esa-sceva/satcom-chunk-collection huggingface dataset:
# How Successful Is Transfer Learning for Detecting Anorexia on Social Media?
## 5 Results  
In this section, we report the results we have obtained by evaluating the systems based on ML. To do so we employ the usual metrics in NLP tasks, including Precision (P), Recall (R), F1-score (F1) and macro-average. The metrics are computed as follows:  
\[P(c)=\frac{TP}{TP+FP} \tag{1}\]  
\[R(c)=\frac{TP}{TP+FN} \tag{2}\]  
where \(c\) is equal to the anorexia class (0, 1), TP = True Positive, FP = False Positive and FN = False Negative.  
\[F1=\frac{2*P*R}{P+R} \tag{3}\]  
We compare the performance of the different models on the SAD dataset. For this purpose, we evaluate our models using the 10-fold cross-validation technique. Table 2 shows the prediction performance for each model.  
On the one hand, regarding the deep neural networks models, it can be observed that there are no significant differences in the results. CNN performs slightly better than LSTM and BiLSTM. These results were to be expected since these types of networks need a large amount of training data. One of the advantages of these methods is that features are automatically deduced and optimally tuned to the desired outcome.  
On the other hand, the Transformer-based models we have explored outperform neural network models including LSTM, CNN and BiLSTM. In particular, the model with the highest performance is the one trained specifically for Spanish (BETO), followed by the XLM-17 model. These results show the capabilities of pre-trained language models that provide the possibility of evaluating a model without the need to rely on large datasets, while at the same time it is an opportunity in some languages such as Spanish where the resources for this type of task are very limited. As previously mentioned, BETO achieves the highest results since it is trained for a specific language, contrary to the other Transformer-based models that have been trained with multilingual corpora such as XLM-100, M-BERT and XLM-17.  
Finally, we compare the performance of our systems with the results obtained by [PERSON] et al. [33]. They employed traditional ML algorithms to evaluate the SAD dataset and the classifiers with the best performance were SVM and MLP, obtaining an F1 score of 91.6%. We would like to highlight that although the results obtained by these classifiers are very good, the Transformer-based models we have explored are more effective. Specifically, BETO obtained an improvement performance of 2.76%, which shows that it is necessary to rely on language model specifically trained for a certain language.
# A Novel Energy-Efficient Contention-Based MAC Protocol Used for OA-UWSN
## 6 Conclusions  
This paper presented a low-power contention-based MAC protocol for the OA-UWSN. The OA-CMAC protocol locates the nodes through acoustic handshaking, performs beam alignment through optical handshaking, and transmits data after successful acoustic and optical handshakes. Through OMNeT++ simulation, it could be seen that when the optical handshaking success ratio  
Figure 9: Optical-acoustic energy-efficient competitive media access control (OA-CMAC) protocol node energy consumption simulation.  
was higher than 50%, the throughput was increased by several folds compared to the underwater acoustic communication MAC protocol. The node's lifetime was increased by 30%, greatly reducing the network cost. Using the OA-CMAC protocol can achieve higher throughput and lower node energy consumption, which is suitable for underwater wireless communication environments with high loads and large data.  
This work was developed as a collaboration among all authors. [PERSON] and [PERSON] designed the system and MAC protocol. [PERSON] conducted protocol simulations and results analysis. [PERSON], [PERSON], and [PERSON] directed the research, collaborating in discussions on the proposed protocol model. The manuscript was mainly drafted by [PERSON] and [PERSON], and was revised and corrected by all co-authors. All authors have read and approved the final manuscript.  
This research was supported by the National Natural Science Foundation of China (No. U1806201, 61671261, 61431004, 61771271), the Key Research and Development Program of Shandong Province (No. 2016 GGX101007), the China Postdoctoral Science Foundation (No. 2017T100490), the University Science and Technology Planning Project of Shandong Province (No. J17 KA058, J17 KB154), the Natural Science Foundation of Shandong Province (No. ZK2017 BF036), and the Applied Basic Research Projects of QingDao (No. 17-1-1-4+jch).  
The authors declare no conflicts of interest.
# Dynamic Fashion Video Synthesis from Static Imagery
## 4 Experiments
### Quantitative Analysis
The marginal distribution \(P(y)\), on the other hand, shows the overall label distribution. The Kullback-Leibler divergence, denoted as \(D_{\text{KL}}\), quantifies how dissimilar these two distributions are. The formula computes the expected value, denoted as \(\mathbb{E}\), of the Kullback-Leibler divergence across all the generated images. Finally, the exponential function \(\exp\) is applied to this expected Kullback-Leibler divergence to yield the IS. Table 1 shows the results of our method in comparison to three other image-to-video models, namely ImaGINator [18], cINNs [23], and Poke [24]. The comparison was made on the Fashion dataset's testing subset [65]. We have downloaded pre-trained models of cINNs and Poke, which were trained on the iPER dataset [68] and Imagintor from the Weizmann Action dataset [76]. The movements performed are similar to those in the Fashion dataset [65], such as turning and stepping from side to side. This allows for a fair and meaningful comparison. Our model has outperformed the previous works in all metrics. The results were particularly impressive for the metrics FVD and VFID I3D. We have performed exceptionally well in the VFID I3D metric because the GAN-based models we compared were trained to generate only 20-30 frames. When they were made to produce more than that, the quality would seriously deteriorate. However, we have trained our model to generate more than 60 frames, which allows it to produce consistent frames and achieve a significantly better score. A quantitative evaluation was also conducted on DreamPose [38], and it scored slightly better than our model. However, it is important to note that DreamPose analyses five consecutive pose frames and adjusts the pose of the image accordingly, taking more of video-to-video translation approach. The metrics do not assess the ability of a model to synthesise spontaneous realistic movements, a feature which DreamPose does not provide. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **IS \(\uparrow\)** & **FVD \(\downarrow\)** & **VFID I3D \(\downarrow\)** \\ \hline ImaGINator [18] & 2.003 & 3383.2 & 32.179 \\ \hline cINNs [23] & 2.560 & 3326.0 & 79.386 \\ \hline Poke [24] & 2.823 & 3514.5 & 25.929 \\ \hline Ours & 2.965 & 1659.5 & 0.867 \\ \hline DreamPose [38] & 3.016 & 1253.6 & 0.653 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Quantitative comparison performed on the testing set of Fashion [65]. Our method has outperformed the other image-to-video models ImaGINator, cINNs, and Poke. Dreampose uses a video-to-video translation approach, which yields the best scores by processing individual video frames, but lacks the ability to generate spontaneous movements. Higher values indicate better results for IS, while lower values are desirable for FVD and VFID I3D.
## 4 Target and Frequency Sweeping Jamming Classification Experiment
### Pre-Experimental Data Preparation  
To avoid the influence of the number of samples on the classification and recognition results, 180 sets of target signal samples and 60 sets each of noise AM frequency sweeping signals, sine AM frequency sweeping signals, and square AM frequency sweeping signals (a total of 360 sets of samples) were selected for the experiment. Using principal component analysis to reduce the dimensionality of a total of eight principal components, we selected the features with a cumulative distribution of the top 95% to form a feature vector for classification and identification, i.e., principal components 1, 2, 3, 4, 5.  
The features are divided into two types: training and testing, in which the training samples are used to calculate a fitness function so that the classifier can be effectively trained, while the testing samples are used to verify the performance of the proposed method.
# Monitoring of Cropland Non-Agriculturalization Based on Google Earth Engine and Multi-Source Data
## 2 Study Area and Data
### Data Sources and Preprocessing
These values were then used to linearly stretch the original image. Moreover, the study used SRTM elevation data with a spatial resolution of 30 m, which was resampled to 10 m to align with the resolution of the Sentinel data.
## 4 Proposed FGPQ Scheme
### Privacy-Preserving LBS Query  
This section achieves the privacy-preserving LBS query by means of the bilinear pairing and the ASPE algorithm, which includes LBS query request generation, LBS searching and request result decryption. Before that, we first define a range query used for location range matching.  
**Definition 4**: _Given the location coordinate \((x_{*},y_{*})\) and the searching spatial range \(T\), a location coordinate \((x_{i},y_{i})\) is within the searching spatial range if and only if_  
\[R=\sqrt{(x_{*}-x_{i})^{2}+(y_{*}-y_{i})^{2}}\leq T, \tag{5}\]  
_where \(R\) is the Euclidean distance used to compute the distance between two coordinates [32]._
# Design and Implementation of a Passive Optical Network for a Small Town +
## 5 Conclusions  
This paper presented the design, implementation, and evaluation of a gigabit-capable passive optical network (GPON) infrastructure for delivering high-speed broadband access in a small-town environment. By adopting a scalable FTTH architecture using passive components, the proposed solution demonstrates an efficient and sustainable alternative to conventional copper-based networks. The detailed design, which includes appropriate branching ratios, OLT-SFP planning, and splitter placement, ensures optimal signal strength, minimal power loss, and high service reliability.  
The implementation approach, which involves modular planning, use of standardized optical components, and careful attention to installation practices, proved effective for both underground and aerial deployments. This versatility makes the system suitable for diverse building types and settlement densities, including residential complexes, individual homes, and resort areas with overhead cable restrictions.  
The practical results confirm the system's ability to meet growing digital communication demands, while offering flexibility for future upgrades and minimal maintenance requirements. This study not only validates the technical feasibility of deploying GPON in small towns but highlights its role in accelerating digital inclusion and regional development.  
Future work may explore the integration of next-generation PON technologies, such as XG-PON and NG-PON2, to further enhance capacity and to support emerging smart city services.  
Conceptualization, B.Z. and F.S.; methodology, F.S., B.Z., and S.G.; software, B.Z. and S.G.; validation, F.S., B.Z., and S.Z.; formal analysis, S.G. and B.Z.; investigation, B.Z.; resources, B.Z. and F.S.; data curation, S.G. and M.P.; writing--original draft preparation, F.S. and B.Z.; writing--review and editing, S.G., S.Z., M.L., and M.P.; visualization, B.Z.; supervision, B.Z.; project administration, B.Z., F.S., S.G., and M.P.; funding acquisition, S.G., M.L., and M.P. All authors have read and agreed to the published version of the manuscript.  
Figure 7: Reach Extension Architecture for G-PON Mid-Span.  
**Funding:** The authors would like to thank the Research and Development Sector at the Technical University of Sofia for the financial support.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors upon request.  
**Acknowledgments:** The authors would like to thank the anonymous reviewers, whose comments contributed to the improvement of the quality of the paper.  
**Conflicts of Interest:** The authors declare no conflicts of interest. The funders had no role in the design of this study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
###### Abstract  
The net primary productivity (NPP) is commonly used for understanding the dynamics of terrestrial ecosystems and their role in carbon cycle. We used a combination of the most recent NDVI and model-based NPP estimates (from five models of the TRENDY project) for the period 1982-2012, to study the role of terrestrial ecosystems in carbon cycle under the prevailing climate conditions. We found that 80% and 67% of the global land area showed positive NPP and NDVI values, respectively, for this period. The global NPP was estimated to be about 63 Pg C \(\cdot\) y\({}^{-1}\), with an increase of 0.214 Pg C \(\cdot\) y\({}^{-1}\). y\({}^{-1}\). Similarly, the global mean NDVI was estimated to be 0.33, with an increasing trend of 0.00041 y\({}^{-1}\). The spatial patterns of NPP and NDVI demonstrated substantial variability, especially at the regional level, for most part of the globe. However, on temporal scale, both global NPP and NDVI showed a corresponding pattern of increase (decrease) for the duration of this study except for few years (e.g., 1990 and 1995-1998). Generally, the Northern Hemisphere showed stronger NDVI and NPP increasing trends over time compared to the Southern Hemisphere; however, NDVI showed larger trends in Temperate regions while NPP showed larger trends in Bengal regions. Among the five models, the maximum and minimum NPP were produced by JULES (72.4 Pg C \(\cdot\) y\({}^{-1}\)) and LPJ (53.72 Pg C \(\cdot\) y\({}^{-1}\)) models, respectively. At latitudinal level, the NDVI and NPP ranges were \(\sim\)0.035 y\({}^{-1}\) to \(\sim\)\(-\)0.016 y\({}^{-1}\) and \(\sim\)0.10 Pg C \(\cdot\) y\({}^{-1}\). y\({}^{-1}\) to \(\sim\)\(-\)0.047 Pg C \(\cdot\) y\({}^{-1}\). y\({}^{-1}\), respectively. Overall, the results of this study suggest that the modeled NPP generally correspond to the NDVI trends in the temporal dimension. The significant variability in spatial patterns of NPP and NDVI trends points to a need for research to understand the causes of these discrepancies between molded and observed ecosystem dynamics, and the carbon cycle.  
carbon cycle; ecosystems; NDVI; NPP; TRENDY models; spatial trends; temporal trends +
Footnote †: journal: Remote Sensing  
8
# Design, Synthesis and Antifungal Activity of Novel Benzoylcarbamates Bearing a Pyridine Moiety
## 3 Results and Discussion
### Antifungal Activity  
The in vivo antifungal activities of target compounds against _S. scleroorum_ and _B. cinerea_ are summarized in Table 1. Compounds **4a-4j** demonstrated moderate antifungal activities against _S. scleroorum_, with EC\({}_{50}\)values of 10.85-15.89 \(\mu\)g/mL. Comparing their EC\({}_{50}\)values with diethofencarb (2.95 \(\mu\)g/mL), chlorothalonil (9.97 \(\mu\)g/mL), and carbendazim (0.24 \(\mu\)g/mL),it might be found that their activity was lower than those of the controls. On the other hand, the synthesized compounds displayed moderate to strong antifungal activities toward _B. cinerea_, the EC\({}_{50}\)values of which were in the range of 6.45-13.03 \(\mu\)g/mL. Interestingly, compounds **4d**, **4f**, **4g**, and **4h** elicited significant activities with EC\({}_{50}\) values of 6.45-6.98 \(\mu\)g/mL, indicating that their activities were near or superior to that of chlorothalonil (EC\({}_{50}\), 6.56 \(\mu\)g/mL), and much higher than that of carbendazim (EC\({}_{50}\), 19.20 \(\mu\)g/mL). However, their activities were lower than that of diethofencarb (EC\({}_{50}\), 4.72 \(\mu\)g/mL). In general, compound **4h** possessed the best activity against both _S. scleroorum_ and _B. cinerea_, and the EC\({}_{50}\) values were 10.85 and 6.45 \(\mu\)g/mL, respectively.  
We analyzed the relationship between the activity and structure of the target compounds and found that the types of substituent at the benzene ring (R), including the electron-donating group (CH\({}_{3}\) or OCH\({}_{3}\)) and the electron-withdrawing group (Cl or Br), had an inconspicuous effect on antifungal activity. However, the positions of the substituent had a notable effect on the activity, and the compounds, and compounds with the substituent at the 4-position possessing higher activity than those with the substituent at the 2- or 3-position. Compounds **4d** (R = 4-CH\({}_{3}\)), **4h** (R = 4-Cl), and **4j** (R = 4-Br), for example, exhibited stronger activity against the two tested fungi in comparison to their 2- or 3-position analogues. On the other hand, due to the introduction of a chlorine atom at the 4-position of the benzene ring, compound **4h** had a suitable oil-water partition coefficient (Log P), therefore, it might easily access the cell wall of fungi and play an effective role in antifungal activity [16; 25].
## 4 Studies of the Molecular Profiling of EVs as Potential Biomarkers
### Metabolome Profiling Analysis of EVs in Multiple Cancers
There were also a few molecules differentially expressed between two subgroups of PCa patients (stages 2 and 3), as acylcarnitine [189]. A proof-of-concept study to detect EVs metabolite biomarkers from plasma of endometrial adenocarcinoma (EAC) in patients versus control subjects revealed a clear separation of metabolites. Furthermore, EVs characterized by TGF-\(\beta\)-treated human pancreatic cell line (PANC 1) showed marked differences compared to the control group [177] (Table 2). A metabolomic study of EVs derived from patient blood before and after chemotherapy also detected the presence of different compounds, i.e., 6-dimethylaminopurine, leucyl proline, alanyl-histidine, and methionine sulfoxide [178] (Table 2). EVs of GBM subtypes were shown to contain significantly distinguishable metabolic content from astrocytoma cells. Furthermore, a significant difference was found in the metabolic profile between GBM-derived EVs and parental cells; with enrichment in glycerol, tryptophan, carnitine, and oxidized glutathione (GSSG) [179] (Table 2). Currently, few metabolome-oriented studies have addressed EVs under tumorigenic conditions. These studies reflect the metabolic plasticity of cancer cells and their tendency to escape dependence on canonical pathways through metabolic reprogramming. Clearly, new and innovative combinatorial analysis strategies are needed to cover the entire spectrum of the metabolome. Moreover, the method of cell culture can also impact the metabolite composition of EVs, and may need to be taken into consideration when comparing results from different studies [180] (Table 2).
# Classification of Children's Sitting Postures Using Machine Learning Algorithms
###### Abstract  
Sitting on a chair in an awkward posture or sitting for a long period of time is a risk factor for musculoskeletal disorders. A postural habit that has been formed cannot be changed easily. It is important to form a proper postural habit from childhood as the lumbar disease during childhood caused by their improper posture is most likely to recur. Thus, there is a need for a monitoring system that classifies children's sitting postures. The purpose of this paper is to develop a system for classifying sitting postures for children using machine learning algorithms. The convolutional neural network (CNN) algorithm was used in addition to the conventional algorithms: Naive Bayes classifier (NB), decision tree (DT), neural network (NN), multinomial logistic regression (MLR), and support vector machine (SVM). To collect data for classifying sitting postures, a sensing cushion was developed by mounting a pressure sensor mat (8 \(\times\) 8) inside children's chair seat cushion. Ten children participated, and sensor data was collected by taking a static posture for the five prescribed postures. The accuracy of CNN was found to be the highest as compared with those of the other algorithms. It is expected that the comprehensive posture monitoring system would be established through future research on enhancing the classification algorithm and providing an effective feedback system.  
consumer products; classification algorithms; image classification; machine learning algorithm; pattern recognition; sensor systems and applications; sitting posture +
Footnote †: journal: Applied Sciences
# Constant-Beamwidth LCMV Beamformer with Rectangular Arrays
## 5 Experimental Results
The proposed method (especially [A]) leads to a better performance in terms of DF for all frequencies compared to the LCKP. The LCKP is marginally better for WNG in lower frequencies and does not suffer from performance loss at approximately 6.4 kHz, caused by using rectangular arrays. Comparing [A] and [B], the former has better results for both WNG and DF, while both have a similar result in maintaining a constant \(\theta_{\rm B}\). This is a direct result of using the SD and DS beamformers in [A], and although [A] has less than half the number of sensors in the CB beamformer than [B], the FNBWs are similar. The beampattern of [B] is more focused than that of [A], but this does not cause a better performance in either beamwidth or directivity. Comparing [C] and [D], both lead to almost identical results for all metrics. This can also be seen by comparing Figure 1c,d, where their beampatterns are almost indistinguishable. This indicates that the SD and DS beamformers are obfuscated by the CB in [C, D], caused by the CB beamformer having too many sensors compared to the other beamformers. Figure 1: Beampattern heatmap for all situations in Table 1. x-axis is direction of source (in \({}^{\rm o}\)), and y-axis is the frequency (in kHz).