esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This comparative study focused on four advanced transformer-based deep learning models aimed at classifying water potability from physicochemical features. The experimental results indicate clear performance differences between the architectures, with enhanced tabular transformers (ETT) showing the best performance. The ETT achieved a test accuracy of nearly 95%, significantly outperforming the other models (SAINT: -92%, TAPE: \(\sim\)89%, and FTTransformer: \(\sim\)83%). The F1-score of ETT (\(\approx\)0.94) was likewise the highest among these, which points to its excellent precision-recall balance for drinkable and non-drinkable inferences.  
Specifically, multi-head self-attention and efficient feature embeddings combined within the enhanced tabular transformer proved very beneficial for the binary classification task. The strong performance of the SAINT continues to resonate with previous reports touting its strengths, emphasizing the importance of its dual-attention mechanism, although it ranked just short of ETT in our evaluations. The TAPE model achieved competitive results aided by an unsupervised autoencoder pre-training phase, suggesting this pre-training helped, although it certainly could not rival ETT's fully supervised approach.  
Meanwhile, the FTTransformer--albeit easy and theoretically powerful on some benchmarks, was the underperformer in this study, perhaps owing to extensive hyperparameter tuning or difficulties associated with the water potability data that are very highly imbalanced. All in all, the study establishes that transformer-based deep learning frameworks are not only feasible but are among the most effective for water quality classification. The enhanced tabular transformer could be a strong basis for the development of intelligent water monitoring systems due to the state-of-the-art results it achieved on the dataset tested. Such findings serve to motivate the further embrace of advanced deep learning in environmental informatics, especially in high-risk applications such as the drinking water safety assessment. Concurrently, the study indicated that one model cannot be simply the best across the board; architecture selection must be relevant to the characteristics of the dataset and constraints in computational resources (for example, SAINT took longer to train due to its elaborate attention, whereas TAPE trained relatively fast but was a bit less accurate).
# Melody Extraction Using Chroma-Level Note Tracking and Pitch Mapping
## 3 Evaluation Results and Discussion
### Qualitative Error Analysis  
A qualitative error analysis is valuable to help further improve the performance of the proposed method. It is noticed that the proposed method performs very well for the first 10 excerpts of ISMIR2004, while it is surpassed by some methods on this dataset. Hence, if the performance on the opera and pop excerpts is improved, the total performance on this dataset has the potential to surpass others. When we look insight into this dataset, the deep vibratos and pitch glides are commonly found, and the lower frequency accompaniments are also very strong. Moreover, the proposed method works much better in MIR1K, where there are 1000 excerpts, exceeding the other two datasets greatly.  
Figure 9: Median results of extracting melody from a polyphonic music recording. (**a**) Spectrogram; (**b**) HPCP; (**c**) chroma-level notes; (**d**) coarsely mapped pitches; (**e**) fine-tuned pitches; (**f**) melody output.  
There is no pitch shifting between different octaves by the proposed method because of the chroma-level note mapping. However, if the accompaniment is strong, the DP tracking may output the same pitch at the end of some notes due to the DP smoothing processing, leading to false alarm estimations. Thus, a more sophisticated voicing detection scheme is needed to further reduce the false alarms.
# Assessing the Functional Response to Streamside Fencing of Pastoral Waikato Streams, New Zealand
## 2 Materials and Methods  
### Study Sites and Design  
Our study was conducted in the Waikato region of the central North Island, New Zealand between March 2011 and April 2012. All measurements were conducted during stable weather conditions in autumn, when streams are generally most stressed due to sustained low flows and warmer temperatures. We sampled 11 pairs of fenced and unfenced stream reaches to assess the effects of riparian fencing on stream health. Fenced reaches had been retired and fenced from adjacent grazed land between 5 and 34 years previously (Table 1), and stocking rates were consistent among unfenced sites. Fenced stream reaches were either located upstream (Raglan and Whatawhata) or downstream of unfenced stream reaches (Waitetuna, Waitomo, Mangawhara, Matarawa, Tapapakanga, Little Waipa and Waitete). At two sites, fenced and unfenced reaches were located on neighbouring, but different streams (Taupo & Kakahu), due to the difficulty of finding fenced sites equal or greater than 30 years old adjacent to grazed sites. Nine of the 11 pairs had been previously studied by [PERSON] et al. [7] to test the effectiveness of fenced riparian buffers on biological and water quality indicators. Catchment size ranged from just over 300 ha to 8600 ha (Whatawhata Stream and Little Waipa River, respectively; Table 1) as determined from the Freshwater Environments of New Zealand database, FENZ, [50]. Eight of the 11 study pairs were located in catchments with high-producing exotic grassland as the predominant land cover upstream and two study pairs with either exotic or indigenous forest, LCDB, [51]. Kakahu was the only site where predominant land use differed between Treatments, with high-producing exotic grassland at the fenced reach and indigenous forest at the unfenced reach. The length of fenced reaches ranged from 480 m (Raglan) to 1700 m (Taupo) (Table 1). Fenced stream length as a proportion of total upstream length ranged from 0.1% (Kakahu) to 33.1% (Matarawa; Table 1). Riparian vegetation, other than grass, was present at both fenced and unfenced sites, except at unfenced Waitete, Kakahu and Taupo sites. Where riparian vegetation other than grass was present, mean vegetation buffer widths ranged from 0.6 m (Mangawhara fenced) to 75 m (Taupo fenced; Table 1) and were wider at the fenced (mean = 23.1 m) than at the unfenced sites (mean = 5.5 m). Riparian area as a proportion of total catchment area ranged from less than 0.01% (unfenced sites at Kakahu, Waitete and Mangawhara) to 1.73% (Whatawhata fenced site; Table 1).
# Influence of Acoustic Streams on the Efficiency of Ultrasonic Particle Agglomeration
###### Abstract  
The article is devoted to the study of ultrasonic agglomeration of PM 2.5 in homogeneous and inhomogeneous ultrasonic fields. The possibility of increasing the efficiency of ultrasonic agglomeration by initiating acoustic streams in a resonant inhomogeneous ultrasonic field is shown. A inhomogeneous ultrasonic field with zones of high and low sound pressure levels formed using a bending-oscillating disk transmitter made it possible to initiate acoustic vortex-type streaming that promotes the movement of particles into the nodal areas of a standing wave and between them. Due to the formation of a inhomogeneous ultrasonic field, the efficiency of particle collection is increased: for PM 2.5, the efficiency reaches 95%; PM 1.5--92%; PM 0.5--85%. The results were obtained under the following conditions: concentration \(2\times 10^{-2}\) g/m\({}^{3}\), sound pressure level 165 dB, flow rate 6.2 m\({}^{3}\)/h. For comparison, when a homogeneous ultrasonic field is formed in the agglomeration chamber (under similar conditions), the efficiency of particle capture by inertial gas cleaning equipment does not exceed the following: for PM 2.5--89%; PM 1.5--85%; and PM 0.5--76%. The obtained research results made it possible to propose a design for an agglomeration chamber that can greatly increase the productivity of ultrasonic flow processing.  
ultrasound; gas purification; agglomeration; acoustic field; resonant gap; vortex flow Article
## 2 Materials and Methods
### Hardware Composition of Sowing Variable Measurement and Control System Unit  
As shown in Figure 2, the sowing variable measurement and control system mainly consists of a drive motor, a chain wheel transmission mechanism, a control terminal, and a motor control unit. The system employs an IPCA-7010 industrial onboard computer, whose Atom motherboard based on the X86 architecture serves as the core. It integrates storage, communication, display, and input-output modules, and has good compatibility and scalability, making it easy to operate and maintain. It also meets well the various needs of multi-source data acquisition and storage, operation monitoring, information analysis, and decision-making in precision agriculture field operations. The terminal integrates a GPS positioning module, CAN bus module, and DTU unit. The GPS positioning module uses the UBlox-Neo M8 module, which supports dual-mode positioning of GPS and BeilDou systems and has a maximum positioning error of \(\leq\)2.5 m.  
Traditional seeders adopt ground wheel transmission which, with the continuous increase of operating speed, causes vehicle slip to become one of the important factors affecting sowing quality. Direct-drive technology can effectively eliminate the adverse effects of mechanical transmission. The electrically driven seeding system mainly uses a DC motor to replace the mechanical transmission structure, directly driving the seeding disc, and obtains the operating speed of the seed drill through speed measuring devices such as speed radar or GPS. Based on the speed combined with the set sowing quantity information, the rotation speed of the seeding disc is adjusted in real time to achieve a rational match between sowing quantity and operating speed. Adopting an electrically  
Figure 2: Unit for Sowing Variable Measurement and Control System. (**a**) Drive motor. (**b**) Chain wheel transmission mechanism. (**c**) Control terminal. (**d**) Motor control unit.  
driven seeding structure not only suits high-speed sowing operations well, but also further realizes independent start and stop control of each seeding unit.  
A BG45 \(\times\) 15 SI integrated DC motor is used as the driving source for the seeding axis and fertilization axis. The motor integrates the drive circuit inside and uses a DC analog voltage signal to dynamically adjust the motor speed, with a signal range of 0-10 V. The motor output power is 52.5 W, working voltage is DC12 V, maximum speed is 3080 rpm, and it is equipped with a PLG52 planetary reducer with a reduction ratio of 50. After the control terminal makes a decision, the control instructions are sent to the seeding and fertilizing driving motors through the CAN bus, and the seeding and fertilization axes are driven to rotate.  
The motor control unit adopts K8516 CAN bus analog output module, with the main technical parameters as follows: 4 channels of output signals, 12-bit DA resolution, output signal range of 0-10 V, and DC9-24 V power supply voltage. It communicates with the control terminal through the CAN bus interface.
## 2 Application of Neural Networks for Water Level Prediction  
Over the past years, artificial neural networks (ANN) have been well developed and employed in many application areas to extract and learn information from various kinds of data [40]. The ANN was inspired by the working principles of the biological brain and the neuron system [41]. Types of ANNs are twofold, based on the connection between neurons as feed forward neural networks and recurrent neural networks (RNN). FFNN does not have a feedback path from the neuron's output to its input, but it does in RNN [42]. Usually, NNs are arranged as interconnected layer architecture [43].  
Different types of ANNs have been exercised in various application areas. Selection of the right ANN type is vital for optimal performance. It is significant to consider how structural differences between multiple types of ANNs, such as feed forward neural networks (FFNN), long short-term memory (LSTM) networks, and gated recurrent units (GRU), affect results. Each type of network has its strengths and weaknesses, and the choice of the network depends on the specific task and desired outcomes. For example, FFNN is good at mapping inputs to outputs in a simple, direct way and often functions for tasks such as image classification and regression. LSTM and GRU networks are better suited for tasks involving sequential data, where the current output depends on previous inputs and outputs, as they can maintain long-term dependencies and handle the temporal dynamics of the data. These networks are often used for language modeling and time series prediction tasks.  
The first type of relationship selected for the investigation is the traditional one, in which wetland water level as a function of daily rainfall to the catchment, daily evaporation, minimum daily temperature, maximum daily temperature, daily relative humidity at daytime, daily relative humidity at nighttime, and daily average wind speed. Since it is a direct mapping from the aforementioned climate parameters to the water level, the FFNN was selected. To investigate the other relationship, the temporal relationship that exists between daily water levels, the LSTM was selected. The daily water level is a time series signal, and there could exist a temporal relationship between water levels of the nearby days.
# Sentinel-1 and -2 Based near Real Time Inland Excess Water Mapping for Optimized Water Management
## 2 Materials and Methods
### Methodology  
A large number of satellite images needs to be processed to be able to create the weekly inland excess water maps. This requires automation of the data processing workflow (Figure 4). For this purpose, a set of Python scripts was developed that combined standard libraries like gdal and numpy  
Figure 3: Validation sites in the study area.  
with arcpy for GIS operations. Two sets of scripts ran with a different interval. The daily set comprised downloading, preprocessing and processing data when new satellite images became available, while the second set ran on a weekly basis and integrated the daily results into the weekly map.
# Deep Learning-Based Crack Detection on Cultural Heritage Surfaces
###### Abstract  
This study employs a deep learning-based object detection model, GoogleNet, to identify cracks in cultural heritage images. Subsequently, a semantic segmentation model, SegNet, is utilized to determine the location and extent of the cracks. To establish a scale ratio between image pixels and real-world dimensions, a parallel laser-based measurement approach is applied, enabling precise crack length calculations. The results indicate that the percentage error between crack lengths estimated using deep learning and those measured with a caliper is approximately 3%, demonstrating the feasibility and reliability of the proposed method. Additionally, the study examines the impact of iteration count, image quantity, and image category on the performance of GoogleNet and SegNet. While increasing the number of iterations significantly improves the models' learning performance in the early stages, excessive iterations lead to overfitting. The optimal performance for GoogleNet was achieved at 75 iterations, whereas SegNet reached its best performance after 45,000 iterations. Similarly, while expanding the training dataset enhances model generalization, an excessive number of images may also contribute to overfitting. GoogleNet exhibited optimal performance with a training set of 66 images, while SegNet achieved the best segmentation accuracy when trained with 300 images. Furthermore, the study investigates the effect of different crack image categories by classifying datasets into four groups: general cracks, plain wall cracks, mottled wall cracks, and brick wall cracks. The findings reveal that training GoogleNet and SegNet with general crack images yielded the highest model performance, whereas training with a single crack category substantially reduced generalization capability.  
deep learning; GoogleNet; SegNet; segmentation; crack detection; cultural heritage +
Footnote †: journal: Applied sciences
## Appendix C  
Consider the following Lyapunov function:  
\[V=\frac{1}{2}{S_{i}}^{2} \tag{101}\]  
By taking the derivative of (32), one can obtain:  
\[\begin{array}{l}\dot{S}_{i}=(t_{f}-t)\left[e^{\sum\limits_{i=1}^{N}{a_{ij}(x_{ 3i}-x_{3j})}}\left(\sum\limits_{j=1}^{N}{a_{ij}(x_{4i}-x_{4j})}\right)^{2}+e^{ \sum\limits_{i=1}^{N}{a_{ij}(x_{3i}-x_{3j})}}\sum\limits_{j=1}^{N}{a_{ij}(x_{ 4i}-\dot{x}_{4j})}\right]+\\ ne^{\sum\limits_{i=1}^{N}{a_{ij}(x_{3i}-x_{3j})}}\sum\limits_{j=1}^{N}{a_{ij}(x _{4i}-x_{4j})}\end{array} \tag{102}\]  
By taking the derivative of (101), then:  
\[\begin{array}{l}\dot{V}={S_{i}}\dot{S}_{i}\\ =(t_{f}-t)e^{\sum\limits_{i=1}^{N}{a_{ij}(x_{3i}-x_{3j})}}{S_{i}(-k_{3}S-k_{4} sign(S_{i})+\frac{w_{qi}}{x_{1i}}-\dot{q}_{j})}\end{array} \tag{103}\]  
Define \(\frac{w_{qi}}{x_{1i}}-\dot{q}_{j}\) as the uncertain disturbance (\(d\)). Then, (103) comes to:  
\[\begin{array}{l}\dot{V}=(t_{f}-t)e^{\sum\limits_{i=1}^{N}{a_{ij}(x_{3i}-x_{ 3j})}}{S_{i}(-k_{3}S_{i}-k_{4}sign(S_{i})+d)}\\ =-k_{3}(t_{f}-t)e^{\sum\limits_{i=1}^{N}{a_{ij}(x_{3i}-x_{3j})}}{S_{i}}^{2}-(t _{f}-t)e^{\sum\limits_{i=1}^{N}{a_{ij}(x_{3i}-x_{3j})}}(k_{4}-\left\|{d} \right\|_{\infty})\\ \leq 0\end{array} \tag{104}\]  
Therefore, the sliding surface (\(S_{i}\)) can converge to zero. When \(S_{i}=0\) is achieved, we can deduce that:  
\[t_{go}e^{\sum\limits_{i=1}^{N}{a_{ij}(x_{3i}-x_{3j})}}\sum\limits_{j=1}^{N}{a _{ij}(x_{4i}-x_{4j})}+\mu(e^{\sum\limits_{i=1}^{N}{a_{ij}(x_{3i}-x_{3j})}}-1)=0 \tag{105}\]  
To simplify the calculation, define \(e^{\sum\limits_{i=1}^{N}{a_{ij}(x_{3i}-x_{3j})}}=I\), and then:  
\[t_{go}\dot{I}+\mu(I-1)=0 \tag{106}\]  
It can be observed that the solution to (106) is given as:  
\[I=C(t_{f}-t)^{\mu}+1 \tag{107}\]  
where:  
\[C=\frac{I(t_{0})-1}{\left(t_{f}-t_{0}\right)^{\mu}} \tag{108}\]  
From (107), one can obtain:  
\[\sum\limits_{j=1}^{N}{a_{ij}(x_{3i}-x_{3j})}=\ln\left(C\left(t_{f}-t\right)^{ n}+1\right) \tag{109}\]Taking the derivative of (A42), we can obtain:  
\[\sum\limits_{j=1}^{N}a_{ij}(x_{4i}-x_{4j})=\frac{Cn\Big{(}t_{f}-t\Big{)}^{n-1}}{C \Big{(}t_{f}-t\Big{)}^{n}+1}\] (A43)  
From (A42) and (A43), one can further obtain:  
\[\begin{split}\lim\limits_{t\to t_{f}j=1}^{N}a_{ij}(x_{3i}-x_{3j})& =0\\ \lim\limits_{t\to t_{f}j=1}^{N}a_{ij}(x_{4i}-x_{4j})& =0\end{split}\] (A44)  
This completes the proof of Theorem 4.
# A Comprehensive Review of the Application Characteristics of Biodiesel Blends in Diesel Engines
## 3 Engine Performance
### Exhaust Gas Temperature  
Density, viscosity, calorific value, cetane number and other characteristics of biodiesel mainly affect the exhaust gas temperature (EGT) [60; 61]. A lower EGT generally means good combustion and less heat loss, indicating the effective usage of thermal energy in the fuel. In general, the heat generated in the cylinder increases with an increase of the rotation speed and load, resulting in an increase in EGT [62].  
Both higher and lower EGT may be observed when using biodiesel. [PERSON] et al. compared the EGT of rice-bran-, neem-, and cottonseed-oil biodiesel, and the results showed that the EGT of biodiesel are higher [34]. Other studies [62; 63] also reached similar conclusions. However, some researchers found that the EGT of pure diesel is lower [60; 64; 65; 66]. [PERSON] et al. tested the changes in EGT of different concentrations of biodiesel at different loads, and found that the average EGR values of B30, B50, and B70 are 2.77%, 8.28%, and 7.17% lower than diesel under the same conditions [65]. EGT is affected by the physical and chemical characteristics of the fuel, such as density, viscosity, calorific value, and cetane number [60]. The higher EGT of biodiesel may be attributed to its higher oxygen content, which promotes combustion [67]. The results are shown in Figure 5. Poor atomization caused by high density and high viscosity will cause unburned fuel in the main combustion period to burn later, resulting in a higher EGT [34]. However, a higher cetane number in the fuel would result in a shorter ignition delay, resulting in an earlier start of combustion, thereby reducing EGT and shortening the cylinder power cycle [64]. The lower calorific value may also lower cylinder temperature [60]. Another study [68] also indicated that the EGT of biodiesel is directly related to its oxygen percentage.  
Figure 4: Coefficient of variation in indicated mean effective pressure (\(COV_{IMEP}\)) of palm biodiesel–diesel blended fuel and mineral diesel [44].  
Figure 5: Variation of exhaust gas temperature (EGT) with different engine loads for different test fuels [65].
# An Improved Ciphertext-Policy Attribute-Based Encryption Scheme in Power Cloud Access Control
## Preliminaries and Definitions
### Linear Secret-Sharing Schemes  
A secret-sharing scheme II over a set of parties \(\mathrm{P}\) is called linear (over \(\mathbb{Z}_{p}\)) if:  
1. _The shares for each party form a vector over_ \(\mathbb{Z}_{p}\)_._
2. _There exists a matrix_ \(M\) _with_ \(l\) _rows and_ \(n\) _columns called the sharing-generating matrix for II. For all_ \(i\ =\ 1,2,\cdots,l\)_, the_ \(i\)_'th row of_ \(M\)_, we let the function_ \(\rho\) _define the party labeling row_ \(i\) _as_ \(\rho(i)\)_. When we consider the column vector_ \(v=(s,r_{2},\cdots,r_{n})\)_, where_ \(s\in\mathbb{Z}_{p}\) _is the secret to be shared, and_ \(r_{2},\cdots,r_{n}\in\mathbb{Z}_{p}\) _are randomly chosen, then_ \(M\cdot v\) _is the vector of_ \(l\) _shares of the secret_ \(s\) _according to II. The share_ \(\left(M\cdot v\right)_{i}\) _belongs to party_ \(\rho(i)\)_._  
Every linear secret-sharing scheme based on the above definition enjoys the linear reconstruction property, defined as follow: Suppose that II is an LSSS for access structure \(\mathbb{A}\). Let \(S\in\mathbb{A}\) be any authorized set, and let \(i\subset\{1,2,\cdots,l\}\) be defined as \(I\ =\ \{i:\rho(i)\in S\}\). Then, there exist constants \(\left\{w_{i}\in\mathbb{Z}_{p}\right\}_{i\in I}\), if \(\{\lambda_{i}\}\) are valid shares of any secret \(s\) according to II, then \(\sum_{i\in I}w_{i}\lambda_{i}\ =\ s\). These constants \(\left\{w_{i}\right\}\) can be found in a time polynomial, in the size of the share-generating matrix \(M\).