esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Validation of the Land-Use Maps Using the Ground Truth Data  
The locations of the 75 field survey points within the study area are presented in Figure 1. The validation of the land-use maps using ground truth data was performed to the 2005, 2009 and 2011 land-use maps derived from the ENVISAT ASAR data and to the land-use map created from the SPOT 2005 image. The accuracy of the ground truth is expected to decrease for data taken earlier than 2011/2012. For validation, the pixel size of the land-use maps was downsampled from 75 m to 150 m, which was the target pixel size of the final land-use maps. The following overall accuracies were achieved:  
* Land-use map 2011 (SAR): 80.0%
* Land-use map 2009 (SAR): 82.7%
* Land-use map 2005 (SAR): 77.3%
* Land-use map 2005 (SPOT 4): 88.2%  
The best overall accuracy with the SAR derived land-use maps was achieved in 2009, which also had the best availability of SAR data. The SPOT 2005 land-use map has a higher overall accuracy than SAR land-use maps, this is mainly explained with the fact that SPOT classification includes two classes less, as the \"Rainy season rice and shrimp culture\" class was merged with the \"Aquaculture\" class and the urban area was not classified. Due to the class division and the image extent only 68 ground truth points were used for the SPOT image. Figure 8: The SPOT results (1987, 1998, 2005) for four districts in Tra Vinh. The ground truth data was collected in October 2013 while the oldest land-use maps validated were from the year 2005. However, based on the information received from the local people during the field survey campaign, most of the significant changes in the study area occurred before the year 2005. A summary of the classification accuracies is provided in Table 6. The producer's accuracy is the percentage of reference points correctly classified. The user's accuracy describes the probability that a classified pixel actually belongs to the class. \"Rainy season shrimp and rice\" was often classified into the \"All rice\" or \"Aquaculture\" class, and thus the accuracies are low. To correctly classify the class more frequent imaging throughout the year is required. Other classes were to some extend classified into the \"Settled areas, sugarcane, coconut and orchard\" class, and thus the producer's accuracy is lower than the user's accuracy. The class has a diversity of different land covers, and thus, confusion with other classes occurs. Rice paddies and water areas were in a few cases classified as \"Aquaculture\";
# Romanian Fake News Detection Using Machine Learning and Transformer-Based Approaches
## 4 Results
### Performance Evaluation Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Trained on** & **Test Set** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1 Score (\%)** & **ROC AUC (Micro)** & \begin{tabular}{c} **ROC AUC** \\ **(Macro)** \\ \end{tabular} \\ \hline FakeRom & FakeRom & 92.8 & 93 & 92.8 & 92.8 & 0.989 & 0.989 \\ \hline NEW & NEW & 95.5 & 95.8 & 95.5 & 95.5 & 0.996 & 0.997 \\ \hline FakeRom & \
\begin{tabular}{c} FakeRom + \\ NEW \\ \end{tabular} & 72.2 & 74.4 & 72.2 & 71.4 & 0.90 & 0.906 \\ \hline NEW &
\begin{tabular}{c} FakeRom + \\ NEW \\ \end{tabular} & 96.5 & 96.7 & 96.5 & 96.5 & 0.998 & 0.998 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results obtained using BERT model. Comparing the metrics from Tables 1-5 among the different implemented models (Naive Bayes, Logistic Regression, Support Vector Machine, BERT, RoBERTa-large), the following characteristics are observed:  
* Naive Bayes shows relatively low performance in accuracy and F1 score but has a quite high ROC AUC, indicating that while it may not be very precise, it is capable of distinguishing between classes effectively. * Logistic Regression and Support Vector Machine offer a balance between precision, recall, and F1 score, with higher accuracy than Naive Bayes and lower log loss, suggesting more reliable predictions. * BERT stands out, with a high F1 score and superior ROC AUC, demonstrating its strong performance for this classification task and good generalization across classes. * RoBERTa-large, although it has slightly lower accuracy and precision compared to BERT, still exhibits a very high ROC AUC, indicating excellent class discrimination capability, especially for imbalanced classes. The visualizations and tables collectively demonstrate that models trained on the NEW dataset consistently outperform those trained on FakeRom. BERT achieves the best overall performance, followed closely by SVM and RoBERTa. Cross-dataset performance varies significantly, highlighting the importance of diverse training data. Transformer-based models show more robust performance across different testing scenarios.
# Deep Learning-Enhanced Spectrogram Analysis for
## 2 Related Work  
Deep learning has significantly transformed biomedical signal processing, particularly in respiratory sound classification, auscultation analysis, and disease detection [13; 14; 15]. Initially, respiratory anomalies were predicted using Recurrent Neural Networks (RNNs) [16] which demonstrated the potential of sequential modeling in biomedical signal analysis. Other approaches incorporated transformation techniques like the 3D-second order difference plot applied to respiratory sounds [17] though these still required hand-crafted features. The emergence of deep-learning-based frameworks, particularly Convolutional Neural Networks (CNNs), enabled end-to-end learning directly from spectrogram inputs, significantly improving performance in respiratory sound classification tasks [18].
# Demonstration of Wastewater Recycling in a Slaughterhouse
###### Abstract  
The implementation of research results in industrial applications is a crucial step in the development of innovative technologies. In this work, slaughterhouse wastewater recycling was successfully realized. The system, comprising a process combination of flotation, membrane bioreactor, and reverse osmosis, was able to treat the wastewater from a medium-sized poultry slaughterhouse in northwestern Tunisia. The process managed to treat approximately one-third of the wastewater to the required standard for agricultural irrigation. An additional 35% was purified to drinking-water quality. The remaining water was discharged as concentrate, meeting the necessary limits for indirect discharge. As a result, the slaughterhouse's fresh water consumption was reduced by 35% and the amount of wastewater by around 70%. With the combined system, average reductions of 99%, 98%, and 96% were achieved for the parameters COD, TN, and electrical conductivity, respectively.  
w +
Footnote †: journal: Applied  
1
Footnote 1: email: {[[https://doi.org/10.3390/appl/1498881](https://doi.org/10.3390/appl/1498881)]([https://doi.org/10.3390/appl/1498881](https://doi.org/10.3390/appl/1498881))}  
activated sludge or a combination of anaerobic and aerobic biological processes, to meet the requirements for discharging into local water bodies [20]. However, the direct recycling of SWW has not yet been explored in practice. This study addresses this gap by investigating the feasibility of direct SWW recycling as a novel approach. A process previously tested in the laboratory at bench scale was implemented in a Tunisian slaughterhouse, with an SWW effluent of about 55 m\({}^{3}\cdot\)d\({}^{-1}\).  
It is a medium-sized industrial slaughterhouse in the north-west of Tunisia. An average of 4000 to 8000 chickens are slaughtered per day, 6 days a week. Turkeys are slaughtered only occasionally. The site already operated an SWW treatment system consisting of screens, a gravitational flotation system, and a cascade-type aeration basin system without sludge recirculation. Based on preliminary testing, a concept was developed to implement an SWW recycling system using the existing equipment. This was then constructed by the company Delta Umwelt-Technik GmbH, Teltow, Germany, in the form of a container plant. The existing activated sludge system was optimized. Three MESNER(r), Adelsdorf, Germany, compact plate diffusers were installed and the old blower was replaced by a KDT 3.80 rotary vane compressor from Becker GmbH, Wuppertal, Germany.  
This article presents the process of implementation from planning to steady-state operation. Not only is the process of the implementation of the SWW recycling system considered, but also the transfer of research results from the laboratory to industrial application.
# Power Battery Scheduling Optimization Based on Double DQN Algorithm with Constraints
## 1 Introduction
### Dataset  
The data set adopted in this study is the substantial and easy\(-\)to\(-\)use battery cycle aging data set, released in 2019 by the Toyota Research Institute in cooperation with MIT and Stanford University [21]. Cyclic aging experiments were conducted on 124 commercial lithium-ion batteries. Cyclic aging occurs in the process of battery charging or discharging. It directly results from various factors such as service mode, temperature condition, and battery current. The battery consists of a LiFePO4 (LFP) positive electrode and a negative graphite electrode, with a nominal capacity of 1.1 Ah and a nominal voltage of 3.3 V.  
All cells in this dataset use a one\(-\)step or two\(-\)step fast\(-\)charging strategy, as shown in Figure 1. The format of the policy is \"C1 (Q1) \(-\) C2 (80%) \(-\)1 C (3.6 V) \(-\)3.6 V (1/50 C)\", this means that the SOC is first charged from 0% to Q1% of the nominal capacity of the battery at a C1 multiplier, then from Q1% to 80% of the SOC at a C2 multiplier. At a 1 C multiplier to a voltage of 3.6 V, then at a constant voltage of 3.6 V to a current of 1/50 C, the battery capacity reaches 100% of the SOC, called a whole charge process. After the whole charging process is completed, the constant current and constant voltage discharge process starts at an interval of 60 s. Discharging at a 4 C rate to a voltage of 2 V, and then discharging at 2 V constant voltage to a current of 1/50 C, is known as a whole discharge process. One full charge and one full discharge is called a small cycle. At the end of each small cycle, measure its SOH, SOH = current discharge capacity/nominal capacity, and end this cycle aging process when SOH = 80%.  
The capacity degradation curves of the same batteries under different charging and discharging protocols are shown in Figure 2. Under different aging conditions, the same battery exhibits different capacity degradation patterns. This paper uses data ranging from new cells to 20% degradation.
## 5 Conclusions  
In summary, the ameliorating effect of AAE on liver cirrhosis was evaluated with a combination of a biochemical analysis and electrical tissue conductivity. The expression levels of \(\alpha\)-SMA and COX-2 in immunohistochemical analysis showed reduced fibrosis and inflammatory response in AAE or lactulose treatment compared with cirrhotic liver. The cirrhotic liver showed decreased conductivity than that of normal liver, but the conductivity of cirrhotic liver with AAE treatment was similar to normal liver. The western blot analysis of \(\alpha\)-SMA and COX-2 protein among the four groups was well correlated with the MR-based electrical conductivity images. When comparing the experimental groups, there was no significant difference in tissue conditions between the AAE and Lac treatment groups. However, cirrhotic liver with AAE treatment showed superior hepato-protective effect on hepatic cirrhosis in the immunohistochemical analysis as well as electrical conductivity. Future studies through comparison with large samples and other herbal extracts are required to clearly identify the ameliorating effect of AAE on liver cirrhosis. For clinical applications, it is necessary to provide the relationship between the biochemical parameters and electrical conductivity. Recent conductivity tensor imaging (CTI), which provides information on conductivity as well as on the cellular environments such as cell density, intra- and extracellular compartments, has been developed. We expect that CTI with high sensitivity regarding tissue changes can provide information on liver tissues in situ that could be utilized for in vivo human imaging.  
Conceptualization, Y.K. and J.W.K.; methodology, B.K.C., N.K. and E.J.Y.; software, N.K.; validation, J.W.K. and E.J.Y.; formal analysis, Y.K. and B.K.C.; investigation, J.S.K., Y.H.H., and S.G.S.; data curation, J.S.K. and S.G.S.; writing--original draft preparation, Y.K. and H.J.K.; writing--review and editing, J.W.K., Y.H.H. and H.J.K.; visualization, Y.H.H.; supervision, H.J.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Research Foundation of Korea (NRF) grants funded by the Korea government (No. 2021R1A2C2004299). This research was also supported by the research fund from Chosun University (2020).  
The study was conducted according to the guidelines of the Institutional Animal Care and Use Committee (IACUC, KHUASP-18-024) of Kyung Hee University.  
Not applicable.  
The data presented in this study are available upon request from the corresponding author. The data are not publicly available for confidentiality reasons.  
The authors declare no conflict of interest.
# Copper Alloys Performance in High-Pressure and Low-Velocity Conditions Using a Custom Tribometer
## 3 Results and Discussion
### Tribological Performance Study of Two Commercial Copper Alloys
#### 3.3.1 Tribological Tests  
The results of the friction coefficient, plotted as a function of pressure and velocity, are presented in Figure 10, with the dashed lines corresponding to the same PV factor and black dots representing each test described in Table 3. The results for the HLB material (Figure 10a) showed a reduction trend with an increase in the PV factor, mainly because of the frictional heating that causes effective smearing of the Pb, which acts as a lubricant phase. The opposite effect is observed in NAB (Figure 10b), with the friction coefficient increasing considerably due to the higher PV factors applied, and a sharp increase in the friction coefficient was observed in the NAB at PV 5.0 tests, which will be further discussed in the results in Section 3.3.2.  
The wear rate values (Figure 11) follow a trend where higher PV factors result in higher k values. The HLB results were around \(10^{-7}\), while NAB showed a significantly lower wear rate of around \(10^{-8}\). This can be attributed to NAB's higher hardness due to the presence of phases \(\kappa_{\rm{IL}}\), \(\kappa_{\rm{III}}\), and \(\kappa_{\rm{IV}}\).  
Figure 10: Comparative friction coefficient. (**a**) HLB alloy, (**b**) NAB alloy.  
Figure 9: HLB microstructure (**a**) and NAB microstructure (**b**). Optical microscopy.  
Regarding the debris collected during the test (Figure 12), the debris from the HLB material was generally coarse metal plates with some fine oxide particles, and progressively, the debris and the size of coarse debris increased due to the increase in the PV factor. In the case of NAB, the fraction of oxidized particles is higher, and there is also the presence of coarse debris. These characteristics are directly and consistently related to the delamination wear mechanism, which was expected in the high-load and low-velocity regime. One point identified in some NAB samples was evidence of an additional abrasive wear mechanism, with the formation of ferrous alloy chips. This may be one of the reasons for the sudden increase in the NAB friction coefficient due to the increase in the PV factor. These characteristics lead to a transition from mild to severe wear regime [5,36].
# Learning Kernel Stein Discrepancy for Training Energy-Based Models
## 6 Experiment  
We introduce the PT KSD GAN for training unnormalized models (Figure 1). Using an energy-based discriminator, it assigns energy values to samples, while the generator, minimizing Kernel Stein Discrepancy, produces samples reflecting true density. Initial tests  
Figure 1: Model structure of PT KSD GAN.  
on 2D toy datasets (Figure 2) validate the generator's alignment with true density and sample diversity.  
We further validate using linear Independent Component Analysis (ICA), a standard for unnormalized model training. ICA decomposes signals into independent sources. Our focus was on retrieving original latent variables from mixtures, with the results shown in Table 1.  
Finally, PT KSD GAN's performance on image datasets like MNIST and CIFAR-10, using a DCGAN framework, is depicted in Figure 3, indicating realistic image generation with room for refinement.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{5}{c}{**Data Dimension**} \\ \cline{2-6}
**Method** & **10** & **20** & **30** & **40** & **50** \\ \hline Max. & \(-10.98\) & \(-18.48\) & \(-21.49\) & \(\mathbf{-23.43}\) & \(-25.53\) \\ Likelihood & \(\mathbf{-10.90}\) & \(\mathbf{-18.35}\) & \(\mathbf{-21.21}\) & \(-25.18\) & \(-25.49\) \\ PT KSD GAN & \(\mathbf{-10.95}\) & \(-18.37\) & \(-21.23\) & \(-25.14\) & \(\mathbf{-25.36}\) \\ LSD & \(-11.13\) & \(-27.20\) & \(-21.48\) & NaN & NaN \\ Score & \(-10.92\) & \(-22.52\) & \(-30.33\) & \(-55.53\) & \(-73.62\) \\ CNCE & \(-11.00\) & \(-18.77\) & \(-24.47\) & \(-37.64\) & \(-36.31\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Linear ICA training results.  
Figure 2: Result of PT KSD GAN on two-dimensional toy datasets. (**Left**): data. (**Middle**): densities of energy-based models. (**Right**): learned densities of the generator.
# Immersive Virtual Reality Experience of Historical Events Using Haptics and Locomotion Simulation
## 3 Results and Discussion
### Limitations  
However, the data analysis also demonstrated some limitations of our system. Locomotion through the treadmill reported by many as unnatural, nevertheless compelling (_Mean_: 3.65, 3.80, _S.D._: 0.81, 0.77, see Table 2). Even though the treadmill is designed to resemble the natural walking motion, after a certain amount of usage time, it becomes uncomfortable. This result complies with previous research [34, 39], that states VR treadmills create a significant sense of presence, still they have a high fatigue effect. Difficulty in walking around the visual environment weakens the sense of presence and creates discomfort in the user experience. This may explain the fact that locomotion, as an important aspect of immersion, received the lowest score (Table 3).  
In our opinion, all of the aforementioned details could be summarized as a design limitation. As we previously mentioned in Section 2.2, we had taken under consideration the possible fatigue effect during the implementation of our application. Nonetheless, the study's results state that there is still room for improvement. An interesting way to address this situation would be to utilize the haptic feedback of the treadmill. Small vibrations on the users' feet could indicate that the user reached the boundaries of a predefined path [40] so that they do not wander around in vain. The tension and duration of the vibrations should be carefully designed so that no further fatigue is added. Overall, this could create a more interactive use of the equipment and assist in the overall sense of presence. Further study can evaluate this approach.  
Another limitation in our paper concerns the conducted survey. While it provided us with thoughtful results and ideas, it consisted of a limited number of users. Furthermore, the sense of presence was measured only by subjective self-reported measures using a limited number of questions from our questionnaire. Additional studies could certainly focus on physiological and psychological measurements for a more comprehensive conclusion. Similar to [41] these measurements can be used along with an evaluation on users' motion sickness.
# Logistics Space: A Literature Review from the Sustainability Perspective
## 5 Discussion
### Recommendations for Logistics Space Planning  
In order to ensure the reasonable distribution of logistics space and determine the optimal location, scale and type of logistics facility activities, a sustainable development blueprint must be planned. First of all, the government should formulate a strategy for the sustainable use of logistics land, make full and reasonable use of land resources, increase land utilization, and curb the idleness and waste of land. Thus the priority is to integrate existing logistics facilities, achieve intensive use of land, and minimize land use conflicts. What's more, to tap the potential of land use and develop new logistics land is necessary. Governments and enterprises should increase the construction of automated warehouses and develop underground logistics systems--a new logistics model that promotes sustainable urban development. In addition, at the required time, the production mode of the desired product is produced in the required quantity. Just-in-time production eliminates inventory, optimizes production logistics, and reduces dependence on storage land. Secondly, to improve the mobility of logistics activities is an important way, including increasing the construction of traffic network and port intermodal transport, and improving the transit transport system. Good traffic accessibility facilitates the organization of transportation routes for logistics activities, reduces transportation distances, and improves the logistics efficiency of enterprises. Finally, the government should promote the joint development of the logistics industry and the manufacturing industry. The spatial planning of the manufacturing industry can effectively guide the transfer of the logistics industry and achieve a balance between supply and demand in the logistics space. The joint development of the manufacturing industry and the logistics industry is not only an important means of adjusting the industrial structure and transforming economic growth, but also a common requirement and an urgent need for the manufacturing and logistics industries.