esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Performance Analysis
### Analysis of Measured Magnetic Target Signals
#### 4.2.3 Extraction of Time-frequency Ridge of Synchrosqueezed Wavelet Transform  
The time-frequency ridge of synchrosqueezed wavelet transform was abstracted and compared with the geomagnetic calibration signal measured at the same position, as shown in Figure 11:  
Figure 10: Draw spectral contour lines for STFT, CWT, SSWT; (**a–c**) are spectral contour lines for SFFT, CWT, and SSWT.  
Figure 9: (40–60 s, 30–70 Hz) resolution STFT, CWT, and SSWT images. (**a–c**) Time-frequency diagrams of STFT, CWT, and WSST are enlarged to clearly show the changes in the time-frequency diagram.  
It can be observed that the 50 Hz signal shook violently when the amplitude of the geomagnetic signal changed significantly and with the time-frequency ridge of the synchrosqueezed wavelet transform. The abstracted time-frequency ridge of the synchrosqueezed wavelet transform conformed to contour distribution. It can be proved that power frequency magnetic disturbance induced by the target movement could be effectively detected by this method.
# Unsupervised Temporal Adaptation in Skeleton-Based Human Action Recognition
## 4 Experiments
### Ablation Study
#### 4.6.1 Pseudo-Labels with TAM  
In this work, **Temporal Adaptation Modeling (TAM)** is proposed to assist with integrating unlabeled target domain data into model training, allowing the joint-domain classifier to receive action knowledge from the target domain. To validate its effectiveness, we ablate the term \(L_{\text{CE-t}}\) proposed in Equation (9) (i.e., \(\lambda=0\) in Equation (9)) and use the exclusive source data in model training using Equation (7). Experimental results presented in Table 8 demonstrate a significant performance discrepancy on the two domains. Since only the source domain data is involved in model training, the resulting model is biased towards the source domain and achieves compromised performance in the target domain. The results demonstrate that **TAM** overcomes the limitation by using pseudo-labels and leads to effective adaptation to the target domain.  
Figure 3: T-SNE visualization on action prototypes on the feature space of ST-GCN (upper row: “vanilla without CPL”; bottom row: “with CPL”). Action prototypes are distinguished by colors where twenty actions are randomly selected among fifty common actions for clarity. The left column represents action clusters of the source domain (NTU RGB+D), and the right column shows clusters of the target domain (PKU-MMD), respectively.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Models** & **Source** & **Target** & **Top1 Acc** & **Standard Deviation** \\ \hline
**without TAM** & NTU RGB+D & - & 59.35\% & 2.12\% \\
**with TAM** & NTU RGB+D & PKU-MMD Part I & 69.34\% & 1.70\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: The effectiveness of using pseudo-labels with TAM.
# Closing Connectivity Gap: An Overview of Mobile Coverage Solutions for Not-Spots in Rural Zones
###### Abstract
Table 1 depicts both MCI and Rural Population Density (RPD)--in percentage units from the total--in order to analyze the gap among fifteen Latin countries. Identifying the locations where coverage is under specific boundaries appears to be suitable for sketching out the Not-Spots' presence in these contexts. Therefore, Figure 1 charts the correlation between the total population and coverage density, segregated by mobile network generation from 2G to novel 5G, in two specific countries of the target region: Colombia (Figure 1a) and Mexico (Figure 1b). This is aimed at recognizing the \begin{table}
\begin{tabular}{l c c} \hline \hline
**Country** & **MCI** & **RPD** \\ \hline Argentina & 67.2 & 8 \\ Bahamas & 68.7 & 17 \\ Brazil & 63.5 & 13 \\ Chile & 73.2 & 12 \\ Colombia & 63.7 & 19 \\ Costa Rica & 63.3 & 20 \\ Dominican Republic & 59.8 & 18 \\ Ecuador & 65.3 & 36 \\ El Salvador & 55.4 & 27 \\ Haiti & 32.8 & 44 \\ Mexico & 67.6 & 20 \\ Panama & 65.3 & 32 \\ Peru & 66.6 & 22 \\ Uruguay & 76.7 & 5 \\ Venezuela & 57.4 & 12 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Contrast between MCI and RPD of 15 Latin America countries [7]. coverage gap inside the mentioned countries. Moreover, it likely identifies regions in which the population cannot access voice and data services.
## 2 Methodology
### The WCDPSO Algorithm
#### 2.1.3 Adding Disturbance Term  
The global and local optimal solutions are adjusted by adding a disturbance term because there may be better particles in the vicinity of the global or local optimal particles. Thus, adding disturbance term can prevent particles from falling into the local optimal solution. Now, the velocity-updating formula of each particle is described as follows:\[V_{id}(t+1)=wV_{id}(t)+c_{1}r_{1}(P_{id}(t)*(1\pm\delta*rand)-X_{id}(t))+c_{2}r_{2} (P_{gd}(t)*(1\pm\delta*rand)-X_{id}(t)) \tag{5}\]  
\(1\pm\delta*rand\) is the added disturbance term and \(\delta\) is a constant. The PSO algorithm is improved by adding a disturbance term, then it is renamed as the DPSO algorithm. The \(1\pm\delta*rand\) is so small that its influence on the searching capability may be ignored at the early iteration stage, but the particle convergence speed becomes smaller and smaller at the late iteration stage. Now, the disturbance term \(1\pm\delta*rand\) can prevent the particle searching speed from dropping to zero and falling into the local optimal.
# Efficient Transmit Antenna Subset Selection for Multiuser Space-Time Line Code Systems
## 4 Complexity Analysis
### Complexity of Proposed SNR-Based Efficient Incremental TAS Selection Algorithm  
From Algorithm 2 with (23) in lines 6 and 7, the numbers of RMs and RSs for the proposed SNR-based efficient incremental TAS selection algorithm can be calculated as Line 6:  
* RM in \(\hat{\mathbf{V}}_{k-1}\hat{\mathbf{H}}_{k,q}\Rightarrow 32K^{2}\),
* RS in \(\hat{\mathbf{V}}_{k-1}\), Line 7:
* RM in \(\mu=\hat{\mathbf{H}}_{k,q}^{\text{H}}\hat{\mathbf{\Theta}}_{k,q}\Rightarrow 3 2K\),
* RS in \(\mu=\hat{\mathbf{H}}_{k,q}^{H}\hat{\mathbf{\Theta}}_{k,q}\Rightarrow 24K-6\),
* RM in \(\Delta=(\mathbf{I}_{2}-\mu)^{-1}\Rightarrow 40\),
* RS in \(\Delta=(\mathbf{I}_{2}-\mu)^{-1}\Rightarrow 26\),
* RM in \(\alpha=\Delta\hat{\mathbf{\Theta}}_{k,q}^{\text{H}}\Rightarrow 32K\)
* RS in \(\alpha=\Delta\hat{\mathbf{\Theta}}_{k,q}^{\text{H}}\Rightarrow 24K\),
* RM in \(\psi=\hat{\mathbf{\Theta}}_{k,q}\alpha\Rightarrow 16K^{2}+8K\),
* RS in \(\psi=\hat{\mathbf{\Theta}}_{k,q}\alpha\Rightarrow 28K^{2}+14K\)
* RS in \(\hat{\mathbf{V}}_{k-1}-\psi\Rightarrow 4K\),
* RS in \(Tr[\psi]\Rightarrow 4K\),  
Thus, the overall computational complexities of the proposed SNR-based incremental TAS selection algorithm (called proposed incremental) are given by  
\[N_{\text{proposed}-incremental}^{RM} =\Big{(}24K^{2}+38K+20\Big{)}\Big{(}2N_{T}N_{S}+N_{S}-N_{S}^{2} \Big{)} \tag{53}\] \[N_{\text{proposed}-incremental}^{RS} =\Big{(}30K^{2}+29K+10\Big{)}\Big{(}2N_{T}N_{S}+N_{S}-N_{S}^{2} \Big{)} \tag{54}\]
# Patient-Tailored Dementia Diagnosis with CNN-Based Brain MRI Classification
## 2 Materials and Methods
### CNN-Based Models for Diagnostic Support of Dementia
#### 2.2.2 Independently Developed CNN-Based Models  
The independently developed models tackling the medical image classification task were constructed from scratch based on conventional CNN architecture, which included convolutional, pooling, normalizing, dropout, flattening, and dense layers.  
The input layer accepted the RGB 192 \(\times\) 192 pixel brain-representative ROIs.  
Convolutional (Conv2D) layers were the most essential components of the independently developed CNN-based models. They employed the rectified linear unit activation function, which enabled the models to learn more complicated patterns. The models varied in terms of the numbers of Conv2D layers and filters used within them, as detailed in Table 2. It was hypothesized that increasing the number of consecutive Conv2D layers and hence, the number of filters would allow the model to extract and refine more detailed features from the inputs, thus resulting in improved classification performance.  
The output layer employed the softmax activation function to generate a three-element vector, with each element reflecting the predicted probability of the output belonging to one of the three classes (AD, CN, and MCI).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Model**} & **No. of Conv2D** & **No. of Filters in the Last** & **General Characteristics** \\  & **Layers** & **Conv2D Layer** & & **Trial params: 2,952,099 (11.26 MB)** \\ \hline \multirow{3}{*}{Custom CNN 128} & \multirow{3}{*}{6} & \multirow{3}{*}{128} & \multirow{3}{*}{Trialable params: 2,951,075 (11.26 MB)} \\  & & & Non-trainable params: 1024 (4.00 KB) \\ \cline{3-4}  & & & Total params: 10,646,307 (40.61 MB) \\ \cline{3-4}  & & & Non-trainable params: 10,644,643 (40.61 MB) \\ \cline{3-4}  & & & Non-trainable params: 1664 (6.50 KB) \\ \cline{3-4}  & & & Total params: 14,288,931 (54.51 MB) \\ \cline{3-4}  & & & Trainable params: 14,285,475 (54.49 MB) \\ \cline{3-4}  & & & Non-trainable params: 3456 (13.50 KB) \\ \cline{3-4}  & & & Total params: 28,848,675 (110.05 MB) \\ \cline{3-4}  & & & Trainable params: 28,841,635 (110.02 MB) \\ \cline{3-4}  & & & Non-trainable params: 7040 (27.50 KB) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Overview of the structure and characteristics of the independently developed CNN-based models.
## 5 Experimental Verification  
Rigorous experimental validation forms a critical component of this comprehensive analysis, establishing the reliability of the theoretical frameworks and simulation methodologies presented in previous sections. The experimental verification process employed a purpose-built prototype incorporating the optimized design features, tested under conditions that accurately replicate the operating environment of flywheel energy storage systems. This section details the experimental setup, measurement methodologies, and validation results that confirm the accuracy of our loss prediction and thermal modeling approaches.  
Figure 12: Comprehensive efficiency enhancement analysis across various operating conditions.
# STFEformer: Spatial-Temporal Fusion Embedding Transformer for Traffic Flow Prediction
## 2 Materials and Methods
### Fusion Embedding Layer
#### 2.3.2 Temporal Embedding  
Due to temporary occurrences (such as traffic accidents and road maintenance) or instantaneous conditions (such as weather changes), traffic flow undergoes significant short-term perturbations and oscillations, manifesting substantial and immediate impacts. To capture the short-term temporal features of traffic flow, we employ a standard convolutional layer that merges the information in the neighboring time slices as follows:  
\[E_{st}=ReLU(Conv_{1}(\mathcal{X})) \tag{3}\]  
where \(E_{st}\in\mathbb{R}^{T\times N\times d_{st}}\) represents the short-term embedding, \(d_{st}\) indicates the dimension of the short-term feature, \(Conv_{1}\) represents a standard convolutional layer, and ReLU is the activation function.  
Additionally, traffic flow, influenced by the daily routines and commuting patterns of people, exhibits a clear periodicity, such as peak hours in the morning and evening, as well as disparities between weekdays and weekends. Therefore, we utilize two embeddings to effectively capture the daily and weekly periodicity of traffic flow, denoted as \(e_{f_{d}(t)}\) and \(e_{f_{w}(t)}\in\mathbb{R}^{d_{gt}}\), respectively. Here, \(d_{gt}\) is the dimension of the periodicity feature and \(f_{d}\) and \(f_{w}\) serve as functions that convert the time t into a minute index within a day (ranging from 1 to 1440) and a day index within a week (spanning from 1 to 7). Specifically, we normalize the hour, minute, and second information from traffic data to a range between 0 and 1, thus obtaining the minute index. The day index is obtained by applying one-hot encoding. Weconcatenate the embeddings \(e_{f_{d}(t)}\) and \(e_{f_{w}(t)}\) of all \(T\) time slices to obtain temporal periodic embeddings, \(E_{d},E_{w}\in\mathbb{R}^{T\times d_{pt}}\).  
In this paper, we set the dimension \(d_{st}=d_{pt}=d_{t}\). We obtain the temporal embedding \(E_{t}\in\mathbb{R}^{T\times d_{t}}\) by simply summing \(E_{st}\), \(E_{d}\), and \(E_{w}\), thereby integrating both the short-term and periodic temporal features in traffic data given as follows:  
\[E_{t}=E_{st}+E_{d}+E_{w} \tag{4}\]
## 7 Performance Analysis
### Communication Cost and Storage Cost  
We compare communication cost overheads among related protocols and proposed protocol during the message confirmation phase in Table 4. We assume that the identity, password, and normal variable needs eight bytes, the time-stamp needs four bytes, an ECC encryption/decryption needs 32 bytes, a bilinear pairing needs 128 bytes, and one-way hash function needs 32 bytes [4]. As the results of the comparison, the proposed protocol is the most efficient when compared with other related protocols. The storage overhead is calculated based on the total number of bytes required to store required parameters in OBU or TPD and RSU. The proposed protocol has 224 bytes storage cost, where OBU has 160 bytes and RSu has 64 bytes. Although the total memory of our protocol is slightly higher than that of other protocols, our protocol ensures security.
# Characteristics of 2.45 GHz Surface-Wave-Sustained Argon Discharge for Bio-Medical Applications
## 3 Results
### Microwave Radiation at the Sample Position  
One of the reasons for not widely using the SWD at atmospheric pressure for biomedical purposes is its operation at 2.45 GHz and the possible residual microwave power at the torch end and treatment sample position. The possible microwave radiation on the biological systems can produce unwanted and unacceptable biological effects. Although for SWD, the wave power is well absorbed by the plasma and usually the reflected power is less than 1 W, this problem requires special attention. The microwave radiation distribution around the sample position is illustrated in Figure 11 at typical discharge conditions. The results show that at all discharge conditions, the microwave radiation at the sample position is from 0.3 mW to 0.45 mW. Thus, we cannot expect any negative effect from the MW radiation on the biological systems due to the plasma treatment. The highest level of microwave radiation (4-5 mW) is at the connection of the surfatron antenna to the coaxial cable. This problem can be easily solved by shielding this region, which will not disturb the sample treatment.  
Figure 10: Effect of argon flow rate on the temperature for 8 mm/4 mm discharge tube.
## 3 System Model  
Our system comprises 100 acoustic sensor nodes anchored to the multi-level depths across the seabed and designed to be used for long-term underwater monitoring applications. Sensor measurements such as pressure, water salinity, and temperature are relayed to a gateway (sonobuoy) on the sea surface. Nodes are able to maintain a connection to the gateway via one-hop. However, due to excessive energy depletion in data transmission over long distances, we considered a clustered network and performed a data collection task via elected cluster heads. We assume interference-free communication in inter-clusters with the use of different carriers or orthogonal spreading codes. All nodes in the network have energy harvesting equipment, and nodes perform energy harvesting only in their idle times, assuming independently and identically distributed (i.i.d.) harvesting states.
# Genetic Engineering Approaches for the Microbial Production of Vanillin
## 3 Identification of Genes and Pathways in Vanillin-Producing Microorganisms
Eugenol and isoeugenol are generated by plants and participate in their defence mechanism. Clove oil is an inexpensive source of both compounds, eugenol being the main compound. However, eugenol is the most challenging substrate due to its high toxicity and low water solubility. Similarly, the major limitation of isoeugenol in vanillin production is its water insolubility. Ferulic acid, a ubiquitous plant constituent, has served as the predominant precursor for the microbial production of vanillin [8]. In plants, ferulic acid primarily forms ester linkages with proteins and saccharides. Free ferulic acid can be obtained from agricultural waste products (such as sugar beet pulp, rice bran, or corn bran) by chemical and enzymatic hydrolysis [8]. Thus, ferulic acid is used as a substrate for natural vanillin production by several companies, including Solvay, Symrise, and Firmenich [8]. Lignin is an essential renewable and inexpensive source of aromatic molecules, and its valorisation can enhance economic and environmental sustainability [63]. Figure 2: **Vanillin biosynthesis and degradation pathways in vanillin-producing microorganisms. ** (**A**) Enzymes involved in vanillin production from different precursors. (**B**) Enzymes involved in vanillin degradation. IEM: isoeugenol monooxygenase; VAO: vanilly alcohol oxidase; EUGH: eugenol hydroxylase; CADH/CALDH: coniferyl alcohol/aldehyde dehydrogenase; FCS: feruloyl-CoA synthetase; ECH/HCHL: enoyl-CoA hydratase/4-hydroxycinamate CoA-hydratase/lyase; Lac: Laccase; DyP: dye-decolourising peroxidase; LiP: lignin peroxidase; MnP: manganese-dependent peroxidase; ALDR/VR: aldehyde reductase/vanillin reductase; VDH: vanillin dehydrogenase; VDC: vanillate decarboxylase; VDM: vanillate demethylase.
## 3 Results
### Sentiment Analysis  
The task of sentiment analysis for low-resource languages like Amharic remains challenging due to the lack of publicly available datasets and the unavailability of required NLP tools. Moreover, there are no attempts of analyzing the complexities of sentiment analysis on social media texts (e.g., Twitter dataset), as the contents are highly context-dependent and influenced by the user experience [58]. Some of the existing works in Amharic either target the generation of sentiment lexicon or are limited to the manual analysis of very small social media texts.  
The work of [PERSON] et al. [59] focuses on the generation of Amharic sentiment lexicon using the English sentiment lexicon. The English lexicon entries are translated to Amharic using a bilingual English-Amharic dictionary.  
The work by [PERSON] [60] builds a rule-based sentiment polarity classification system. Using movie reviews, 955 sentiment lexicon entries are generated. The system is built to detect the presence and absence of the positive and negative sentiment lexicon entries to classify the polarity of the document.  
For this work, we considered the recently released sentiment classification datasets, a total of 9400 k tweets where each tweet is annotated by three users [20]. The tweets are sampled using the extended sentiment lexicons from [PERSON] [60] (to a total of 1194 lexicon entries). We split the dataset into training, testing, and development sets with the 80:10:10 splitting strategy.  
We built document-based sentiment classifiers using the different semantic models. For a classical classification approach, we used the term frequency-inverse document frequency (TF-IDF) features using different algorithms from the sci-kit-learn Python machine learning framework. For the deep learning approach, we used the TextClassifier document classification model from the FLAIR framework, which uses the _DocumentRNNEmbeddings_ computed from the different word embeddings. Table 8 shows the different experimental results.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Precision** & **Recall** & **F1** \\ \hline \multicolumn{4}{c}{CRFSuite Word Features} \\ \hline CRFSuite & 80.88 & 63.89 & 71.39 \\ \hline \multicolumn{4}{c}{Word Embeddings} \\ \hline AmWord2 Vec & 68.11 & 57.47 & 62.34 \\ fastText & 79.01 & 55.56 & 65.24 \\ \hline \multicolumn{4}{c}{Contextual Embeddings} \\ \hline AmRoBERTa & 25.62 & 30.56 & 27.87 \\ AmFlair & 79.70 & 74.31 & 76.91 \\ MultFlairFT & **81.21** & **74.31** & **77.61** \\ MultFlair & 75.58 & 61.81 & 68.00 \\ \hline \multicolumn{4}{c}{Graph Embeddings} \\ \hline DeepWalk & 73.95 & 64.06 & 68.65 \\ Role2 Vec & 74.00 & 60.76 & 66.73 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Experimental results for NER classification (macro-averages).
## 6 Conclusions  
By analyzing the characteristics of particle size in the sediments from Lake Ta-Lung-Chi in the Tian Shan, the possibility of using sediment grain size to reconstruct past changes of regional precipitation was discussed. The following conclusions are drawn.  
The core sediment (TLC01) with 50-cm length extracted from Lake Ta-Lung-Chi recorded the environmental changes covering over the past 90 years (1928-2018). The average sedimentation rate is about 0.55 cm year\({}^{-1}\), and the average content of clay fraction (<4 \(\upmu\)m), fine-silty fraction (4-16 \(\upmu\)m), silty fraction (16-32 \(\upmu\)m), coarse-silty fraction (32-64 \(\upmu\)m), and sandy fraction (>64 \(\upmu\)m) was 16.94%, 25.03%, 13.24%, 15.37%, and 29.42%, respectively.  
The grain size of lake sediments in the Tianshan Mountains sensitively reflects the changes in precipitation in the basin, and it can be used as a useful indicator for quantitative reconstruction of long-term paleoprecipitation. The three-point moving average curve of silty fraction (16-32 \(\upmu\)m) content is significantly consistent with the total precipitation curve from April to September since 1950. The total content of clay and fine-silty fraction (0-16 \(\upmu\)m) is clearly consistent with the monthly precipitation in July.  
The reconstructed precipitation (1950 years ago) with grain size of lake sediments was significantly different from the CRU database, which was due to the lack of meteorological data from monitoring stations in China before 1950, The total precipitation from April to September showed a significant downward trend from 1930 to 1975, and an overall increasing trend began in 1975.  
Conceptualization, L.M. and J.A.; methodology, L.M. and Z.H.; validation, L.M. and J.A.; investigation, L.M. and W.L.; data curation, W.L.; writing--original draft preparation, L.M. and W.L.; writing--review and editing, L.M. and J.A.; visualization, W.L.; project administration, L.M. and J.A.; funding acquisition, L.M., and J.A. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (U1903115), West Light Foundation of Chinese Academy of Sciences (2016-QNXZ-A-4), Tianshan Youth Project of Xinjiang Uygur Autonomous Region of China (2018Q008) and the high-level training project of Xinjiang Institute of Ecology and Geography, CAS (E050030101).  
We thank the two anonymous reviewers for their professional comments and detailed suggestions, which significantly improved our manuscript. The meteorological data collected at the Bayanbulak Meteorological station for the study were obtained from the China Meteorological Data Service Center ([[http://data.cma.cr/](http://data.cma.cr/)]([http://data.cma.cr/](http://data.cma.cr/))).  
The authors declare no conflict of interest.