esa-sceva/satcom-chunk-collection huggingface dataset:
# Prediction of Blade Tip Timing Sensor Waveforms Based on Radial Basis Function Neural Network
## 4 Validating the Prediction Accuracy of RBF Neural Network
### Predicting Static Circumferential Characteristic Curves
The polynomial function curve and the static circumferential characteristic curve in Figure 17f have a higher overlap in \(10-60^{\circ}\). Beyond this range, the overlap is lower and separate, this curve demonstrates that the polynomial function fit curve has as high an accuracy as the other three models only in the \(10-60^{\circ}\) range. Figure 17: Plots of the RBF model, Kriging model, BP model and polynomial function against the static circumferential characteristic curve about the angle at \(0.15\) mm for the clearance, together with the curves of the Kriging model, BP model and RBF model. (**a**) Comparing the RBF model and the static circumferential characteristic curve. (**b**) Comparing the Kriging model and the static circumferential characteristic curve. (**c**) Comparing the BP model and the static circumferential characteristic curve. (**d**) Comparing the prediction curves of the Kriging model and RBF model. (**e**) Comparing the prediction curves of the BP model and RBF model. (**f**) Comparative graphs of the polynomial function curves and static circumferential characteristic curves. From Figure 18, it is found that the prediction curves of the four models have high overlapping with the static calibration data in the range of angle \(10-60^{\circ}\), but outside of this range the overlap among the models is lower. Figure 19 depicts the prediction error at a clearance of 0.15 mm. The Kriging model predicts data with high accuracy close to the sample point, which gradually decreases as it moves away from the center of their points in Figure 19a,b. Figure 19d shows that the general trend of the RBF model is more stable, it has less fluctuation and high approximation accuracy in the process of changing to the center of the data points. However, the polynomial function fitting curves cannot assure a great degree of accuracy for all samples, where large errors exist. Figure 20 shows the prediction error under six sets of sample points when the clearance is 0.15 mm, whose absolute prediction error is in the range of 10 mV. While the prediction error is smaller and more accurate near the center of the data point, whose prediction accuracy gradually diminishes when moving away from the center of the data point. Figure 19: The prediction error comparison of different prediction models at a clearance of 0.2 mm: (**a**) with ten groups of data points using Kriging models; (**b**) with six groups of data points using Kriging models; (**c**) the polynomial function fitting model under ten sets of sample points; (**d**) with six groups of data points using RBF models. Figure 18: Where \(x=0.15\) mm, the RBF model, Kriging model, BP model and polynomial function fitting prediction curve compared with the static radial feature curve.
# A Survey of Ledger Technology-Based Databases
## 3 Centralised Ledger Databases
### LedgerDB
#### 3.1.2 Transaction Processing  
The state of the ledger can be modified through transactions, which result in journal entries that will be added to the ledger. A block is made up of multiple journal entries. Instead of the usual order-then-execute approach, LedgerDB uses a new execute-commit-index transaction model, as is depicted in Figure 1. This model makes the most of the centralised structure and connects the execution and validation phases. There is no consensus mechanism implemented, as a single service provider manages the network.  
The use of permissionless or permissioned blockchain verification models for data management, such as smart contract or UTXO, continue to give rise to issues regarding performance. For example, a recursive search is necessary for the UTXO verification model to locate the appropriate data. As a result, LedgerDB uses clues, which are user-specified labels (keys) that realise business logic. Clues can be ordered for given assets with a unique ID. Therefore, as opposed to key-value models, a clue indicates every state of an asset is connected through only one clue index.  
**Execute Phase:** the transaction sent from the client is verified and checked for necessary permissions for the operations. If checks are successful, the transaction is stored in the storage layer, decreasing communication and storage costs. After execution is completed and transactions have been executed, successfully or unsuccessfully, they are sent to an appropriate ledger server.  
**Commit Phase:** following the sending of transactions by a ledger proxy to an appropriate ledger server, the journals of the previous phase are collected and are then processed in a group. These collected executed transactions are ordered in this phase in a global order. The transactions are forwarded to the storage system, where they are stored. The success of a transaction is determined in the commit phase, and only successful transactions are stored. A client is issued a receipt regarding their transactions, regardless of the transaction's success. Thus, clients receive updates in the form of these receipts regarding the status of their transactions.  
**Index Phase:** the index phase is important due to its retrieve and verification mechanisms. The necessary indexes for data stored in the commit phase are created. There are three types of indexes in LedgerDB: clue index, bAMT accumulator, and block information. The block information is not a digest of a single block, rather it is a summary of the entire ledger. Thus, all ledger information is easily globally verifiable as they were all created at a given time using this summary. After indexes are built at the end of the index phase, the receipt is sent back to the client.
## 2 Experimental and Analytical Procedure
### PR Measurements and Average Strain Determination
According to the deformation potential theory [23], the energy gap increases for decreasing tensile stress, and indeed, this is our case. Therefore, the tensile strain in the previously masked wire sections of the hydrogenated multi-wires sample (see Figure 2) is expected to largely decrease, in good agreement with the observation of a compressive strain produced on GaAsN wires by a lattice expansion in the hydrogenated barriers [11]. Figure 2: (**a**) Room-temperature PR spectra of the multi-wires sample; (**b**) the same for the untreated GaAsN reference sample. The PR features near 1.26 eV are typical of dilute nitrides and mark the redshifted GaAsN fundamental gap (E- band) both in the spectrum of the multi-wires sample (E-\({}^{w}\)) and in the one of the untreated GaAsN sample (E-\({}^{u}\)). In panel (a), note the blueshift of the SO transition energy E\({}_{0}\)’ + \(\Delta_{0}\)’ (due to PR signal coming from the hydrogenated sections of the multi-wires sample) with respect to the one of GaAs (E\({}_{0}\) + \(\Delta_{0}\)). At the same time, also note that in the wire sections, the increased energy of the E- band (with respect to the untreated sample) highlights the reduced tensile character of their strain state. In both panels, arrows mark the transition energy, as obtained from the best fit (solid continuous lines) of the experimental line-shape. The transition energy of the GaAs fundamental gap E\({}_{0}\) and SO band E\({}_{0}\) + \(\Delta_{0}\) (as obtained from literature data, see Ref. [27]) and of the GaAs\({}_{0.991}\)N\({}_{0.009}\):H fundamental gap E\({}_{0}\)’ (as obtained from simulation results) are also reported (dashed bars). 3. After lowering the temperature of the sample, the improved experimental conditions allowed the doublet in the E- band of the spectra of the untreated GaAsN sample to be resolved (not shown) and to measure a valence band splitting \(\Delta\)E = (14 \(\pm\) 2) meV, that corresponds [10; 30] to an in-plane tensile strain \(\varepsilon_{//}\approx\) + 1.8 \(\times\) 10\({}^{-3}\). It must be pointed out that this value is very close to that expected for the coherent growth of a GaAs\({}_{0.991}\)N\({}_{0.009}\) layer on GaAs. We note that, due to the increased broadening parameters, the same procedure could not be applied in the case of the multi-wires sample.
## 3 Results  
### Overview of Lysis Device  
Figure 1 shows a schematic diagram of the blood cell lysis device and its operation. As shown in Figure 1a, the device features one main microchannel and two electrical ground (GND) buffer microchannels connected by a Nafion nanochannel membrane, which is a commercial ion perm-selective membrane. The lysis operation region is highlighted with green rectangles, which are detailed in Figure 1b,c. As shown in the right image of Figure 1a, the device was fabricated using conventional polydimethylsiloxane (PDMS) soft lithography and a glass bonding technique for easy mass production and transparent observation. The edge of the microchannels was punched with holes to inject the blood sample and buffer solution. The electric field for cell lysis was applied using rod-shaped electrodes located at the reservoirs of the inlet and ground buffers. The outlet reservoir remained open for the sample to naturally flow and arrive at the outlet via a pressure-driven flow from the inlet. The channel dimensions of the lysis device were determined based on our previous work [36], in which the micro/nanosized particles were efficiently separated depending on their electrophoretic mobility. The device utilized in this study had optimized channel dimensions; the height of the channel was 35 \(\upmu\)m, and the channel width of the cell lysis section was designed to be 600 \(\upmu\)m to enable effective cell lysis.  
Figure 1b,c demonstrate the operation of the blood cell lysis device when an electric field was applied. When there was no electric field in the device, the pressure-driven flow from the blood cell inlet applied a hydraulic drag force to the cells. Because there was no external force to compete with the pressure flow, the blood cells flowed to the end of the microchannel and arrived at the outlet reservoirs, as shown in Figure 1b. When an electric field was applied, as shown in Figure 1c, an electrophoretic force inversely proportional to the direction of the drag force was applied to the blood cells. Expectedly, the electric field was concentrated near the nanochannel, which was the Nafion membrane in our device. Therefore, the electrophoretic force was strongly amplified near the Nafion membrane and competed with the drag force. Owing to the resultant force balance, the cells decelerated and were eventually trapped near the Nafion membrane, providing the concentrated electric field with sufficient time to disrupt their membrane. We observed that the time required for blood cell lysis was less than 1 s.  
Figure 1: Schematic of the rapid blood cell lysis device. (**a**) Schematic diagram (**left**) and fabricated device (**right**) with the microchannel highlighted by color dye solutions (scale bar: 3 mm). (**b**) Schematic of the lysis region without an electric field. (**c**) Schematic of the lysis region with an electric field for blood cell lysis.
# Qualitative Assessment of Hazardous Gas Emissions from Sewage Sludge-Derived Biochar
## 1 Introduction
### Sewage Sludge Treatment Options  
To date, the options to utilize sewage sludge as a resource are limited. Sewage sludge has a relatively high water content, directly affecting the techno-economic and processing options [3]. Dewatering, thickening, conditioning, drying, and incineration reduce its volume at a relatively high energy input. However, thermal disposal methods are recommended for highly contaminated sludge.  
Interest in the thermal processing of sewage sludge for resource recovery is growing. Pyrolysis is a thermal process in which a feedstock undergoes decomposition at high temperatures without oxygen. Pyrozed sewage sludge generates tar oils, syngas components, and biochar that can potentially be used as a value-added by-product in agriculture or as an alternative fuel [4; 5; 6]. A summary of the sludge treatment options is presented in Figure 1[7].  
The properties of sludge-derived biochar depend mainly on the pyrolysis process conditions and feedstock [11; 12]. Biochar derived from sewage sludge has a sufficiently high carbon content and can, therefore, be used as a soil amendment as well as a source of energy. Conversion of sewage sludge into biochar can reduce the bioavailability of heavy metals and other pollutants and reduce the waste volume [4]. However, sewage sludge-derived biochar tends to be of lower quality compared to plant biomass-derived biochar, such as lower specific surface area and higher ash content.
## 3 Results and Reports
### Testing to Assess the Normality of Distribution
#### 3.5.2 Normality Test Using a Quantile-Quantile Probability Plot  
A Quantile-Quantile (Q-Q) is a graphical probability plot method that compares two probability distributions by plotting their quantiles against each other. This approach displays the observed values against normally distributed data, represented by the red line. Normally distributed data are represented by blue dots and fall along this line. Figure 12 illustrates the Q-Q plots applied to the sample used in the statistical test. It can explicitly show the closeness of the data points, thus describing the linearity of the normally distributed sample. We could also see the outliers that could probably be represented during the second classification phase. The theoretical distribution is the normal distribution with mean 0 and standard deviation 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Systolic** & **Normality Test** & **S-Value** & \(p\)**-Value** & **Result** \\ \hline \multirow{4}{*}{Dts\_1} & AD & 0.299 & 0.781 & ✓ \\  & SW & 0.996 & 0.284 & ✓ \\  & KS & 1.0 & 0.0 & ✓ \\  & DP & 2.653 & 0.26 & ✓ \\ \hline \multirow{4}{*}{Dts\_2} & AD & 0.221 & 0.781 & ✓ \\  & SW & 0.996 & 0.274 & ✓ \\  & KS & 1.0 & 0.0 & ✓ \\  & DP & 3.324 & 0.19 & ✓ \\ \hline \multirow{4}{*}{Dts\_3} & AD & 0.492 & 0.781 & ✓ \\  & SW & 0.996 & 0.339 & ✓ \\  & KS & 1.0 & 0.0 & ✓ \\  & DP & 0.035 & 0.983 & ✓ \\ \hline \multirow{4}{*}{Dts\_1} & AD & 0.274 & 0.781 & ✓ \\  & SW & 0.998 & 0.676 & ✓ \\  & KS & 1.0 & 0.0 & ✓ \\  & DP & 0.056 & 0.972 & ✓ \\ \hline \multirow{4}{*}{Dts\_2} & AD & 0.166 & 0.781 & ✓ \\  & SW & 0.998 & 0.923 & ✓ \\ \cline{1-1}  
The results obtained from the normality test using the Q-Q plot show that there were no grounds to reject the null hypothesis that the data were normally distributed, a conclusion that agreed with that obtained using the AD, KS, SW and DP statistical tests.
## 4 Experimental Evaluation  
In this section, we conduct experiments to verify the practicality of our proposed IBFD system prototype, which is introduced in Section 2. Here, we firstly investigate and validate the SI cancellation performance, and then evaluate the whole system performance of the proposed system. The experiments were carried out with the proposed system implemen  
Figure 8: SI cancellation by adaptive filter.  
tation mentioned in Section 2 in a NICT anechoic chamber in Yokosuka, Kanagawa, Japan. A picture of the experimental scene in the NICT anechoic chamber is shown in Figure 9. In the conducted experiments, in order to evaluate the SI cancellation performance of the antenna and AC board, in addition to the devices described in Section 2, a spectrum analyzer is used to measure the signal power of antenna and AC boards, as shown in Figure 9. Additionally, the system experimental parameters are summarized in Table 2. It is noted that in this experiment we only use a single carrier to transmit data with OFDM modulation. Additionally, in this work, we focus on implementing a basic IBFD system prototype to investigate and show the possibilities of it, so that we choose the system parameters based on the criterion of maximizing the system performance considering the capabilities of the instruments and devices employed. The experiments are conducted by the following steps:  
* Step 1: Turn on SI signal;
* Step 2: Evaluate SI cancellation of antenna;
* Step 3: Evaluate SI cancellation of AC;
* Step 4: Turn on desired signal and combine desired signal with SI;
* Step 5: Evaluate constellations, EVMs, and BERs before and after turning on AF-based digital SI cancellation.  
The results of the conducted experiments are given and discussed in the following subsections.
## 2 Materials and Methods  
The study applied multi-band Sentinel-2 satellite imagery and ERA5-Land climate model data. The spectral bands of Sentinel-2 used in the research were B2, B3, B4, B8, B8A, B11, and B12, with derived indices like NDVI, NDWI, NDSI, SI1-SI5, and BI for analysis of vegetation cover, soil moisture, and surface mineralization. Soil water volume content (VSWL1-VSWL4) was taken from the ERA5-Land model for four depths (0-7 cm, 7-28 cm, 28-100 cm, and 100-289 cm) to take into account the influence of water on salinity processes. Three geographically diverse regions were selected for investigation: the previous bottom of the Aral Sea (high salinity), Bozaigyr village in the Akmola region (moderate salinity and typical vegetation), and Shol in the Aktobe region (sandy soil with sparse vegetation), which allowed for testing in full on diverse grounds. A map of these sites superimposed on a Sentinel-2 background with EPSG:32643 coordinates is presented in Figure 1. The dataset covers 2019-2024 and comprises multi-seasonal images from April to October (2019-2021), which capture pre-cultivation, vegetative, and post-harvest growth stages. This setup captures spatial and seasonal variation, thus enhancing the robustness and generalizability of the model.  
Figure 2 illustrates the methodology of this approach to soil salinity clustering and prediction that combines machine learning methods. The flow chart shows the sequential data processing stages, from preprocessing and pseudo-label creation to ensemble learning and multi-task neural modeling.
# Experimental and Numerical Study in Dynamic Compaction of Weakly-Cohesive Soils
## 5 Discussion  
We identified several aspects that need further consideration to improve terrain compaction modeling (in general) and analytical terrain compaction models (for cohesionless soil). The most important aspects are  
1. _Initial measurements_: Standardized laboratory tests of some parameters are used to identify the nature of the soil (moisture content, state of natural compaction, initial dry density, etc.). It is necessary to widen the range of parameters that lead to the identification of the nature of the soil in the state before the start of the compaction operation.
2. _Establishment of the model boundary condition_: The type of dynamic loading and the boundary conditions are specified for a vibratory compactor and a weakly cohesive soil. Further research should investigate the effects of another type of soil in the compaction process.
3. _Soil deformation calculation_: The analytical approach as one layer may prevent the accurate estimation of soil deformations. Future research could focus on the direction of developing a new analytical model that will take into account the different deformation behaviors between sub-base layers.
4. _Assessment of the soil compaction degree_: We found small differences (under 1%) between soil deformation behavior results of the in situ tests during road construction and simulation results. However, more investigation is needed to accurately clarify the in situ soil deformation (consolidation) evolution under dynamic load in a short time to find the intercorrelation laws between soil mechanical properties measured by laboratory investigations. Also, the experimental investigations highlighted the fast compaction effect achieved by the compactor even when it operated with a single vibratory roller. One possible explanation is the terrain's characteristics, specifically its weakly cohesive soil and low initial moisture content (\(\mathrm{w_{i}}=11.5\%\) instead of \(\mathrm{w_{opt}}=18.54\%\)). These factors made it difficult to obtain stable geotechnical samples until after three initial static passes were completed. Afterward, the required soil consistency was achieved, though the moisture content decreased to 9.8%. However, this process resulted in an increase in the initial compaction degree to 85%, which is significantly higher than typically found in natural soils.
###### Abstract  
Super-resolution (SR) plays an important role in the processing and display of mixed-resolution (MR) stereoscopic images. Therefore, a stereoscopic image SR method based on view incorporation and convolutional neural networks (CNN) is proposed. For a given MR stereoscopic image, the left view of which is observed in full resolution, while the right view is viewed in low resolution, the SR method is implemented in two stages. In the first stage, a view difference image is defined to represent the correlation between views. It is estimated by using the full-resolution left view and the interpolated right view as input to the modified CNN. Accordingly, a high-precision view difference image is obtained. In the second stage, to incorporate the estimated right view in the first stage, a global reconstruction constraint is presented to make the estimated right view consistent with the low-resolution right view in terms of the MR stereoscopic image observation model. Experimental results demonstrated that, compared with the SR convolutional neural network (SRCNN) method and depth map based SR method, the proposed method improved the reconstructed right view quality by 0.54 dB and 1.14 dB, respectively, in the Peak Signal to Noise Ratio (PSNR), and subjective evaluation also implied that the proposed method produced better reconstructed stereoscopic images.  
Keywords:stereoscopic imaging and coding; mixed-resolution stereoscopic image; super-resolution; view difference; convolutional neural networks +
Footnote †: journal: Applied
# A CIA Triad-Based Taxonomy of Prompt Attacks on Large Language Models
## 6 Mitigation Strategies for CIA Dimensions in LLM Attacks
### Mitigating Availability Attacks  
Availability ensures that the LLM remains operational and responsive to legitimate users. Attacks such as prompt-based Denial-of-Service (DoS) and context flooding degrade the system's usability. The following mitigation strategies focus on managing resources effectively:  
* **Rate Limiting:** Imposing restrictions on the number of requests from a single user or IP address helps to prevent resource exhaustion. A prominent example occurred when a customer service chatbot implemented rate-limiting to counter large-scale coordinated prompt attacks aimed at overwhelming the system during peak hours.
* **Context Management:** Limiting the size and complexity of inputs prevents the system from being overwhelmed by excessively large prompts. This approach was critical in thwarting an attack where adversaries exploited the context window to introduce irrelevant or recursive loops, causing the model to exceed memory limits.  
* **Anomaly Detection:** Real-time monitoring systems can detect and block abnormal input patterns that indicate ongoing attacks. For example, monitoring tools deployed in a retail chatbot were used to detect and neutralize an attack involving botnets that repeatedly introduced malformed prompts to disrupt order-processing systems [68; 69; 70].
* **Load Balancing for LLM Infrastructure:** Incorporating intelligent load-balancing strategies can mitigate distributed DoS attacks targeting cloud-based LLM deployments. Providers such as AWS and Azure have implemented these strategies to ensure consistent model performance even under high-demand scenarios.