esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Results  
#### 3.4.1 Land Use Categorization  
Class imbalance and expected results are some of the considerations taken into account in the selection of metrics to measure the performance of a classifier. There is no one metric that measures well the performance of a classifier in every scenario. In this research, the classifier is of the multi-class type (many possible results that are mutually exclusive) [40], so it is also important to calculate the metrics for each class \(c_{1}\ldots c_{n}\) individually, and then calculate the classifier metrics generally, as proposed in [27]: accuracy, precision, recall, and F1-score. Since the classes are unbalanced (some classes have more data, so they are more likely to appear than others) the prioritized indicator is the F1-score [37]. Results for each model are shown in Table 7.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Feature** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline TF-IDF & 0.830 & 0.911 & 0.702 & 0.744 \\ TF-IDF/lemma & 0.836 & 0.907 & 0.730 & 0.772 \\ \hline Unigram & 0.884 & 0.896 & 0.810 & 0.838 \\ Unigram/lemma & 0.884 & 0.886 & 0.832 & 0.851 \\ \hline Bigram & 0.837 & 0.887 & 0.770 & 0.805 \\ Bigram/lemma & 0.843 & 0.868 & 0.784 & 0.813 \\ \hline Trigram & 0.615 & 0.843 & 0.492 & 0.559 \\ Trigram/lemma & 0.651 & 0.855 & 0.551 & 0.624 \\ \hline N-gram (1,2,3) & 0.894 & 0.899 & 0.844 & 0.863 \\ N-gram (1,2,3)/lemma & **0.904** & **0.900** & **0.870** & **0.880** \\ \hline \hline \end{tabular}
\end{table}
Table 7: Metrics with different combinations of characteristics.  
The corpus is used with two variations: one, with the text in its lemmatized form and the other with the original text, without lemmatization. The values achieved with these two variations are very close to each other, but the use of the lemmatized text always provides slightly better results (see Table 7, where two rows for each feature is shown, with and without lemma). Furthermore, comparing the results of the models it is clear that by using all n-grams (unigram + bigram + trigram) together the highest accuracy is achieved. Therefore, this model is chosen for the classification of application data resulting in tweets labeled to be positioned in a Land Use Map (a sample of the results achieved with real data is shown in Table 8).  
Out of the 924 tweets used by the classifier, 327 were classified in the commercial category, 248 in the institutional category, 35 in residential, 14 in unbuilt land, and 8 in industrial. There were 292 tweets left that did not reach the cut-off point established for the classifier, so they were not labeled (non-classified) assuming that the text does not refer to a location.
###### Abstract  
Land use and land cover (LULC) change has become an important research topic for global environmental change and sustainable development. As an important part of worldwide land conservation, sustainable development and management of water resources, developing countries must ensure the use of innovative technology and tools that support their various decision making systems. This study provides the most recent LULC change analysis for the last six years (2015-2021) of Coatzacoalcos, Veracruz, Mexico, one of the most important petrochemical cities in the world and host of the ongoing Interoceanic Corridor project. The analysis was carried out using Landsat 8 Operational Land Imager (OLI) satellite images, ancillary data and ground-based surveys and the Normalized Difference Vegetation Index (NDVI) to identify and to ameliorate the discrimination between four main macro-classes and fourteen classes. The LULC classification was performed using the maximum likelihood classifier (MLC) to produce maps for each year, as it was found to be the best approach when compared to minimum distance (MDM) and spectral angle mapping (SAM) methods. The macro-classes were water, built-up, vegetation and bare soil, whereas the classes were an improved classification within those. Our study achieved both user accuracy (UA) and producer accuracy (PA) above 90% for the proposed macro-classes and classes. The average Kappa coefficient for macro-classes was 0.93, while for classes it was 0.96, both comparable to previous studies. The results from the LULC analysis show that residential, industry and commercial areas slowed down their growth throughout the study period. These changes were associated with socio-economical drivers such as insecurity and lack of economic investments. Groves and trees presented steady behaviors, with small increments during the five-year period. Swamps, on the other hand, significantly degraded, being about 2% of the study area in 2015 and 0.93% in 2021. Dunes and medium and high vegetation densities (\(\sim\) 80%) transitioned mostly to low vegetation densities. This behavior is associated with rainfall below the annual reference and increments of surface runoff due to the loss of vegetation cover. Lastly, the present study seeks to highlight the importance of remote sensing for a better understanding of the dynamics between human-nature interactions and to provide information to assist planners and decision-makers for more sustainable land development.  
remote sensing; land use classification; GIS; Coatzacoalcos +
Footnote †: journal: Applied Sciences
# A Comparative Analysis for Air Quality Estimation from Traffic and Meteorological Data
## 2 Materials and Methods
### Data Sources and Acquisition
The positioning of these cells is visible in Figure 1. In total, exploring the three-year period considered for this work, we analysed 10 different pollutant categories, each one measured by at least one sensor in the central area of Milan. The count of unique measurements varies greatly with the severity and the intrinsic relevance of the pollutant for the definition of an air quality index, which is typically identified by a combination of \(NO_{2}\), \(O_{3}\) and particulate matter, as described in Section 2.3. Specifically, nitrogen oxides (\(NO_{2}\), \(NO_{x}\)) are represented, respectively, by 8 and 7 unique sensors distributed around the city center, \(C6_{H}6\) and \(CO\) by 4, \(O_{3}\) and particulate matter (\(PM_{10}\), \(PM_{2.5}\)) by 3, \(BC\) by 2. Lastly, \(SO_{2}\) and \(NH_{3}\) are represented by a single measurement only. As for the meteorological data, logs for the pollutants provided by ARPA correspond to an hourly aggregation by average, with the exception of particulate matter (\(PM_{10}\) and \(PM_{2.5}\)), where measurements are stored with daily interval, representing a 24-h mean value. While this is sub-optimal given the consistency of the remaining data, it does not represent a major issue and it is somewhat expected since the vast majority of air quality indicators only make use of a daily average for particulate matter. The last feature type employed in this work is represented by recorded hourly transits in the congestion charge surrounding the center of Milan. In this case, data is provided by Agenzia Mobilita Ambiente e Territorio (AMAT) ([[https://www.amat-mi.it/](https://www.amat-mi.it/)]([https://www.amat-mi.it/](https://www.amat-mi.it/))) and encompasses the C Area, corresponding to the urban region of Cerchia dei Bastioni. The area is accessible by a total of 42 gates, displayed in Figure 1, each one monitored by fixed cameras continuously monitoring and recording the plates of any vehicle entering the city center. The logs were cross-referenced with data from the regional Department of Motor Vehicles, in order to obtain, for each plate, a list of characteristics of the corresponding vehicle, including fuel type and potential category of European emission standard. The complete dataset is subdivided into three main CSV files: the first containing information about the monitoring gates, the second providing detailed information about vehicles and the last one providing the transit records in the city center, during the three-year period. Altogether, these files contain information about four main features: (i) the emission standard category, from Euro 0 to Euro 6; (ii) the fuel type, namely petrol, diesel, electricity, gas or hybrid; (iii) the category of vehicle, public transport, cargo or normal cars; and (iv) additional information such as whether the vehicle was authorized into the area, or whether it is a service vehicle or if it belongs to residents.
# An NLOS Ranging Error Mitigation Method for 5G Positioning in Indoor Environments
## 5 Conclusions  
In this paper, we propose a method aimed at addressing the challenge of mitigating NLOS errors in 5G indoor localization. Our approach involves several key steps for 5G terminals: firstly, we performed the classification and identification of NLOS observations based on decoupling wireless signal features, where indoor propagation channels are categorized into multiple classes to assess the severity of NLOS effects based on channel identification results. Secondly, we established 5G NLOS measurement identification and classification criteria for filtering ranging results. Finally, we introduced a position estimation method based on the SRUKF to ensure the numerical stability of the filter position estimation output results during the fusion of observations. The theoretical simulations and field experiment demonstrated the superior positioning accuracy compared to four advanced methods: SVM-REM, VTL, VBS and IRUKF. The method proposed in this article provides a new idea for NLOS error suppression in indoor 5G positioning and can also be extended to NLOS error suppression for other wireless signals through appropriate adjustments. The research results of this article show good NLOS suppression effects, and the proposed method can be directly applied in real environments. This can make important contributions to high-precision positioning based on commercial 5G base stations and provide crucial support for location-based services in commercial 5G networks.  
Conceptualization, Z.D.; methodology, J.L. and E.H.; software, J.L.; validation, J.L. and E.H.; formal analysis, J.L.; investigation, Z.D. and J.L.; resources, J.L.; original draft preparation, J.L. and E.H.; editing, J.L. and Z.D.; visualization, J.L.; project administration, Z.D. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China (grant numbers: 2022 YFB3904702 and 2022 YFB3904603).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors.  
The authors thank [PERSON] for resource support and project administration.  
The authors declare no conflicts of interest.
# Hybrid Data Hiding Based on AMBTC Using Enhanced Hamming Code
## 2 Preliminaries
### [PERSON] and [PERSON]'s Method  
For DH, the AMBTC algorithm is applied to the original cover image to obtain a low mean, a high mean, and a bitmap for every block. Then, the secret message is concealed in the AMBTC compressed \(trio(a,b,BM)\). The merit of AMBTC is that it achieves a higher payload compared to other DH schemes performed in the compression domain. Here, it performs AMBTC DH in two phases. The method proposed by [PERSON] and [PERSON] is composed of two stages. One of them is to embed three bits in two quantization levels in \(trio(a,b,BM)\) by using HC(\(7\), \(4\)). The detailed process of this method is as follows.  
**Step 1:**: For each \(trio\), obtain seven bits from the two pixels at two quantization levels, and rearrange the seven bits to form a seven bit unit. Let \(a=(a_{8}\ a_{7}\ a_{6}\ a_{5}\ a_{4}\ a_{3}\ a_{2}\ a_{1})\) and \(b=(b_{8}\ b_{7}\ b_{6}\ b_{5}\ b_{4}\ b_{3}\ b_{2}\ b_{1})\) be the two original pixels. The rearranged seven bit unit is obtained by \(y=(a_{4},a_{3},a_{2},a_{1}||b_{3},b_{2},b_{1})\), where the symbol \(||\) denotes that the four bits from \(a\) are concatenated with the three bits from \(b\). Three secret message bits \((m=(m_{1},m_{2},m_{3}))\) are read from the secret bit set \(M\).
**Step 2:**: Compute the syndrome \(S(=Hy\oplus m)\) of the codeword \(y\), and then, the value is changed into a decimal value and is assigned to the variable \(i\). To obtain the stego codeword \(\tilde{y}=(y_{1},y_{2},y_{3},y_{4},y_{5},y_{6},y_{7})\), flip the \(i\)th bit of the codeword \(y\).
**Step 3:**: To reconstruct two quantization levels with the codeword \(y\), \((y_{7},y_{6},y_{5},y_{4})\) is replaced with four LSBs of the low-mean value \(a\), and \((y_{3},y_{2},y_{1})\) is replaced with three LSBs of the high-mean value \(b\).
**Step 4:**: It is possible to hide an additional bit by using the order of two quantization levels and the difference between them. In this case, it may be acceptable to embed an additional bit when the criterion \((b-a\geq 8)\) is satisfied. Otherwise, it is not accepted to embed anadditional bit. If the bit to be embedded is \"1\", swap the order of the two quantization levels as \((trio(b,a,BM))\), otherwise no change is conducted.  
In Step 4, it is possible to embed an additional bit only under the given condition \((b-a\geq 8)\). The reason for the condition is necessary; if the difference between the values of \(a\) and \(b\) is small, the order of the two values may be reversed as a result of the computation of the Hamming code. An ambiguous result in the decoding procedure may occur.
# High-Accuracy Calibration Based on Linearity Adjustment for Eddy Current Displacement Sensor
## 4 Calibration Accuracy Verification
### Accumulated Displacement Error Verification  
In the process of ADE verification, the ECDSs are also placed off the measured target with a certain offset, which is defined as the initial position. Then, the output voltages are recorded at intervals of 0.25 mm until reaching the full scale, and the errors between the measured displacement values and the true values are calculated. Figure 12 illustrates the accumulated displacement error band curves of each sensor, which is also significantly reduced after adjustment, from 11-25 \(\upmu\)m to 1-10 \(\upmu\)m, which indicates that the sensors have better accuracy.  
Then, the 95% confidence interval of accumulated displacement errors of each sensor are given in Figure 13, whose statistical data are shown in Table 5. In order to better reflect the measurement deviation characteristics of ECDSs, outliers are listed separately in the form of the red '+'. It can be seen that the original ADE medians of sensor 1 to 4 are 8.0314 \(\upmu\)m, 11.9520 \(\upmu\)m, 8.4100 \(\upmu\)m and 17.6790 \(\upmu\)m, respectively, which are reduced to 2.2914 \(\upmu\)m, 0.3472 \(\upmu\)m, 2.0165 \(\upmu\)m and 6.7096 \(\upmu\)m after adjustment. Moreover, the error fluctuation range of each sensor (from minimum to maximum) is also decreased from 13.2838 \(\upmu\)m, 14.8441 \(\upmu\)m, 17.4496 \(\upmu\)m and 24.4002 \(\upmu\)m (more than \(\pm\)10 \(\upmu\)m before adjustment) to 3.6297 \(\upmu\)m, 1.1052 \(\upmu\)m, 2.2354 \(\upmu\)m and 7.5956 \(\upmu\)m (less than \(\pm\)5 \(\upmu\)m after adjustment). It indicates that each adjusted sensor has a better accuracy, and as well the error fluctuation is markedly reduced. Therefore, the proposed calibration method can effectively improve both accuracy and precision of sensor calibration and measurement.  
Figure 11: Equidistance displacement error (EDE) curves before and after adjusted.  
Figure 12: Accumulated displacement error (ADE) band curves of each sensor: (**a**) Sensor 1; (**b**) Sensor 2; (**c**) Sensor 3; (**d**) Sensor 4.
## 3 Results and Discussion
### Contact Angle  
Table 3 shows measurements of contact angles for various pure liquids in film samples. Low contact angle demonstrates that the strong surface presents hydrophilic attributes, while high contact points uncover hydrophobic properties. When PP and DSP were added, the WCA value decreased, as shown in Table 2 (and in Figure 5) [46]. The WCA of papaya films was comparable with soy protein isolate, polyvinyl alcohol, and glycerol blend films [47; 48]. Water contact point (\(\theta\)) \(\theta\) < 95 represents hydrophilic properties and \(\theta\) > 95 indicates hydrophobic properties [49]. Contact angle values were observed to be slightly higher on the airside than on the film support side, as was also observed in studies of papaya and defatted soy protein films. The lowest values were observed for the DSP base film, but with the addition of PP in gelatin base film, the WCA significantly increased from 39\({}^{\circ}\) to about 47\({}^{\circ}\), respectively (Table 2). No distinctions were seen between the contact points of tests with various thicknesses [45].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sample** & **T (\%)** & **CA (\({}^{\circ}\))** \\ \hline G-2 & 15.20 & 65.13 \(\pm\) 5.13 \\ G/PP-2 & 25.9 & 47.97 \(\pm\) 4.28 \\ G/PP/SP-2 & 6.08 & 41.91 \(\pm\) 5.23 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Optical and wetting properties of edible films.  
Figure 4: Transparency of edible film (white line indicates the film area): (**A**) gelatin film, (**B**) papaya gelatin film, (**C**) soy protein papaya gelatin film.
## 5 Results and Discussion
### Discussion
#### 5.3.4 NLP-BiGRU  
Figure 13 shows that the NLP-BiGRU combination was the one that gave the best results for the training accuracy (100%). However, the validation accuracy obtained (98%) did not vary concerning BiLSTM (98%) and GRU (98%). On the other hand, the time to achieve its training execution was 320s, i.e., 33% more than the time processed by GRU.
# Washing Batch Test of Contaminated Sediment:
## 3 Results
### TPH Removal Efficiency  
The assessment of potential treatment capacity of sediment washing in terms of hydrocarbon removal was performed only in the second experimental survey campaign and on the sediment fraction less than 63 \(\upmu\)m, since it is more contaminated (17,800.002 mg kgDW\({}^{-1}\)) than the fraction above 63 \(\upmu\)m (9500.028 mg kgDW\({}^{-1}\)). In this sense, it is necessary to underline that the analysis, in this second experimental phase, was preceded by a series of preliminary tests aimed at evaluating and validating the observations previously detected (including for heavy metals, already discussed previously). In this context, for these tests was adopted the concentration of 0.05 M and as extracting agents EDTA, citric acid, and EDDS.  
With regard to the results, obtained at controlled pH, in Figure 4 the residual hydrocarbon concentrations are reported after the relative washing tests.  
It should be noted that the best results were obtained with citric acid at pH 4 and contact time of 4 h: in general, the basic environment favors the dissolution of the pollutants only with EDDS  
Figure 4: Residual TPH concentration after controlled pH washing tests.  
and EDTA; on the other hand, citric acid seems little influenced by the alkaline environment, indeed favored by the further lowering of pH.  
It is important to underline that also in this case the citric acid is satisfactory, even at neutral pH: only the batch test conducted in a strongly alkaline environment with EDTA showed an increase in performance compared to citric acid at the same boundary conditions. In this sense, a cost-benefit analysis would be useful to integrate the survey, with the aim of evaluating both the management simplification deriving from a neutral pH treatment, therefore, without pH corrections, and the economic savings linked to the use of citric acid.  
Moreover, the results obtained for the removal of organic contaminants (TPH) show that for all three extracting agents analyzed, extraction efficiencies have increased with increasing contact time except in the case where EDTA was used at pH 4. At this pH value, the best extracting agent was found to be citric acid, as it achieved a removal efficiency of 99%, while EDTA showed a constant efficiency of 85% and the EDDS of 83%. At pH 7, citric acid and EDTA both achieved extraction efficiencies of about 87-88%, higher than EDDS. Finally, at pH 9, EDTA was found to be the best extracting agent achieving a 90% extraction efficiency.
## 5 The Limitations of the Study  
Despite the valuable insights provided by this study, several limitations should be acknowledged. The invasive nature of the experimental setup, involving the placement of hNIRS optodes and LDF probes directly on the dura mater, limits its direct applicability to clinical settings. While this approach minimized motion artifacts and ensured high-quality data, non-invasive methodologies would be more suitable for real-world clinical applications. Future research should focus on developing and validating robust non-invasive techniques for cerebral oxygenation and metabolism monitoring under similar conditions.  
The sample size of ten pigs, while sufficient for detecting significant trends, imposes constraints on the generalizability of the results for the CPR and ROSC epochs. The small cohort size limited our ability to perform detailed subgroup analyses or assess the effects of specific interventions, such as varying doses of epinephrine, on cerebral metabolism. Previous studies [13; 35] have shown that the adrenaline (epinephrine) dosage significantly influences resuscitation success rates, highlighting its critical role during CPR. Future research with larger sample sizes should aim to assess how pharmacological interventions influence cerebral oxygen metabolism and neurological outcomes during cardiac arrest and resuscitation.  
The limited number of cases with a ROSC further restricted the scope of analysis for this phase. Expanding the sample size in future studies could provide a more comprehensive understanding of the physiological responses and improve the statistical power.  
Lastly, the lack of baseline measurements for the absolute concentrations of cytochrome C oxidase represents another limitation. While the relative changes in _rCCO_ provided valuable insights, absolute values would have enhanced the interpretation of mitochondrial function during cardiac arrest and resuscitation. Incorporating baseline measurements in future studies could offer a more detailed characterization of the physiological changes occurring during these critical events.
# A Mask-Based Adversarial Defense Scheme
## 4 Results  
### Ablation Study  
The ablation study focuses on the effect of masking on both the training phase and the test phase. There are four choice combinations are (1) no masking being applied, (2) to apply masking only at the training phase, (3) to apply masking only at the test phase, and (4) to apply masking both at the training phase and the test phase. We choose the model VGG16 for the CIFAR-10 data set, \(3/4\) as the mask rate, \(8\times 8\) grids for training and \(4\times 4\) grids for testing. We use a benign sample together with adversarial samples generated from BIM Linf (\(\epsilon=0.01\)), PGD Linf (\(\epsilon=0.01\)), CW L2 (\(\epsilon=1.0\)), and DeepFool Linf (\(\epsilon=0.01\)) algorithms.  
Table 2 shows the results of the ablation experiment. We find that the model with masking applied at both the training phase and the test phase greatly outperforms other combinations when facing adversarial samples. In the case of the clean sample, applying masking at both phases still have an acceptable precision of 82.65%, close to the best performance of 84.65% where masking is only applied at training.