esa-sceva/satcom-chunk-collection huggingface dataset:
# Commonsense Knowledge-Aware Prompt Tuning for Few-Shot NOTA Relation Classification
###### Abstract  
Compared with the traditional few-shot task, the few-shot none-of-the-above (NOTA) relation classification focuses on the realistic scenario of few-shot learning, in which a test instance might not belong to any of the target categories. This undoubtedly increases the task's difficulty because given only a few support samples, this cannot represent the distribution of NOTA categories in space. The model needs to make full use of the syntactic information and word meaning information learned in the pre-training stage to distinguish the NOTA category and the support sample category in the embedding space. However, previous fine-tuning methods mainly focus on optimizing the extra classifiers (on top of pre-trained language models (PLMs)) and neglect the connection between pre-training objectives and downstream tasks. In this paper, we propose the commonsense knowledge-aware prompt tuning (CKPT) method for a few-shot NOTA relation classification task. First, a simple and effective prompt-learning method is developed by constructing relation-oriented templates, which can further stimulate the rich knowledge distributed in PLMs to better serve downstream tasks. Second, external knowledge is incorporated into the model by a label-extension operation, which forms knowledgeable prompt tuning to improve and stabilize prompt tuning. Third, to distinguish the NOTA pairs and positive pairs in embedding space more accurately, a learned scoring strategy is proposed, which introduces a learned threshold classification function and improves the loss function by adding a new term focused on NOTA identification. Experiments on two widely used benchmarks (FewRel 2.0 and Few-shot TACRED) show that our method is a simple and effective framework, and a new state of the art is established in the few-shot classification field.  
comonsense knowledge-aware prompt tuning; few-shot none-of-the-above relation classification; pre-trained language models; scoring strategy
# Improving a Process-Based Model to Simulate Forest Carbon Allocation under Varied Stand Density
## 4 Discussion  
### Module Improvement  
The method of determining the allocation ratio according to the relative intensity of the water, light, and nutrient constraints (\(W\), \(L\), and \(N\)) was better than for the method that used a fixed proportion in the original model. Nutrition (such as nitrogen and phosphorus) plays an important role in the carbon cycle and plant growth. Previous field measurements and theoretical studies have shown that nitrogen limitation has a significant effect on the response of the carbon cycle to a change in stand density [42]. When the stand density is too high or during the rapid growth stage of trees, nutrition is a limiting factor for growth, and the impact of water and light on plants is lower. The allocation of carbon in plants, especially the allocation to stems owing to nutrition, cannot be fully determined without considering nutrition-limiting factors [43]. The soil in the study area is often heavily weathered, and nutrients, such as \(N\), are low in concentration and availability. The \(W\), \(L\), and \(N\) module, where nitrogen limitation is roughly controlled by the temperature and water availability [44], can better simulate the allocation change constrained by the nutrient availability. Furthermore, there was one stand that underestimated the biomass using \(W\), \(L\), and \(N\) model; this could have been caused by soil texture factors. The particle composition and total porosity of different texture soils are different. These factors affect the distribution of the plant biomass by affecting water, gas, heat, and nutrition.
# Sequence Deep Learning for Seismic Ground Response Modeling: 1D-CNN, LSTM, and Transformer Approach
## 5 Results and Discussion  
### Prediction Performance  
#### 5.1.1 Response Spectra  
We first evaluate the prediction performance of the response spectra. Figures 11-22 show the prediction results for the sites explained in Table 1. Note that the weighted moving average is applied to the prediction for reducing the prediction noise as performed by [PERSON] et al. (2018). The subfigures \"a\" to \"f\" in each figure show the six representative response spectra prediction results among 10 testing datasets for the corresponding site. The data-driven models--CNN-, LSTM-, and transformer-based models--generally show good prediction performances. The physics-based model, SHAKE, tends to overestimate the responses for most cases.  
The data-driven models predict the overall spectral trends well and capture the critical natural period where the response shows the peak amplitude and its amplitude. For example, in Figure 11a (site FKSH18), the predicted spectra align well with the true spectral trend. Further, the true peak amplitude and the predicted peak amplitudes show a good agreement at around the critical natural period of 0.96 s. These successful predictions are observed throughout the majority of the test datasets. In contrast, the physics-based model tends to overestimate the amplitudes, particularly for the sites FKSH17 (Figure 11), IWTH05 (Figure 16), IWTH21 (Figure 19), and IWTH22 (Figure 20).  
In a few instances, the data-driven models deviate from the true spectrum, such as Figure 13 for the site FKSH19, Figure 14c for the site IBRH13, and Figure 16a,b for the site IWTH05. In these cases, the models fail to capture the overall trends of the response spectra and the peak amplitudes at the corresponding natural periods. For example, in Figure 14c, the models tend to show the peak amplitude at around 0.1-0.2 s, while the true peak amplitude occurs at 0.3 s. Additionally, the CNN model exhibits larger overestimations compared to the other models.  
Among the models, the CNN occasionally makes a large overestimation and fails to capture the overall spectral trend (e.g., Figure 14c,d for site IBRH13 and Figure 17d for site IWTH12). These excessive overestimations and mispredictions are less frequently observed in the LSTM and transformer models. This can be attributed to the limited ability of the CNN to learn feature dependencies over long sequences, as it primarily focuses on adjacent features. In contrast, the LSTM and transformer models are inherently designed to consider the long sequences and their feature dependencies.
# An Audio Distortion Dynamic Range Index for Evaluating the Performance of Audio Systems
## 3 Critical Influence Factors
### Step Size of the Moving Threshold  
The subdivision ability of ADDR is affected by the step size of the moving threshold. A smaller step size can improve the accuracy of the ADDR plot describing distorted spectral components, and it is computationally advantageous to use a larger step size.  
Noise spectral components mainly affect the region with larger values in the ADDR plot. Due to the average operation, the magnitude range of noise spectral components is relatively much smaller than the harmonic and spurious spectral components. It is more sensitive to the step size than the harmonic and spurious spectral components.  
In contrast, harmonics and spurious spectral components mainly affect the variation characteristics of the ADDR plot from the maximum value to the region of small values, which has great significance to the area comparison. An optimal step size can follow all the harmonic and spurious components of different magnitudes, but in the presence of many harmonics and spurious components with small magnitude differences, this will significantly increase the complexity degree of ADDR calculation. Therefore, a step size determination method that balances the computational complexity and the resolution ability of ADDR is as follows:  
Step 1: Arrange the harmonic, spurious and the highest noise spectral components in the spectrum in order from high to low magnitude: \(M_{1}\), \(M_{2}\), \(M_{3}\),..., \(M_{n}\) (\(n\) is the number of the harmonic, spurious and the highest noise components);  
Step 2: Compute the magnitude differences of two adjacent spectral components listed in step 1, respectively: \(\Delta M_{1}\), \(\Delta M_{2}\), \(\Delta M_{3}\),..., \(\Delta M_{n-1}\), where \(\Delta M_{k}=M_{k+1}-M_{k}\), (k = 1, 2, 3,..., \(n-1\));  
Step 3: Update the magnitude differences by merging the magnitude differences whose absolute value is smaller than a specified resolution into its corresponding latter magnitude difference, respectively.  
Step 4: Determine the step size sequence \(T_{step}\) as the magnitude differences calculated in step 3 by order.  
The number of turning of the sawtooth line in part 'B' in Figure 1b is the same as the order of harmonics, which reveals that this is an efficient and effective method.
## 7 Discussion
### Hydrothermal Alteration  
Potassic and sericitic alteration is spatially related to the main stage of sulfide mineralization, whereas propylitic alteration hosts insignificant amounts of pyrite and chalcopyrite. The potassic alteration formed during the emplacement of the quartz monzonite and is characterized by the presence of quartz, hydrothermal biotite and K-feldspar, magnetite, chalcopyrite, bornite, molybden, and pyrite.  
Propylitic alteration (epidote and chlorite) was likely coincident with the late stage of formation of the potassic alteration. Pyrite and chalcopyrite are found in trace amounts in the propylitic alteration, suggesting a slight increase in the sulfur fugacity of the hydrothermal fluids and/or a rise of the fluid temperature [67].  
Sericitic alteration is expressed by the almost complete replacement of magmatic K-feldspar, clinopyroxene, hornblende, and biotite and by the extensive presence of sericiite and quartz in the ground mass. Kaolinite, dolomite, and rutile constitute minor phases. Acicular to prismatic rutile crystals were formed as a result of Ti removal from precursor biotite during alteration [68]. The sericitic alteration overprints the potassic and propylitic alteration. The areas of sericitic alteration with the intense feldspar destructive textures possess relatively high sulfur content (S \(\approx\)1.30 wt %). The magmatic fluid activity that resulted in the formation of this alteration zone during the late phases of the porphyry system was mainly controlled by the major E-W trending fault at the eastern part of Ragain 1 Hill. In this location, minor quartz veins and abundant mineralized D-type veins crosscut the host rocks.
## 9 TR-ARPES Spectra Measurements  
### Static ARPES Spectra of Cu (111)  
Except for the investigation of space charge effect, static ARPES spectra of single-crystal copper were also measured at room temperature, using the EUV beamline with photon energy H13 (20.15 eV). The EUV photon flux was adjusted to \(\sim\)6 \(\times\) 10\({}^{8}\) ph/s and the EUV spot area at the sample was 170 \(\upmu\)m \(\times\) 120 \(\upmu\)m. Grating G2 was used and the silt width was Ws = 100 \(\upmu\)m. The exposure time for acquiring data was \(\sim\)20 min. Figure 12a shows a snapshot of the Shockley surface state of Cu (111), along \(\Gamma\)-K direction in the surface Brillouin zone. For comparison, Figure 12b shows the similar ARPES image obtained with radiation from an He-I discharge lamp (21.22 eV).  
Figure 11: ARPES spectra of the Fermi edge of Cu (111) as a function of incident EUV flux on the sample material. EUV photon energy H13 (20.15 eV); monochromator grating G2 and slit width = 100 \(\upmu\)m. (**a**) spectra measured with the following EUV photon fluxes (ph/s): (1) 6 \(\times\) 10\({}^{8}\); (2) 1.5 \(\times\) 10\({}^{9}\); (3) 3.6 \(\times\) 10\({}^{9}\); (4) 4.8 \(\times\) 10\({}^{9}\); (5) 5.28 \(\times\) 10\({}^{9}\); (6) 5.68 \(\times\) 10\({}^{9}\); (**b**) the Fermi edge measured with photon flux \(\sim\)3.6 \(\times\) 10\({}^{9}\) ph/s, corresponding to a pulse photon density \(\sim\)180 (ph/\(\upmu\)m\({}^{2}\)). The black fitting curve has a FWHM = 190 meV.
# Secure and Privacy-Aware Blockchain Design: Requirements, Challenges and Solutions
## 2 Blockchain: Background Knowledge
### Overview of Blockchain Technology  
A blockchain is a transparent and decentralized database, secured with cryptography techniques to enforce the integrity of its contents, stored under the form of transactions grouped in blocks linked to each other [13]. A block is based on two main parts which are the block header and the transactions. The block header comprises of different fields such as block version, the Merkle tree Root Hash, timestamp, nonce, and parent block hash. Transactions include information such as transaction time, transaction amount, recipient address, etc. and their contents can be verified by any user on the network. Transactions are broadcast through the network of nodes and are eventually added to a block after a process called mining.  
Concretely, mining is the process of adding a block to the blockchain using consensus mechanisms (e.g., proof of work, proof of stake, etc.) to validate the transactions in a block [14]. It helps to verify transactions from this block and secure the blockchain against a double-spending attack. Miners receive rewards to validate the block (e.g., new coin and transaction fees).  
In order to add each block in the chain, the proof of work mechanism requires miners to solve complex mathematical calculations that must be accepted by all the miners. Once the miners validate the transactions, a block is added to the blockchain network. For this solution to work, it must be difficult for an attacker to compromise more than half of the hashing power on the network. It is easy and fast to verify the proof and its correctness. However, proof of work is inefficient due to high computational power to solve complex mathematical calculations.  
To cope with this issue, other consensus mechanisms such as proof of stake [15] allow miners to validate block transactions according to the amount of digital currency (stake) a miner owns [1]. As compared to proof of work it saves more energy because it eliminates the high amount of computing power required from the consensus mechanism. Unfortunately, it may lead to unwanted centralization since rewards are distributed based on the amount staked. Wealthy node operators will earn the most rewards, making them even more wealthy and in time, enable them to operate the majority of nodes. Different methods exist, such as the coin age selection approach that is used to avoid the wealthiest node in the network [16]. The coin age selection method selects nodes based on how long their tokens have been staked for. In this approach, older and larger sets of coins have a higher chance of mining the next block. Once a user has mined a block, their coin age is reset to zero and then they must wait a certain period of time to mine another block.
## 3 Proposed Approaches
### Spectrum Attention Mechanism
#### 3.2.3 Frequency Domain Weighting  
The computed attention weight matrix \(A\) is multiplied by the frequency domain signal \(X_{f}\) to obtain the weighted frequency domain signal \(X^{\prime}_{f}\). This ensures that the model emphasizes the crucial frequency information in the signal. As depicted in (7), \(X^{\prime}_{f}\) represents the weighted sum of the frequency domain signal.  
\[X^{\prime}_{f}=A\cdot X_{f} \tag{7}\]
# Applications of Thermal Plasmas for the Environment
## 5 Conclusions  
It can be concluded that all materials can be decomposed with thermal plasmas. The electrical energy from the torches goes to the plasma which transfers its energy to the waste in the plasma-chemical reactor, activating two simultaneous processes: the organic waste is thermally decomposed into a synthesis gas (syngas) consisting mainly of carbon monoxide and hydrogen, with a more complete conversion of C to gas than with conventional technologies), and inorganic materials are converted into an inert, non-leachable vitrified slag. Thermal plasma treatment can thus be considered as a completely closed processing system. Plasma treatment of organic waste mainly aims at the production of syngas, while the main purpose of combustion is the decomposition of materials. Thermal plasma technology is an environmentally friendly process for the treatment of waste and a very flexible tool as it can work in a wide temperature range with almost any chemical composition of waste The main advantages of thermal plasma-assisted waste processing processes over other processes are lower exhaust, greater volume reduction, smaller plant footprints, faster start-up and shutdown times, and lower costs for a given throughput.  
The market of thermal plasma treatment of wastes is gaining momentum globally. The scaling-up of existing plants should be explored employing advanced plasma-based technologies Because plasma torches need a lot of electrical power, research on ways to increase the efficiency of the thermal plasma treatment processes is very important in a collective effort by scientists and engineers, environmentalists and governmental policy makers The choice of a specific thermal plasma technology depends on the type of waste, local regulations and existing auxiliary installations. Thorough computational fluid dynamics (CFD) modeling combined with chemical reaction modeling can greatly support the reactor design [5].  
This research received no external funding.  
Not applicable.  
The author declares no conflict of interest.
## 3 Calculation Methodology
### Initial and Boundary Conditions  
As reported in our previous work [2], two-way coupling was suitable for this simulation. This study adopted a computational two-way coupling approach for the CFD-DPM model with a three-dimensional transient simulation using ANSYS 2022 R2. The turbulence model was based on the selection of the RSM. Both pressure-velocity coupling and convective diffusion were applied using SIMPLEC and QUICK, respectively. For the wall boundary, no-slip boundary conditions were utilized. Standard wall functions were applied to the near wall treatment. It is notable that to improve the accuracy of the calculations, we used boundary layers near the wall. For a standard wall function, the cell centroid that is the closest to the wall should be located within the log-law layer \(y+\), approximately 30-300. The boundary condition of the coarse material outlet was 'wall'. The working conditions of the simulation were established as follows: the outlet mass flow rate, rotor speed, and feed rate were 6.125 kg/s, 280 rpm, and 1.574 kg/s, respectively. These simulation conditions were chosen based on the findings of [PERSON] et al. [2] and the practical working conditions of the plant. The trajectory of the particles was simulated using the discrete phase model (DPM). Material particles with sizes in the range of 20-800 \(\upmu\)m were introduced with gas from the inlet. The particle size distribution of the raw material is shown in Figure 2. The influence of the presence of the particles on the fluid flow structure was considered. In brief, it was assumed that the solid phase consisted of spherical particles. Under the working conditions of negative pressure and low-velocity flow, the airflow within the classifier was assumed to be incompressible. The computational parameters used in the simulation are listed in Table 1.  
Figure 2: The feed particle size distribution of quartz.
# Deep Learning-Based Optical Camera Communication with a 2D MIMO-OOK Scheme for IoT Networks
## 3 System Architecture
### Start of Frame Addition  
The preamble is the start of a symbol frame. It is designed to facilitate synchronization and the start of frame detection. This part serves a crucial role in helping the receiver accurately identify the start of a frame for suitable data decoding. Since the preamble does not carry real data, this length should be carefully considered to optimize OCC efficiency.  
Figure 1: Optical Camera Communication for IoT system based 2D MIMO-OOK scheme.  
In our proposed scheme, we define a preamble length of 6 bits, with a Start Frame (SF) part of \"011100\".
## 6 Discussion and Conclusions  
CEP, also known as event or stream processing, is the usage of technology for querying data before storing it within a database or, in some cases, without ever being stored [8].  
A CEP system is aimed at solving the velocity problem of big data, while data comes as a stream of predefined events. The sliding window approach used by CEP systems ensures that only a portion of actual data simultaneously passes into the main memory, whereas the old events may be discarded or archived [65]. This way, all the data does not have to fit into system memory, and the most recent events can still be efficiently analyzed [9].  
Multiple applications for CEP approaches include business activity monitoring, aiming at identifying problems and opportunities in their early stages; network sensors that monitor industrial facilities; and market data, such as stock and commodity prices, that need to be derived from several events and their relationships [66].  
CEP is mainly used to address latency requirements, where latency must be low, the volume of input events is high, and event patterns to be detected are complex, such as patterns based on temporal or spatial relationships [8].  
In this paper, multiple tools used for CEP are introduced and showcased, presenting their advantages and shortfalls. While Apache Spark and Apache Kafka are designed to implement streaming processing applications and processing large datasets, Microsoft Power BI and the Tableau Suite help transform data into appealing and intuitive information. This is just a small selection of tools available for CEP, with alternatives and complements available from major companies [66]. Additional solutions for discussion include Amazon Kinesis Analytics, Apache Flink, Apache Samza, Apache Storm, Hadoop, Microsoft Azure Stream Analytics, and Oracle Stream Analytics [8].  
As for future work, it is expected to identify and showcase bleeding-edge technology in CEP, as well as how CEP may impact manufacturing and everyone's daily life.  
Conceptualization, R.S. and N.L.; Methodology, R.S., R.M., A.M., C.A., N.L., and J.M.; software, R.M. and A.M.; investigation, R.S., R.M., A.M.; writing--original draft preparation, R.S. and N.L.; writing--review and editing, R.S., C.A., and N.L.; visualization, R.S., R.M., and A.M.; supervision, N.L. and J.M.; project administration, J.M.; funding acquisition, J.M. All authors have read and agreed to the published version of the manuscript.  
Not applicable.  
We thank the administrative staff of the University of Minho for their availability.