esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
_Pasteurella multocida_ (_Pm_) is one of the major pathogens of bovine respiratory disease (BRD), which can develop drug resistance to many of the commonly used antibiotics. Our earlier research group found that with clinical use of enrofloxacin, \(Pm\) was more likely to develop drug resistance to enrofloxacin. In order to better understand the resistance mechanism of \(Pm\) to enrofloxacin, we isolated \(Pm\)S and \(PmR\) strains with the same PFGE typing in vitro, and artificially induced \(PmR\) to obtain the highly resistant phenotype, \(PmHR\). Then transcriptome sequencing of clinically isolated sensitive strains, resistant and highly drug-resistant strains, treated with enrofloxacin at sub-inhibitory concentrations, were performed. The \(satP\) gene, of which the expression changed significantly with the increase in drug resistance, was screened. In order to further confirm the function of this gene, we constructed a \(satP\) deletion (\(\Delta Pm\)) strain using suicide vector plasmid pRE112, and constructed the \(C\)-\(Pm\) strain using pBBR1-MCS, and further analyzed the function of the \(satP\) gene. Through a continuously induced resistance test, it was found that the resistance rate of \(\Delta Pm\) was obviously lower than that of \(Pm\) in vitro. MDK\({}_{99}\), agar diffusion and mutation frequency experiments showed significantly lower tolerance of \(\Delta Pm\) than the wild-type strains. The pathogenicity of \(\Delta Pm\) and \(Pm\) was measured by an acute pathogenicity test in mice, and it was found that the pathogenicity of \(\Delta Pm\) was reduced by about 400 times. Therefore, this study found that the \(satP\) gene was related to the tolerance and pathogenicity of \(Pm\), and may be used as a target of enrofloxacin synergistic effect.  
bovine respiratory disease; _Pasteurella multocida_; drug resistance; antibiotic tolerance; \(satP\)
## 3 Resources and Demand Assessment of the Case Study
### Global Horizontal Irradiation and Wind Speed  
Providing the latitude and longitude of Kibran Gabriel island to HOMER Pro software, GHI and wind speed data of NASA can be found via Internet connection. According to the data obtained, daily average GHI is 5.94 kWh/m2/day. As depicted in Figure 3, the daily average GHI for each month varies from the minimum 5.05 kWh/m2/day in July to the maximum 6.68 kWh/m2/day in April. The sky is relatively clearer in winter (November to May) than summer (June to October).  
Similarly, the annual average wind speed data of the site taken from NASA. The annual average wind speed is 3.78 m/s at 50 m above the surface of the earth. As shown in Figure 4, the relative minimum and the maximum monthly average wind speeds are measured in October (2.89 m/s) and June (4.7 m/s), respectively.  
Figure 3: Average global horizontal irradiation data.  
Figure 2: Geographic location of Kibran Gabriel island.
## 5 Simulations and Discussions
### Simulations Results
Figure 11: Combined output (Scenario 1). Figure 9: Wind farm (WF) output (Scenario 1). Figure 14: SOC of the Li-ion battery (Scenario 1). Figure 12: Fluctuation of WF output (Scenario 1). Figure 13: Fluctuation of combined output (Scenario 1). Figure 15: SOC of the NaS battery (Scenario 1). \begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Scenario 1**} & \multicolumn{3}{c}{**Scenario 2**} & \multicolumn{3}{c}{**Scenario 3**} & \multicolumn{3}{c}{**Scenario 4**} & \multicolumn{3}{c}{**Scenario 5**} \\  & **Conv.** & **Pro. (MA)** & **Pro. (Opt.)** & **Conv.** & **Pro. (MA)** & **Pro. (Opt.)** & **Conv.** & **Pro. (MA)** & **Pro. (Opt.)** & **Conv.** & **Pro. (MA)** & **Pro. (Opt.)** & **Conv.** & **Pro. (MA)** & **Pro. (Opt.)** \\ \hline Maximum 1-Min & 22.1 & 17.0 & 17.2 & 21.8 & 15.5 & 15.5 & 18.6 & 12.4 & 13.0 & 19.9 & 14.3 & 14.4 & 17.9 & 12.8 & 14.2 \\ Introduction of \(\mathcal{P}_{\text{T}}\) [s] & & & & & & & & & & & & & & & \\ Average of 1-Min & 9.3 & 6.42 & 7.37 & 9.31 & 6.34 & 7.35 & 9.60 & 6.61 & 7.66 & 9.96 & 6.96 & 7.78 & 9.88 & 6.78 & 7.70 \\ Modulation of \(\mathcal{P}_{\text{T}}\) [s] & & & & & & & & & & & & & & & \\ SD of _Pro. [s]_ & 0.0507 & 0.0411 & 0.045 & 0.065 & 0.0345 & 0.087 & 0.0421 & 0.0306 & 0.0353 & 0.096 & 0.0400 & 0.0424 & 0.0523 & 0.0433 & 0.0461 \\ Maximum 1-Min & 9.86 & 9.65 & 9.97 & 8.24 & 8.21 & 8.35 & 7.28 & 6.72 & 7.25 & 8.50 & 9.10 & 9.19 & 7.22 & 7.20 & 7.68 \\ Average of 1-Min & & & & & & & & & & & & & & \\ Median of \(\mathcal{P}_{\text{T}}\) [s] & & & & & & & & & & & & & & \\ Average of 1-Min & 3.39 & 3.27 & 3.76 & 3.32 & 3.21 & 3.73 & 3.47 & 3.37 & 3.90 & 3.56 & 3.51 & 3.93 & 3.55 & 3.42 & 3.90 \\ SD of \(\mathcal{P}_{\text{T}}\) [s] & & & & & & & & & & & & & & & \\ Maximum power & 7126.8 & 3092.0 & 3970.7 & 6713.4 & 3507.5 & 3070.3 & 6472.3 & 3294.9 & 3334.7 & 7846.9 & 4203.2 & 4310.5 & 7400.3 & 3002.8 & 3668.0 \\ \hline \(\mathcal{P}_{\text{T}}\) [s] [W/m] & – & 367.1 & 192.0 & – & 349.6 & 162.9 & – & 3502.3 & 166.8 & – & 6494.7 & 345.3 & – & 589.9 & 229.9 \\ \(\mathcal{P}_{\text{T}}\) [s] [W/m] & & & & & & & & & & & & & & & \\ Integral & & & & & & & & & & & & & & & & \\ WF output [M/m] & & & & & & & & & & & & & & & & \\ Battery life (1-iso) [s] & 5.83 & 5.48 & 4.54 & 6.43 & 7.16 & 5.22 & 5.45 & 5.40 & 4.68 & 5.16 & 4.54 & 4.02 & 5.49 & 5.48 & 4.18 \\ Battery life (Na) [s] & 15 & 15 & 15 & 15 & 15 & 15 & 15 & 15 & 15 & 15 & 15 & 15 & 15 & 15 & 15 & 15 & 15 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Simulation results.
# FL-LoRaMAC: A Novel Framework for Enabling On-Device Learning for LoRa-Based IoT Applications
## 5 Simulation Details and Parameter Selection
### Network Setup and System Model  
We assume a network where a large number of end devices are considered to be placed uniformly in a circular LoRa cell with the gateway placed at the center. It is assumed that, at any moment, the gateway has enough resources available to demodulate any number of valid receptions. The network server is connected to the application server. The application server is assumed to contain the untrained global model and it is also responsible for performing the federated averaging on parameters received from the nodes.  
Out of all end devices, we assume that five devices are running the ECG anomaly detection application, with other devices potentially used for other applications. All five of those devices will be taking part in the federated learning process. All end devices share the same communication channel and gateway, and hence can interfere with each other's transmissions.  
The training dataset consists of 80% of the whole dataset, and the remaining 20% was used for testing purposes. To simulate the real-world scenario, the data samples from the training subset were equally and randomly distributed among the end nodes participating in learning as shown in Figure 9. In a real world scenario, these data will be collected by the devices from local sensing.
# Deep Neural Network for Automatic Image Recognition of Engineering Diagrams
## 3 Results
### Effect of Clustering Methods  
In Table 3, it is shown in a confusion matrix to depict the performance of PN_DAC model.  
The PN_DAC model exhibits the optimum performance in SR and DR, with 98.08% and 0.39%, respectively. Though both PN models-k-means and DAC-had a low dummy detection rate, the PN_DAC model recorded a lower score in dummy detection than the PN_Kmeans model by approximately 1%. This resulted from the differences between the image clustering methods. The k-means clustering is an iterative algorithm based upon Euclidian distance, which represents a simple quantitative distance between entities in feature space. It does not consider the direction of the feature. Consequently, the algorithm is too weak to construct with high-dimensional data such as that contained within image representations. In contrast, the PN_DAC model obtains the latent features of the data by performing efficient feature extraction using a convolutional network. As shown in Table 3, most of the confusion is created among these symbol classes, except for the three-way valves, ball valves, and sensor symbols. We also calculate F1 scores for each symbol in PN_DAC model, as given in Table 4. By solving the pairwise binary classification problem with adaptive parameters, the model delivered good performance that could be interpreted as a good analysis of the hidden patterns in the regions. DAC has configured the negative class to make it easier to distinguish between the classes, thereby increasing its performance by entering well-defined data into the model.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Actual**}} & \multicolumn{1}{c}{**Jway\_Vlv**} & \multicolumn{1}{c}{**Ball\_Vlv**} & \multicolumn{1}{c}{**Gate\_Vlv**} & \multicolumn{1}{c}{**Butterfly\_Vlv**} & \multicolumn{1}{c}{**Check\_Vlv**} & \multicolumn{1}{c}{**Refief\_Vlv**} & \multicolumn{1}{c}{**Globe\_Vlv**} & \multicolumn{1}{c}{**Utility**} & \multicolumn{1}{c}{**Sensor**} & \multicolumn{1}{c}{**PLC**} & \multicolumn{1}{c}{**Dummy**} & \multicolumn{1}{c}{**Total**} \\ \hline
3 way\_Vlv & 4 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 5 \\ Ball\_Vlv & 0 & 5 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 5 \\ Gate\_Vlv & 0 & 0 & 214 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 5 & 219 \\ Butterfly\_Vlv & 0 & 0 & 0 & 9 & 0 & 0 & 0 & 0 & 0 & 0 & 9 \\ Check\_Vlv & 0 & 0 & 0 & 11 & 0 & 0 & 0 & 0 & 0 & 2 & 13 \\ Relief\_Vlv & 0 & 0 & 0 & 0 & 0 & 3 & 0 & 0 & 0 & 1 & 4 \\ Gobe\_Vlv & 0 & 0 & 0 & 0 & 0 & 28 & 0 & 0 & 0 & 2 & 30 \\ Utility & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 18 & 0 & 0 & 2 & 20 \\ Sensor & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 190 & 0 & 0 & 190 \\ PLC & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 21 & 2 & 23 \\ Dummy & 0 & 0 & 5 & 1 & 2 & 1 & 2 & 2 & 0 & 2 & 291 & 306 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Confusion matrix: PN_DAC model.
# Global-Local Self-Attention Based Transformer for Speaker Verification
## 5 Conclusions  
In this work, we proposed a Transformer-based speaker embedding extractor for speaker verification with a novel global-local self-attention mechanism. The method balances the ability to model long-distance dependencies and capture local features by performing local and global attention in parallel. We aggregated features at different levels in the encoder and decoder to obtain more powerful speaker embeddings. The combination of these designs enables our proposed method to achieve excellent results compared with several strong baselines. Finetuning the parameters and more adequate training may further improve results. In future work, we will further improve the performance by combining this method with other techniques, such as pre-training, while exploring how to better apply Transformer to speaker recognition tasks.  
Methodology, F.X. and D.Z.; software, F.X.; validation, D.Z. and C.L.; writing--original draft preparation, F.X.; writing--review and editing, C.L.; supervision, D.Z.; project administration, D.Z.; funding acquisition, C.L. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Architecture**} & \multirow{2}{*}{**Window Size**} & \multicolumn{2}{c}{**VoxCeleb2 Dev**} \\ \cline{3-4}  & & **EER (\%)** & **MinDCF** \\ \hline  & 20 & 2.50 & 0.25 \\ Our work & 25 & 2.44 & 0.23 \\  & 30 & 2.52 & 0.27 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The window size w changed from 15 to 30 frames.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Architecture**} & \multicolumn{2}{c}{**Voxceleb2 Dev**} \\ \cline{2-3}  & **EER (\%)** & **MinDCF** \\ \hline Our work & 2.44 & 0.23 \\ (a) Original full self-attention & 2.62 & 0.24 \\ (b) No sub-block aggregation net & 3.17 & 0.28 \\ (c) No LePE & 2.53 & 0.26 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ablation study of our proposed architecture.  
**Funding:** This research was funded by the National Key Research and Development Program of China (No. 2018********* 4402, 2020 YFB1712401).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare that they have no conflict of interest.
# Efficacy Improvement of Anomaly Detection by Using Intelligence Sharing Scheme
## 4 Proposed Architecture
### Proposed Algorithm
#### 4.2.1 Global Reputation Model (GRM) as an Outlier Ensemble  
In order to use the outlier ensemble abstraction, the transformation of inputs from the form of network flows with anomaly scores into a local reputation model (LRM), is necessary (step 1. in Figure 2). This is represented by treating each flow's anomaly score as an estimate of the corresponding hostname anomaly score. Then the host-name anomaly scores are estimated by averaging the anomaly scores of all flows that communicate with said hostname in a single network. This simple procedure arrives at the local reputation model.  
Figure 2: Overview of the global reputation model (GRM) illustrated on three networks.  
The GRM can be viewed as a data-centered outlier ensemble with horizontal sampling. Let \(H\) be the set of all existent hostnames and \(Hn\ \subseteq H\) the set of all hostnames that were observed in the network \(n\ \in N\). Let \(fn:\ Hn\ \rightarrow\ \mathbb{R}\) be the anomaly score of a hostname measured in the network \(n\). Now, \(Hn\) is the horizontal sampling of \(H\) and the sets of anomaly scores observed in individual networks form an ensemble \(Eh:\)  
\[Eh=\left\{\left\{fn\left(h\right)\mid h\in Hn\right\}\ \mid\ n\in N\ \right\} \tag{1}\]  
Horizontally sampled ensembles are routinely used in classification. Classical examples are bootstrap aggregating [38] and random forests [39]. Intuitively, this is understandable; horizontal sampling leads to dilution of dense groups of data points which significantly hampers our ability to identify outliers. To be able to utilize the literature around outlier ensembles, the reputation model is re-defined as an ensemble with vertical sampling by allowing the existence of missing values. The ensemble is now:  
\[Ev=\left\{\left[fn\left(h\right)\mid n\in N\right]\mid h\in H\right\} \tag{2}\]  
In the above equation, \(\left\{fn\left(h\right)\mid n\in N\right\}\) is an ordered set of the anomaly scores of \(h\) in all networks. Since not all of the hostnames are observed in every network (the domain of \(fn\) is a subset of \(H\)), there are missing values in the ensemble. The difference between these two representations is illustrated in Figure 3. The figure shows that, in the case of horizontally-sampled ensemble (left), we treat each network as a sampling \(Hn\) of a set of hostnames \(H\). In the case of the vertically-sampled ensemble (right), each network is treated as a dimension \(Dn\) in a feature space of hostnames. Sampling is then conducted over the dimensions of the feature space, one dimension at a time, and values are missing whenever a hostname was not observed in a given network [40].  
By considering the reputation model as a vertically-sampled ensemble, explicitly dealing with missing values is necessary. The shortcomings of existing algorithms designed for estimation with missing values is necessary (discussed in Section 3.2) to motivate the design of a novel ensemble normalization algorithm described later in this section.
# NFT Technology for Enhanced Global Digital Registers: A Novel Approach to Tokenization
## 9 Practical Applications and Use Cases
### Evaluation of the Proposed Approach  
* Feasibility. The feasibility of our proposed approach relies on the effective integration of blockchain technology and NFTs with existing and new global digital registers. To ensure its practicality, it is necessary to develop standards and protocols that facilitate seamless data exchange, interoperability, and collaboration among different systems and stakeholders.
* Scalability. Scalability is a crucial consideration for the widespread adoption of NFT-based tokenization for global digital registers. The system must be capable of handling an increasing number of transactions and participants without compromising performance. Leveraging blockchain technologies with efficient consensus mechanisms, such as proof-of-stake (PoS) or sharding, can help address scalability concerns.
* Long-term Sustainability. The long-term sustainability of our approach depends on its adaptability to evolving technological advancements, legal frameworks, and user demands. Continuous research, development, and collaboration among stakeholders, including governments, industry players, and academics, are vital to ensuring the system's ongoing relevance and growth. Challenges and Future Research Directions: Some potential challenges and future research directions include the following: 1. Developing privacy-preserving techniques that maintain data confidentiality while ensuring the integrity and transparency of the system; 2. Establishing legal frameworks and international agreements to address IP, copyright, and regulatory concerns; 3. Investigating the integration of our system with emerging technologies, such as quantum computing and advanced cryptographic techniques, to enhance security and performance.
## 2 Related Works
### AdaBound  
AMSGrad designed a strategy of non-increasing learning rates to tackle extreme learning rate problems. However, recent work has pointed out AMSGrad does not show evident improvement over Adam [21]. [PERSON] et al. [20] speculate that [PERSON]'s extremely large and small learning rates are likely to account for weak generalization ability. To this end, they proposed AdaBound which implemented a gradual transformation from Adam to SGD (M) by employing dynamic bounds to clip extreme ones. However, AdaBound only tackles the extreme learning rates at the end. AdaBound is described in the following Equations (16) to (23).  
The general updating rule for weights in AdaBound:  
\[\theta_{t+1}=\theta_{t}-\hat{\eta}_{t}\times\hat{m}_{t} \tag{16}\]  
Calculating the EMA indicator of gradients:  
\[m_{t}=\beta_{1}\times m_{t-1}+(1-\beta_{1})\times g_{t} \tag{17}\]  
Calculating the bias correction for EMA indicator of gradient:  
\[\hat{m}_{t}=\frac{m_{t}}{1-\beta_{1}^{t}} \tag{18}\]Calculating the EMA predictor of square gradient:  
\[v_{t}=\beta_{2}\times v_{t-1}+(1-\beta_{2})\times g_{t}^{2} \tag{19}\]  
Calculating the bias correction for EMA predictor of square gradient:  
\[\hat{v}_{t}=\frac{v_{t}}{1-\beta_{2}^{t}} \tag{20}\]  
Setting the lower bound function for [PERSON]:  
\[\eta_{l}=final\_lr\times(1-\frac{1}{(1-\gamma)\times t+1}) \tag{21}\]  
Setting the upper bound function for [PERSON]:  
\[\eta_{u}=final\_lr\times(1+\frac{1}{(1-\gamma)\times t}) \tag{22}\]  
Constraining the learning rates of [PERSON] in these two bounds:  
\[\hat{\eta}_{t}=Clip(\frac{\alpha}{\sqrt{\hat{\eta}_{t}}+\epsilon},\eta_{l}, \eta_{u}) \tag{23}\]  
where the \(\eta_{l}\) is the lower bound and \(\eta_{u}\) is the upper bound, and \(final\_lr\) is considered as a global constant learning rate to the end. The value \(\gamma\) controls the transition speed from Adam to SGD (M). The authors do the grid search on these two parameters, and they are not sensitive to accuracy. The \(\gamma\) and the \(final\_lr\) are given to 0.999 and 0.1 for all tasks, respectively. The rest of the parameters are the same as Adam, which mention in the last Section 2.5. In this setting, the bound range is empirically hand-designed without data information, which is an exhausting way. As a result, it is unknown whether bounds of AdaBound can properly truncate the unreasonable learning rates, because the unreasonable learning rates of a certain task are not state clearly. Intuitively, AdaBound uses the same bounds for all tasks because parameters are not sensitive, which may hurt the generalization performance of the algorithm [23].
# Improved Smart Pillow for Remote Health Care System
## 1 Introduction
### Evolution of Measurement of Core Body Temperature (MCBT)
They offered an exceptional sensor data fusion technique through different sensing modalities and the design of a novel activity learning model by adding behavioral observations. They claim that the new system enhances accuracy to 98.17% from the original value of 80%. A secure thermal-energy aware routing protocol for Wireless Body Area Networks (WBAN) based on Blockchain technology is proposed by [PERSON] et al. [15], with stability and efficiency prioritized in their design when managing the limitations of temperature and energy. They applied the Hyperledger Caliper, a benchmarking tool, and Castalia, a simulation tool, to prove the improvement of their protocol in preserving residual energy and balancing of temperature. [PERSON] et al. [16] review wearable sensors for remote health monitoring, first introducing various health monitoring systems, and then discussing textile-based wearable sensors and the different options of wireless technologies. They are concerned with information privacy, data security, energy efficiency and reliable communication, all of which are emphasized in our proposed system.
## 3 Development of the System
### Design of the Data Model  
The data model needed for the system was very basic, needing just two tables. The first table contained historical data of doctor attendance, meteorological data and environmental quality, this table was used to generate our predictive model. The second table stored the results and input parameters of the prediction. Table 2 shows the attributes of the tables needed for the functioning of the decision support system.  
Figure 3: Decision support system architecture.
## 1 Introduction
[22] conducted simulations and analyses to examine the effects of different waste rock reinforcement methods on the seismic performance of tailings dams. The study focused on the impact of seismic wave frequencies and found that low-frequency waves have a greater influence on tailings dams compared to high-frequency waves. The application of non-uniform seismic loads leads to a 10-30% decrease in the dynamic response of concrete panel rockfill dams compared to that of uniform loads. Furthermore, the edges of the concrete panels experience higher dynamic tensile stresses [23; 24]. [PERSON] and [PERSON] employed an equivalent viscoelasticity model to simulate the dynamic response of different types of tailings dams to seismic forces. They considered the nonlinear properties of the foundation soil and the deposited tailings in order to identify the potential instability modes and seismic failure characteristics of these dams [25]. Most current studies on the dynamic characteristics of tailings dams rely on numerical simulation techniques like the limit equilibrium method and finite element method, which typically provide satisfactory results. Among the various types of numerical simulation methods, the discrete unit method can make up for some of the shortcomings of the finite element method when handling uranium tailings sand due to its discrete nature [26]. This paper examines the dynamic response characteristics of a uranium tailings dam under seismic load using the discrete element method. This study investigates the acceleration, displacement, and velocity change patterns of the uranium tailings dam. Furthermore, it quantitatively analyzes the factors influencing the dynamic response characteristics of the uranium tailings dam, such as the friction angle of the tailings sand and seismic wave amplitude. By controlling the variables, the study calculates the impact of these factors on the dynamic response of the tailings dam. The research findings can be utilized in the development of tailings ponds, providing a theoretical basis and reference for the safe operation of dams. This is of significant importance in safeguarding the safety of downstream residents and protecting the ecological environment.