esa-sceva/satcom-chunk-collection huggingface dataset:
# Performing Arithmetic Operations with Locally Homogeneous Spiking Neural P Systems
## 4 An Locally Homogeneous Spiking Neural P System for Subtraction
### An LHSNP System with 1 Input Neuron for 2 Number Subtraction
#### 2.2.3 Subtrahend Input  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{\(t\)} & \multirow{2}{*}{\(in\)} & \multirow{2}{*}{\(aux_{4}\)} & \multicolumn{4}{c}{\(m_{6}\)} & \multicolumn{4}{c}{\(aux_{1,3}\)} & \multirow{2}{*}{\(aux_{1,9}\)} \\  & & & & & & & & & & & & & & & & & & \\  & & & & & & & & & & & & & & & & & \\ \hline \(t=0\) & - & 0 & 0 & \(\ldots\) & 0 & 1 & 1 & 0 & \(\ldots\)  
The spiking corresponding to the subtrahend bits is received by \(\sigma_{\text{in}}\) in descending order and then transmitted along \(\sigma_{s}\) to the \(\sigma_{\text{sub}}\). The transmission of the subtrahend is as follows:  
1. \(t=0\) to \(8\), \(\sigma_{s}\) did not generate spiking output.
2. \(t=9\) to \(16\), the spiking corresponding to the subtrahend bits are received by \(\sigma_{in}\) in the order of \(1\), \(1\), \(1\), \(0\), \(0\), \(0\), \(0\), \(0\) from low to high.
3. \(t=10\) to \(17\), the corresponding spiking of the subtrahend bits are sequentially received by \(\sigma_{s}\). At the same time, \(\sigma_{s}\) also receives \(1\) spiking sent by \(\sigma_{aux_{2},0}\), \(\sigma_{aux_{2},1}\) at each time slice. \(\sigma_{s}\) has \(3\), \(3\), \(3\), \(2\), \(2\), \(2\), \(2\), \(2\) spiking in each time slice, using rule \(a^{2}\to crlambda\) or \(a^{3}\to a\) direction sends spiking to \(\sigma_{sub}\).
4. \(t=11\) to \(18\), the spiking corresponding to the subtrahend bits \(1\), \(1\), \(1\), \(0\), \(0\), \(0\), \(0\) is sequentially received by \(\sigma_{sub}\).
4. Subtraction Operation  
At \(t=12\) to \(19\) time slice, \(\sigma_{sub}\) sequentially sends the calculation results to the environment. After the calculation of the highest bits of the two subtractions in \(\sigma_{sub}\) is completed, the spiking sent by \(\sigma_{aux_{3}}\) is used to detect whether there is a carry generation. If there is a carry generation, the rule \(a^{6}\to a^{4}\) is used to send \(4\) spiking to the environment, indicating data overflow. The calculation process is as follows:  
1. \(t=1\) to \(11\), \(\sigma_{sub}\) did not generate spiking output.
2. \(t=11\) to \(18\), the minuend and subtrahend bits are sequentially received by \(\sigma_{sub}\), and \(\sigma_{sub}\) has \(2\), \(3\), \(3\), \(2\), \(2\), \(2\), \(2\) spiking in each time slice.
3. \(t=12\) to \(19\), \(\sigma_{sub}\) sends the operation results \(0\), \(1\), \(1\), \(0\), \(0\), \(0\), \(0\) to the environment in sequence. At \(t=19\) time slice, \(\sigma_{sub}\) received \(5\) spikings sent by \(\sigma_{aux_{3}}\), no rules can be used, and there is no data overflow.  
From Table 5, it can be seen that the operation result of two natural numbers is \(00000110_{2}=6\), which is correct and verifies that the system can achieve subtraction operation.
###### Abstract  
In the life sciences, automation solutions are primarily established in the field of drug discovery. However, there is also an increasing need for automated solutions in the field of medical diagnostics, e.g., for the determination of vitamins, medication or drug abuse. While the actual metrological determination is highly automated today, the necessary sample preparation processes are still mainly carried out manually. In the laboratory, flexible solutions are required that can be used to determine different substances in different matrices. A suitable system based on an automated liquid handler was implemented. It has been tested and validated for the determination of three cannabinoid metabolites in blood, urine and saliva. To extract \(\Delta\)9-tetrahydroocannabinol-D3 (\(\Delta\)9-THC-D3), 11-hydroxy-\(\Delta\)9-tetrahydroocannabinol (THC-COOH) and 11-nor-9-carboxy-\(\Delta\)9-tetrahydroocannabinol (THC-COOH) from serum, urine and saliva both rapidly and cost-effectively, three sample preparation methods automated with a liquid handling robot are presented in this article, the basic framework of which is an identical SPE method so that they can be quickly exchanged against each other when the matrix is changed. If necessary, the three matrices could also be prepared in parallel. For the sensitive detection of analytes, protein precipitation is used when preparing serum before SPE and basic hydrolysis is used for urine to cleave the glucuronide conjugate. Recoveries of developed methods are >77%. Coefficients of variation are <4%. LODs are below 1 ng/mL and a comparison with the manual process shows a significant cost reduction.  
automation; liquid handling; sample preparation; biological matrices; micro solid-phase extraction; SPE; cannabinoid determination; LC-MS; cost reduction Article
## 2 Datasets  
The TID2013 dataset comprises 25 reference photos and 3000 distorted images created with 24 various forms of distortion, such as JPEG compression, Gaussian blur, and white noise. The collection also contains subjective quality rankings for each warped image, which were gathered from human observers during a subjective experiment. The subjective quality scores range from 0 to 9, with 0 being the poorest and 9 representing the best.  
The CSIQ dataset contains 30 reference photos and 866 distorted images created with six distinct forms of distortion, including JPEG2000 compression, JPEG compression, and Gaussian blur. The subjective quality scores for each warped image were also collected from human observers in a subjective experiment, and they range from 1 to 5, with 1 being the poorest quality and 5 being the greatest quality.  
The LIVE dataset is more current, with 29 reference photos and 779 distorted images made using 5 different forms of distortion, including JPEG compression, Gaussian blur, and white noise. The subjective quality scores for each deformed image were determined by human observers in a subjective experiment, and they range from 0 to 100, with 0 being the lowest quality and 100 representing the greatest quality.  
While these datasets have been frequently used to test the effectiveness of blind image quality evaluation algorithms in the literature, they do have certain drawbacks. For example, the distortion types contained in these datasets may not completely reflect the spectrum of real-world image distortions. Nonetheless, these datasets serve as a valuable standard for comparing the performance of various approaches in the field of blind image quality evaluation. Figure 1 represents the sample images from the datasets.
# A Vulnerability Scanning Method for Web Services in Embedded Firmware
## 1 Introduction
Regrettably, the efficient analysis of web services in embedded systems for vulnerability detection encounters two primary hindrances: (1) the intricate interactions and implicit dependencies between the front end and back end preclude the precise determination of contamination sources [12]; (2) during data flow tracing, achieving an optimal balance between efficiency and accuracy proves difficult. **Our approach. ** For web services within embedded systems, we track data flows between the front end and back end and develop rules to filter out boundary binary functions that are deemed risky yet prove to be harmless. Additionally, we designed a lightweight taint analysis methodology to facilitate vulnerability discovery. Specifically, our approach uses the relationship between the front end and back end to identify boundary binary files. We then discern functions that introduce external data as source points based on summarized characteristics. Furthermore, we formulate rules to detect and reduce hazardous functions viewed as sink points. Finally, we apply a coarse-grained taint analysis to trace data flows, facilitating vulnerability queries from source to sink points. In summary, we have developed a vulnerability scanning method that enables data flow tracing between the front end and back end. The contributions of this paper are as follows:  
* We offer data flow tracing based on front end-back end associations, introducing a novel method of pinpointing boundary binaries and external data entry points. * We provide rule parsing based on abstract syntax tree (AST) nodes derived from decompiled code, utilizing these AST nodes to filter hazardous functions. * We designed coarse-grained taint propagation rules to facilitate data flow tracking from external data entry points to hazardous functions. * We designed and implemented a prototype system, WFinder, and evaluated it using 10 real-world firmware samples. This evaluation revealed 13 unknown bugs, 8 of which were assigned CNVD numbers.
###### Abstract
In this research, heat transfer through radiation is not taken into account since its effect is negligible. The level of heat transfer is crucial to evaluate the accuracy of the viscosity measurement in almost all types of viscometers [17]. However, there are only a few studies available in the literature which focus on the viscosity measurement errors caused by the uneven temperature fields within the fluids [18; 19; 20; 21]. This research includes a much expanded and deeper analysis of these previous studies. In this research, the effects of non-uniform temperature fields are analysed detailly in order to confirm that even in limited amounts, serious viscosity and temperature deviations may exist. It is shown that even for small volumes and even in very controlled environments, an almost 35% viscosity measurement error occurs. The final results are supported by experimental data and numerical simulations using OpenFOAM [22]. Finally, as a solution to this problem, a new non-dimensional parameter for optimum heating speed called the Akpek number is proposed. The Akpek number enables the estimation of _VME_s in any possible case, thus assisting researchers in calculating viscosities in any possible case.
## 2 Preliminaries
### Biaffine Attention  
The biaffine attention mechanism is used as an alternative to the traditional MLP-based attention mechanism and affine label classifier, not using the LSTM recurrent state in the dual affine transformation, but instead first reducing dimensionality through MLP operations. Based on the aforementioned performance characteristics, the biaffine attention mechanism is employed to capture the probability distribution of the relationship between every pair of words in a sentence, as described by:  
\[h_{i}^{a}=\text{MLP}_{a}(h_{i}) \tag{8}\]  
\[h_{j}^{o}=\text{MLP}_{o}(h_{j}) \tag{9}\]  
The hidden states \(h_{i}\) and \(h_{j}\) obtained from the word embeddings \(W_{i}\) and \(W_{j}\) are processed through a multilayer perceptron to obtain the aspect feature representation and the opinion feature representation \(h_{j}^{o}\). The adjacency matrix \(R\in\mathbb{R}^{n\times n\times m}\) is obtained through Equations (10)-(12), which represents the relationships between words.  
\[g_{i,j}=h_{i}^{nT}U_{1}h_{j}^{o}+U_{2}(h_{i}^{a}\oplus h_{j}^{o})+b \tag{10}\]  
\[r_{i,j,k}=\frac{\exp(g_{i,j,k})}{\sum_{l=1}^{m}\exp(g_{i,j,l})} \tag{11}\]  
\[R{=}\text{Biaffine}(\text{MLP}_{a}(H),\text{MLP}_{o}(H)) \tag{12}\]  
where, \(U_{1}\), \(U_{2}\), and \(b\) are trainable weights and bias; \(\oplus\) denotes concatenation; \(g_{i,j}\) represents the relationship between \(w_{i}\) and \(w_{j}\); \(m\) is the number of relationship types; \(r_{i,j,k}\) represents the score for the \(k\)th relationship type of the word pair \((w_{i},w_{j})\). Biaffine is the integration of the aforementioned steps. Figure 4 shows a schematic diagram structure of model.
## 3 Results
### Fault Diagnosis
#### 3.2.1 Short-Time Fourier Transform (STFT)  
Before filtering the signal, it is advisable to study it in its raw version to prevent the loss of important information on account of a premature and inaccurate pre-treatment.  
The first applied technique is the Short-Time Fourier Transform, defined on the time-frequency domain, assuming a Hamming window with a length equal to 1024 and without overlap, as suggested in [13].  
It is possible to detect a general amplitude increase in the spectral components of bearing 3 starting from the 12 th second (of the last 49 considered), even more evident beyond the 45 th (Figure 16), with the appearance of the fourth and third harmonics of the BPFI (297 Hz), respectively, still completely invisible in the first days of screening (Figure 17); in the chromatic graph related to bearing 4's signal (Figure 18), a clear display of the fourth harmonic of the BPFO is detectable (although considering 238 Hz instead of 236 Hz, probably because of slipping among parts) as well as the presence of the second and sixth harmonics of the BSF (278 Hz). It is worth noting that traces of the same harmonic series are visible even in the signal acquired by the third accelerometer on bearing 2 (Figure 19), especially those related to the fault occurring on bearing 3, which is contiguous to it on the shaft.  
Figure 16: STFT applied to the signal from sensor 6 on bearing 3 (last 49 s).
# Word Sense Disambiguation Using Clustered Sense Labels
## 3 Deep-Learning Model for Word Sense Disambiguation  
A sequence labeling model is used for WSD. We chose a BERT-based model, which performed well, especially in WSD [15]. A transformer [25] encoder is added above the BERT to obtain the overall sentence information. We chose a single-task model [20], because, in our case, a multi-task model would not work well [26]. Figure 3 shows the architecture processing a sample sentence with clustered sense tags. Every word is divided into word pieces for input and the sense number is given to the first token of the word piece in the output.
In Mexico, varieties have been developed through natural mutation of the regional creole varieties, including those of white calyces. The characteristic of this variety (name as \"white\") are green stems, yellow calyces that dry acquire a light brown color, with an average yield of 570 kg dry calyces/ha [9]. On the other hand, combining the method of extraction with the solvent used is important to get efficient extraction of bioactive compounds. The most used methods to extract metabolites from a solid matrix are solid-liquid extractions (S-L) with solvents of different polarity (acetone, methanol, and water among the most common) [10], considering that acetone and alcoholic solutions are more used for phenols extraction. Ethanol is frequently used in extraction of phenolic compounds because it is considered a cheap, non-toxic and efficient green solvent [11]. During extraction step, the plant material swells enhance the accessibility of solvent into plant cells improving extraction and solubilization [11]. However, solid-liquid methods require long extraction times, high energy consumption and use of high solvent volumes [12]. Also, some compounds have an intracellular location, making their extraction difficult, because dissolution and transport processes are hindered [13]. That is why more efficient techniques have been implemented, such as ultrasound-assisted extraction (UAE), among others [14]. UAE can improve the extraction efficiency by promoting the mass transfer due to possible rupture of the cell wall and reduction of particle size by the acoustic effect of cavitation [15]. Through these structural changes and cell disruptions, phenolic compounds can be more easily extracted from the plants [16]. It is a simple, economic alternative, with higher yields, lower energy use and solvent consumption [17]. Since ultrasound-assisted extraction is a convenient method for take-out bioactive compounds from vegetal, the present work was focused on getting phenols from red and white calyces of two _H. sabdariffa_ varieties, using ultrasound-assisted extraction at different treatment times, and determine the yield of phenols, anthocyanins, flavonoids, tannins, antioxidant activity (DPPH) and antigenotoxic effect (comet assay). A conventional, traditional solid-liquid extraction was applied as a reference.
## 2 Materials and Methods
### Biofilm Prevention Concentration (BPC) Assay  
To analyze the CHG effect in preventing _S. mutans_ biofilm formation, an overnight culture of _S. mutans_ Clarke ATCC 25,175 was diluted up to 2 \(\times\) 10\({}^{8}\) CFU/mL in a fresh BHI medium containing 2% glucose to induce biofilm formation. CHG stock (200 mg/mL) and CHG formulations (2 mg/mL) were diluted up to 20 \(\upmu\)g/mL in sterile 2% glucose BHI broth. Then, two-fold dilutions, 100 \(\upmu\)L each, were prepared in a 96-well-microtiter plate in duplicate. One-hundred microliters of sterile 2% glucose BHI broth was used as a growth medium for positive controls. Negative controls were performed using 100 \(\upmu\)g/mL ampicillin in the same broth. Thereafter, 100 \(\upmu\)L of the bacterial inoculum prepared at 2 \(\times\) 10\({}^{8}\) CFU/mL was added to each well. The final CHG concentrations tested were 10, 5, 2.5, 1.25, 0.6, 0.3 and 0.15 \(\upmu\)g/mL in the presence of 10\({}^{6}\) CFU/mL of _S. mutans_. Plates were incubated at 37 \({}^{\circ}\)C for 48 h, and growth was monitored by measuring optical density at 600 nm.  
The plate was incubated at 37 \({}^{\circ}\)C overnight. The day after, supernatants were removed, and each well was washed 3 times with 150 \(\upmu\)L of (phosphate buffered saline) PBS 1X to remove nonadherent cells. An aliquot of 100 \(\upmu\)L of 0.1% CV was added to each well (CV was diluted in MilliQ water), and the plate was incubated at RT for 40 min in the dark. The CV was then removed, and 100 \(\upmu\)L of 95% ethanol was added to each well and mixed by pipetting. The absorbance of the samples was read at \(\uplambda\) = 540 nm.
# A Comprehensive Review of Open Caisson Modeling Technology: Current Practices and Future Prospects
## 5 Challenges and Future Directions
### Future Directions
#### 5.2.5 Centrifuge Transparent Soil Modeling  
In view of its advantages in modeling soil behavior in geotechnical events, the centrifuge modeling technique can overcome the challenges of identifying representative soil displacements caused by shaft construction [210]. Modern technological advancements have enabled precise monitoring of soil deformations anywhere in small-scale centrifuge models [211; 212]. Transparent soils were utilized in centrifuge experiments to investigate different mechanism like pullout capability, anchor rotation of VDPA and embedment loss during keying [213; 214]. [PERSON] [60] created a centrifuge modeling method that combines transparent soil with laser imaging technologies and was verified by conducting experiments on a strip foundation. Future advancements in centrifuge testing allowed for the accurate simulation of gravity effects and realistic stress states in small-scale models using transparent soil masses. This technique held significant potential for studying ultra-deep shafts and addressing the complexities of heterogeneous soil strata. Another interesting approach could be to incorporate real-time monitoring and multi-sensor systems into centrifuge transparent soil models. Combining technologies such as fiber-optic sensors, piezoelectric transducers, and particle image velocimetry (PIV) allowed for simultaneous monitoring of displacement, strain, and pore pressure during testing. This multi-parameter method generated a larger dataset for verifying numerical models and expanding prediction competencies. Future research should also look into the effects of factors like variations in temperature and the flow of groundwater on soil dynamics in transparent models, specifically in the setting of climate change implications on infrastructure, which has remained unexplored.
## 2 Localization Solution
### Base Operation  
The localization system employs a sequential ultrasound emission strategy in combination with a time difference measurement approach. This method circumvents the need for beacon-receiver synchronization typical in ToF systems. The operational sequence and signal processing technique are as follows:  
Signal Emission: The system operates by firing the beacons in pairs according to a pre-determined sequence. Each pair of beacons emits an ultrasound signal simultaneously. This paired emission strategy allows for relative time measurements, reducing the complexity and potential errors associated with absolute time synchronization. By using pairs of beacons, the system can create distinct time differences that are crucial for the localization process.  
Time Difference Measurement: As the signals travel from the beacons to the receivers, the system measures the time differences (\(\Delta t\)) between the arrivals of these signals at each receiver. These time differences are then used to calculate the relative distances between the beacons and the robot, based on the known speed of sound in the medium. By using time differences rather than absolute times, the need for precise clock synchronization between beacons and receivers is eliminated.  
Position Calculation: By analyzing the time differences from multiple pairs of beacons, the system can triangulate the precise position of the robot. The calculation utilizes hyperbolic multilateration techniques, where each time difference measurement defines a hyperbolic curve on which the robot must lie. The intersection of these curves, derived from multiple beacon pairs, pinpoints the robot's position with high accuracy. This approach, based on the known positions of the beacons and the measured time differences, allows for high-accuracy localization without the need for complex synchronization mechanisms.  
Figure 1: Overview of the ultrasound-based localization system, showing the relationship between ceiling-mounted beacons and the mobile-robot-mounted receivers.
## 7 Parameter Tuning Method of the HCS of the MAR System  
Through numerous simulation experiments and prototype testing experiments, this paper summarizes the parameter tuning method of the HCS of the MAR system:  
The parameter tuning method of SYSTD is described in Section 5.3.  
ESO Parameter Tuning: There are three parameters to be tuned in the ESO, namely, \(\beta_{c1}\), \(\beta_{c2}\), and \(\beta_{c3}\). \(\beta_{c3}\) is the most important among the three parameters.  
When tuning the parameters, first, select the parameter \(\beta_{c3}\). If \(\beta_{c3}\) is too small, the observation accuracy of \(z_{3}(m)\) will be insufficient, and \(z_{1}(m)\) and \(z_{2}(m)\) will lag behind \(x_{1}(m)\)and \(x_{2}(m)\). If \(\beta_{e3}\) is too large, it will increase system fluctuations and even cause system oscillation. Therefore, \(\beta_{e3}\) is adjusted first to ensure the accuracy of the ESO.  
Then, adjust \(\beta_{e1}\) and \(\beta_{e2}\) from small to large to reduce the oscillation of the ESO output. Since there is coupling in the horizontal system of the MAR system, these two parameter values should be as small as possible while ensuring the stable output of the ESO.  
In addition, \(\delta\) has a significant impact on \(\beta_{e1}\), \(\beta_{e2}\), and \(\beta_{e3}\). Before adjusting these three parameters, the value of \(\delta\) needs to be determined first. In this paper, \(\delta=h\).  
NLSEF Parameter Tuning: There are three parameters to be tuned in the NLSEE, namely, \(\beta_{f1}\), \(\beta_{f2}\), and \(b_{0}\). \(b_{0}\) is the coefficient of the control variable \(u\). The adjustment methods of \(\beta_{f1}\) and \(\beta_{f2}\) are similar to those of PD parameters.