esa-sceva/satcom-chunk-collection huggingface dataset:
# Image-Captioning Model Compression
## 3 Methodology
### Decoder Compression
#### 3.2.1 Architecture Changes  
Following [9], the decoder model has three important logical parts:  
* Embeddings Calculation In this part, one-hot encoded vectors representing words are transformed to word vectors. Let \(\Pi\) be one-hot encoding of the input word and \(W_{e}\in\mathbb{R}^{E\times|\Sigma|}\) is a word-embedding matrix for a vocabulary \(\Sigma\). Then, word vector \(\pi\) could be obtained by the following equation: \[\pi=W_{e}\Pi\] (1) Here, the size of the parameter matrix depends on embedding dimension \(E\) and the size of the vocabulary \(|\Sigma|\). As the size of the vocabulary is a fixed value based on the preprocessing of the dataset, we experimented with changing \(E\) and explored its influence on the overall model performance as well as on the model size.
* Top-Down Attention LSTM and Language LSTM As these two modules are similar to each other in terms of number of parameters and inner representations, we treat them as one logical part. Both modules are LSTMs, so generally the operation of them over a single time step is the following: \[h_{t}=\text{LSTM}(x_{t},h_{t-1}),\] (2) where \(h_{t}\in\mathbb{R}^{M}\). In this case, the sizes of the parameter matrices of LSTM strongly depend on parameter \(M\). We manipulated this parameter by trying to reduce it without causing huge losses in the model's quality.
* Attention Module The third important module of the Up-Down architecture is called Attention Module. It is used to calculate attention weights \(\alpha_{t}\) for the encoder's features \(v_{i}\). It works in the following way: \[\alpha_{i,t}=w_{a}^{T}\text{tanh}(W_{va}v_{i}+W_{ha}h_{t}^{1})\] (3) \[\alpha_{t}=\text{softmax}(a_{t}),\] (4) where \(W_{va}\in\mathbb{R}^{H\times V}\), \(W_{ha}\in\mathbb{R}^{H\times M}\). The main constants that influence the amount of weights in this module are \(H\), \(V\) and \(M\). \(V\) is the size of the encoder vectors, which is fixed by encoder choice, \(M\) has been discussed previously, and \(H\) is the dimension of input attention representation. We investigated how changing \(H\) influenced both model size and its performance along with other parameters.  
Thus, in trying to reach a smaller model size without harming its performance metrics, we manipulated it with model parameters such as \(E\), \(M\) and \(H\).  
The model AoANet described in [10], similarly to Up-Down, has both embedding calculation and LSTM parts. So, the reasoning for parameters \(E\) and \(M\) is similar to the previous subsection. However, the other parts of the architecture are different, so we don't experiment with them in this paper.  
We will reduce the number of all the parameters multiplying \(E\), \(M\) and \(H\) for Up-Down and \(E\) and \(M\) for AoANet used in the original papers by the equal scale factor \(\gamma\).
# A Blockchain and IoT-Enabled Framework for Ethical and Secure Coffee Supply Chains
## 1 Introduction
### The Need for Blockchain and IoT in the Coffee Supply Chain
#### 1.3.2. IoT for Automated and Real-Time Data Monitoring and Collection  
IoT devices such as RFID tags, GPS modules, digital weighing scales, and environmental sensors can be installed throughout the supply chain to collect data continuously and automatically. These devices reduce reliance on manual entry, which helps to eliminate human error and delays in data reporting. For example, GPS modules can monitor transit routes in real time, temperature sensors can alert handlers to spoilage risks, and RFID tags can preserve identity records for individual coffee batches. This automation provides the foundation for real-time visibility and enables proactive decision-making across the supply chain.
## 4 Investigations Results
### Analyses of Properties of the Considered Networks
It can also be seen that under the considered operating conditions, already at frequency \(f=10\) kHz and the cooling system A, the junction temperature of the transistor reaches a value close to the maximum allowable value given by the manufacturer. On the other hand, an increase in frequency to 400 kHz causes an increase by more than twenty-fold in the value of the average power dissipated in the transistor. Figure 12c shows that the quotient of the peak-to-peak value of the junction temperature ripple of the transistor and the average value of the junction temperature excess above the ambient temperature is a decreasing function of frequency, and at \(f=10\) kHz, it can even reach several percent. Figure 13 shows the waveforms of the power dissipated at the steady state in the transistor included in the boost converter and cooperating with system C. The calculations were made for selected frequency values. As can be seen, the actual value of the power dissipated in the transistor exceeds 2 kW. At the same time, the value of conduction losses do not exceed 5 W. This means that in the high frequency range of the boost converter, the value of the average power dissipated in the transistor is determined by dynamic losses related to the transistor switching process. They can be up to twenty times bigger than the losses due to the current flowing through the switched transistor in the on-state. Figure 14 shows the waveform of the junction temperature of the transistor working in the boost converter, determined at frequency \(f=10\) kHz and resistance \(R_{0}=15\)\(\Omega\). The presented results concern the transistor cooperating with system C.  
As can be seen, under the considered operating conditions, ripples of the junction temperature of the transistor are visible, and their peak-to-peak value exceeds 4 K.
# An Intrusion Resistant SCADA Framework Based on Quantum and Post-Quantum Scheme
## 4 Proposed Scheme
### Encryption and Digital Signature Algorithm
#### 4.2.4 Verification  
The MTU receives the package and deciphers the cipher using the same quantum key and, obtains the signature \(Sign_{h}\), \(Sign_{wots}\), _Authentication Path_. The RTU derives the digest of cipher by using the OTS parameters hidden in _Sign_. Using the SPHINCS tree algorithm, it generates and validates the authentication path. The MTU also obtains its public key, \(PK_{derived}\), to validate the signature.
# Contributions of DNA Sequencing Technologies to the Integrative Monitoring of Karstic Caves
## 5 Taxonomic and Functional Assessment of Cave Microorganisms by Metagenomics
### Workflow, Pitfalls, and Care for the Metagenomic Approach  
Metagenomic data represent complex and large datasets, which complicates computational analyses. The type of sequencing platform used is a crucial issue, as it generates highly contrasted numbers of reads as well as contrasted read sizes. Illumina HiSeq or MiSeq technologies are used most of the time. [PERSON] et al. (2016) performed a direct comparison of Illumina HiSeq 2000 (that generated 67.2 \(\times\) 10\({}^{6}\) reads; 6.7 Gb of sequences), MiSeq v-300 (37.5 \(\times\) 10\({}^{6}\) reads; 5.8 Gb), and MiSeq v3-600 (59.0 \(\times\) 10\({}^{6}\) reads; 15.6 Gb) [101]. MiSeq technology provided longer reads (150 bp for v-2 chemistry, 300 cycles and even 300 bp for v-3, 600 cycles, versus 100 bp for HiSeq), which may improve the efficiency of de novo assembly of contigs, and thus improve the detection of the uncultivated fraction in the MiSeq dataset. A previous study conducted in Tjuv'Ante's Cave (Sweden) generated 39 million sequences with HiSeq technologies, allowing the detection of 29 different bacterial phyla (Supplementary Table S1) [121]. More recently, NovaSeq Illumina platforms greatly expanded sequencing capabilities and sequencing depth, reaching 6 Tb and 20 billion sequences in less than 48 h, and making it thus possible to fully investigate the rarest taxa present in ecosystems. The application of NovaSeq sequencing in caves gave the first insights into Movie Cave sediment functioning [124].  
Sequencing depth is also an element to be considered for the study of the diversity of microbial communities in caves. Shallow Shotgun Metagenome Sequencing (SSMS) involves sequencing samples at a shallower depth than in full shotgun metagenome sequencing, and most of the time is preferred because of lower sequencing cost. By combining many more samples into a single sequencing run and using a modified protocol that uses a lower volume of reagents for sequencing library preparation, SSMS is an economical way to provide compositional and functional sequencing data of the most abundant microorganisms. It gives more accurate functional profiles and more precise taxonomic resolution than 16S rRNA gene sequencing [56]. SSMS was used in Movie Cave (Romania) and elucidated the distribution of aerobic methylotrophic microorganisms within microbial mats and sediments [116]. However, shallow shotgun sequencing is not intended to replace deep whole-genome shotgun sequencing for strain-level analysis or novel gene and genome assembly.
# Neural Network Approach to MPPT Control and Irradiance Estimation
## 1 Introduction
Among them, the most promising are algorithms that do not use irradiation measurements, because irradiance sensors are costly and difficult to calibrate. It has been shown that the usage of the irradiance sensors can be effectively avoided by using the irradiance estimators [29; 40; 41]. However, implementation of the irradiance estimators additionally increases the computational cost. Here we propose a simple MPPT algorithm that is based on the neural network (NN) model of the photovoltaic module. The expression for the output current of the NN model is used to develop am analytical, gradient MPPT algorithm, which can provide high prediction accuracy of the maximal power. Finally, to avoid the usage of the pyranometer, a simple irradiance estimator, which is also based on the identified NN model, has been proposed. The presented algorithm has smaller computational complexity compared to the other NN-based MPPT algorithms, in which the MPP position is predicted by one multilayer NN or by two single-layer NNs. The performance improvement of the proposed approach over the available methods is confirmed by numerical examples with simulated and experimental data. The rest of the paper is organized as follows: The procedure of modeling the photovoltaic cell/module and MPPT problem are described in Section 2. The proposed MPTT algorithm and irradiance estimator are presented in Section 3. This is followed by numerical results and discussion in Sections 4 and 5.
# Calculation of Theoretical Travel Time and Automatic Picking of Actual Travel Time in Seismic Data
###### Abstract  
We used the ray tracing technique based on the IASP91 Earth model to calculate the travel times in order to identify the phases. This technique can calculate the travel times for the seismic phases in the conventional travel time tables. The waveform data received from the stations in the Guangxi area are selected for analysis and discussion. The outcomes of the numerical modeling and its use demonstrate that there is good agreement in terms of the absolute differences between the calculated and theoretical travel times from the IASP91 tables. The relative residuals are determined directly from the actual arrival times picking during the correlation analysis, and the validity of the travel time method for picking seismic phases by correlation analysis can be demonstrated.  
IASP91 Earth model; theoretical travel time calculation; actual travel time automatic picking 10.3390/app13031341
# A Source Code Similarity Based on Siamese Neural Network
## 4 Experiments
### Experimental Results  
In this paper, to investigate the performance of WICE-SNN and baseline on the task, we have carried out the experiments about 10 times on dataset, and took the average value as the experimental result. The performance results of all model are shown in Table 1. We also used word2 vec to get each word vector and compute the cosine value based on their average weights(Word2 Vec) or TF-IDF weights(WICE) as the similarity value. We can find our proposed method achieves the best performance, with the improvement by up to 0.67, 0.83, and 0.74 in Precision, Recall and F1. Our method improve 0.41 in recall and 0.18 than word embedding and WICE, however, the precision has been reduced than the previous two methods. For N-gram, we set n from 1 to 5, and get the average values as the result. From the results, we can find N-gram model have almost same performance with Code2 Vec one this dataset. In our experiment, we also found N-gram gets the best performance when n is 2. For Code2 Vec is only designed for functions, so in the data preprocessing, the _main_ function or the function that implements the main algorithm is retained, and other codes are deleted. In addition, we deleted all comments in code snippets as the astminer library can not process comments when extracting code blocks into abstract syntax trees.  
At the same time, in order to test the best performance of each method, we change the similarity threshold, which increases from 0 to 1 in steps of 0.1. The test results are shown in Figure 3. The abscissa in the figure shows the change of the threshold value. It can be seen from the figure that when the threshold value of the first two methods is about 0.9, the F1 performance is the best, and the third method, when the threshold value is about 0.6, the performance is the best.  
N-gram is a statistical method that needs less computing resources and it takes about 11.96 s to get results. Word2 Vec and WICE belong to shallow neural networks. They take about 57 min to complete the process. Our model and code2 vec are deep neural networks. Their computational cost is decided by their size of networks. They will take more time than shallow neural network. Especially, code2 vec need to parse all source codes into an abstract syntax tree (AST) and extract the paths from their ASTs. So preprocessing is also a time-consuming thing. For our model, no more preprocessing is required, and the size of kernels are small. The computational cost is mainly spent on model training. It takes about 7 h to completely train a model. For code2 vce, we take about 10.5 h to train a model.
## 3 Results
### Statistical Process Optimization  
The process optimization was conducted using fractional factorial experimental design (FFED) under various combinations of five self-determining variables. The FEED analysis and the quadratic regression eq. obtained from ANOVA illustrate the probable specifics and combined effect of the factors for the DB15-HNT system [39; 40; 41; 42; 43; 44; 45]. For the ANOVA analysis, the \(p\)-value \(<\) 0.05% with 95% assurance span was considered noteworthy [44; 45; 46; 47; 48; 49; 50].  
The graphs in Figure 13 represent a strong correlation between the particle data and the responses calculated with the FFED model. In addition, A, C, D, A\({}^{2}\), C\({}^{2}\), D\({}^{2}\), and E\({}^{2}\) are considerable model conditions for the DB15-HNT system. The regression Equation (1) obtained for DB15-HNT system is shown below:  
Figure 12: (**a**) Determination of enthalpy and Gibbs free energy: plot of thermodynamic equilibrium constant against 1/T and (**b**) Determination of activation energy: plot of pseudo-second order kinetic constant against 1/T.  
The regression coefficient [51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61; 62; 63] indicates the effect of various features affecting the adsorption capacity on the adsorption capacity [64; 65; 66; 67]. The surface and contour plots represented in Figure 14 show the combined effect of two features on the process of adsorption. Statistical optimization experiment was 112 mg g\({}^{-1}\) with conditions optimized pH = 1, the adsorbent dosage of 0.5 g L\({}^{-1}\), and an initial dye concentration of 412 mg L\({}^{-1}\) for an adsorption time of 177 min with orbital shaking of 165 rpm at temperature 20 \({}^{\circ}\)C.
# Review of 3D Imaging by Coded Aperture Correlation Holography (COACH) +
## 2 System Architectures
### Coded Aperture Digital Holography
#### 2.1.3 Single Camera Shot I-COACH
If the system is linear and shift invariant, the overall intensity response \(I_{D}\) on the sensor plane is given by a sum of the entire individual shifted impulse responses, as follows  
\[I_{D}=\sum\limits_{j}G_{1}\bigg{(}u-\frac{z_{h}u_{j}}{f_{0}}-\frac{U}{4},v- \frac{z_{h}v_{j}}{f_{0}}\bigg{)}+\sum\limits_{j}G_{2}\bigg{(}u-\frac{z_{h}u_{ j}}{f_{0}}+\frac{U}{4},v-\frac{z_{h}v_{j}}{f_{0}}\bigg{)} \tag{7}\]  
The resulting bipolar object hologram \(H_{OBJ}\) obtained by subtracting the individual responses is  
\[H_{OBJ}=\sum\limits_{j}G_{1}\bigg{(}u-\frac{z_{h}u_{j}}{f_{0}},v-\frac{z_{h}v _{j}}{f_{0}}\bigg{)}-\sum\limits_{j}G_{2}\bigg{(}u-\frac{z_{h}u_{j}}{f_{0}},v -\frac{z_{h}v_{j}}{f_{0}}\bigg{)} \tag{8}\]  
The object image is reconstructed by cross-correlating the object hologram with the PSH(\(z=0\)) as  
\[ \begin{array}{l}I_{img}=H_{OBJ}\otimes H_{PSH}=\left[\sum\limits_{j}G_{1} \bigg{(}u-\frac{z_{h}u_{j}}{f_{0}},v-\frac{z_{h}v_{j}}{f_{0}}\bigg{)}-\sum \limits_{j}G_{2}\bigg{(}u-\frac{z_{h}u_{j}}{f_{0}},v-\frac{z_{h}v_{j}}{f_{0}} \bigg{)}\right]\\ \hskip 14.226378 pt\otimes\left[G_{1}(u,v)-G_{2}(u,v)\right]\\ \hskip 14.226378 pt=\sum\limits_{j}\Lambda\bigg{(}u_{o}-\frac{z_{h}u_{j}}{f_{0} },v_{o}-\frac{z_{h}v_{j}}{f_{0}}\bigg{)}\\ \hskip 14.226378 pt\cong O\bigg{(}\frac{f_{0}u_{o}}{z_{h}},\frac{f_{0}v_{o}}{z_ {h}}\bigg{)}=O\bigg{(}\frac{u_{o}}{M_{T}},\frac{v_{o}}{M_{T}}\bigg{)}\end{array}  \tag{9}\]  
where \(\otimes\) represents a two-dimensional correlation and \(M_{T}=z_{h}\)/\(f_{0}\) is the lateral magnification of the imaging. The GSA was designed to limit the area of the uniform magnitude on the sensor plane to be only 4.3 \(\times\) 4.3 mm out of 14.3 \(\times\) 14.3 mm. The linear phase values for the two CPMs were chosen to be \(\pm\)0.7\({}^{\circ}\) such that, for \(z_{h}\) = 25.4 cm, the intensity patterns are shifted by approximately 3 mm from the optical axis. The system was trained using a pinhole with a diameter of 25 \(\upmu\)m. Two objects, element 5 and 6 of group 3 of USAF resolution targets, were used. The relative distance between the two objects was shifted by 4 mm in steps of 1 mm. The intensity responses for the input pinhole and the corresponding bipolar hologram are shown in Figure 10(a),b, respectively. The image responses for the input of the two-plane object and the object hologram are shown in Figure 10(c),d, respectively. Various object holograms were recorded for the different locations of the two objects, and the resulting bipolar holograms were reconstructed by a cross-correlation with the PSH library. The reconstruction results are shown in Figure 12, demonstrating that although the FOV is limited, the background noise is relatively low for a single camera shot. Because SCS-I-COACH is a single shot recording, it can be used for recording the dynamic scene and for creating holographic videos of moving objects.
# Dual-Neighborhood Tabu Search for Computing Stable Extensions in Abstract Argumentation Frameworks
## 2 Preliminary Definitions  
### Argumentation Frameworks  
**Definition 1**.: _An abstract argumentation framework is denoted by a pair, \(AF=(A,R)\), where \(A\) is a finite set of arguments and \(R\subseteq A\times A\) is an attack relation built on set \(A\)._  
For two arguments, \(a\), \(b\in A\), argument \(a\) is said to attack argument \(b\) if \((a,b)\in R\). Set \(E\subseteq A\) attacks \(b\) if any argument in \(E\) attacks \(b\). \(\{a\}-\) refers to the set of arguments attacking the argument \(a\).  
In general, we depict \(AF\) as a directed graph, where vertices denote arguments and edges denote attack relationships between arguments. Figure 2 illustrates a set of abstract argumentation frameworks.  
**Example 1**.: _Argumentation frameworks: \(AF1=(\{a\},\{\})\), there is only one argument node with no attack relationship; \(AF2=(\{a,b\},\{(a,b\}))\), two arguments, \(a\) and \(b\), and \(a\) attacks \(b\); \(AF3=(\{a,b\},\{(a,b),(b,a)\})\), denotes mutual attacks between arguments, \(a\) attacks \(b\) and \(b\) attacks \(a\); \(AF4=(\{a,b,c\},\{(a,b),(b,c)\})\), comprising three arguments, \(a\), \(b\), and \(c\), where \(a\) attacks \(b\), \(b\) attacks \(c\), and \(c\) is defended by \(a\)._
## 4 Materials and Methods
### The Reformulated MLR (rMLR) Model  
The MLR model [31] shows that \(J_{\rm{a}}\) can be mechanistically estimated from \(q_{\rm{L}}\), \(\Phi_{\rm{Pmax}}\), and the chlorophyll fluorescence flux density emitted from the photosystem II (PSII) across the full ChIP emission spectrum (ChIF\({}_{\rm{P\_F}}\), \(\mu\)mol m\({}^{-2}\) s\({}^{-1}\)). At the photosystem level,  
\[J_{\rm{a}}=\frac{q_{\rm{L}}\times\Phi_{\rm{Pmax}}\times(1+K_{\rm{DF}})\times{ \rm{ChIF}}_{\rm{P\_F}}}{(1-\Phi_{\rm{Pmax}})}, \tag{1}\]  
where \(K_{\rm{DF}}\) is the ratio between the rate constants for constitutive heat loss (\(K_{\rm{D}}\)) and fluorescence (\(K_{\rm{F}}\)) and is assumed to be 9 [46]. However, \(q_{\rm{L}}\) is much less studied than the other PAM parameters [31]. A previous study [46] shows that the role of \(q_{\rm{L}}\) and \(\Phi_{\rm{Pmax}}\) in the original MLR model can be replaced by \(\Phi_{\rm{P}}\) and NPQ:  
\[J_{\rm{a}}=\frac{\Phi_{\rm{P}}\times(1+{\rm{NPQ}})\times(1+K_{\rm{DF}})\times {\rm{ChIF}}_{\rm{P\_F}}}{(1-\Phi_{\rm{P}})}, \tag{2}\]  
The practical advantage of Equation (2) is that both \(\Phi_{\rm{P}}\) and NPQ (dimensionless) can be estimated from air temperature (\(T_{\rm{air}}\), \({}^{\circ}\)C) and PAR (see the section on the estimation of \(\Phi_{\rm{P}}\) and NPQ). Note that \(J_{\rm{a}}\) in Equations (1) and (2) is already balanced by carboxylation and photorespiration [31; 47]. To differentiate from \(J_{\rm{a\_PAM}}\), in this study, \(J_{\rm{a}}\) was calculated from ChIP emission using Equation (2).  
One can obtain \(A_{\rm{net}}\) for C\({}_{3}\) and C\({}_{4}\) species:  
\[A_{\rm{net}}=A_{\rm{g}}-R_{\rm{d}}=\left\{\begin{array}{l}\frac{C_{\rm{c}}- \Gamma s}{4C_{\rm{c}}+8\Gamma s}\times\frac{\Phi_{\rm{P}}\times(1+{\rm{NPQ}}) \times(1+K_{\rm{DF}})\times{\rm{ChIF}}_{\rm{P\_F}}}{(1-\[PERSON]_{\rm{P}})}-R_{\rm{ d}}\ C_{3}\\ \frac{1-\zeta}{3}\times\frac{\[PERSON]_{\rm{P}}\times(1+{\rm{NPQ}})\times(1+K_{\rm{DF }})\times{\rm{ChIF}}_{\rm{P\_F}}}{(1-\[PERSON]_{\rm{P}})}-R_{\rm{d}}\ \ \ C_{4}\end{array}\right., \tag{3}\]  
where \(A_{\rm{g}}\) represents gross photosynthesis (\(\mu\)mol m\({}^{-2}\) s\({}^{-1}\)); \(R_{\rm{d}}\) is the daytime respiration (\(\mu\)mol m\({}^{-2}\) s\({}^{-1}\)); \(C_{\rm{c}}\) is the chloroplastic CO\({}_{2}\) partial pressure (\(\mu\)mol mol\({}^{-1}\)); \(\Gamma^{*}\) is the chloroplastic compensation point of CO\({}_{2}\)\(\mu\)mol mol\({}^{-1}\)[31; 65]; \(\zeta\) is the fraction of total electron transport of mesophyll and bundle sheath allocated to mesophyll, assumed to be 0.4 [66]. We refer to Equation (3) as the rMLR model. The procedure for quantifying \(A_{\rm{net}}\) from the observed SIF is illustrated in Figure 12.