esa-sceva/satcom-chunk-collection huggingface dataset:
# Anticancer Efficacy of Antibacterial Quinobenzothiazazines
###### Abstract  
The antitumor potency of a series of designed and prepared antibacterial quinobenzothiazazines was evaluated against different types of human cancer cell lines, such as glioblastoma SNB-19, lung adenocarcinoma A549 and breast cancer T47D, and the activities of the compounds were compared to cisplatin and doxorubicin. 9-Propoxy-5-methyl-12\(H\)-quino[3,4-\(b\)][1,4]benzo-thiazol chloride (**4a**), 9-allyloxy-5-methyl-12\(H\)-quino[3,4-\(b\)][1,4]benzothiamin chloride (**4d**) and 11-benzyl-5-methyl-12\(H\)-quino[3,4-\(b\)][1,4]benzothiazinium chloride (**4l**) were the most active compounds; their IC\({}_{50}\) values against all three cell lines ranged from 5.3 to 9.3 \(\upmu\)M. The effective derivatives showed no cytotoxic effect up to 100 \(\upmu\)M on normal human dermal fibroblasts (NHDFs). To explore the structure-activity relationship, the effect of the type/nature and position of the substituents on the tetracycline quinobenzothiazazine system on the anticancer activity was investigated. Additionally, the receptor-dependent approach was used to specify the mutual ligand-enzyme (bio)compositions that might be potentially valid for the antitumor characteristics of new quinobenzothiazazine derivatives. In particular, the molecular docking procedure was applied for the most potent agents against the human breast cancer line T47D in order to obtain comprehensive knowledge about the aromatase-inhibitor binding mode. The docking study revealed that some regularities in the spatial atomic distribution and nonbonding interactions (e.g., hydrophobic patterns) can be observed for the most active molecules. The surface of the electron-rich aromatic rings of **4d** and **4l** molecules could also contribute to \(\pi\)-\(\pi\) stacking interactions with protoporphyrin IX (HEM) as well as to the formation of \(\pi\)-cation interactions with the adjacent iron cofactor.  
quinobenzothiazazines; human glioblastoma cells; lung adenocarcinoma cells; breast cancer cells; anticancer activity +
Footnote †: journal: Medical Biology, Faculty of Science, Palacky University Olomouc, Sheditelta 27, 783 71 Olomouc, Czech Republic 5
# Interference-Robust Transmission in Wireless Sensor Networks
###### Abstract  
Low-power wireless sensor networks (WSNs) operating in unlicensed spectrum bands may seriously suffer from interference from other coexisting radio systems, such as IEEE 802.11 wireless local area networks. In this paper, we consider the improvement of the transmission performance of low-power WSNs by adjusting the transmission rate and the payload size in response to the change of co-channel interference. We estimate the probability of transmission failure and the data throughput and then determine the payload size to maximize the throughput performance. We investigate that the transmission time maximizing the normalized throughput is not much affected by the transmission rate, but rather by the interference condition. We adjust the transmission rate and the transmission time in response to the change of the channel and interference condition, respectively. Finally, we verify the performance of the proposed scheme by computer simulation. The simulation results show that the proposed scheme significantly improves data throughput compared with conventional schemes while preserving energy efficiency even in the presence of interference.  
co-channel interference; low-power wireless sensor network; transmission scheme +
Footnote †: journal: Sensors
# Temperature Dynamics in Early Pregnancy: Implications for Improving In Vitro Fertilization Outcomes
## 4 Discussion  
This study aimed to characterize the fluctuations in CBT of mice in early pregnancy, considered a close proxy for the fallopian tube, which is recognized as an optimal environment for early embryogenesis. Specifically, our study investigated temperature patterns in the initial five days post-fertilization to identify distinct profiles that could serve as benchmarks for regulating incubator temperatures. The main findings of this study are as follows:  
1. The mean CBT for the five days before mating was 37.23 \({}^{\circ}\)C in the pregnant group and 37.28 \({}^{\circ}\)C in the non-pregnant group, aligning with the standard CBT typically reported in mice. Furthermore, no abnormal behaviors were observed, and none of the mice showed signs of illness, suggesting that implanting the temperature loggers did not negatively impact their well-being.
2. The mean CBT of the pregnant mice exhibited a significant increase from 37.23 \({}^{\circ}\)C to 37.56 \({}^{\circ}\)C, indicating a rise of 0.33 \({}^{\circ}\)C. This change was observed over a 10-day period, encompassing the five days prior to mating and the five days following mating.
3. Examining the circadian rhythm of CBT, no changes were observed during the dark phase. However, during the light phase, all pregnant mice exhibited an increase in CBT, with a statistically significant average rise of 0.58 \({}^{\circ}\)C.
4. Consequently, the amplitude of the CBT circadian rhythm decreased from 0.75 \({}^{\circ}\)C to 0.27 \({}^{\circ}\)C, with mean temperatures of 37.42 \({}^{\circ}\)C during the light phase and 37.69 \({}^{\circ}\)C during the dark phase.
5. The Fourier amplitude spectra analysis revealed that the CBT rhythm had the largest 24 h component, followed by significant 12, 8, and 6 h components. In the pregnant mice, the 24 h component showed the most substantial and statistically significant amplitude decrease of 57%, reducing the amplitude to 0.24 \({}^{\circ}\)C. The amplitudes of the 12 and 8 h components were 0.15 \({}^{\circ}\)C and 0.11 \({}^{\circ}\)C, respectively, while the 6 h component remained unaltered at 0.17 \({}^{\circ}\)C.
6. All the pregnant mice showed a statistically significant decrease in the variability of the irregular fluctuation component, resulting in a standard deviation of 0.28 \({}^{\circ}\)C. However, no such reduced-variability trend was observed in the non-pregnant group.
7. In the pregnant mice, the sample entropy increased across all the scales (statistically significant from scale factors 1 to 11), whereas in the non-pregnant mice, no changes in the complexity of CBT variability were observed.
# A Graph Convolutional Network Based on Sentiment Support for Aspect-Level Sentiment Analysis
## 4 Experimental Analysis
### Ablation Experiments  
In this study, the effectiveness of each module in the SSGCN was checked through ablation experiments. As shown in Table 7, the SSGCN was taken as the benchmark model. Without SSWs denotes that only the weight of the sentiment support was removed, while without SSWMask denotes that only the enhancement of the meaning of sentiment support words and grammatical information was removed. Without AspMask represents the enhancement of only removing aspect word meaning and grammatical information, while without SSWAspMask represents the enhancement of semantic and grammatical information and removing both sentiment support words and aspect words. The following conclusions can be drawn from the results in Table 7:  
1. By comparing without SSWs with the SSGCN, we showed that the sentiment support weight obtained by combining the syntactic and semantic information of SSWs could improve the performance. Specifically, it improved the accuracy by 0.55-1.2% and macro F1 by 0.08-2.23%.
2. By comparing without SSWMask with the benchmark model, the validity of the enhancement module of the sentiment support word meaning and grammatical information was verified.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{2}{c}{**Acc**} & \multicolumn{2}{c}{**Macro F1**} \\ \cline{2-5}  & **P** & **Significance** & **P** & **Significance** \\ \hline _Restaurant_ & 0.003 & Remarkable & 0.010 & Remarkable \\ _Laptop_ & 0.0001 & Remarkable & 0.146 & Not remarkable \\ _Twitter_ & 0.017 & Remarkable & 0.037 & Remarkable \\ \hline \hline \end{tabular}
\end{table}
Table 6: \(t\)-test results.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Model Concatenation Methods** & **Acc** & **Macro F1** \\ \hline Concat & **84.36** & **77.67** \\ Add & 83.47 & 76.71 \\ Multiply & 83.29 & 75.26 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Concatenation method results.  
3. By comparing without AspMask with the benchmark model, the validity of the enhancement module of aspect word meaning and syntactic information was verified.
4. By comparing without SSWMask and without AspMask with the benchmark model, we demonstrated the effectiveness of the feature concatenation module.
5. According to the comparison between without SSWAspMask and the benchmark model, the accuracy and macro F1 of the model decreased when the semantic and grammatical information of aspect words and SSWs were not enhanced, further indicating the importance and effectiveness of the grammatical and semantic information of aspect words and SSWs.
## 4 Simulation and Experiment
### Experiment
From the results, the root mean square of the joint position error is equal to 1.06 degrees. Additionally, the maximal absolute value of the steady-state error is 1.91 degrees, which is better than the correlative studies about the PAM exoskeleton systems (\(\pm 2\) degrees in PAM-functional electrical stimulation (FES) rehabilitation exoskeleton [15], 3.27 degrees in a PAM-driven exoskeleton [33], and 2.29 degrees in the previous study of the authors [14]). Moreover, by using the original method, the tracking error converges by 2.5 degrees within 5 s and the root mean square value is 1.79 degrees. These values are both slightly higher than the proposed method. Then, the original method can only work when the initial error is within the boundary defined by the prescribed performance function. The above results can indicate the high tracking performance of the proposed method in the control of exoskeleton systems. It is noteworthy that the proposed  
Figure 11: Experimental setups: one-air source; two-air filter regulator; three-high-speed valve; four-force sensor (used to calculate the joint torque); five-inertial measurement unit (IMU); six-one-DOF exoskeleton and a manikin arm; seven-microcontroller(Arduino Mega 2560, employed for signal acquisition); eight-host; nine-microcontroller (Arduino Mega 2560, used for transmitting the control signal); ten-printed circuit board (adopted to drive the high-speed valve). Figure 12: Block diagram of the experimental process. The experimental results show that the tracking errors of the proposed control steady-state error can converge into the predesigned boundary, and the root mean squared of the tracking error is equal to 1.06 degrees. Subsequent work will be dedicated (but not limited) to improving a safer exoskeleton for rehabilitation training tasks and extending the developed method to a more complex MIMO system for position tracking tasks (e.g., a multi-DOF exoskeleton system). To be specific, the presented controller will be integrated with the virtual impedance control method for the exoskeleton system to evaluate human movement in active robot-assisted rehabilitation. Literature search, program, data collection and writing--original draft, Z.Z. ; data interpretation, L.H., J.X. and H.J. ; methodology, Z.Z. ; experiment and editing, Z.Z. and H.Z. All authors have read and agreed to the published version of the manuscript. This work was supported by the National Natural Science Foundation of China under grant number 62073063. Not applicable. Data Availability StatementData is contained within the article. The authors thank [PERSON] and [PERSON] for their helpful advice and comments on this study. The authors declare no conflict of interest.
# Inference Acceleration with Adaptive Distributed DNN Partition over Dynamic Video Stream
## 5 Performance Evaluation
### Results
#### 5.2.3 Comparison of Feature Map Segmentation Methods  
The first half of the DNN model in the horizontal cut was executed distributively inside the device cluster. Figure 8 shows the average inference latency of the three vertical partitioning methods based on feature map partitioning for the different number of devices. Overall, within a device cluster, **FSP** performs better compared to other feature map partitioning methods. This is due to the strong adaptability of the **FSP** algorithm to DNN networks with branching structure and optimization of the input feature maps. MoDNN suffers from the effect of different devices waiting for each other and has the lowest parallelism and, therefore, the highest average latency. The two-dimensional feature-map-cutting method in DeepThings brings a larger overlapping region, and frequent overlapping data synchronization leads to the poorer performance of DeepThings than **FSP**. DeepSlicing has better memory and communication optimization, but the complex collaborative approach brings an extra burden when facing multiple devices collecting data simultaneously. Therefore, the additional cost increases as the number of workers increases.
# Wireless Accelerometer Architecture for Bridge SHM: From Sensor Design to System Deployment
## 2 Wireless Sensor Network Architecture
### Gateway  
The network gateway, powered by a Raspberry Pi, serves as a pivotal component for data aggregation, management, and communication. Acting as an message queuing telemetry transport (MQTT) broker, it allows sensor nodes to publish data on specific topics, facilitating communication between the nodes and the central server. Furthermore, the gateway enables remote configuration of critical parameters such as sampling frequency and duty cycle of the sensor nodes.  
The Raspberry Pi gateway connects to the internet via a 4G modem, providing reliable connectivity even in remote locations. It is powered by a battery system that is charged using a solar panel, ensuring continuous operation independent of the power grid. The gateway also generates a Wi-Fi network, enabling wireless connectivity for the sensor nodes. To ensure that the connection is accessible for all nodes, one or more Wi-Fi repeaters may be needed if ranges higher than 150 m are necessary for long bridge monitoring. The next section will detail the structure and implementation of these repeaters.
## 4 System Architecture  
The proposed work follows a cloud storage architecture that has three entities: data owner, cloud service provider (CSP), and authorized user [21; 22]. Figure 2 illustrates the interaction between the three entities of the system.  
The data owner (DO) is the one who possesses data and intends to consume the cloud data services.  
The cloud service provider (CSP) is an establishment that offers various services to its users, one of which is storage service.  
The authorized user (AU) is authorized to use the files stored by the data owner. He/she can download the files from the cloud in an encrypted form and can decrypt them by generating the actual key using a shared secret key to get the original data.  
The system architecture explains the interaction between the three entities of cloud system architecture.  
1. The data owner has sensitive data/files (personally identifiable information) that needs to be stored in the cloud in encrypted form (detailed in the proposed system architecture: steps 1 through 4).
2. Data owners and authorized users share a single secret key. On both sides, a new key is generated for encryption/decryption using the neural key exchange protocol. The user can be an owner as well (detailed in the proposed system architecture: steps 8 and 9).
3. When an authorized user wants to recover the original data/file, he/she downloads the encrypted files from the cloud, generates the key and executes the decryption algorithm, and gets back the corresponding original data/files (detailed in the proposed system architecture: steps 5, 6 and 7).
# Fine-Grained Mapping Between Daily Activity Features in Smart Homes
## 2 Related Work
### DAR Model Transfer  
To alleviate the burden of labeling, DAR model transfer has gradually gained attention. The goal of DAR model transfer is to adapt an established model for use in recognizing daily activities in another smart home. Two key focuses in DAR model transfer are unifying the feature space of daily activities and minimizing the differences between data distributions across different smart homes.  
As mentioned earlier, equipped sensors are primary features for daily activity recognition, and sensors are typically categorized by their type and the functional areas they serve [49]. Sensor mapping is a crucial method for unifying the feature space of daily activities [50]. To the best of our knowledge, existing sensor mapping approaches typically group sensors by category and/or function area. Sensors from different smart homes that share the same group label are then mapped together. In some cases, the sensor mapping process can be further refined manually [51].  
For the second focus of DAR model transfer, transfer learning has been widely adopted by most existing approaches. [PERSON] et al. introduced a feature-based knowledge transfer framework [14], which utilized transfer learning to relax the requirement that training and testing datasets must have highly similar distributions. Their evaluation showed that the framework outperformed non-transfer learning models by 8% in terms of accuracy. [PERSON] et al. developed a shared semantic feature space, where the parameters of the space were optimized according to the differences between data distributions. Model-based transfer learning has also been used for DAR [52]. For example, [PERSON] et al. employed divergence, based on the probability distributions of sensors, to evaluate the similarity between instances of daily activities. The most similar activity instances were assigned the same labels [53]. [PERSON] et al. proposed a novel heterogeneous transfer learning algorithm called Heterogeneous Daily Living Activity Learning (HDLAL) [54]. HDLAL projected data from two domains into a common feature space using the maximum mean discrepancy. From this, a domain-invariant feature representation was derived from the cross-domain data distribution. An ensemble classification algorithm was then used to train a multi-label classifier in the new feature space. Finally, the projected data was used to predict the labels of daily activity instances. In addition to transfer learning, ontology-based approaches have also been explored. [PERSON] et al. developed ontologies for daily activity recognition in two smart homes. The differences between activity instances were addressed by extracting parameters from the respective ontologies. An integrated classifier was then employed for DAR based on these differences [51].
## 5 Simulation Results under Irregular Conditionsfor Various Wave Period and Height
### i-MET Control Strategy  
We determined \(\bar{P}_{t}^{*}\) and \(\eta_{t}^{*}\) by performing \(T_{m}=5000\) s simulations whilst using the i-MET controlled OWC-WEC system with \(k_{p}=5\). This was done in a PM wave environment with a peak period (\(T_{p}\)) of 6\(\sim\)18 s and significant wave height (\(H_{s}\)) of 0.4\(\sim\)5 m.  
Figure 13 depicts the results when the i-MET controller is applied. Figure 13a shows the relative time average power extraction \(\bar{P}_{t}^{*}\), and Figure 13b displays the relative turbine's efficiency \(\bar{\eta}_{t}^{*}\). In all areas, \(\bar{P}_{t}^{*}\) is at or above 0.94, with some areas exhibiting a performance above one. \(\bar{\eta}_{t}^{*}\) exhibits values greater than 0.95. We observed that \(\bar{P}_{t}^{*}\) is optimal in \(9\leq T_{p}\leq 13\) and \(0.4\leq H_{s}\leq 1.4\) and \(\bar{\eta}_{t}^{*}\) is optimal in \(12\leq T_{p}\) and \(1\leq H_{s}\leq 2\). Thus, the correlation between the two seems minimal.  
Figure 12: Time averaged results for maximum power extraction with \(b_{g}\) fixed in time under the Pierson-Moskowitz (PM) irregular wave model with various \(T_{p}\) and \(H_{s}\) for \(T_{m}=5000\) s: (**a**) maximum power extraction from turbine; (**b**) generator damping coefficient; (**c**) relative turbine efficiency.
## 4 Microanalysis  
### Sample Preparation and Test Procedures  
As presented above, the composite additive with the optimal proportion solidified the Caofeidian dredger fill effectively. A microanalysis was carried out by SEM test and XRD test to characterise the mechanism of solidification for the composite additives. For comparison, the unsolidified soil sample and solidified soil samples with single additives were also investigated. The proportion design for the microanalysis tests is presented in Table 8.  
For the SEM test, a \(2\times 2\times 2\) mm\({}^{3}\) cubical core sample was extracted from the sample of unconfined compression test after 28 days of curing. The gold sputtering coating treatment was applied to the cubical sample in vacuum before the test. An SU8010 cold-field emission scanning electron microscope (Hitachi, Japan) was used in the test. Next, an incidental energy-dispersive spectrometer (EDS) analysis was applied to a selected representative region, as shown in Appendix A.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Number**}} & \multirow{2}{*}{**Description**} & \multicolumn{4}{c}{**Proportion of Solidifying Agent/\%**} \\ \cline{3-6}  & & **Slag** & **Gypsum Powder (\%)** & **Sodium Silicate** & **Quicklime** \\  & & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline B1 & Unsolidified soil & 0 & 0 & 0 & 0 \\ B2 & Slag + Gypsum powder & 10 & 1.5 & 0 & 0 \\ B3 & Slag + Sodium silicate & 10 & 0 & 0.8 & 0 \\ B4 & Slag + quicklime & 10 & 0 & 0 & 1 \\ B5 & Slag + all & 10 & 1.5 & 0.8 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Proportion design for microanalysis.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**T**}} & \multicolumn{4}{c}{**7d**} & \multicolumn{4}{c}{**28d**} \\ \hline  & \multirow{2}{*}{**Quicklime**} & **Sodium** & **Silicate** & **Gypsum Powder** & **Quicklime** & **Sodium Silicate** & **Gypsum Powder** \\ \hline T\({}_{1}\) & 5145 & 5200 & 5070 & 8667 & 8365 & 8424 \\ T\({}_{2}\) & 5800 & 5668 & 5327 & 9590 & 9043 & 8607 \\ T\({}_{3}\) & 4967 & 5048 & 5515 & 7415 & 8264 & 8641 \\ t\({}_{1}\) & 171  
For the XRD test, the core sample for the unconfined compression test after 28 days of curing was extracted and ground into powder (<360 mesh). Next, a SmartLab X-ray polycrystalline diffractometer (Rigaku, Japan) was applied to perform the XRD pattern analysis.