esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
In autoptic human hippocampi with AD, both mRNA or protein expressions of NR1, NR2A, and NR2B subunits were lower than in age-related controls, although there have been conflicting data here as well [30; 31; 32]. It is suggested that AD-evoked changes in NMDA receptors could result from direct effects of oligomeric A\(\beta\). Experiments on cell cultures indicate that A\(\beta\) oligomers can activate NMDA receptors through both NR1/NR2A or NR1/NR2B heteromers [33; 34], with downstream changes in signalling pathways [35]. With respect to brain lateralization, our earlier analysis did not reveal significant differences between the right (R) and left (L) frontal cortices of young male Sprague-Dawley rats in NR1, NR2A, or NR2B subunit expressions [29]. Only mild L/R laterality (index of laterality \(<\) +0.100) was found in NR2B expression in the frontal cortex of young male Long Evans rats [36]. On the other hand, there seems to be the same asymmetry in NR2B expression in the hippocampus, reported at a synaptic level [37]. Age- and SD-dependent changes in NMDA receptors raise an intriguing possibility of alteration in NOS signalling, a key molecular partner of NMDA receptor and a major regulator of neural activity [29; 38], including adjustments of the sleep-wake cycle, via inducible NOS (iNOS). It is suggested that NO elevation via iNOS is a specific homeostatic mechanism for producing non-REM recovery of sleep following SD [39; 40; 41]. Neuronal NOS (nNOS) seems to play a complementary role in the recovery of sleep induction processes [39; 42]. Although the physiological role of endothelial NOS (eNOS) in the sleep-wake cycle is not known yet in a detail, this isoform appears to be very vulnerable to effects of SD, with 24 h general SD reducing endothelial-dependent vasodilation [43]. Moreover,normal aging impairs the mechanism by which NO induces sleep [44]. Previously, we have compared age-related changes in activities of nNOS, eNOS, and iNOS in Sprague-Dawley, Wistar, and Long Evans rats and found the most marked bilateral changes in the activity of eNOS when compared to nNOS or iNOS [29; 38]. On the other hand, analysis of human autoptic hippocampus revealed a lateral asymmetry in eNOS or iNOS activities in nondemented controls and increased activities of nNOS, eNOS, and iNOS, especially in the L hemisphere in subjects with AD, in line with the notion of higher susceptibility of L hemisphere to aging and AD [45]. Thus, our previous results suggested that SD-evoked effects could be different in the R and L hemisphere in the case of NOS isoforms, rather than in NMDA receptor subunits. This study extends the analysis of NR1, NR2A, and NR2B expressions with nNOS, eNOS, and iNOS activities in the cortex of young and old rats exposed to acute or chronic SD, to elucidate their changes related with aging and sleep impairments, with implications for age-related cognitive decline and pathobiology of AD.
# Seismic Responses of a Cable-Stayed Bridge with Consideration of Uniform Temperature Load
## 5 Case Study and Discussion
### Deck to pylon Connections
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Responses**} & \multicolumn{2}{c}{**Connection 1**} & \multicolumn{2}{c}{**Connection 2**} & \multicolumn{2}{c}{**Connection 3**} \\  & \(\Delta T=0\) (\({}^{\circ}\)C) & \(\Delta T=\pm 35\) (\({}^{\circ}\)C) & \(\Delta T=0\) (\({}^{\circ}\)C) & \(\Delta T=\pm 35\) (\({}^{\circ}\)C) & \(\Delta T=0\) (\({}^{\circ}\)C) & \(\Delta T=\pm 35\) (\({}^{\circ}\)C) \\ \hline \(P_{comp. }\)1 (kN) & 58,888 & 60,877 & 60,213 & 63,193 & 77,877 & 79,850 \\ \(P_{transic}\)2 (kN) & 25,193 & 26,982 & 28,937 & 29,075 & 21,747 & 56,249 \\ \(P_{spanol}\)3 (kN) & 2501 & 6156 & 2621 & 6266 & 2600 & 6625 \\ \(\Delta_{bursting}\)4 (m) & 0.11 & 0.25 & 0.11 & 0.25 & 0.12 & 0.24 \\ \(M_{pylon}\)5 (kN-m) & 1,176,896 & 1,347,783 & 1,295,675 & 1,513,304 & 2,390,025 & 3,918,211 \\ \(\Delta_{pylon}\)6 (m) & 0.16 & 0.24 & 0.16 & 0.26 & 0.24 & 0.34 \\ \(\Delta_{pisssuitable for the connection of a cable-stayed bridge since the responses of the bridge will be sharply increased under either earthquake loading or temperature loading. Thus, Connection 1 or Connection 2 can be considered as the candidate of both seismic excitation and temperature loadings.
# Spoken Language Identification System Using Convolutional Recurrent Neural Network
## 4 Proposed Spoken Language Identification System
### Proposed CRNN Architecture  
CNN has been used widely in various application areas, including image classification and speech recognition. In these domains, A CNN has achieved state-of-the-art levels of performance. The convolutional layer in a CNN facilitates feature extraction, which is performed using convolutional processing and filtering. A sliding window (filter) is applied to the inputs to perform this filtering operation. By calculating the convolution product between the window and the considered input portion, the applied filter will represent the characteristic extracted by the network, with its parameters estimated during network training. The objective of subsampling layers is to reduce the dimensionality of the characteristics that result from the convolution without significant loss of information. Standard methods are max-pooling and average-pooling.  
In the RNN family, the LSTM network is a unique subclass [46]. This network has been shown to yield remarkable results for learning long-time dependencies. Recursion  
Figure 2: Data preprocessing step.  
is the critical feature of recurrent networks. In particular, this network creates loops in the network diagram that can preserve information. As a result, they can essentially \"remember\" previous states and use that knowledge to determine what will happen next. This property makes them ideal for working with time series data. LSTM units are used for the construction of recurrent networks. Each LSTM unit is a cell with four gates: the input gate, the external input gate, the forget gate, and the output gate. The most important aspect of the cell is its internal state, which enables it to store and preserve values over time.  
This article proposes a CRNN architecture that combines CNN with LSTM, as demonstrated above. CNN is an excellent network for feature extractions, while the LSTM has proved its ability to identify the language in sequence-to-sequence series. Figure 3 shows the architecture of the proposed CRNN.  
The proposed CRNN architecture, as shown in Figure 3, contains CNN layers for feature extraction on the input data, as well as LSTM to support long-term temporal dynamics. The novelty of the proposed CRNN architecture is in using three layers to convert the type of input data depending on the networks used in CRNN: a sequence folding layer, a sequence unfolding layer, and a flatten layer.  
The sequence folding layer converts the sequences of images to an array of images [47] for CNN to be able to extract spatial features from the input array. The sequence unfolding layer converts this array of images back to image sequences [48], and the flatten layer converts image sequences to feature vectors [49] for input to the LSTM layer. Finally, to  
Figure 3: CRNN architecture.  
carry out the final prediction, the results are provided as input, fully connected layers, and output layers in the prediction block [50].
## 5 Results and Discussion
### Effect of Contamination on Compaction Parameters  
A series of compaction tests were performed on AC, BC and KC specimens to investigate the effect of contamination on optimum moisture content (wopt) and maximum dry density (ydmax). As shown in Figure 7a, addition of atrazine resulted in a decreasing trend for OMC values of the contaminated mixtures. For instance, addition of the 2%, 4%, and 6% atrazine, caused a reduction from 20.12% to 18.74% for AC specimens while the OMC value for uncontaminated AC was 21.35%. The addition of atrazine showed an identical trend for BC and KC specimens. For instance, a decreasing OMC trend value in the range of 19.7% \(\leq\) wopt \(\leq\) 22.74% and 17.8% \(\leq\) wopt \(\leq\) 18.69% for the atrazine range of 2% \(\leq\) ATZ \(\leq\) 6% were recorded for BC and KC specimens. The OMC values for uncontaminated BC and KC values were 23.45% and 19.74%. Similarly, the recorded MDD values showed a decreasing trend with the addition of atrazine as shown in Figure 7b. For instance, the addition of 2%, 4%, and 6% atrazine increased the MDD values of the AC to 1.85, 1.82 and 1.78 T/m\({}^{3}\). The identical increasing trend for BC and KC specimens were recorded. For example, the MDD for BC and KC were between 1.86 \(\leq\)ydmax \(\leq\) 1.98 T/m\({}^{3}\) and 1.69 \(\leq\)ydmax \(\leq\) 1.81 T/m\({}^{3}\) after addition of the atrazine between 2% \(\leq\) ATZ \(\leq\) 6% whereas the MDD for uncontaminated BC and KC were 1.91 and 1.72 T/m\({}^{3}\). The acquired results in this section is in good agreement with [27]. Viscosity has a vital role in reducing the optimum moisture contents and maximum dry density. The used atrazine has a viscosity higher than water, and this characteristic can be the main reason for reduction in MDD and OMD when the atrazine content of the soil increased [60; 61; 62].  
Figure 6: Atterberg limit test results for Amber Clay (AC), Black Clay (BC), and Kaolinite Clay (KC) specimens contaminated at different Atrazine contents (**a**) Liquid Limit (LL) versus Atrazine content; (**b**) Plastic Limit (PL) versus Atrazine content.
## 2 Drilling Data Collection  
### Signal Acquisition Plan While Drilling  
The 112,206 return air passage of the No. 1 coal mine operated by Shaanxi Xiaobaodang Mining Co., Ltd., Yulin, China. is situated within the coal seam of the 11 th panel of the second coal seam, with the 112,206 working face oriented to the west. The cross-section of the roadway is rectangular, and the support method employs a combination of anchor mesh, cable, and beam support. The lithology of the immediate roof of the 112,206 return air channel consists of siltstone, with a rock thickness ranging from 1.041 to 1.85 m and an average thickness of 1.45 m. The basic roof is composed of medium-grained sandstone, which locally transitions to coarse-grained sandstone, exhibiting a rock thickness between 18.92 and 25.9 m, with an average thickness of 22.41 m. During the tunnel excavation and support process, numerous anchor cable boreholes are constructed. The drilling process involves the interaction between the drilling tool and the rock mass. The drilling signal serves as the physical feedback from the drilling tool, and fluctuations in this signal occur in the weak areas of the surrounding rock during drilling. The dynamic characteristics of the drilling device can be used to assess the condition of the tunnel's surrounding rock. In this study, ideal data are obtained by capturing the drilling signal, preprocessing and denoising the original data, and then performing feature selection to establish an AdaBoost model for predicting the strength of the coal and rock mass. The overall scheme is illustrated in Figure 1.  
The 112,206 return air channel reinforcement support utilizes a pneumatic handheld drilling rig for anchor cable support. It is primarily designed for drilling in coal roadways and semi-coal roadways with a rock hardness of \(\leq\)f6. Additionally, it can be employed for mixing and installing resin anchor rods, as well as for drilling small-diameter hydrological, gas, or blasting holes. This drilling rig features a compact structure, lightweight design, low noise operation, simple usability, convenient maintenance, and an extended service life. The relevant parameters of the drilling rig are presented in Table 1.
## 1 Introduction
### Opportunistic Algorithms  
Due to the wide variety of use cases, many algorithms are developed [7]. A defining property of an ON algorithm is whether it generates extra message copies. _Forwarding_ protocols route a single copy of the message; _Flooding_ protocols produce multiple replicas to increase the delivery rate at the cost of increased network overhead.  
Two big classes of algorithms are _stateless_ and _stateful_. The former is based on efficiently disseminating the copies of a message, so it is also known as \"dissemination-based\". The later algorithms collect data on the ON behavior and attempt to use this knowledge to make more effective routing decisions. Many different techniques are used. Distance-vector style algorithms are popular, but more complex methods are also proposed.  
We propose using machine learning (ML) techniques, specifically reinforcement learning (RL) and Q-learning, to solve the routing problem. As we will see below, multiple works use this approach. However, our proposal uses a particular way of modeling the network that captures more information from the network's behavior. In our previous work [8], this model was applied for ML routing in ON; however, in that work, the training was performed offline using recorded traces. In this work, the ML process is performed online, and the model is built and maintained during the network's lifetime. We show that the application of this network model improves the network's performance compared to the conventional Q-learning application. It is also competitive with conventional protocols, especially regarding network overhead, as it does not depend on additional traffic for routing information.
## 2 Materials and Methods  
### Chemicals  
Tables 1 and 2 present the characteristics of semi-synthetic, synthetic, and natural emollients (vegetable oils) selected for testing. In addition, vitamin E (Tocopheryl Acetate) from Ecospa, Warsaw, Poland was used for research.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Designation**} & **INCI Name/Trade** & \multirow{2}{*}{**Chemical Family/Name**} & **Molecular Weight** & \multirow{2}{*}{**Appearance:**} & **Spreading Value** \\  & **Name/Manufacturer** & & & **[g/mol]** & & **[mm2/10 min]** \\ \hline \multirow{4}{*}{CCTG} & Caprylic/Capric & fatty acid esters, & & & \\  & Triglyceride/Crodamol & esters/Mix of tri-esters & & & \\  & GTCC/Croda; & with carbon chains of C8 & 408.58 & Liquid & 550 \\  & Cracow, Poland & and C10 derived from & & & \\  & coconut oil and glycerin; & & & \\ \hline \multirow{4}{*}{DA} & Dibutyl Adipute/Cetiol & esters/adypinian & & & \\  & B/Basf; Warsaw, Poland & dibutylu; diester of butyl & 258.35 & Liquid & 1000 \\  & & alcohol and adipic acid & & & \\ \hline \multirow{4}{*}{OD} & Octyldodecanol/Eutanol & primary branched-chain & & & \\  & G/Basf; Warsaw, Poland & alkohol/2-Octyl-1- & & 298.50 & Liquid & 600 \\ \hline \multirow{4}{*}{DC} & Dicaprylyl & Esters/dioctyl carbonate; & & & \\  & Carbonate/Cetiol & diester of carbonic acid & & & \\ \cline{1-1}  & CC/Basf; & and octanol & & & \\ \cline{1-1}  & Warsaw, Poland & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of semi-synthetic emollients (Caprylic/Capric Triglyceride) and synthetic emollients (Dibutyl Adipute, Octyldodecanol, and Dicaprylyl Carbonate).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{4}{*}{**Designation**} & **INCI Name/Common** & **Fatty Acids Composition of** & \\  & **Name/Manufacturer** & **Vegetable Oils**[37; 38; 39; 40; 41; 42; 43; 44; 45] & **Appearance:** \\ \hline \multirow{4}{*}{MTSO} & Oleic acid 54–68\% & & \\  & Macadamia Ternifolia Seed & Palmitic acid 16–23\% & \\  & Oil/Macadamia nut oil/Ecospa; & & & \\  & Warsaw, Poland & & & \\  & & & & \\  & & & & \\  & & & \\ \hline \multirow{4}{*}{PADO} & Prunus Amygdala Dulcis (Sweet & Linoleic acid 13.38–27.69\% & \\  & Almond) Oil/Sweet almond & Palmitic acid 5.59–8.15\% & Liquid \\  & oil/Ecospa; Warsaw, Poland & & & \\  & & & & \\  & & & \\ \hline \multirow{4}{*}{RCSO} & Ricinoleic acid 74–84\% & & \\  & & Linoleic acid 7.3–10.32\% & \\  & Ricinus Communis (Castor) Seed & Oleic acid 5.5–7.55\% & \\  & Oil/Castor oil/Ecospa; & & & \\  & Warsaw, Poland & & \\  & & & \\  & & & \\  & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Characteristics of natural emollients.
# An Empirical Study of Cluster-Based MOEA/D Bare Bones PSO for Data Clustering +
## 4 Results
### Investigating Swarm Size and Maximum Number of Iterations  
In Table 9, the performance of CM-BBPSO, using the following swarm-size and maximum iteration pair settings ss-mi, 20-100, 30-100, 30-200 and 40-100, is presented. Experiments with ss-mi of 40-100 achieved the best results in 6 out of 13 datasets. Further experimentation using this setting will be considered. Overall, the results were considered competitive for the different settings.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline
**Dataset** & **CM-BBPSO** & **M-Fuzzy** & **M-BBPSO** & **SP-QPSO** & **BBPSO** & **IB-Fuzzy** & **IB-QPSO** & **M-QPSO** & **MG-BBPSO** & **MG-QPSO** & **MG-Fuzzy** \\ \hline Appendicitis & 54 & **12** & 46 & 30 & 35 & 29 & 61 & 32 & 37 & 47 & **12** \\ CMC & **20** & **11** & 33 & 31 & 39 & 33 & 44 & 25 & 36 * & 36 * & 33 \\ Dermatology & 36 & 34 & **23** & **15** & 25 & 51 & 56 & 27 & 42 & 29 & 57 \\ Ecoli & 36 & 34 & 32 & **24** & 50 & 52 & 37 & **23** & 26 & 27 & 52 \\ Glass & 48 & 27 & **22** & **20** & 46 & 52 & 43 & 28 & 28 & 27 & 50 \\ Iris & 44 & 37 & 33 & 40 & **10** & 51 & 33 & 38 & **19** & 22 & 57 \\ Seeds & 46 & 31 & 22 & 32 & 6 & 56 & 49 & 15 & 29 & 43 & 65 \\ Sonar & 26 & 24 & 36 & 57 & 49 & **18** & 41 & 36 & 39 & 51 & **17** \\ WDBC & 31 & **10** & **16** & 58 & 41 & 38 & 51 & 27 & 46 & 53 & 24 \\ Wine & 34 & **19** & **18** & 34 & 23 & 55 & 47 & 22 & 34 & 54 & 52 \\ WOBC & 53 & **16** & 25 & **15** & 51 & 25 & 63 & 33 & 31 & 48 & 34 \\ Segment & 35 & **11** & **18** & 24 & 28 & 33 & 46 & 21 & 36 * & 36 * & 36 * \\ Vehicle & 28 & **10** & 22 & 29 & **19** & 29 & 35 & 36 * & 28 & 36 * & 36 * \\ \hline Total & 491 & 276 & 346 & 409 & 422 & 522 & 606 & 363 * & 431 * & 509 * & 525 * \\ \hline \hline \end{tabular}
\end{table}
Table 7: Sum rank values of algorithms for all 6 metrics for each dataset with best two algorithms presented in bold.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**CMC**} & \multicolumn{2}{c}{**WOBC**} & \multicolumn{2}{c}{**Iris**} & \multicolumn{2}{c}{**Glass**} & \multicolumn{2}{c}{**Wine**} \\ \cline{2-11}  & **Acc** & **F1** & **Acc** & **F1** & **Acc** & **F1** & **Acc** & **F1** & **Acc** & **F1** \\ \hline CM-BBPSO & 0.402 & 0.371 & 0.896 & 0.865 & 0.721 & 0.672 & 0.429 & 0.351 & 0.719 & 0.691 \\ HT-MOC (best) [20] & & & 0.961 & & & & & & & 0.951 & \\ MOFC-TMS [25] & & 0.653 & & 0.97 & & 0.836 & & 0.501 & & 0.696 \\ GenClustMOO [32] & & & 0.647 & & 0.969 & & 0.823 & & 0.503 & & 0.698 \\ VAMOSA [31] & & 0.593 & & 0.816 & & 0.682 & & 0.415 & & 0.516 \\ MOAC [26] & 0.401 & & & & 0.218 & & 0.893 & & & \\ ES-NMPBFO [27] & 0.448 & 0.478 & & 0.886 & 0.834 & & & \\ CBPSO [24] & 0.398 & & 0.961 & & 0.908 & & 0.509 & & 0.711 & \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison of CM-BBPSO with other reported cluster-based MOC, MOC and BBPSO clustering algorithms.
# Analysis of Bluetooth RSSI for Proximity Detection of Ship Passengers
## 3 The PACT Proximity Datasets
If the device proximity of a smartphone is known in addition to the COVID-19 patient and close contacts, then distance data can be tagged, and a machine learning method, such as the DBSCAN algorithm, is used to discover trends. The computer can find close contacts by learning the rules on its own, which will result in a significant increase in efficiency and accuracy. Close contacts are isolated according to epidemic prevention regulations after accurately identifying connections in a short period of time. An electronic barrier is set up in the isolation area of close contacts. Suppose a close contact leaves the isolation area without permission. In that case, a warning is sent to the monitoring room via a cell phone voice triggering device, and personnel can intervene in time to decrease the danger of viral transmission. As a result, distance recognition accuracy becomes highly critical. Figure 2: Floor plan of the HANNARA ship student housing quarters. For proximity detection applications, the ship does not require additional equipment but only a smartphone in the hands of persons onboard. Generally, wireless networks are installed on newly built ships. The ship's wireless network covers the active area of the ship's occupants. RSSI data received from another person on the cell phone estimates proximity. Estimation results can be transmitted back to the control center by using WiFi. The server will store data in the local database to make it easier for the program to read the data. In older ships, a ship's wireless network has to be installed to be able to obtain proximity detection results from the user's device and to transmit information to the control center. Furthermore, as satellite-based marine networks, such as the Starlink program, are gradually implemented [22], the problem of data backhaul on all types of boats may be solved. We quantify possible parameters influencing proximity detection results in this study and provide a solid reference base for future proximity detection algorithm development.
# AWMC: Abnormal-Weather Monitoring and Curation Service Based on Dynamic Graph Embedding
## 3 Method
### Dynamic Graph Embedding Based on Entropy  
The graph similarity algorithm to find the most similar graph corresponding to each graph is proposed. The graph similarity algorithm is as follows.  
\[d(e(G_{i}),e(G_{j}))=\sqrt{\|e(G_{i})-e(G_{j})\|_{2}^{2}} \tag{6}\]  
Then, we can obtain the set of graphs that are most similar to each graph. This set is called the supervised dynamic matrix that contains the most similar graph for each graph. The set is formulated as \(\mathcal{S}=\{(S_{t}\to S_{t}+1)S_{i}\in\mathcal{S},t\in[0,T]\}\), where \(T\) is the number of time intervals, \(S_{i}\) is the graph at time interval \(i\), and \(S_{t}\) indicates the precedent graph of \(S_{t}+1\). The embedding model includes two autoencoders. An autoencoder is used to reconstruct the dynamic weather graph, and another autoencoder is used to reconstruct the dynamic supervised graph. The embedding vector is calculated by the autoencoder. More specifically, the encoder extracts the features of the graph for mapping to the embedding space, and the embedding vector is calculated by reversing the encoder's computation. The loss function is based on graph entropy similarity to shorten similar graphs in the distance in the embedding space. The formula of the loss function based on calculating graph entropy similarity in the embedding space is as follows.  
\[\mathcal{L}=\frac{1}{T}\sum_{t=1}^{T}\|e(g_{t})-e(s_{t})\|_{2}^{2}+\frac{1}{T} \sum_{t=1}^{T}\|G_{t}-\widehat{G_{t}}\|_{2}^{2}+\frac{1}{2}\sum_{i=0}^{I}(\|W \|_{2}^{2}+\|\widehat{W}\|_{2}^{2}) \tag{7}\]  
where \(W^{i}\) and \(\widehat{W^{i}}\) indicate the weight in the i-th layer of the encoder and decoder, respectively, and \(g_{t}\) and \(s_{t}\) indicate the embedding vectors of graphs \(G_{t}\) and \(S_{t}\), respectively.  
In addition, the gradient-descent [33] and backpropagation [34] algorithms were used to train the model, and update the weight and basis. Then, we obtained the final dynamic graph embedding model.