esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data  
The aim of this paper is to analyze and compare firms' financial sustainability across NUTs II regions, using key financial indicators. For it, firms with the primary code of NACE Rev. 2 code 37--Sewerage from Portugal and Spain were selected in the SABI database, from Bureau van Dijk. After it, in the same database, the firm's financial reporting was collected. Only private firms and active in 2019 were included. The final sample comprises 47 Portuguese firms and 175 Spanish firms for 4 years (from 2016 to 2019). The last year with available data was 2019. We decide to analyze 4 years of observations to understand the evolution of a firm's financial situation since a singular year does not provide us insights into its future financial sustainability. The data is an unbalanced panel, summing 173 and 646 observations to Portugal and Spain, respectively. Table 1 characterizes the sample, showing the firm's distribution across regions and by size, as well as the number of individuals per km2 (population density). Panel A presents information about Portuguese firms, while panel B presents information about Spanish firms. In Portugal, the northern region concentrates 36.2% of WWTPs, followed by Lisbon with 34% of firms in the sample. The islands (Azores and Madeira) concentrate only 4.2% of firms. Most firms are SMEs (44 out of 47 firms, 93.6% of the sample), which is also most of the Portuguese firms. Large-size firms are only located in Lisbon (4.3%) and in the north (2.1%). Moreover, Lisbon is the capital of Portugal and where most of the individuals concentrated, and it is the 2 nd region with the highest representation in the sample. Madeira, while it is the second region with a higher population density, only has one private firm in the sewage sector. Regarding Spain, the distribution across regions is more similar. The region of Catalonia concentrates 17.2% of the WWTPs, followed by Andalusia with 13.9%. The smallest regional share is to Cantabria, with 0.6%, followed by Asturias and Extremadura (both with 1.2%). SMEs also prevail in Spain (171 out of 173 firms), with a representation of 98.8% of the total firms. Only in Valencian community and Madrid is one large firm in this sector. Madrid, the capital of Spain, has the highest population density, but it is the 3 rd region with the highest regional share of WWTPs. Castilla y Leon is the region with fewer individuals per km2 and is the 7 th region with more firms in the sample. Cantabria is a region with great desertification. The information in Table 1 exhibits regional asymmetries not only in terms of the number of firms in this sector as well as population density, which calls the need to understand the impact of regional differences in a firm's financial sustainability. Table 2 shows the mean values of annual accounting information, namely total assets, and total equity (from balance sheet), sales and net income (from income statement), number of employees, and age of the company, per region.
# Massive MIMO Indoor Transmissions at 38 and 65 GHz Applying Novel HBF Techniques for 5G
## 4 Results
### Throughput Analysis
#### 4.2.2 Performance According to Algorithm
With these conditions, 25 Gbit/s was achieved to 10 m. However, in our case, at the 38 GHz band, when the HBF algorithm was applied, along with the MCS 27 (256 QAM) scheme, two streams, and the same bandwidth, it was possible to reach 5.35 Gbit/s at up to 4.10 m. Therefore, if 16 streams were used instead of 2, a throughput of 42.8 Gbit/s at 4.10 m was achieved. Note that our maximum distance was 4.10 m, and since our parameters were different, it was not directly comparable.
###### Abstract  
The cross-lingual text classification task remains a long-standing challenge that aims to train a classifier on high-resource source languages and apply it to classify texts in low-resource target languages, bridging linguistic gaps while maintaining accuracy. Most existing methods achieve exceptional performance by relying on multilingual pretrained language models to transfer knowledge across languages. However, little attention has been paid to factors beyond semantic similarity, which leads to the degradation of classification performance in the target languages. This study proposes a novel framework, a heterogeneous graph neural network with multi-view contrastive learning for cross-lingual text classification, which integrates a heterogeneous graph architecture with multi-view contrastive learning for the cross-lingual text classification task. This study constructs a heterogeneous graph to capture both syntactic and semantic knowledge by connecting document and word nodes using different types of edges, including Part-of-Speech tagging, dependency, similarity, and translation edges. A Graph Attention Network is applied to aggregate information from neighboring nodes. Furthermore, this study devises a multi-view contrastive learning strategy to enhance model performance by pulling positive examples closer together and pushing negative examples further apart. Extensive experiments show that the framework outperforms the previous state-of-the-art model, achieving improvements of 2.20% in accuracy and 1.96% in F1-score on the XGLUE and Amazon Review datasets, respectively. These findings demonstrate that the proposed model makes a positive impact on the cross-lingual text classification task overall.  
Keywords:cross-lingual text classification; contrastive learning; graph attention network; multi-view
# A Review of Fabrication Technologies for Carbon Electrode-Based Micro-Supercapacitors
## 5 Fabrication Technologies of Carbon Electrode-Based Micro-Supercapacitors
### Printing Technologies
#### 5.4.1 Inkjet Printing  
Commercially available inkjet printers can easily print high resolution patterns with precise control of the width, thickness, distance, and porosity, through the ejection of ink droplets from nozzles onto substrates [186]. Inkjet printing can be used with versatile, environment friendly, and cost-effective precursors by creating small drops on various substrates. This method is a fast process, low cost, and can be up-scaled. To have an efficient inkjet printing process, the ink should have a high solids content and high stability. Moreover, printable ink formulations should have appropriate properties, such as the surface tension and viscosity of the ink.  
Inkjet printing was used for the fabrication of a solid-state symmetrical micro-supercapacitor with excellent cycling stability and mechanical flexibility. Therefore, a highly concentrated ink was formulated with two-dimensional 8-MnO\({}_{2}\) nanosheets with a lateral size of 89 nm and around 1 nm thickness, and this ink was printed on polyimide film substrates, avoiding the coffee-ring effect [186]. Piezoelectric inkjet printing was developed for one-step printing of liquid carbon precursors with a piezo-driven micro-pipetting system on a 3D printer that enables precise positioning of the ink and thus, high-resolution (down to 200 \(\upmu\)m with only small spreading) interdigitated micro-supercapacitors with polyvinyl alcohol/hydrogen sulfate hydrogel electrolyte [187], as illustrated in Figure 12. The liquid carbon precursors used in this study were binder-free and did not contain additives, so it converted directly into porous carbon via a pyrolysis step without further processing.
## 3 Architectural System Design
### Dataset Acquisition  
The research utilizes the National Family Health Survey-5 (NFHS-5) dataset, which is publicly available and can be accessed via platforms such as Kaggle ([[https://www.kaggle.com/datasets/ravisinghiitbhu/nfls5](https://www.kaggle.com/datasets/ravisinghiitbhu/nfls5)]([https://www.kaggle.com/datasets/ravisinghiitbhu/nfls5](https://www.kaggle.com/datasets/ravisinghiitbhu/nfls5)), accessed on 1 February 2025). The dataset contains 136,136 samples and 95 variables. The variables include essential clinical, demographic, and lifestyle factors related to maternal health, such as BMI, hemoglobin concentration, blood pressure resolution, age, education, previous pregnancy, or if they have breastfed, etc. The target variable shows whether there is the presence or absence of pregnancy complications, modeled as a binary classification problem [40].  
Figure 2 presents a snapshot of the NFHS-5 dataset, illustrating features such as respondent age, education level, household assets, and healthcare access. This tabular view highlights the diversity and richness of the collected data, forming the foundation for pregnancy complication prediction.  
Figure 3 depicts the class imbalance in the target variable, with approximately 75.7% of instances indicating pregnancy complications and 24.3% representing non-complications. This imbalance was addressed using the NearMiss-SMOTE technique during preprocessing.  
Figure 2: Sample view of the NFHS-5 dataset.
# Forgery Detection for Anti-Counterfeiting Patterns Using Deep Single Classifier
## 4 Experimental Results and Analysis
### Experimental Setup
#### 4.2.3 Classification Criteria  
The classification performance is evaluated by several metrics as follows:  
(1) TP, TN, FP and FN. The four metrics TP (True Negative), TN (True Positive), FP (False Positive) and FN (False Negative) are the most basic metrics.  
(2) Precision. The precision rate represents the proportion of samples with positive predicted results, where the ground truth is also positive. The expression is  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}}\]  
(3) Recall. The recall rate represents the proportion of samples with positive predicted results among all samples that are positive in the real situation. The expression is  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}}\]  
(4) F\({}_{1}\) Score is an indicator used in statistics to measure the accuracy of binary classification models. It takes into account both the accuracy and recall of the classification  
Figure 10: Scatterplot and the optimized decision boundary.  
model. The F\({}_{1}\) score can be seen as a harmonic average of model precision and recall. The expression is  
\[\text{F}_{1}=2\times\frac{\text{Precision}\times\text{Recall}}{\text{Precision}+ \text{Recall}}\]  
Generally speaking, the higher the values of precision, recall and F\({}_{1}\) score, the better the performance of an algorithm.
# A Size-Controlled AFGAN Model for Ship Acoustic Fault Expansion
###### Abstract  
Identifying changes in the properties of acoustical sources based on a small number of sample data from measurements has been a challenge for decades. Typical problems are the increasing sound power from a vibrating source, decreasing transmission loss of a structure, and decreasing insertion loss of vibration mounts. Limited access to structural and acoustical data from complex acoustical systems makes it challenging to extract complete information of the system and, in practice, often only a small amount of test data is available for detecting changes. Although sample expansion via interpolation can be implemented based on the priori knowledge of the system, the size of the expanded samples also affects identification performance. In this paper, a generative adversarial network (GAN) is employed to expand the acoustic fault vibration signals, and an Acoustic Fault Generative Adversarial Network (AFGAN) model is proposed. Moreover, a size-controlled AFGAN is designed, which includes two sub-models: the generator sub-model generates expanded samples and also determines the optimal sample size based on the information entropy equivalence principle, while the discriminator sub-model outputs the probabilities of the input samples belonging to the real samples and provides the generator with information to guide sample size considerations. Some real data experiments have been conducted to verify the effectiveness of this method.  
acoustic fault identification; sample expansion; generative adversarial network; size control +
Footnote †: journal: Applied Physics Letters
## 2 Materials and Methods  
The presented study concerns sound insulation of a cross-laminated timber (CLT) floor with a thickness of 180 mm, which is the reference structure and has additional layers for enhanced acoustic behavior in 12 versions (configurations). The reference floor is 4000 mm long and 3100 mm wide and comprises a five-ply massive CLT slab, as illustrated in Figure 11 (Appendix A).  
The materials involved in this study are tabulated with their relevant properties in Table 1, and the examined floor configurations are presented in Table 2. The thickness of the layers, their density, and the area density (or mass per unit area) are commonly used in product specifications and research [2; 8; 34].  
Notably, some details may be unique in the measurement setup:  
(a) No glue was used between the layers, nor any other connecting elements such as screws or spikes; the additional layers just lay one on top of another, covering the full area of the test floor.  
The measurements were performed in the acoustic laboratory of LTH (Lund University, Lund, Sweden) between the years 2020 and 2021, following the ISO 10140 procedures for airborne and impact sound insulation [35; 36], in the extended frequency range of 50-5000 Hz in 1/3 octave bands.  
Specifically, the airborne sound reduction index \(R\) (in dB) was estimated according to ISO 10140-2 [35] for each frequency band as follows:  
\[R(f)=L_{S}(f)-L_{R}(f)+10 log\left(\frac{A_{R}(f)}{S_{R}}\right) \tag{1}\]  
where  
\(L_{s}\) is the measured sound pressure level in the sending (source) room in dB;  
\(L_{R}\) is the measured sound pressure level in the receiving room in dB;  
\(S\) is the sample area of the testing partition in m\({}^{2}\), which is 12.3 m\({}^{2}\);  
\(A_{R}\) is the equivalent sound absorption area of the receiving room in m\({}^{2}\).  
Similarly, the normalized impact sound pressure levels \(L_{n}\) were calculated according to ISO 10140-3 [36] as follows:  
\[L_{n}(f)=L_{i}(f)+10 log\left(\frac{A_{R}(f)}{A_{0}}\right) \tag{2}\]  
where  
\(L_{i}\) is the measured sound pressure level in the receiving room in dB;  
\(A_{0}\) is the reference equivalent sound absorption area, equal to 10 m\({}^{2}\).  
Then, the single-number quantities (SNQs) for insulation ratings were calculated following ISO 717, with part 1 defining the following: \(R_{w}\), \(R_{w,\ 100}\) (=\(R_{w}+C_{100-3150}\)), and \(R_{w,\ 50}\) (=\(R_{w}+C_{50-3150}\)) for airborne sound [19], and part 2 defining the following: \(L_{n,w}\), \(L_{n,w,100}\) (=\(L_{n,w}+C_{J,100-2500}\)), and \(L_{n,w,50}\) (= \(L_{n,w}+C_{J,50-2500}\)) for impact sound [20].
# Feasibility of Wave Simulation in Typhoon Using WaveWATCH-III Forced by Remote-Sensed Wind
###### Abstract  
The purpose of our work was to assess the feasibility of hindcasting waves using WAVEWATCH-III (WW3) in a typhoon by assembling winds from multiple remote-sensed products. During the typhoon season in 2021-2022, the swath wind products in the Western Pacific Ocean were collected from scatterometers and radiometers. Cyclonic winds with a spatial resolution of 0.125\({}^{\circ}\) at intervals of 6 h were obtained by assembling the remote-sensed winds from those satellites. The maximum wind speeds, \(V_{max}\), were verified using the reanalysis data from the National Hurricane Center (NHC), yielding a root-mean-squared error (RMSE) of 4.79 m/s and a scatter index (SI) value of 0.2. The simulated wave spectrum was compared with the measurements from Surface Waves Investigation and Monitoring (SWIM) carried out on the Chinese-French Oceanography Satellite (CFOSAT), yielding a correlation coefficient (Cor) of 0.80, squared error (Err) of 0.49, RMSE of significant wave height (SWH) of 0.48 m with an SI of 0.25, and an RMSE of the peak wave period (PWP) of 0.95 s with an SI of 0.10. The bias of wave (WW3 minus European Centre for Medium-Range Weather Forecasts (ECMWFs) reanalysis (ERA-5)) concerning the bias of wind (assembling minus ERA-5) showed that the WW3-simulated SWH with the assembling wind forcing was significantly higher than that with the ERA-5 wind forcing. Moreover, the bias of SWH gradually increased with an increasing bias of wind speed; i.e., the bias of SWH increased up to 4 m as the bias of wind speed reached 30 m/s. It was concluded that the assembling wind from multiple scatterometers and radiometers is a promising source for wave simulations via WW3 in typhoons.  
wave; WAVEWATCH-III; assembling wind +
Footnote †: journal: Marine Science and Engineering
## 5 Conclusions  
This paper introduces an adaptive scale factor method derived from orthogonal basis function decomposition. This method addresses the challenges faced by the OBF detector in conventional magnetic anomaly detection, particularly when confronted with unknown relative velocities or non-ideal uniform motion. In simulations, we employed techniques to adjust the relative velocity between the detection platform and the magnetic target, along with modifying the characteristic time. By constructing a viable mathematical model and fitting it to a Gauss function curve, we achieved adaptive detection of magnetic anomaly signals under an unknown motion state. In the context of non-ideal uniform motion, an adaptive selection of the optimal scaling factor \(\beta\) is employed based on measured data under unknown velocity states. This approach aims to enhance the output signal-to-noise ratio of magnetic anomaly targets in non-ideal conditions. Furthermore, the traditional orthogonal basis function decomposition energy value was effectively improved by 17.7%. Additionally, the time when the magnetic target passes through the closest point of approach (CPA) aligned with the peak of the orthogonal basis decomposition energy detection, resulting in a 54.1% increase in precision compared to the conventional method. Consequently, this enhancement significantly bolsters the detector's performance. In conclusion, this method provides a new approach for the magnetic anomaly detection technology under an unknown motion state, which can improve the accuracy and performance of magnetic anomaly signal detection in practical applications. This method can also be applied to other fields of signal processing and target detection. Finally, the scale factor approach also provides reference and guidance for subsequent research. Therefore, this method is not only important for researchers in related fields but also provides a feasible improvement for practical applications.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the data varies under different conditions. We are grateful to the School of Marine Science and Technology for providing the test site and measurement instruments. The authors declare no conflicts of interest.
# An LDPC-RS Concatenation and Decoding Scheme to Lower the Error Floor for FTN Signaling
## 3 LDPC-RS Concatenation Code  
### Occurrence of an Error Floor in FTN Signaling  
Inter-symbol interference caused by FTN signaling introduced relevance between symbols and code words. Additionally, shortening or tail cutting of the pulse-shaping filter and the channel response further complicate symbol relevance canceling. The LDPC code shows good performance with the AWGN channel, where symbols and noise are all independent. While applying FTN signaling and M-BCJR detection, there is residual relevance (interference) between symbols since we cannot accurately simulate an infinite pulse-shaping filter or channel response. On the other hand, LDPC code sometimes independently suffers from error propagation, stopping and trapping sets of its factor graph [17]. Thus, FTN signaling makes the LDPC code's weakness obvious, and therefore an error floor occurs. The smaller the acceleration factor value is, the more severe the ISI that will occur, and thus, the BER performance of the LDPC will be poor, and the error floor will still occur.  
The error floor is caused by FTN signaling and residual relevance (interference) between symbols. Since the M-BCJR algorithm is already a good detection scheme from the decoder perspective, we used shortening to provide some absolutely right bits of the LDPC--known bits with a high LLR in the factor graph in order to help the BP decoder partially overcome error propagation. Furthermore, we utilized perturbation to add low power noise to calculation of all check nodes during BP decoding. Since the operation is summation, confident nodes with high LLR values will not be affected, but those nodes of low reliability (a low magnitude of LLR) will be perturbed and may be corrected or move away from stopping or trapping sets, which are believed to be a reason for the error floor.  
Figure 2: BER vs. SNR of 5C LDPC (\(7680,3840\)) code with three turbo iterations and 4-BCJR detection.
# A Review of the Artificial Neural Network Models for Water Quality Prediction
## 6 Discussion
### Data Processing Is Key
Data preprocessing problem: Most studies use the normalization method for pre-processing data, but it does not disclose specific details. This is probably due to the use of built-in functions to deal with normalization in many platforms. However, this basic information should be clearly defined, because different scaling ranges have different effects on the final result of the model. In the face of missing data, researchers will simply delete it. This approach is not worth advocating because data is precious. Researchers can adopt appropriate populating strategies to deal with missing data. Some imputation methods besides linear interpolation--such as the improved mean value method that can solve the breakpoint phenomenon of linear interpolation, and designing filling schemes such as missing-refilling schemes or gap-filling to obtain continuous records--are worthy of exploring. The restricted condition of the model forecasting methods using prediction values to fill the missing vales is that the data are appropriately and normally distributed. Therefore, it is uncertain whether the above method can be applied to other prediction tasks that do not meet the above conditions. Existing literature has shown that the identification of error and abnormal data is a difficult task because they are difficult to define in water quality prediction. How to deal with such data still needs further exploration by researchers.