esa-sceva/satcom-chunk-collection huggingface dataset:
# Short-Term Power Load Forecasting Based on an EPT-VMD-TCN-TPA Model
## 2 Related Theories and Methods
### Temporal Pattern Attention (TPA)  
The attention mechanism is inspired by an information processing mechanism derived from human vision. The core of the attention mechanism is to assign different weights to the input feature information, focus on important information, and ignore irrelevant information, so as to capture the global and local connections, optimize the prediction model, and improve the prediction accuracy. The temporal pattern attention mechanism proposed by [PERSON] et al. [46] can effectively solve the problem that the traditional attention mechanism only relies on weighting the time steps, and it can also better consider multivariate time series and thus can more accurately predict the relationship between variables. The TPA model is shown in Figure 6.  
Figure 5: Residual blocks.  
Figure 6: The TPA mechanism.  
In the model in this paper, TPA firstly extracts fixed-length temporal patterns from the output information of the implicit layer of the TCN through CNN filters, determines the weights of each temporal pattern using a scoring function, and fuses the final output information according to the magnitude of the weights, as follows.  
1. The TCN processing time series. The original multivariate time series is processed using TCN to obtain the hidden state \(h_{i}\) (column vector) for each time step, where each \(h_{i}\) has dimension \(m\) and \(w\) is the length of the time window representing the selected range of historical data to obtain the hidden state matrix \(H=\{h_{t-w},h_{t-w+1},\cdots,h_{t-1}\}\) and the hidden state \(h_{t}\) at the last moment.
2. CNN convolution. \(k\) convolution kernels (\(1\times w\)) are used to capture the signal patterns with variable hidden states, with the following equation: \[H_{i,j}^{C}=\sum_{l=1}^{w}H_{i,(t-w-1+l)}\times C_{j,T-w+l}\] (25)
3. The attention weight. Let \(query=h_{t}\), \(key=H^{C}\), then \(Scoring\) Function is: \[f(H_{i}^{C},h_{t})=(H_{i}^{C})^{T}W_{a}h_{t}\] (26)  
The attention weight \(\alpha_{i}\) is obtained by normalizing using the sigmod function, as in the following equation:  
\[\alpha_{i}=sigmoid(f(H_{i}^{C},h_{t})) \tag{27}\]  
Using the attention weight \(\alpha_{i}\), a weighted summation is performed for each row \(H^{C}\) to obtain the context vector \(v_{t}\), as in the following equation:  
\[v_{t}=\sum_{i=1}^{m}\alpha_{i}H_{i}^{C} \tag{28}\]
4. Fusion. Finally, \(v_{t}\) and \(h_{t}\) are fused and the output of the attention layer is obtained by linear transformation:  
\[h_{t}^{\prime}=W_{h}h_{t}+W_{v}v_{t} \tag{29}\]  
\[y_{t-1+\Delta}=W_{h^{\prime}}h_{t}^{\prime} \tag{30}\]
## 2 Related Works  
### Traditional AMC Methods  
The likelihood-theory-based approach is proposed, which introduces the basic concept of the likelihood function and explains how to build likelihood functions from the known statistical properties of a signal. It also discusses how to apply the likelihood ratio method under different channel conditions. As a result of the computational complexity of LB-AMC, researchers have turned their attention to FB-AMC. Many feature extraction algorithms, such as cyclic features, high-order moments, and high-order cumulants, and transform domain features, such as wavelet transforms, exist. [PERSON] et al. (2018) select time-domain features such as instantaneous frequency, amplitude, and phase to extract the features. Their method achieves the identification of modulation schemes such as ASK, BPSK, and FSK. [PERSON] et al. (2018) select second-order, fourth-order, sixth-order, and eighth-order cyclic cumulants as features. This method achieves recognition of modulation schemes such as ASK, BPSK, QAM, and APSK. [PERSON] et al. (2018) choose constellation images as the feature; different modulation schemes present different distribution shapes and densities in constellation images. This method can identify BPSK, QPSK, and 8 PSK in the presence of inter-symbol interference (ISI). [PERSON] et al. (2018) choose the cyclic spectrum and the quadratic cyclic spectrum as features. The cyclic spectrum can be used to detect the periodic frequency components of the signal and the quadratic cyclic spectrum is more sensitive to the nonlinear characteristics of the signal. This method can classify PSK, FSK,  
Figure 1: A semi-supervised learning framework for signal recognition.  
QAM, MSK, and OFDM modulation schemes in the presence of a multipath effect. It can be seen that the computational complexity of FB-AMC is much reduced. However, FB-AMC has a high demand for features. Feature extraction is dependent on expert experience. Researchers are searching for an algorithm that can automatically extract features.
## 2 Literature Review
### Disaggregate Demand Models on Transit Consideration and Mode Choice
#### 2.1.2 Binary and Multinomial Mode Choice Models for Transit Demand  
Several studies have utilized binary logit models to explore whether commuters use transit or not [24; 25; 26]. [PERSON] et al. (2023) revealed that suburban rail passengers prioritize fare differences over travel time savings [27]. [PERSON] et al. (2014) identified accessibility and transit time as key factors influencing rail mode choice [28]. Similar studies were carried out for urban buses, as well. [PERSON] et al. (2014) investigated factors influencing bus choice for different distances, finding that long-distance commuters prioritize comfort and safety, medium-distance commuters focus on travel time and headway, and short-distance travelers focus on punctuality and distance to the bus station [25]. [PERSON] and [PERSON] (2017) highlighted travel time as a crucial factor in mode choice between buses and cars [24]. They reported that direct bus service policies were observed to be more impactful than park-and-ride facilities in enhancing the Wollongong bus system. [PERSON] et al. (2019) highlighted that bus reliability positively influenced choice, whereas income had a negative impact [26].  
The shortcoming of binary choice models is that they disregard the characteristics and availability of other means of travel, especially if the data is collected over a short period, such as a single day. To relax these limitations of binary models, many studies have employed multinomial models of transit choice. Empirical findings indicate the significance of in-vehicle travel time, out-of-vehicle travel time, and travel cost [6]. Furthermore, subjective factors, such as positive perceptions regarding comfort and crowding levels inside buses, encourage public transit use [29]. Additionally, this study also found that some commuters shift from private vehicles to the metro because of its cost-effectiveness and time savings, compared to private vehicles.  
Although disaggregate choice models offer better spatial representation of home and work locations, they suffer from small sample sizes and fail to capture transit usage frequency. Thus, they identify individual differences and non-user responses to policies but do not fully explain footfall. Moreover, the influence of first- and last-mile connectivity, walkability, travel perceptions, and personal or work characteristics on metro consideration remains underexplored across segments.
###### Abstract  
In this paper, a watermark embedding scheme based on ghost image encryption and cellular automata transformation is proposed. In this scheme, the watermark forms speckle through different light intensities into a key, and the cellular automata transformation algorithm is embedded into the 3D image. Compared with the traditional watermarking encryption method, this scheme combines ghost imaging and the cellular automata transformation algorithm, which double guarantees and increases the confidentiality of the watermark. The binary computing ghost imaging discussed in this paper saves the storage space of password text and makes the transmission of password text more convenient and faster. Experiments on this method also verify that the watermark-embedded image has higher imperceptibility and higher robustness against attacks, and that the extracted watermark has good integrity.  
Keywords:ghost imaging encryption; cellular automata transform; 3D image security; watermark embedding and extraction +
Footnote †: journal: Computer Phys. Commun.
# Toward a Comprehensive Understanding and Evaluation of the Sustainability of E-Health Solutions
## 7 Case Study
### Case Study: Evaluating the Hospital Information System
#### 7.2.2 Organization  
The assigned weight for the organization dimension was 0.25. All aspects were selected as applicable. The top management supported the adoption of the system at first. However, this support was not sustained after the system was found to have limited features and more funds were needed to upgrade it. Furthermore, this system was supposed to work as a comprehensive health information system for the hospital. However, the features of the adopted basic version were limited to tasks in specific departments in the hospital, which hindered the integration of work processes. Although the IT department requested to purchase the HIS and got the approval on the basic version, they did not know that its features were limited to only pharmacists and laboratory tests without covering the physicians' and nurses' needs. This indicated a lack of proper planning, and that the important stakeholders were not involved in the planning and decision on the appropriate system to choose. Instead, the decision was taken based on the system with the lowest price and meeting the minimum users' needs, without consulting all stakeholders. The culture of information use among users and in the organization was ready to adopt the system. In addition, the needs, demands, and outcomes of adopting the system were not identified and analyzed. Therefore, the goals of adopting the system were not achieved. Instead, some of the tasks in the hospital were still paper-based, because the system did not have all features that could replace the paperwork. The received score of each aspect in the organization dimension is presented in Table 15.
## 3 Results and Discussion
### Physicochemical Properties of SEY
#### 3.1.4 T\({}_{2}\) Relaxation Time  
In general, the relaxation time provides insights into the flow state of different water and lipid components, while the peak area serves as an indicator of their content [26]. As shown in Figure 1c, the T\({}_{2}\) relaxation time spectrum of SEY under different cooking methods consisted of four peaks, where 0-2 ms was attributed to T\({}_{21}\), 2-350 ms to T\({}_{22}\), and more than 350 ms to T\({}_{23}\). They represented water that was tightly bound to proteins, non-mobile water  
Figure 1: Physicochemical properties analysis of SEY. Salt content, cooking loss, and oil exudation (**a**); instrumental color (L*/a*/b*/\(\Delta\)E) (**b**); LF-NMR relaxation spectrum (**c**); peak area of LF-NMR relaxation spectrum (**d**); ATR-FTIR curves (**e**); secondary structure existed in the amide I (**f**). Different lowercase letters in the Figures indicated significant differences (_p_ < 0.05).  
associated with lipid signaling, and the more mobile lipids in egg yolks, respectively [27]. It could be observed that the T\({}_{22}\) proton signals were stronger in the steaming group than in the microwave and baking groups, which might be attributed to the fact that there were still significant differences in the degree of unfolding of proteins and exposure of mobile water molecules by the different heating methods, although heating causes disruption of the lipoprotein structure and frees the lipid molecules from the proteins. Meanwhile, the T\({}_{23}\) proton signal was stronger in the baking group, suggesting a potential higher free lipid content in the egg yolks, which was also consistent with the results of oil exudation detected by the conventional method. The percentage of the three peak areas was depicted in Figure 1d, which was a more visual reflection of the water-oil distribution in the samples under the three heating modes. The microwave group exhibited the highest levels of P\({}_{21}\), potentially attributed to enhanced protein-water and protein-lipid interactions, leading to the conversion of a portion of T\({}_{22}\) into T\({}_{21}\).
###### Abstract  
Social media serves as a platform for netizens to stay informed and express their opinions through the Internet. Currently, the social media discourse environment faces a significant security threat--offensive comments. A group of users posts comments that are provocative, discriminatory, and objectionable, intending to disrupt online discussions, provoke others, and incite intergroup conflict. These comments undermine citizens' legitimate rights, disrupt social order, and may even lead to real-world violent incidents. However, current automatic detection of offensive language primarily focuses on a few high-resource languages, leaving low-resource languages, such as Malay, with insufficient annotated corpora for effective detection. To address this, we propose a zero-shot, cross-language unsupervised offensive language detection (OLD) method using a dual-branch mBERT transfer approach. Firstly, using the multi-language BERT (mBERT) model as the foundational language model, the first network branch automatically extracts features from both source and target domain data. Subsequently, Sinkhorn distance is employed to measure the discrepancy between the source and target language feature representations. By estimating the Sinkhorn distance between the labeled source language (e.g., English) and the unlabeled target language (e.g., Malay) feature representations, the method minimizes the Sinkhorn distance adversarially to provide more stable gradients, thereby extracting effective domain-shared features. Finally, offensive pivot words from the source and target language training sets are identified. These pivot words are then removed from the training data in a second network branch, which employs the same architecture. This process constructs an auxiliary OLD task. By concealing offensive pivot words in the training data, the model reduces overfitting and enhances robustness to the target language. In the end-to-end framework training, the combination of cross-lingual shared features and independent features culminates in unsupervised detection of offensive speech in the target language. The experimental results demonstrate that employing cross-language model transfer learning can achieve unsupervised detection of offensive content in low-resource languages. The number of labeled samples in the source language is positively correlated with transfer performance, and a greater similarity between the source and target languages leads to better transfer effects. The proposed method achieves the best performance in OLD on the Malay dataset, achieving an F1 score of 80.7%. It accurately identifies features of offensive speech, such as sarcasm, mockery, and implicit expressions, and showcases strong generalization and excellent stability across different target languages.  
cross-language model; offensive language detection; mBERT; unsupervised methods; transfer learning +
Footnote †: journal: Applied Sciences
## 5 Experiments
### Evaluation of Standard HTS Vices  
In order to set an upper limit for the scores, we obtained the scores of the four objective measures for several high quality synthetic voices. These voices were trained using about four hours of high quality recordings from professional speakers. The training was a standard HTS training [57]. Six synthetic voices were used for this evaluation, obtained from three professional speakers--two females and one male--each speaker recorded both in Spanish (ES) and Basque (EU). The objective measures are language independent, so the language should not affect the results. The same sentences used to train the voices were used to obtain the scores because no other recordings were available from the same speakers. The number of available sentences for each speaker is shown in Table 1.  
Using the six synthetic voices, the objective measures were computed by averaging the scores obtained over all the sentences. Notice that except for NISQA, the original recordings are used as reference to calculate the score. The mean values and the standard  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Voice** & **Speaker** & **Language** & **Gender** & **\# of Sentences** \\ \hline F1-ES & F1 & ES & F & 3994 \\ F1-EU & F1 & EU & F & 3797 \\ F2-ES & F2 & ES & F & 3712 \\ F2-EU & F2 & EU & F & 3831 \\ M1-ES & M1 & ES & M & 3995 \\ M1-EU & M1 & EU & M & 3799 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Standard HTS voices training corpus.  
deviations obtained for all the voices and for each measure are shown in Figure 1. As it can be seen, the synthetic voices obtain similar scores for a given measure, without significant differences among them. However, we can find some exceptions: for STOI and ESTOI, voice M1-ES stands out for its high scores; for SIIB, there are no significant differences; and finally for NISQA voice F2-ES has significantly higher score than the others, and M1-ES has the lowest score. When looking at the data from the language perspective, it is remarkable that the two voices from speaker M1 obtain significantly different values for the two languages, showing lower scores for Spanish (ES) than for Basque (EU) only in NISQA, and the other way round for STOI and ESTOI.
# The Recent Advancement in Unmanned Aerial Vehicle Tracking Antenna: A Review
## 3 Antenna Overview
### Wire Antenna  
Wire antenna is a radio antenna with a long wire suspended above the ground. Although ground terminal is very crucial in antenna transmission range, in this specific design, the ground plays a dominant role. The length of the wire has no effect on the wavelength of the radio used, but for convenience instead [41; 42]. The wire can be straight or strung forth and back between walls and trees. This type of antenna is not as effective as those antennas whose length are adjustable to resonate to the wavelength intended.
# SVPWM Method for Multilevel Indirect Matrix Converter with Eliminate Common Mode Voltage
###### Abstract
The SVPWM modulation method with zero CMV (ZCMV) for the multilevel NPC inverter was first presented in [21]. This method uses the six active middle vectors and one zero vector due to their ability to create zero common mode voltage. However, the average value of the neutral-point current within one sampling period still exists, and it affects the neutral-point voltage. In this paper, the new SVPWM utilizes a group of four active vectors to produce the reference output voltage. These four active vectors consist of two active vectors closest to the reference vector and its two neighbor vectors. By apply four active vectors to synthesize the reference output voltage vector, the average value of neutral-point current is zero while the voltage transfer ratio was not affected. As compared to [3; 14; 15; 16; 17; 18], the presented topology with proposed SVPWM method in this paper can provide the zero CMV. The rest of this paper is organized as follows. Section 2 introduces the multilevel IMC topology and the SVPWM method to eliminate the CMV with canceling the neutral-point current. In Section 3, some simulation and experimental results are provided to illustrate the efficiency of the proposed method. Finally, conclusions are included in Section 4.
# Establishment and Application of Crowd-Sensing-Based System for Bridge Structural Crack Detection
## 3 Development of the Crowd Sensing Crack Collection System
### Cloud Server Configuration
#### 3.1.1 Cloud Platform Selection  
There are currently two operating modes for cloud platforms: private cloud platform and public cloud platform. Private cloud is mainly built for individual users. Building a private cloud platform requires purchasing dedicated servers and configuring auxiliary measures such as management rooms, power supply, and cooling system to ensure its normal operation. Since it is self-built, it has the advantages of ensuring data security and effective service quality. However, private cloud platforms require purchasing hardware to meet the system's expansion needs, configuring dedicated technical personnel for maintenance and operation and requiring extra resources for cooling and other auxiliary measures. Therefore, the cost of private clouds is high, which is not conducive to development testing and large-scale popularity.  
Public cloud platforms are cloud platforms built on cloud services provided by third-party vendors. Users can access them via the internet and configure and purchase according to their needs. The main advantage of public clouds is the convenience for data sharing and scalability. Users do not need to configure dedicated computer rooms, networks, IP addresses, cooling equipment, or technical managers. Instead, they can pay on-demand and receive virtual private cloud services. Users distributed in various regions can quickly access them via the internet, making data sharing and management convenient. As the data volume or processing requirement increases, public cloud platforms can be flexibly upgraded for data storage and processing. The expansion potential of storage space and computing power is quite huge.  
Figure 2: Architecture of the crowd sensing crack collection system.  
Common vendors in the current market for cloud service platforms include Amazon Web Services, Microsoft Azure, Alibaba Cloud, and Huawei Cloud. For the deployment of cloud servers in this section, Alibaba Cloud has been chosen as the cloud platform. Founded in 2009, Alibaba Cloud has rich and mature service experience in fields such as manufacturing, financial services, government services, traffic monitoring, healthcare services, telecommunications services, and energy supply. Therefore, in this section, Alibaba Cloud is used as the public cloud platform for deploying the cloud server.
## 3 Algorithmic Approaches used in the Methodology
### Long Short-Term Memory (LSTM)  
The traditional topic modeling approaches have some limitations when it comes to context learning. For any language model, temporal aspects are also fundamental. Therefore, a well suitable method is required to perform this task. An RNN type, LSTM, can effectively learn the context and temporal features and can better classify or predict, primarily when we have large data sets with time-series information.  
Figure 3: Plate Notation of Latent Dirichlet Allocation (LDA).  
RNNs are the type of networks that generate recurrent connections to memorize. Language models based on recurrent neural networks have lately established state-of-the-art performance in different applications. The dynamic temporal behavior of RNN makes them favorable for sequential classification-based problems.  
For training, it takes the first-word w from the sequence of input; the output h0 along with the next word w1 is taken as input in the next step, and so on. This way, it keeps remembering the context while training, as shown in Figure 4.  
Simply defining an RNN, we can elaborate it in terms of (\(\sum\), S, \(\delta\)) where \(\sum\) represents the inputs, S represents the states, and \(\delta\) is the transition function of the neural network. For example, a traditional language model based on RNN considers a document as a sequence. To predict the next word, an LSTM is trained which also takes into account the previous words. Hence, it maximizes p (wt\(|\)wt\(-\)1\(|\), wt\(-\)2,..., w0; model). The input words are transformed to vectors in \(\Sigma\) which eventually is used for the LSTM state update. For the output, a projection of s\({}_{t}\) is required into a vector of the size of the dictionary and is followed by an activation function, e.g., Softmax. However, challenges occur with more extensive size dictionaries.