esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Learning Methods for Bug Bite Classification: An End-to-End System
## 3 Results
### Bug Bites Classification Results
According to the normalized confusion matrix (Figure 14), this model, compared to the other models, performs best when there are no bites. It also shows the best results in classifying bee stings. **Neural Network Stacking Ensemble. ** The most accurate results were achieved using the classifier fusion approach with the neural network stacking ensemble. The largest accuracy obtained by a single deep learning model is 78% on the test dataset, provided by the DenseNet169-based architecture. The stacked model performed considerably better, with an accuracy of 86%, a macro-average of precision and an F1-score also with values of 86%, and a macro-average of recall with a value of 87%. The weighted average of precision and recall in the case of the stacked architecture is 86%, whereas this value calculated for the F1-score is 85% (Table 14). The class-wise precision, recall, and F1-score of the ensemble model are also represented in Table 14. It is evident that some of the precision and recall scores for a single target label are lower than in the cases of some of the individual models. However, the \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Label** & **Precision** & **Recall** & **F1-Score** \\ \hline Ant bites & 0.88 & 0.71 & 0.79 \\ Bed bug bites & 0.68 & 0.79 & 0.73 \\ Bee stings & 0.69 & 0.75 & 0.72 \\ Chigger bites & 0.95 & 0.85 & 0.89 \\ Flea bites & 0.82 & 0.74 & 0.78 \\ Mosquito bites & 0.79 & 0.94 & 0.86 \\ No bites & 0.62 & 0.64 & 0.63 \\ Spider bites & 0.83 & 0.80 & 0.81 \\ Tick bites & 0.84 & 0.80 & 0.82 \\ \hline Accuracy & & & 0.78 \\ Macro-average & 0.79 & 0.78 & 0.78 \\ Weighted average & 0.79 & 0.78 & 0.78 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Evaluation metrics for DenseNet169 model. Figure 14: Normalized confusion matrix for DenseNet169. general metrics of the ensemble model have improved for at least 7% on the overall test dataset. The results support our expectation that the combination of models with different predictive accuracies on specific labels would lead to a more robust and precise model. The confusion matrix for the ensemble model is shown in Figure 15. As we can see, the greatest percentage of misclassification is related to flea bites. They are misclassified as bites from ants, bed bugs, and stings from bees (10% each) or as mosquito bites (6%). The highest degree of confusion (19%) is between bee stings (expected) and spider bites (classified). The bites from chiggers and non-bite images are perfectly classified. Also, there is a high degree of correctly classified images achieved by the stacked model when classifying ant (93%), tick (91%), and mosquito bites (90%).
# Effects of Adding Batteries in Household Installations: Savings, Efficiency and Emissions
## 5 Data Analysis  
Data analysis was performed by means of different simulations of an iterative algorithm by using MATLAB and representing the year 2020 with the data presented in previous sections. The goal is to determine the differences between the systems presented in this study, comparing their efficiencies, emissions and the associated annual costs. In systems with battery, the initial load percentage is 40%. With the aim of protecting the battery, when the charging status is below a 20%, regardless whether this percentage was reached in peak or off-peak hours, it is mandatory that the battery starts charging. On the other hand, in order to maximize the effectiveness, it is considered a time discrimination period according to Spanish standards where, due to the presence of the battery, it is possible to accumulate energy in off-peak hours, being this energy consumed later during peak hours. In the following paragraphs, the methodology used in the algorithm to obtain the results is presented. Then, this section is divided as follows: first, the energy demand throughout the year is compared, then the battery systems in several days of the year are analyzed. and finally, the electricity costs in various months are shown.  
In order to obtain the efficiency of each system, the AC/DC conversion was taken into account. The electrical appliances in the current AC system are connected directly to the electrical network, so the losses are produced in each electrical device. Similarly, in the AC system with a battery, losses appear in the household appliances due to their AC connection--as well as due to the storage possibilities in the battery input and output. The domestic appliances in the AC/DC system with battery are powered directly by DC from the battery output, so, in this case, they were just considered losses in the battery input.  
The emissions produced in the house for the evaluated systems were calculated using the calculations of household demand from the electrical network obtained with Equation (1) and CO\({}_{2}\)-emission-intensity levels [20].  
In the same way as in the previous case, once the household demand is obtained from the electrical network and, by relating each period of time with the hourly costs, the variable costs were calculated. Then, adding the fixed costs related to the power factor, the annual electric costs of each system were determined.
# An Evaluation Survey of Knowledge-Based Approaches in Telecommunication Applications
## 3 Evaluation of Different Knowledge-Based Approaches
### Assessment Results
#### 3.3.8 Neural Network-Based Formalisms  
Neural network-based approaches, including DNN, CNN, RNN, LSTM, DRL, DQN, etc., encode knowledge implicitly in their weights and activations, lacking explicit representation in a formalized language. These models often achieve high accuracy in predictions or classifications, especially with large datasets. However, while they capture data patterns and features well, they might lack precise definitions of explicit rules or constraints, impacting precision. Their coverage spans a wide range of data patterns, but may suffer from biases in training data, affecting coverage of less frequent patterns. Despite good generalization within trained data distributions, they might lack robustness when applied to unseen or significantly different data distributions. Neural networks are often considered black-box models, posing challenges in explaining decisions, particularly in deep and complex architectures.  
Concerning procedural qualities, they lack mechanisms to encode uncertainty or confidence levels explicitly, resulting in non-probabilistic outputs. With a lack of mechanisms dealing with conflicting knowledge, these models might overfit or underfit data, leading to inconsistencies in predictions. While techniques like softmax probabilities estimate confidence, accuracy in reflecting true confidence might vary. Furthermore, via scalable with increased data sizes and handling complex relationships, integrating new knowledge often demands retraining or fine-tuning, posing challenges in incorporating new information without compromising prior learning.  
In summary, neural networks showcase high accuracy and scalability, being potent tools for pattern recognition and complex data processing. However, their limitations lie in the explicit handling of uncertainty, explainability, expandability, and generalization to new or diverse data.
## 2 Materials and Methods  
### Process and Samples  
Typically, in the kraft processes of wood, the black liquor initially obtained after digestion goes through multiple effect evaporators, where the SC is gradually increased due to water evaporation. Black liquors at different evaporation stages have different SCs and these can be classified as weak,intermediate, half-strong, or strong black liquors. Weak black liquor has the lowest SC of about 15-20%, whilst strong black liquor has the highest SC of approximately 65-75% [1; 2; 3; 6], even reaching up to 80-85% [4]. Strong black liquor is the final concentrated liquor that is sent to a draft recovery boiler for energy production and chemical recovery.  
In the current work, we investigated samples of weak, intermediate, and half-strong black liquors that were collected at different process stages in a pulp mill. Strong black liquor was not included in the study because its properties, such as high viscosity and temperatures, made it difficult to pump through the online TD-NMR system. The SCs of the collected black liquors were determined gravimetrically after drying at 105 \({}^{\circ}\)C until a constant weight was reached:  
\[\mathrm{SC}=\mathrm{w_{dry}}/\mathrm{w_{s}}\cdot 100\%, \tag{1}\]  
where SC is the solids content expressed as a%, and \(\mathrm{w_{dry}}\) and \(\mathrm{w_{s}}\) are the weights of the dried solids and initial sample, respectively.
# Detection of Azo Dyes in Curry Powder Using a 1064-nm Dispersive Point-Scan Raman System
## 2 Materials and Methods  
### Point-Scan Raman System  
The Raman spectrograph uses a high throughput volume phase grating (VPG) (BaySpec, Inc., San Jose, CA, USA) optimized for 1064 nm laser excitation [28]. The scattering Raman signal from the sample is directed to the VPG through a concave mirror in the spectrograph. The VPG diffracts the incoming light into different angular output paths. The dispersed light is reflected to the 512 pixels Indium-Gallium-Arsenide (InGaAs) detector (Nunavut, Bayspec, Inc., San Jose, CA, USA). A USB cable connects the detector directly with the computer for detector control and data transfer. The detector is thermoelectrically cooled to \(-55\,^{\circ}\)C during spectral acquisition to minimize the dark current.  
The sample is held and moved in two perpendicular directions using a two-axis motorized positioning table (MAXY4009W1-S4, Velmex, Bloomfield, NY, USA). The sample movement is controlled by stepper motor controller. The sample is moved in X- and Y-axes, below the fixed position Raman probe, collecting Raman spectra of the sample by point-scan method. The Raman spectra are accumulated to obtain a hyperspectral Raman image of the sample, which can be analyzed both spectrally and spatially.  
An interface software is developed in-house for parameter setup and data transfer. The software is used to control the operational parameters of the system such as initialization, adjusting exposure time, spectral acquisition and display, sample movement, and data transfer and storage. The interface software was developed using the software development kit (SDK) of the InGaAs detector and the positioning table. The hyperspectral data acquired by the interface software is stored in band interleaved by pixel (BIP) format, which can be analyzed by ENVI (ITT Visual Information Solutions, Boulder, CO, USA) and Matlab (MathWorks, Natick, MA, USA).  
Spectral calibration of the Raman system was done using polystyrene and naphthalene. After spectral calibration, the Raman system covered the wavenumber range of 142 cm\({}^{-1}\) to 1820 cm\({}^{-1}\). The system has a spectral resolution of 12 cm\({}^{-1}\) at full width half maximum (FWHM). A standard resolution test chart (Edmund Optics Inc., Barrington, NJ, USA) was used to evaluate the spatial resolution of the system. The system has a spatial resolution of 0.1 mm.
# Performance Analysis of the Pilot and Data Component of a CSS Signal for LEO-PNT +
## 1 Introduction
Note that the ranging component was already introduced and evaluated in [4] and [11], respectively. In this paper, we include the design and evaluation of the data component, and we relate it with the design of the ranging component. To do so, the rest of this paper is organized as follows: Section 2 explains the signal model for the CSS-based PNT signal by showing the general expression of a CSS signal, the challenges we find in PNT, and how to solve them by including a pilot and data component. Then, Section 3 goes into detail about the signal processing and design of the CSS-based PNT signal, and Section 4 provides the performance analysis of the proposed CSS signal. Finally, Section 5 concludes the paper.
## Appendix B FLUXNET2015 Sites Used in This Study  
Tables 10-11 list the flux tower sites used in this study along with corresponding information. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Code** & **Name** & **Latitude** & **Longitude** & **Veg. Type** & **Year Start** & **Year End** \\ \hline CN-Cha & Changbaishan & 42.4025 & 128.096 & MF & 2003 & 2005 \\ CN-Dan & Dangxiong & 30.4978 & 91.0664 & GRA & 2004 & 2005 \\ CN-Din & Dinghushan & 23.1733 & 112.536 & EBF & 2003 & 2005 \\ CN-Du2 & Duolun\_grassland (D01) & 42.0467 & 116.284 & GRA & 2006 & 2008 \\ CN-Ha2 & Haibei Shrubland & 37.6086 & 101.327 & WET & 2003 & 2005 \\ CN-HaM & Haibei Alpine Tibet site & 37.3700 & 101.180 & GRA & 2002 & 2004 \\ CN-Qia & Qianyanzhou & 26.7414 & 115.058 & ENF & 2003 & 2005 \\ CN-Sw2 & Siziwang Grazed (SZWG) & 41.7902 & 111.897 & GRA & 2010 & 2012 \\ CZ-BK1 & Bily Kriz forest & 49.5021 & 18.5369 & ENF & 2004 & 2008 \\ CZ-BK2 & Bily Kriz grassland & 49.4944 & 18.5429 & GRA & 2004 & 2006 \\ CZ-wet & CZECHWET & 49.0247 & 14.7704 & WET & 2006 & 2014 \\ DE-Akm & Anklam & 53.8662 & 13.6834 & WET & 2009 & 2014 \\ DE-Geb & Gebesee & 51.1001 & 10.9143 & CRO & 2001 & 2014 \\ DE-Gri & Grillenburg & 50.9495 & 13.5125 & GRA & 2004 & 2014 \\ DE-Hai & Hainich & 51.0792 & 10.4530 & DBF & 2000 & 2012 \\ DE-Kli & Klingenberg & 50.8929 & 13.5225 & CRO & 2004 & 2014 \\ DE-Lkb & Lackenberg & 49.0996 & 13.3047 & ENF & 2009 & 2013 \\ DE-Obe & Oberbarenburg & 50.7836 & 13.7196 & ENF & 2008 & 2014 \\ DE-RuR & Rollesbroich & 50.6219 & 6.30410 & GRA & 2011 & 2014 \\ DE-Seh & Selhausen & 50.8706 & 6.44970 & CRO & 2007 & 2010 \\ DE-Spw & Spreewald & 51.8923 & 14.0337 & WET & 2010 & 2014 \\ DE-Tha & Tharandt & 50.9636 & 13.5669 & ENF & 1996 & 2014 \\ DK-Fou & Foulum & 56.4842 & 9.58720 & CRO & 2005 & 2005 \\ DK-Sor & Soroe & 55.4859 & 11.6446 & DBF & 1996 & 2014 \\ DK-ZaF & Zackenberg Fen & 74.4814 & \(-\)20.5545 & WET & 2008 & 2011 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Listing of flux tower sites used in this study (cont. ).
## 4 Conclusions  
We have presented the manufacturing of a beam shaping module consisting of two lens arrays and an aperture, mounted in a 3D printed housing. The lens arrays consist of 64 lenses having a focal length of 10 mm and a square footprint of 1 mm \(\times\) 1 mm are fabricated by femtosecond pulsed laser ablation and CO\({}_{2}\) laser polishing. Good agreement between the designed geometry and the fabricated elements is found while the roughness is measured to be 25 nm. Testing of the lens arrays show, that an excellent Top-Hat beam profile is generated, having an edge length of 4.5 mm, exactly as being calculated. After testing, the arrays are mounted into an additively manufactured housing which is fabricated via stereolithographic technology. The housing is designed to reveal a length  
Figure 6: (**a**) Designed housing (lower part) for beam homogenization with the assembled lens arrays and aperture and (**b**) schematic of the additive manufacturing process using stereolithography for building the designed object layer by layer out of photosensitive resin.  
Figure 7: (**a**) Final beam shaping module consisting of two lens arrays and an aperture, assembled within a 3D printed housing and (**b**) Top-Hat beam profile formed by the beam shaping module.  
of only 16 mm and a diameter suitable for being mounted into commercial 1\" optic holders. With the laser-cutted aperture, the finalized module demonstrates good beam shaping quality.  
[PERSON], [PERSON] and [PERSON] conceived, designed and performed the experiments and analyzed the data. [PERSON], [PERSON] and [PERSON] wrote the paper. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data presented in this manuscript can be obtained from the corresponding author upon request.  
The authors declare no conflict of interest.
## 4 Experiments
### Rescue Mission  
The mission assigned to our group consisted of an emergency response to a disaster in a natural environment. Other participants in this mission were the Provincial Fire Department of Malaga and the Spanish military emergency unit (UME).  
The simulated scenario consisted of an earthquake that caused a fire with victims trapped inside crushed vehicles in a storm water drainage tunnel with a difficult access environment, as illustrated in Figure 8. The area of this scenario (number 7 in Figure 7) has a difference in elevation of 7 m, with an average slope on the access track to the ravine of 27%, equivalent to 15.5\({}^{\circ}\) (0.27 rad).  
A mission protocol was agreed with the participant organizations for cooperation between heterogeneous robot teams, sensor networks, and first-responder teams. The major steps are the following:  
1. An initially available map of the environment is used to establish a boundary area (hot zone) in which the existence of victims is possible.
2. Aerial mapping to obtain a DEM and an orthophoto of the area of operations, which is required for the path planning of cooperative robotic agents.
3. Deployment of the agents throughout the area of operations to execute the SAR mission. Cooperative robots support the SAR mission by detecting risks and PVs using on-board sensors, which can trigger the path planner. In any case, human coordinators are the decision makers that use the SAR-FIS interface to specify which and where rescue and robotic teams are deployed.
4. Cooperative extraction and evacuation of victims is performed.
5. A new aerial mapping and orthophoto are performed to update the DEM and the perimeter of the operation area. This operation task is conditional to the detection of a new risk outside the current zone.
6. Steps 3, 4 and 5 can be repeated if the SAR mission requires it.  
Figure 8: _Cont._The purpose of the aerial visual perimeter exploration (step 5) is to identify new zones with the possibility of finding victims and thus expand the initial area of operations. In particular, as a result of this aerial exploration, during this rescue mission, the presence of vehicles with possible victims inside was identified in the vicinity of a storm water drainage tunnel. This finding required the intervention of rescue teams in the new area for the evacuation of possible victims.
# Construction of Power Fault Knowledge Graph Based on Deep Learning
## 4 Experiments and Results
### Experiment 2-BBiGRU-GA-CRF
#### 4.2.4 Model Parameters  
The experimental parameter settings are shown in Table 6.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Total Number of Logs** & **Total Time (s)** & **Average Time (ms)** & **Time Cost** \\ \hline PCA 26,433 & 117 & 4.43 & 1 \\ DeepLog 26,433 & 101 & 3.82 & 0.86 \\ GRU 26,433 & 85 & 3.21 & 0.72 \\ BiSGRU-GA 26,433 & 73 & 2.76 & 0.62 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Efficiency comparison of PCA, DeepLog, GRU.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Data Set** & **Training Set** & **Validation Set** & **Test Set** \\ \hline Power System Fault & & & \\ Knowledge Manual & 24,162 & 8154 & 8469 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Specific size of data set for entity extraction.  
In order to verify the advanced nature of the BBiGRU-GA-CRF entity recognition model in the field of fault text recognition, we selected the following classical entity recognition models for comparative experiments and analyzed the following models:  
1. BiLSTM-CRF [30] model: a BiLSTM network is used to capture two-way semantic dependencies, and then capture global features;
2. BiGRU-CRF [31] model: replaces the BiLSTM network with a BiGRU network which uses fewer parameters;
3. BERT-BiLSTM-MHA-CRF [32] model: a Multi-head Attention Mechanism is introduced to capture local features;
4. BBiGRU-GA-CRF model: the model proposed in this paper.
## 3 Unconfined Compression Test
### Test Results
#### 3.2.2 Composite-Additive  
The results of the unconfined compression test performed on solidified Caofeidiani dredger fill with composite additives are presented in Table 6.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Number**} & \multicolumn{2}{c}{**Unconfined Compressive Strength (kPa)**} \\ \cline{2-3}  & **7 d** & **28 d** \\ \hline A10 & 1483 & 2764 \\ A11 & 1981 & 3033 \\ A12 & 1681 & 2870 \\ A13 & 1886 & 3200 \\ A14 & 2007 & 3370 \\ A15 & 1907 & 3020 \\ A16 & 1827 & 2401 \\ A17 & 1680 & 2640 \\ A18 & 1460 & 2374 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Test results of composite-additive samples.  
Figure 2: Unconfined compressive strength of samples with different single additives: (**a**) quicklime; (**b**) sodium silicate; (**c**) gypsum powder.  
Table 7 presents the range analysis of the orthogonal test. \(T_{i}\) is the sum of the strength at Level \(i\), and \(t_{i}\) is the average strength. The range is calculated as \(t_{i\text{-}max}\)-\(t_{i\text{-}min}\), which indicates the degree of influence of the three additives on the unconfined compressive strength. Based on the results, it can be stated that quicklime was the key additive. It had the maximum range of values for both 7 day and 28 day strength. It was followed by sodium silicate and, next, by gypsum powder. In addition, the optimal proportion scheme could be obtained based on strength tests and orthogonal analysis as 10% slag, 1% quicklime, 0.8% sodium silicate, and 1.5% gypsum powder. The unconfined compressive strength of the solidified Caofeidian dredger fill with the optimal proportion attained 2007 kPa in 7 d and 3370 kPa in 28 d. These results were slightly lower than or close to the results from [22], in which a different slag composite curing agent was applied to the Caofeidian coastal saline soil.
# A Comparative Evaluation of Transformers and Deep Learning Models for Arabic Meter Classification
## 3 Materials and Methods
### Data Preprocessing
#### 3.2.3 Label Encoding  
Label encoding is especially efficient when the target variable comprises discrete classes as it assigns a unique integer to each category. In this study, we used label encoding to map each of the 14 m classes to an integer ranging from 0 to 13, enabling faster processing during model training.