esa-sceva/satcom-chunk-collection huggingface dataset:
# Informative Language Encoding by Variational Autoencoders Using Transformer
## 4 Experiments
### Results  
#### 4.1.1 Intrinsic Evaluation  
As described in the previous section, we compared our model, the VAE-Transformer, with VAE-LSTM, LSTM LM, Transformer Decoder, and VAE-GPT using intrinsic evaluation.  
Table 2 shows that the VAE-Transformer outperforms the LSTM-based variational autoencoder, LSTM language modeling, and Transformer Decoder in terms of reconstruction and representation learning. It can be observed that the proposed model, the VAE-Transformer, is better than VAE-LSTM regardless of the KL annealing strategy. Table 2 also shows that using a pre-trained model with VAE improves the performance of a language model. This is because of the knowledge transfer and the size of the pre-trained model. To illustrate the training performance, we show the learning curves of cross entropy and KL divergence according to epochs for VAE-Transformer and VAE-LSTM in the training session of PTB, as shown in Figure 9. In terms of cross entropy, the VAE-Transformer is much lower than the LSTM. Moreover, it converges faster than LSTM. In each of the KL  
Figure 8: Comparison between linear annealing and cyclic annealing scheduling.  
annealing strategies, the KL divergence of the VAE-Transformer was larger than that of VAE-LSTM.  
The experimental results indicate that the latent variables of our model contain more useful information from input sentences than the VAE-LSTM. Furthermore, in comparison with LSTM-LM and Transformer Decoder in reconstruction measurement (PPL), the proposed model predicts the next word better than the compared models. This indicates the informative representation \(z\) of the proposed model. In PTB and Wikitext2, VAE-GPT has the best results for reconstruction measurement (PPL), while its KL divergence and MI are low. This implies that posterior collapse happens naturally in those cases because of the complexity and power of the used pre-trained model. Taking advantage of pre-trained knowledge, the pre-trained model is able to reproduce outputs without relying on latent variables. On the contrary, the results of SST2 and AIR, which consist of a small amount of data, are quite different, and the decoder of the variational autoencoder has to rely on latent variables in reproducing the sentences.
# Channel Estimation and Iterative Decoding for Underwater Acoustic OTFS Communication Systems
## 2 Related Work
have designed a practical UWA OTFS system, incorporating Doppler compensation and channel estimation in DD domain [22]. Additionally, [PERSON] et al. have discussed input-output relationships for OTFS under overspread channels while providing a frame design method tailored to the characteristics of UWA channels [23]. In summary, an increasing number of scholars have studied the performance of OTFS in UWA communication. However, most of these studies concentrate on theoretical applications in radio communications, often assuming that the channel is underspread, which is typically not the case for UWA channels that are frequently overspread. Moreover, most UWA OTFS system designs assume perfect channel estimation or only consider the influence of integer Doppler factors. While theoretical investigations into fractional Doppler shifts have been conducted, practical implementation remains challenging. Neglecting fractional Doppler shifts in UWA channels can lead to non-negligible inter-Doppler interference (IDI), severely compromising performance, particularly in UWA communication systems characterized by significant Doppler effects. Therefore, developing a practical OTFS design that is suitable for real-world UWA communication scenarios is imperative. Filling this gap was a key motivation of this work.
# A Survey of Secure Time Synchronization
## 3 Current Time Synchronization Methods
### Timing-Sync Protocol for Sensor Networks (TPSN)  
The TPSN was proposed by [PERSON] et al. for network-wide time synchronization [28]. A sender-receiver structure is used in the TPSN model; the receiver synchronizes its clock with the clock of the sender according to the two-way handshake, as shown in Figure 4 [36; 44; 45]:  
Node A sends a pulse packet at \(T1\) to start the synchronization which includes its level number and value of \(T1\) according to its local clock [32; 36; 44]. Node B receives this message at \(T2\), and \(T2=T1+D+d\), where \(T1\) is the message from Node A, \(D\) is the relative clock drift between Node A and Node B, and \(d\) is the propagation delay of the pulse sent between the nodes. Node B sends an acknowledgement packet at time \(T3\), which includes the level number of Node B and values of \(T1\), \(T2\), and \(T3\). Node A synchronizes itself to Node B after calculating the clock drift and propagation delay as shown below [21; 36; 44]:  
\[D=\frac{(T_{1}-T_{2})-(T_{4}-T_{3})}{2} \tag{6}\]  
\[\text{offset}\ =\frac{(T_{2}-T_{1})+(T_{3}-T_{4})}{2} \tag{7}\]  
The TPSN works in two phases: first, the discovery phase, and then the synchronization phase [46]. The aim of the first phase is to assign a level to each node and create a hierarchical topology in the network [25]. Only the root node is assigned Level 0 as shown below in the Figure 5 [47; 48].  
In the second phase, all nodes connect to the parent node in the hierarchical structure in a two-way handshake message similar to the NTP shown above [31]. In this way, all nodes are synchronized to the root, and network-wide synchronization is achieved by considering a two-way handshake message between two Nodes A and B, as shown in the figure above [32].  
Figure 4: Synchronization between two nodes.  
Figure 3: Two-way handshake between a pair of nodes.  
Similar to the NTP, the TPSN is also based on a hierarchical structure (as shown in Figure 5 above) which provides scalability [47]. Synchronization depends on the nodes' parents in the hierarchical structure; therefore, high synchronization accuracy can be achieved even while the size of the network increases. However, in the case of failure, maintenance of this structure increases the energy consumption [31]. Because the connectivity of a node in the hierarchical structure changes when the node moves, the structure needs to be formed accordingly, since, in the TPSN, a node adjusts its clock according to its parent node [21]. This complex maintenance and energy consumption makes the TPSN unsuitable for secure time synchronization because, in WSNs, nodes can be consistently moving.
###### Abstract
For example, alanine (Ala) and serine (Ser) are known to produce 2-methylpropanal, noted for its sweet aroma, via Strecker degradation [19], while aspartic acid (Asp) and glutamic acid (Glu) result in acidic and umami substances through decarboxylation reactions [20]. Recently, an advanced analytical platform utilizing ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) equipped with multiple reaction monitoring (MRM) technology has been developed. This platform has enabled researchers to quantitatively determine the dynamic interplay between AA variations and the accumulation of ester aroma compounds during the fermentation of rice wine, revealing that valine (Val) enhances the production of ethyl acetate [21]. In the daylily (_Hemercallis_ spp. ), a marked correlation between the metabolic flux of phenylalanine (Phe) and the synthesis of benzaldehyde has underscored the regulatory role of AA metabolic networks in defining characteristic floral scents [1]. Despite these advancements, research on the interaction mechanisms between floral VOCs and AAs is still limited, particularly in systematic multi-cultivar studies of woody ornamental plants such as crabapple, which remain relatively rare. In this study, we constructed a predictive interaction network model between AAs and VOCs in crabapple flowers. By integrating headspace solid-phase microextraction coupled with gas chromatography-mass spectrometry (HS-SPME-GC/MS) and UPLC-MS/MS, we systematically analyzed the volatile compound and AA profiles of 18 crabapple cultivars. We established an aroma phenotype classification model based on odor activity values (OAVs) and validated metabolic differences between groups through hierarchical cluster analysis (HCA). A VOC-AA network model was developed for crabapple flowers using Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA). This model elucidated the metabolic basis of crabapple floral scent formation and yielded an aroma-type prediction model based on VOCs and AA data. This multi-omics framework provides new insights into dissecting the molecular mechanisms of crabapple flower fragrance, facilitates directed breeding for scent, and offers a basis for developing functional horticultural products (e.g., floral teas, aromatic essential oils).
## 4 Results and Discussion
### Two-stage NN  
In 2-stage NNs, future temperatures were unavailable but were simulated first. Based on the same 198 cases used in simple NN, 2-stage NN models predicted RSL with average R\({}^{2}\) of 0.61 (Fig. 2), when daily temperatures of 2 to 5 days were used. There was no statistical difference in R\({}^{2}\) between the four 2-stage NN models. Two-stage NN modeling has been used in many engineering processes [14].  
Figure 2: Ability of simple and 2-stage NN models to predict remaining shelf life (RSL) of living lettuce where model inputs include the current average daily temperature _t_(_c_) plus one to four average daily temperatures from 1, 2, 3 or 4 days prior to the current day. For example, “daily temperatures required” of 2 included _t_(_c_) and _t_(_c_-1), and the value 5 included _t_(_c_), _t_(_c_-1), _t_(_c_-2), _t_(_c_-3), and _t_(_c_-4). Good fit was indicated by higher R\({}^{2}\) in 2-stage NN (R\({}^{2}\)=0.61) compared to simple NN (R\({}^{2}\)=0.57) with LSD=0.016, according to the Proc GLM of SAS software package (SAS 9.1.3). Each bar represents a mean and its standard error obtained with testing of randomly selected testing data set. Each bar represents the mean of 2 to 4 tests.  
When 1\({}^{\circ}\)C storage was interrupted with 5\({}^{\circ}\)C or 10\({}^{\circ}\)C on day 5, the RSL was shortened from 11 days to 9 or 4 days, respectively (Fig. 3).
# Securing Wireless Sensor Networks Using Machine Learning and Blockchain: A Review
## 3 WSN Security Requirements  
The most important WSN security requirements include integrity, availability, scalability, non-repudiation, mutual authentication, confidentiality, and data freshness, defined in turn as follows:  
1. Integrity: transmitted messages cannot be tampered with due to illegal actions when moving from one node to the other.
2. Availability: legitimate (and authenticated) nodes can effectively access the network/provided services.
3. Scalability: the network should be able to cope with increases in size and to adapt to the dynamic addition and removal of various nodes, and node functionalities must be incorporated with sensor nodes for every service without affecting the network's security level.
4. Mutual Authentication: the identities of any pair of nodes engaged in communication must be recognized before they interact.
5. Non-repudiation: the nodes cannot deny the implemented operations or alter the messages they send.
6. Confidentiality: the privacy of sensitive data transmitted over the network medium must be preserved by ensuring that any intruder or other neighboring network intercepting the communication channels cannot obtain any confidential information.
7. Data Freshness: the data must be recent in order to ensure that no old messages have been replayed and that attackers cannot confuse the network by replaying captured messages [33; 34].
## 3 Results  
### Field Measurement Results of Gas Distribution  
After the gas monitoring pipelines were installed in the gob, a gas sample was collected at each measuring point every day at the same time during the normal working face caving and analyzed by gas chromatograph for oxygen concentration.  
The definition of an oxidation zone is generally based on three parameters, air leakage speed, temperature rise rate, and oxygen concentration in the gob. The first two are mostly used in theoretical calculations and are significantly affected by caving conditions. In particular, it is difficult to obtain the actual temperature rise rate and air leakage speed of a gob. Therefore, in this study, an area with the oxygen concentration in the range of 8-18% is defined as an oxidation zone. The measured oxygen concentrations on the intake airway side, the middle, and the return airway side of the gob were classified, compared, and analyzed.  
As can be seen from Figure 5, the oxygen concentration in the goat generally presents a downward trend but there is a certain fluctuation phenomenon. This is mainly due to the complex law of air leakage along the gob-side entry retaining. In addition, due to the different temperatures each day, the underground air pressure is different and the changes in natural wind pressure will also lead to the distribution of oxygen in the goat. Figure 5a shows that, when the 1# measuring point advanced to 136.4 m, the oxygen concentration drops from 18.76% to 17.07% and the point evolves from heat dissipation zone to oxidation zone. When the 2# measuring point advanced to 158.1 m, the oxygen concentration drops from 20% to 17.94% and it enters the oxidation zone from the heat dissipation zone. According to Figure 5b, because its position is close to the return airway, the 3# measuring point does not enter the oxidation zone from the heat dissipation zone until advancing to 210.2 m. At this point, the oxygen concentration drops from 18.62% to 17.72%. The oxygen concentration at 4# measuring point in the return airway also shows a decreasing trend as the working face advanced. It enters the oxidation zone as it advances to 160.1 m when the oxygen concentration drops from 19.29% to 17.85%. It can be explained that the oxidation of residual coal in the gob gradually consumes the oxygen, and the fresh air flow cannot effectively compensate for the consumed oxygen through the air leakage channel. In addition, the oxidation gradually reduces the amount of residual coal available for the reaction. Therefore, due to the limitation of field conditions, it is difficult to accurately define the range of gob spontaneous combustion \"three zones\" only by field measurement. It is necessary to conduct numerical simulation to explore the gob flow field distribution law of the ultra-long working face. The field measurement results can function as the foundation for the subsequent study on the gob porosity and flow field.
## 1 Introduction
### Comparative Study of Different Types of Peer Relationships  
Despite the large number of studies that have explored the association between different types of peer relationships and academic performance, there are fewer comparative studies of different types of peer relationships [30; 31]. However, research has found that the impacts of different types of peer relationships are different [32]. In order to deepen theoretical understanding and support educational practice planning, comparative studies between peer relationships are essential.  
Several studies have compared same sex and opposite sex relationships. A study of 246 emerging adults at a Midwestern university found that academic support from friends and academic support from partners are two different concepts. Academic support from friends was positively correlated with a student's GPA, while that from a partner was not [31]. Results from another study of 1436 high school students (670 boys, 756 girls) showed similar conclusions: adolescents' same sex and opposite sex peer relationships positively affected their academic performance in different ways [30]. A social network is a social structure comprising a set of participants and the mutual ties between these participants [33]. The first of the above studies limited its focus to the closest friends and romantic relationships and did not cover the overall comparison between same sex and opposite sex relationships. The second study, while comparing the effects of same sex and opposite sex relationships on students' academic performance, failed to further analyze the structure of social networks. In fact, different social network structures have significant effects on students' academic performance [6; 16; 34]. Additionally, all of the above studies relied on self-reporting, which may not objectively reflect the real situation of social network structures.  
Another study compared the differences between roommate and classmate relationships. One study analyzed peer relationship networks based on the daily behavioral data of 4738 undergraduate students enrolled in 2018. The results showed that students in the same dormitory were the first to form peer relationships, but then relationships shifted towards being classmate-centered. The number of classmates in the social network was positively correlated with the probability of receiving a scholarship [35], revealing the different effects of roommate and classmate relationships on academic performance.  
Based on existing research, this study contributed improvements in objectivity, the holistic nature of relationship networks, and category segmentation. We comprehensively compared four types of peer relationships (classmates, roommates, opposite sex, and others), both qualitatively and quantitatively, on an objective dataset. The 'other' category refers to relationships that appear in the overall social relationships but do not fall into the first three categories, such as peer relationships with students from other colleges. This comparison can help us recognize which type of relationship is most strongly associated with academic performance.
# Planar Array Diagnostic Tool for Millimeter-Wave Wireless Communication Systems
###### Abstract  
In this paper, a diagnostic tool or procedure based on Bayesian compressive sensing (BCS) is proposed for identification of failed element(s) which manifest in millimeter-wave planar antenna arrays. With adequate a priori knowledge of the reference antenna array radiation pattern, a diagnostic problem of faulty elements was formulated. Sparse recovery algorithms, including total variation (TV), mixed \(\ell_{1}/\ell_{2}\) norm, and minimization of the \(\ell_{1}\), are readily available in the literature, and were used to diagnose the array under test (AUT) from measurement points, consequently providing faster and better diagnostic schemes than the traditional mechanisms, such as the back propagation algorithm, matrix method algorithm, etc. However, these approaches exhibit some drawbacks in terms of effectiveness and reliability in noisy data, and a large number of measurement data points. To overcome these problems, a methodology based on BCS was adapted in this paper. From far-field radiation pattern samples, planar array diagnosis was formulated as a sparse signal recovery problem where BCS was applied to recover the locations of the faults using relevance vector machine (RVM). The resulted BCS approach was validated through simulations and experiments to provide suitable guidelines for users, as well as insight into the features and potential of the proposed procedure. A _Ka_-band (28.9 GHz) \(10\times 10\) rectangular microstrip patch antenna array that emulates failure with zero excitation was designed for far-field measurements in an anechoic chamber. Both simulated and measured far-field samples were used to test the proposed approach. The proposed technique is demonstrated to detect diagnostic problems with fewer measurements provided the prior knowledge of the array radiation pattern is known, and the number of faults is relatively smaller than the array size. The effectiveness and reliability of the technique is verified experimentally and via simulation. In addition to a faster diagnosis and better reconstruction accuracy, the BCS-based technique shows more robustness to additive noisy data compared to other compressive sensing methods. The proposed procedure can be applied to next-generation transceivers, aerospace systems, radar systems, and other communication systems.  
far-field; antenna array; diagnosis procedure; noisy data; BCS; millimeter-wave +
Footnote †: journal: Electronics Letters
# Collaborative Analysis of Learners' Emotional States Based on Cross-Modal Higher-Order Reasoning
## 3 Methods
### Emotion Prediction Module  
The emotion prediction module has been designed to integrate multimodal information, fully exploit the correlations between different modalities, and achieve precise prediction of emotional information using deep learning methods. This module concatenates the cross-modal feature vectors extracted via the cross-modal cooperative attention interaction module and feeds them into a multi-layer perceptron (MLP) for processing. The MLP, as a deep neural network model, is capable of nonlinear mapping and modeling complex relationships within input features, thereby better capturing the inherent patterns and semantic information among features.  
After the attention processing across the three modalities described above, we obtained interaction feature vectors for each modality. These feature vectors were then fused through a tanh-activated MLP after being concatenated, ultimately yielding the predicted value \(\hat{y}\), as shown in Equation (22):  
\[\hat{y}=\text{MLP}(\text{concat}[F_{pw},F_{aw},F_{up}]) \tag{22}\]  
The model utilizes mean squared error (MSE) as the loss function. MSE is a method to measure the difference between predicted values and true values via computing the squared difference between predicted values and true values to evaluate the model's performance. Its formula is shown as Equation (23):  
\[L=\frac{1}{N}\sum_{i=0}^{N}\left(y_{i}-\hat{y}_{i}\right)^{2} \tag{23}\]