(\upmu\)M), both for the compound enclosed in the carrier and for the carrier itself. This may be due to the presence of excipients such as mannitol. Carriers represent a promising development route for xanthone derivatives, while the impact of their use on therapeutic activity requires further study [67].  
Figure 7: Chemical structures of Cudraxanthone derivatives.
## 4 Numerical Simulation
### Ground Subsidence  
The displacement cloud chart in the Z direction of the stratum under the unreinforced and reinforced conditions is shown in Figure 17. The 13 th excavation section (underpass culvert section) of the left line tunnel was selected for observation and analysis. Based on the nephogram, stratum floating could be found in local areas of the culvert exit with amounts up to 22.4 mm under the unreinforced condition. Moreover, the maximum subsidence could reach as much as 219 mm because its sand layer was located above the tunnel, and the groundwater was abundant. In other words, the shield passing underneath the culvert without ground reinforcement may lead to foundation instability or ground collapse. In the reinforced condition, the maximum subsidence of the stratum can be controlled at 8.86 mm.  
Figure 18 shows the transverse ground settlement curves of the 13 th excavation section of the tunnel on the left line with and without reinforcement. In the case of no reinforcement, the maximum surface settlement appeared above the rear tunnel vault with a settlement value of 11.6 mm and a formation loss rate of 0.19%. In the right area of the left tunnel vault, the surface settlement value decreased sharply to 2.65 mm owing to the release of the stress concentration. If the difference of the settlement value is too large, the corresponding superstructure produces extra stress, and when it exceeds a certain limit, cracks, tilt, and even damages occur. Strengthening the section reduced the formation loss rate by more than 50%. The maximum surface settlement was 5.30 mm. This maximum settlement value was close to the surface settlement value above the left tunnel vault with a soft-rock height ratio of 0.5 in the measured monitoring data. It did not appear to have a significant differential settlement value; thus, the MJS method of reinforcement significantly controls the influence of the surface stress concentration of release.  
Figure 17: Z-direction displacement cloud image before and after reinforcement. (**a**) no reinforcement; (**b**) with reinforcement.  
Figure 18: Transverse surface settlement curve of the left and right tunnel excavation at the 13 th step.
## 4 Theoretical and Experimental Analyses
### Applicability  
In this paper, we utilize the \((k\to h,n)\) scheme to encrypt images and embed additional data in the encryption process, which is then stored on multiple participants. In this way, even if one or more participants are breached, the carrier images and secrets can still be recovered from other shares. Therefore, it has strong disaster tolerance. Moreover, the thresholds are dynamically changeable and can change with changes in the current security environment, which has stronger applicability and can be adapted to the application requirements of secure multiparty computing, federated learning, blockchain,  
Figure 12: Comparison of ER for different schemes [25; 27; 28; 29; 30; 31; 34].  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Maximum ER** (bpp)} & \multirow{2}{*}{\(n=2\)} & \multirow{2}{*}{\(n=3\)} & \multicolumn{3}{c}{\(n=4\)} & \multicolumn{3}{c}{\(n=5\)} \\ \cline{2-11}  & & & & & & & & & & \\ \hline \(k=2\) & 4.00 & 2.67 & 2.67 & 2.00 & 2.00 & 2.00 & 1.60 & 1.60 & 1.60 & 1.60 \\ \(k=3\) & \(\backslash\) & 5.33 & 5.33 & 4.00 & 4.00 & 4.00 & 3.20 & 3.20 & 3.20 & 3.20 \\ \(k=4\) & \(\backslash\) & \(\backslash\) & \(\backslash\) & 6.00 & 6.00 & 6.00 & 4.80 & 4.80 & 4.80 \\ \hline \hline \end{tabular}
\end{table}
Table 3: ER of the proposed scheme at different thresholds.  
multiparty copyright protection, and other scenarios in the cloud computing environment. In future applications, medical image transmission systems, distributed security authentication systems, identity authentication systems, and military information transmission systems can be designed based on the algorithm experiment of this paper, which has wide application value.
## 2 Materials and Methods
### Biofilm Assays on SSC  
5.1 Biofilm Cell Enumeration after Incubation under Different Sub-Inhibitory BAC Concentrations (1.25 or 2.5 mg/L, 48 h)  
Stainless steel coupons (SSCs) with 1 mm thick, 1 \(\times\) 1 cm, type 304, 2B finish (Metalurgica Quinacorte, Lda, Lousa, Portugal) were used to produce the biofilms. The procedures for cleaning, degreasing and sterilizing the coupons were described elsewhere [16; 27]. Briefly, coupons were first cleaned in acetone to remove grease, rinsed in distilled water, and consecutively immersed into a phosphoric-acid-based cleaner (CIP 200; Steris Corp.) for 20 min. Then, the coupons were rinsed again and autoclaved in test tubes before use. The procedure for the preparation of the inocula, incubation and detaching of the biofilms were as described in [PERSON] et al. [27]. Briefly, the cleaned and sterilized coupons were immersed in 1.5 mL of the appropriate inocula suspension (approximately 10\({}^{7}\) CFU/mL in TSB-YE or in TSB-YE with 1.25 or 2.5 mg/L of BAC), in Parafilm-sealed 24-well microplates (Orange Scientific). Incubation proceeded without agitation for 48 h at 25 \({}^{\circ}\)C (room temperature). Afterwards, coupons were removed and rinsed with 1 mL of Ringer's solution (RS) on both faces in order to remove planktonic cells. The rinsed coupons were placed in a new 24-well microplate with 1 mL of RS with glass beads (\(\O\) = 3 mm) under and above the coupons. The microplate was then exposed to vortex agitation (Tittertek DSG, Flowlabs, Germany) for 1 min at maximum speed. The resulting suspensions were decimal-diluted, inoculated onto TSA-YE plates, and incubated overnight at 37 \({}^{\circ}\)C, for CFU counting. The assays were replicated using two biological replicates, with two technical replicates each (four replicates).
###### Abstract  
As the world transitions to decarbonized fuels, understanding the impact of engine oil on emissions remains crucial. Lubricant-derived particulate emissions can influence air quality and regulatory compliance in future transport. Researchers have predominantly focused on transient driving cycles to replicate real-world conditions and capture the full range of particle size. This emphasis has led to a lack of comprehensive data on oil-related particulate emissions during steady-state operations, particularly for particles smaller than 23 nm. This paper addresses this gap as upcoming regulations, such as Euro 7, are expected to impose stricter limits by extending measurement thresholds down to 10 nm. The investigation was conducted on a 1.0 L gasoline direct injection engine, assessing total particulate number (TPN) emissions using three oil formulations: a baseline oil with mid-ash content and mid-volatility, a low-ash and low-volatility oil (LoLo), and a high-ash and high-volatility oil (HiHi). A DMS500, with and without a catalytic stripper, measured particle size distribution and TPN. Two digital filters were applied to obtain particle number (PN) metrics comparable to condensation particle counters: \"F1-PN > 23\" with d50 = 23 nm and \"F3-PN > 10\" with d50 = 10 nm. Sub-23 nm particles dominated emissions, with baseline oil generally producing higher PN emissions except at low loads. Using F1-PN > 23, HiHi exhibited higher PN counts across moderate to high speeds, while F3-PN > 10 revealed lower PN emissions for HiHi at specific conditions, excluding 2250 rpm-fast idle. By a weighted arithmetic mean, HiHi's emissions were 9.7% higher than LoLo with F1-PN > 23 and 3.6% higher with F3-PN > 10. Oil formulation did not influence nucleation mode diameter. A three-way ANOVA demonstrated that load and speed were the predominant factors affecting emissions over the entire testing map; albeit at specific operating conditions the effect of the oil is evident. This suggests that under steady-state conditions, carbon-based fuel still plays a key role in particle formation. Future work will investigate decarbonised fuels to further isolate the effect of oil on emissions.  
gasoline direct injection (GDI); oil formulation; sub-23 nm; particle number (PN); catalytic stripper; DMS500; soot; particulate emission +
Footnote †: journal: Applied sciences