esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiments
### Error Analysis  
In this section, we analyze the misclassified satirical articles of the best Arabic satire classification model. The best model was the BERT model. When further analyzing the articles that were correctly predicted as true positives, we found that 1010 out of 1073 articles in the testing set were correctly predicted. Notably, we found explicit satirical content in these articles, portrayed as adjectives and intensifiers.  
However, 63 satirical articles were wrongly classified as non-satire, containing many journalistic registers imitating formal news articles, and had a satirical sense ingrained within the content, thereby presenting implicit satire. Implicit satire is challenging to unravel, since satirical and non-satirical articles share formal terms used in news articles. However, these terms have differing meanings depending on the article's overall tone. For example, the term \"highness\" occurred frequently in both non-satirical and satirical articles. In the former, it refers to royal figures, and in the latter, it is used to refer to objects of higher  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**BERT**} & \multicolumn{3}{c}{**GPT**} \\
**P** & **R** & **F1** & **P** & **R** & **F1** \\ \hline
94.0 & 97.0 & 95.0 & 82.0 & 89.1 & 85.0 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Results of BERT and GPT models.  
superiors with a mocking tone, such as \"his Highness the beloved cat\". Figure 4 shows an example of such a misclassified satirical article, which is a form of implicit satire. It is written in a formal journalistic form; however, it contains one phrase \"hotline\", mocking the lack of services.  
Due to BERT's contextualized embeddings and fine-tuning process, satirical content, even when written in a journalistic manner, was effectively analyzed and identified, and thus, classified. This unique feature found in BERT's bidirectional nature and contextualized word embeddings aided in understanding the context and capturing the subtleties present in satirical texts. However, the low accuracy of textual features could be explained by the lack of satirical content in the form of emotions or linguistics that could be used to train the model. This is understandable given that the articles were written in a journalistic style with minimal satirical content.
# A New Approach to Privacy-Preserving Multiple Independent Data Publishing
## 4 Merging Method
### Formulation
#### 4.1.4 Cell Generalization  
A cell is the cross-section of a column and a row. In our problem definition, a cell consists of a column and an equivalence class. Given a microdata table \(T\), a column \(C_{e}\) and an equivalence class \(E_{e}\) make a cell \(CE_{(ij)}\), where \((1\leq i\leq c)\) and \((1\leq j\leq e)\). For example, in Table 3 above, the column {(Age, Sex)} and the first equivalence class which consists of tuples \(\{t_{1},t_{2},t_{3},t_{4}\}\) form the first cell of the table. A cell generalization for \(CE_{(ij)}\) generalizes each attribute value of \(CE_{(ij)}\) to satisfy privacy requirements.  
In the anonymization, the attribute values in the equivalence class will be shuffled randomly to break the association of each tuple, in order to increase personal privacy. Random shuffling will break the association of the tuple but it will create some invalid records and, in some cases, it may increase the likelihood of privacy breach for a particular set of sensitive values [22]. For these special circumstances, we have introduced cell generalization to enhance the privacy of the equivalence class. Because cell generalization does not generalize the whole equivalence class, it allows better data utility than column generalization or full generalization of the table.  
Cell generalization satisfies the \(k\)_-anonymity_ and \(l\)_-diversity_ privacy requirements for the particular cell and increases the probability of false matches of the attributes. In addition, cell generalization helps to reduce the curse of dimensionality associated with full generalization of the microdata table, and it certainly increases the data utility of the published dataset.
## 2 Related Works
### Existing Works  
Existing works in the detection of protein function modules could be divided into three categories:  
Graph theory-based approach. Similar to a computer network, the graph theory is introduced to improve the detection of protein functional modules, mainly based on three approaches: hierarchical algorithm, partitioning algorithm, and density algorithm [16]. The hierarchical algorithm (such as, modularity division-based method [17], etc.) is based on similarity of the connections between each node. For the partitioning algorithm, the most representative method is based on restricted neighborhood search clustering (RNSC) [18]. Although both hierarchical and partitioning algorithms are easy to understand and implement, the clustering number should be determined beforehand and the function modules cannot be overlapped.  
Machine learning-based approaches. Considering the disadvantages (poor scalability and low clustering) of original Markov Clustering (MCL) algorithm, [PERSON] et al. (2015) proposed an improved MCL clustering algorithm for PPIN [19] via importing two parameters: punishment and mutation factors. This approach improves convergence speed but leads to substantial computation complexity. In literature [20], [PERSON] proposed a rapid and lightweight hidden layer neural network prediction algorithm based on the Extreme Learning Machine (ELM) algorithm. It uses the speed advantage of the ELM algorithm and achieves better protein function module prediction results.  
Intelligence algorithm-based approaches. Swarm intelligence algorithms are also implemented for PPIN function module detection. Examples of these algorithms include Ant Colony Optimization (ACO) [21], Particle Swarm Optimization (PSO) [22], and Artificial Bee Colony (ABC) [23]. [PERSON] applied ACO algorithm to the PPIN complex clustering problem, and further proposed the optimization strategies in protein interaction networks [24]. In 2012, [PERSON] introduced a novel ACO-based functional module detection (NACO-FDM) [25] algorithm to improve the efficiency in searching for an optimal path. However, this algorithm easily falls into the local optimum. In literature, another ACO-MAE mechanism that combines ACO with the idea of multi-agent evolution (MAE) was developed to achieve better prediction accuracy.  
Besides, some other works have demonstrated higher performance with a mixture of graph-based approach and machine learning-based approaches [26; 27]. The algorithm is based on prior calculation of parameters on the protein residue networks and later machine learning. However, this approach still needs a lot of noted samples for training.
###### Abstract  
The potential for lithium-ion (Li-ion) batteries and supercapacitors (SCs) to overcome long-term (one day) and short-term (a few minutes) solar irradiance fluctuations with high-temporal-resolution (one s) on a photovoltaic-powered reverse osmosis membrane (PV-membrane) system was investigated. Experiments were conducted using synthetic brackish water (5-g/L sodium chloride) with varied battery capacities (100, 70, 50, 40, 30 and 20 Ah) to evaluate the effect of decreasing the energy storage capacities. A comparison was made between SCs and batteries to determine system performance on a \"partly cloudyday\". With fully charged batteries, clean drinking water was produced at an average specific energy consumption (SEC) of 4 kWh/m3. The daily water production improved from 663 L to 767 L (16% increase) and average electrical conductivity decreased from 310 pS/cm to 274 pS/cm (12% improvement), compared to the battery-less system. Enhanced water production occurred when the initial battery capacity was >50 Ah. On a \"sunny\" and \"very cloudy\" day with fully charged batteries, water production increased by 15% and 80%, while water quality improved by 18% and 21%, respectively. The SCs enabled a 9% increase in water production and 13% improvement in the average SEC on the \"partly cloudy day\" when compared to the reference system performance (without SCs).  
lithium-ion battery; supercapacitors; photovoltaics; desalination; membranes +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Comparison of Measured Indoor Temperatures  
The room temperature measured by the ventilation system was compared to the room temperatures measured by a TESTO device in each room during one year; from 7 November 2017 to 12 November 2018 (see Figures 10 and 11).  
In general, the room temperatures measured by the ventilation system were somewhat higher than the temperatures measured by the TESTO devices. This was particularly the case during the heating season. When comparing the average temperatures measured over the heating season (7 November-15 May), the difference between the average temperature measured by the ventilation system and that by the TESTO devices was 0.27 \({}^{\circ}\)C in room A and 0.24 \({}^{\circ}\)C in room B. This is only a small difference, and could be explained by the measurements having been made in two different places in the rooms.  
Figure 10: Daily mean indoor temperatures in room A, as measured by the ventilation system (Room A) and by the TESTO device (TESTO Room A).
## 2 Materials and Methods
### Histological, Immunohistochemical, Western Blot, GPx Activity and TAC Analysis  
Routine hematoxylin and eosin (H&E) staining was performed to conduct the histological analysis. To investigate the expression level of TNF-\(\alpha\) and IL1\(\beta\) in the pulp tissues, immunohistochemical staining was performed using anti-TNF-\(\alpha\) and anti-IL1\(\beta\) antibodies (Santa Cruz Biotech). Briefly, the deparaffinized slides were treated with 1 mg of porcine trypsin (Sigma-Aldrich) for 10 min to allow antigen retrieval. Endogenous peroxidase activity was blocked following the treatment with 30% H2O2 for 7 min. The slides were then washed and subjected to a protein block step for 1 h. Then, the primary antibodies (dilution ratio 1:50) were applied and the slides were covered with parafilm. The slides were then incubated in a humid chamber at 4 \({}^{\circ}\)C for 8 h. After washing, a universal secondary antibody (Dako REAL(tm) EnVision(tm)/HRP, Rabbit/Mouse; Dako North America Inc., Carpinteria, CA, USA) was applied. The unreacted secondary antibodies were washed with PBS and the slides were stained with a chromogen substrate (Dako REAL(tm) DAB+Chromogen and Dako REAL(tm) Substrate Buffer; Dako North America Inc.).  
Fresh mandibular incisors on the affected side were separated from the mandible, homogenized in a reagent buffer and protease inhibitor cocktail and then evaluated using Western blot analysis as described above (Section 2.3). The pulp tissue in the incisor was processed and homogenized for further TAC and GPx analyses. Subsequent procedures were performed as described above (Section 2.2). The relative activities of TAC and GPx in the no-injury group were assessed for comparison with 4 HR and ointment base-only (control) groups.
## 2 Materials and Methods
### Preparation of Activated Carbons  
The activated carbons (ACs) were prepared using either potassium hydroxide (KOH), iron chloride (FeCl\({}_{3}\)) or phosphoric acid (H\({}_{3}\)PO\({}_{4}\)), which were physically mixed with the ground hydrochar at different activating agent to HC ratios of 4:1, 3:1 and 2:1 (w:w). In the case of H\({}_{3}\)PO\({}_{4}\), the mixture was left overnight at 60 \({}^{\circ}\)C and then heated at 500 \({}^{\circ}\)C for 2 h under continuous N\({}_{2}\) flow (100 NmL\(\cdot\)min\({}^{-1}\)) in a stainless steel tube placed in an electrical furnace. The activation temperature was reached at 10 \({}^{\circ}\)C\(\cdot\)min\({}^{-1}\) heating rate. Activation with KOH and FeCl\({}_{3}\) was performed similarly but at 750 \({}^{\circ}\)C for 1 h [44; 45; 46; 47]. After cooling under nitrogen flow, the ACs were washed with HCl or NaOH 0.1 M until neutral pH; then were washed with distilled water; and finally, were dried at 105 \({}^{\circ}\)C overnight. The ACs were denoted as GSHC, followed by the activating agent and the activating agent:hydrochar (w:w) ratio. For instance, GSHC-KOH-3 represents the activated carbon obtained with KOH using 3:1 activating agent to hydrochar ratio.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multirow{2}{*}{**Yield (\%)**} & \multicolumn{4}{c}{**Proximate Analysis (\%)**} & \multicolumn{4}{c}{**Ultimate Analysis (\%)**} \\ \cline{3-10}  & & **Volatile Matter** & **Fixed Carbon** & **Ash** & **C** & **H** & **N** & **S** & **O 1** \\ \hline GS & - & 73.3 & 24.3 & 2.4 & 56.5 & 6.6 & 1.8 & 0.13 & 32.6 \\ GS-180-40 & 75 & 66.0 & 31.0 & 3.0 & 61.7 & 6.6 & 1.6 & 0.02 & 27.1 \\ GS-200-40 & 77 & 59.2 & 37.6 & 3.2 & 63.8 & 6.4 & 1.7 & 0.01 & 24.9 \\ GS-220-40 & 59 & 55.9 & 40.7 & 3.4 & 69.0 & 6.4 & 1.7 & 0.02 & 19.5 \\ GS-240-40 & 59 & 55.2 & 41.6 & 3.2 & 69.2 & 6.2 & 2.0 & 0.02 & 19.4 \\ GS-260-10 & 53 & 50.2 & 46.8 & 3.0 & 70.5 & 6.1 & 1.7 & 0.08 & 18.6 \\ GS-260-20 & 62 & 49.8 & 46.6 & 3.6 & 70.9 & 6.4 & 1.8 & 0.04 & 17.3 \\ GS-260-30 & 64 & 50.4 & 46.3 & 3.3 & 71.2 & 6.2 & 1.9 & 0.06 & 17.4 \\ GS-260-40 & 63 & 50.3 & 46.4 & 3.3 & 70.7 & 6.3 & 1.9 & 0.05 & 17.8 \\ GS-260-50 & 62 & 49.6 & 46.8 & 3.6 & 69.8 & 6.1 & 2.0 & 0.03 & 18.5 \\ GS-260-60 & 64 & 50.6 & 45.9 & 3.5 & 70.0 & 6.2 & 2.1 & 0.02 & 18.1 \\ GS-280-40 & 62 & 47.0 & 49.8 & 3.2 & 72.6 & 6.1 & 2.2 & 0.02 & 15.9 \\ GS-300-40 & 48 & 43.8 & 51.8 & 4.4 & 74.0 & 5.8 & 2.3 & 0.01 & 13.5 \\ \hline \hline \end{tabular}  
* Calculated by difference \(\text{O}=100-\text{(C}+\text{H}+\text{N}+\text{S}+\text{Ash})\).  
\end{table}
Table 1: Proximate and ultimate analyses of grape seeds (GSs) and hydrochars (wt.% dry basis).
## 2 Materials and Methods
### Crop LAI and Above-Ground Biomass Collection from the SMAPVEX16-MB Field Campaign
Crews were instructed to take one photo then move approximately 5 m to take the next photos. Field crews positioned themselves relative to the sun to minimize shadowing in the photos. These photos were post-processed to estimate LAI using the CanEye software. All 14 photos were averaged to provide one estimate of LAI per sample site. Prior to the use of CanEye, the photos were enhanced to increase the quality through the use of ViewNX-2 (Nikon) software. Once enhancements were implemented, the images were then imported in the CanEye software for classification and LAI calculation. The parameters of the software were configured in accordance with the photos, i.e., number of lines and columns, julian date and latitude. Masking for any non-soil or data acquisition anomalies were applied where necessary in addition to any removal of photos that were problematic. In the data processing, all green leave, stems, and spikelets are included. Since the measurements were taken in summer, the plants did not senescence at the time of measurements. Therefore, the ground LAI measurements include total green elements. This corresponds well to Sentinel-2-derived LAI as the simple ratio index is sensitive to all elements with chlorophyll pigments.
# Independent Random Recurrent Neural Networks for Infrared Spatial Point Targets Classification
## 3 Classification of IR Radiation Intensity Sequence Based on IRRNN  
Aiming at the spatial point target shape classification problem studied in this paper, and according to the characteristics of the target infrared radiation intensity time series samples, this paper proposes an Independent Random RNN algorithm structure. The main idea is to use the independent neuron structure to extend the length of the RNN neural network and introduce a Random RNN (RRNN) algorithm to add all historical network output values before the current time, together with the random weight matrix, to the input part of the network.
## 2 Design and Implementation of On-Line PD Monitoring System  
### General Structure of On-Line PD Monitoring System  
In Figure 1, the schematic diagram of the general structure of the developed on-line partial discharges monitoring system is shown, whose unique feature is the possibility of simultaneous registration of PD pulses with the use of three different diagnostic techniques, i.e., high-frequency, ultra-high frequency, and acoustic emission.  
High-frequency current transformer (labeled in the system as sensor ISO-002-HF) and active dielectric windows (ADW) with built-in acoustic emission sensor and mounted UHF antenna (the set was labeled as sensor ISO-002-ADW/UHF) were employed as PD detectors. Each PD detector has an additional housing, in which there is a signal conditioning module and analog-to-digital (A/D) signal processing module. A module for fiber-optic signal transmission was included as well.  
Figure 1: Schematic diagram of the general structure of the developed on-line partial discharges monitoring system for power transformers.  
This type of transmission allows to minimize the influence of external EMI occurring on substations. The transmitted digital data is received by the multi-channel data concentrator labeled as MPD-001. It is a specialized modular device, prepared for operation in a substation environment, equipped with multi-port network communication cards. It performs the initial integration of data frames received from many PD sensors into a form that facilitates their transmission to the server and performing more advanced calculations (e.g., phased-resolved partial discharge patterns or data trend analysis).  
The process of designing and producing PD sensors and main elements of the monitoring system, i.e., module for conditioning and digital processing of PD pulses, data concentrator, and modules of server software are discussed in detail later in the article.
## 3 Industrial Applications  
Metal AM opens up new opportunities to improve manufacturing capability. Short lead times, access to new materials, material waste reduction, and fabrication of novel complex geometries and difficult to machine materials are key factors to encourage industry sectors to adopt metal AM technologies [67]. This section presents a range of recent applications in different industries along with several case studies where AM technologies have been successfully applied.
## 5 Data Processing and Results
### Determination of the Weights of Predicators  
The objective-weighting Python library ([[https://pypi.org/project/objective-weighting/](https://pypi.org/project/objective-weighting/)]([https://pypi.org/project/objective-weighting/](https://pypi.org/project/objective-weighting/))) (accessed on 1 March 2025) is used as the basis for the calculation of the weights of the predictors. The reason why we cannot use that library as it is is because the entropy weighting function uses a sum normalization instead of the min-max normalization. The  
Figure 4: Comparison of MSE for linear regression (with polynomial features) and gradient boosting regression on the raw data for all predictors in the reference dataset \(R\).  
Figure 3: Comparison of MSE for linear regression (with polynomial features) and gradient boosting regression on the raw data for all predictors in the dataset \(O\).  
purpose of sum normalization is to scale the values in a vector so that the sum of the scaled values equals to 1. On the other hand, the min-max normalization would scale all values to a range between 0 and 1. Hence, for our study, the min-max normalization is used. The source code of the entropy weighting function in the objective-weighting python library is modified accordingly. The weights calculated this way are shown in Figure 5 for our dataset, and shown in Figure 6 for the reference dataset.  
As can be seen, for the technological innovation subsystem in dataset \(O\), the largest weight is 0.138 (for \(X_{12}\)), and the smallest weight is 0.035 (for \(X_{31}\)). For the financial development subsystem in dataset \(O\), the largest weight is 0.149 (for \(Y_{23}\)), and the smallest weight is 0.046 (for \(Y_{32}\)).  
For the technological innovation subsystem in the dataset \(R\), the largest weight is 0.227 (for \(X_{16}\)), and the smallest weight is 0.079 (for \(X_{17}\)). For the financial development subsystem in the dataset \(R\), the largest weight is 0.229 (for \(Y_{17}\)), and the smallest weight is 0.059 (for \(X_{16}\)).  
The ratio between the largest weight and the smallest weight for all subsystems and for both datasets is roughly between 3 and 4. Hence, no predictor can be apparently ignored during the calculation for the comprehensive evaluation index. In other words, every predictor would make some impact to the comprehensive evaluation index to the subsystem, and eventually may exert some impact on the degree of coupling and degree of coordination.  
Figure 5: The weights for 12 predictors for the technological innovation subsystem (**left**), and the weights for the 12 predictors for the financial development subsystem (**right**) in the dataset \(O\).  
Figure 6: The weights for 7 predictors for the technological innovation subsystem (**left**), and the weights for the 7 predictors for the financial development subsystem (**right**) in the dataset \(R\).
## 2 Development of a New Analytical Model Based on the Virtual Solid Flame Concept
### Evaluation of the Fire Geometry and Its Position in the Compartment
#### 2.2.2 Power and Fire Load Density  
The rate of heat release density RHR\({}_{f}\), given as input, is a constant. The parameter R is here introduced as the variable value of the rate of heat release density (kW/m\({}^{2}\)) which can vary at each time-step: it may change during the course of the fire depending on the ventilation conditions. The initial value of R is RHR\({}_{f}\), and the following simple assumption is considered: R is (momentarily) decreased when the fire is air controlled, and such situation is evaluated through Equation (3) from EN 1991-1-2 Annex E [13]. This equation considers that the power is limited by the oxygen which can enter the compartment through the openings, using the size of the openings to consider the ventilation limitation. The first use of this parameter is attributed to [PERSON] [12]. The empirical observation was made that wood fires in rooms with small windows appeared to burn at a rate approximately stoichiometric [14]. The present methodology disregards the influence of the environment on the fire plume (such as the boundary conditions, for example the effect of isolated openings), as well as other complex phenomena's (combustion efficiency, evolution of the temperature, chemical reaction kinetics ([PERSON] and [PERSON] [15])). These considerations lead to the procedure described below.  
The total power \(Q\) (MW), released by the fire in the compartment, is evaluated through the following equation (\(A_{band}\) being the area of one burning band, i.e., d\({}_{x}\) multiplied by b):  
\[Q=(R/1000).A_{band}\ \sum_{i=1}^{n}\ burning(i) \tag{1}\]  
The limit value \(Q_{lim}\) given by the following equation (EN1991-1-2 [13]):  
\[Q_{lim}=(1/10)\.\ m\.\ H\.\ A_{v}\.\ h_{eq}\ ^{0.5} \tag{2}\]  
If \(Q\leq Q_{lim}\) then \(R=\) RHR\({}_{f}\).  
If \(Q>Q_{lim}\) then \(R\) is adapted to reach \(Q=Q_{lim}\), namely:  
\[R=\frac{Q_{lim}}{\sum_{i=1}^{n}A_{band}\.\ burning(i)} \tag{3}\]  
The fire load density \(q\) in a band depends on the band \(i\) and on time. The initial value is set to \(q_{\rm f,d}\) in each band, and this value can decrease during the course of the fire. In each band \(i\), the variation of the fire load density as a function of time depends on the value of the variable burning and on the value of \(R\):  
\[\frac{dq(i)}{dt}=0\qquad\text{ if }burning(i)=0 \tag{4}\]  
\[\frac{dq(i)}{dt}=-R\qquad\text{ if }burning(i)=1 \tag{5}\]