esa-sceva/satcom-chunk-collection huggingface dataset:
# Data Exchange in Cluster Structure for Longevity of IoT
## 4 Performance Evaluation  
### Experiment Environment  
In order to evaluate the proposed method in IoT networks, we measure the lifetime of the network and average energy consumption rates. To evaluate the performance of the proposed system, we have used MinT [16] to configure the simulation nodes for IoT device and customized the TOSSIM [32] to simulate the IoT environment. The MinT is middleware to configure the IoT device easily and quickly and also used for in-loop simulation. The TOSSIM is a simulator of TinyOS to simulate the WSN environment. We called the new simulator MinTOSSIM. The performance of the proposed method is measured using the MinTOSSIM. We configure the testbed with a size of 100 m \(\times\) 100 m and deploy the nodes randomly. As the number of nodes increases to 50, 70, and 100, we measure the variation of network traffic. All the nodes deployed in the network have data exchanging opportunities of 100, 150, or 200 times per round. As the probability of 0.3 per one round, each node issues a query to an arbitrary property. Figure 5 shows the deployment map of our IoT networks, which are comprised of 100 randomly deployed sensor nodes.  
Figure 4: Data-exchange phase sequence diagram.  
We use the first-order radio model as the communication style [32]. Figure 6 shows the structure of the chosen radio model. After a data packet of k bits in the sender is sent through the transmitter and the signal is amplified, it can be transmitted as a radio signal. The receiving node receives the data as a radio signal and decodes them into data packets. In this model, the energy for data transmit-receive operations was proposed according to the following Equations (1), (2). In these equations, k is data size(bit), d is distance(meter), Eamp is the power consumption in the signal amplifier (5 pJ), and Eelec is the power consumption in the radio module (1 nJ).  
\[\mathrm{E_{T_{x}}(k,d)=E_{elec}\times k+E_{amp}\times k\times d^{2}} \tag{1}\]  
\[\mathrm{E_{R_{x}}(k,d)=E_{elec}\times k} \tag{2}\]
# The Automatic Algorithm of Optimizing the Position of Structured Light Sensors
## 2 Optimization Method for the Number and Position of Multiple Sensors  
Objects with high reflection coefficients, such as metals, are also objects of three-dimensional measurement. However, in actual measurements, there are often several specular reflection areas, which leads to a loss of measurement information and affects the measurement. The specular reflection areas are shown in Figure 1. Multi-angle is one of the methods used to solve the problem of specular reflection. The principle of a multi-angle sensor is shown in Figure 2, taking two sets of sensors as an example. The specular reflection areas of the two sets of sensors are different, and the non-specular reflection areas of the two sets of sensors are complementary. By fusing the non-specular areas of two sets of sensors, the problem of specular reflection is solved. During this process, the coverage area and specular reflection area of the sensor need to be manually adjusted and optimized. Even for professionals, facing objects with diverse shapes can take a long time, and the optimized sensor positions and quantities may not necessarily be optimal.  
However, some people's research has focused on the method of multi-angle sensors, and no one has paid attention to this issue: when using multi-angle methods in the presence of specular reflection areas, optimize the corresponding optimal number of sensors and specific positions. Aiming at this issue, this article proposes an automatic algorithm for optimizing the position of structured light sensors.  
Figure 1: Specular reflection during measurement (the strong white light area on an object during specular reflection).  
Figure 2: Schematic diagram of a multi-angle sensor. The red dashed lines of sensors 1 and 3 indicate their respective fields of view; The blue dashed lines of sensors 2 and 4 indicate their respective fields of view.  
The overall idea of the system optimization algorithm is shown in Figure 3. First, the 3D point cloud data is processed, and then the sensor position information is initialized. A 3D point cloud can be obtained from the design document. Next, the sensor coverage area is analyzed based on the calculation of the coverage area of the sensor group. Furthermore, the specular reflection area of the sensor group is calculated, and finally the position and pose of the sensor are optimized.
## 5 Course Control Based on ANF-FOID Controller
### Course Control Based on ANF-FOPID Controller  
The ANFM trained in Section 4 is connected in parallel with the FO PI\({}^{\lambda}\)D\({}^{\mu}\) controller to construct ANF-FOPID controller for ship course control. The structure of ANF-FOPID controller is shown in Figure 7.  
After learning the inverse dynamics model of the ship course, the ANFM I with trained structure and parameters is obtained and copied into ANFM II, as shown in the dashed box in the Figure 7, and connected in parallel with the FO PI\({}^{\lambda}\)D\({}^{\mu}\) controller (ANF-FOPID controller) to achieve course control of ship.  
In the control process, an adaptive law with the output of the FO PI\({}^{\lambda}\)D\({}^{\mu}\) controller as the independent variable is introduced to update the ANFM output layer weight matrix \(W\) online to compensate for modelling errors and to cope with the effects of time-varying uncertainty in the modelling parameters. ANF-FOPID controller generates the control input rudder angle \(\delta\) as  
\[\delta=\delta_{FOPID}+\delta_{ANFM} \tag{43}\]  
In which, ANFM is used to generate the control compensation signal and is the master controller, the detailed expression of which is shown in Equation (22). The FO PI\({}^{\lambda}\)D\({}^{\mu}\) controller is introduced to achieve faster and more accurate tracking performance and to ensure reliable control.  
The squared error term between the expected control quantity and the actual control quantity of ANFM is  
\[E(k)=\frac{1}{2}[\delta(k)-\delta_{ANFM}(k)]^{2} \tag{44}\]  
Figure 7: ANF-FOPID control system.  
Substituting Equation (44), that is  
\[E(k)=\frac{1}{2}\delta^{2}{}_{FOPID}(k) \tag{45}\]  
The adaptive law for designing the online adjustment weight matrix \(W\) using the gradient descent method is as follows,  
\[W(k+1)=W(k)-\eta\frac{\partial E(k)}{\partial W(k)} \tag{46}\]  
where, \(\eta>0\) is the learning rate.  
From Equations (22), (44) and (46), it follows that  
\[\frac{\partial E(k)}{\partial W(k)}=\frac{\partial E(k)}{\partial\delta_{ANFM }(k)}\cdot\frac{\partial\delta_{ANFM}(k)}{\partial W(k)} \tag{47}\]  
that is  
\[\frac{\partial E(k)}{\partial W(k)}=-[\delta(k)-\delta_{ANFM}(k)]\cdot\Psi(k) \tag{48}\]  
The adaptive law is as follows, which is expressed as a function of the output of the fractional PI\({}^{\lambda}\)D\({}^{\mu}\) controller and the output matrix vector of the ANFM normalization layer:  
\[W(k+1)=W(k)+\eta\delta_{FOPID}(k)\cdot\Psi(k) \tag{49}\]  
Therefore, the ANF-FOPID controller achieves online control of the course by adjusting the output layer weight matrix of the ANFM that meets the training requirements, in conjunction with the FO PI\({}^{\lambda}\)D\({}^{\mu}\) controller.
# Correlation of Gravity and Magnetic Field Changes Preceding Strong Earthquakes in Yunnan Province
## 2 Data Processing and Accuracy  
### Gravity and Geomagnetic Field Data Acquisition Instrument and Research Area  
There are 249 gravity measurement points in Yunnan Province and its surrounding areas, including nine absolute gravity points. The absolute observation adopts the FG  
Figure 1: The spatial distribution of the annual difference in gravity and magnetic anomaly fields for each of the two years that precede strong earthquakes in the Yunnan area. (**a**) Annual changes in gravity from 2013 to 2014; (**b**) Annual changes in total geomagnetism from 2013 to 2014; (**c**) Annual changes in gravity from 2019 to 2020; (**d**) Annual changes in geomagnetism from 2019 to 2020. Epicenter and focal mechanism are shown for earthquakes that occurred within a year of the respective time period end.  
5 absolute gravimeter, and the observation accuracy is 2 \(\times\) 10\({}^{-8}\) m\(\cdot\)s\({}^{-2}\). The relative observation instrument is CG-5. The relative gravimeter has an observation accuracy of 10 \(\times\) 10\({}^{-8}\) m\(\cdot\)s\({}^{-2}\); there are 130 geomagnetic vector measurement points, of which the GSM-19T proton precession magnetometer (PPM) is used to observe the total geomagnetic intensity, the instrument resolution is 0.1 nT, and the observation accuracy is 0.5 nT.  
The research area of this paper is 21-29\({}^{\circ}\) N, 97-106\({}^{\circ}\) E, and the research objects are mainly the observation data of gravity and geomagnetic measurement points in this area. The distribution of measuring points and measuring lines is shown in Figure 2.
# An Attention-Based Convolutional Neural Network for Acute Lymphoblastic Leukemia Classification
## 6 Conclusions  
In this study, we proposed a diagnostic support system based on CNN architecture with an ECA module to accurately classify ALL cancer and healthy cell images. VGG16 is used as our backbone to extract the features from the source images. The ECA module was incorporated after each convolutional block to further enhance the relevance of extracted features from VGG16. The performance comparison between the VGG16 model with and without the ECA module showed that the attention mechanism helps in improving the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **Accuracy** & **Year** \\ \hline NASNet-Large with VGG19 [36] & 0.965 & 2020 \\ Hybrid model (VGG16 + MobileNet) [37] & 0.961 & 2019 \\ LeukoNet [38] & 0.896 & 2018 \\ \hline
**Proposed Method** & **0.911** & **2021** \\ \hline \hline \end{tabular}
\end{table}
Table 7: Performance comparisons between our proposed method and other approaches.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Methods** & **F1-Score** \\ \hline SDCT-AuxNet [35] & 0.948 \\ Neighborhood-correction algorithm (NCA) [54] & 0.910 \\ Ensemble model based on MobileNetV2 [55] & 0.894 \\ Deep Multi-model Ensemble Network (DeepMEN) [50] & 0.885 \\ Ensemble CNN based on SENet and PNASNet [56] & 0.879 \\ Deep Bagging Ensemble Learning [57] & 0.876 \\ LSTM-DENSE [58] & 0.866 \\ Ensemble CNN model [59] & 0.855 \\ Multi-stream model [60] & 0.848 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Methods with the top entry of C-NMC 2019 Challenge.  
model accuracy since it explores the relationship between channels and obtains better feature representation. The findings of this study revealed that our proposed deep learning-based model outperforms the state-of-the-art approaches. Integrating the attention module in the deep learning architectures may yield a significant performance gain. We evaluated our method on the C-NMC 2019 dataset. The obtained results showed a mean accuracy of 0.911 on this challenging dataset. In the future researchers can focus more on reducing the false positive rate to further improve the final accuracy.  
Conceptualization, Y.Z. and M.Z.U.; methodology, M.Z.U.; software, M.Z.U. and J.S.; validation, J.S., S.A., G.D.K. and C.X.; formal analysis, L.W.; investigation, M.Z.U.; resources, Y.Z.; data curation, M.Z.U.; writing--original draft preparation, M.Z.U.; writing--review and editing, L.W.; visualization, M.Z.U.; supervision, Y.Z.; project administration, Y.Z.; funding acquisition, Y.Z. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the Taishan Scholar Project of Shandong Province (TSHW20150 2038); Natural Science Foundation of Shandong Province (ZR2019 ZD04 ZR2018 ZB0419); National Natural Science Foundation of China (61773246, 81871508).  
Not applicable.  
Data Availability StatementData underlying the results presented in this paper are available in [8; 9; 10; 11; 12].  
The authors have no relevant conflicts of interest to disclose.
## 3 Automatically Choosing the Number of Clusters
### Density Peaks Algorithm
 \tag{43}\]  
where \(\sigma_{\rho_{i}}\) and \(\sigma_{\theta_{i}}\) are the standard deviations of \(\rho_{i}\) and \(\theta_{i}\) for all the snakes. The two parameters \(\alpha_{0}\) and \(\beta_{0}\) are user-defined parameters used to identify the cluster center and compute the \(TH\) value. We specify a snake \(T_{i}\) as a class center when the minimum distance \(\theta_{i}>TH_{d}(\rho_{i})\). However, the performance of the density peaks algorithm depends on the-offdistance \(d_{c}\). To avoid this limitation, the value of \(d_{c}\) is automatically extracted from the set of snakes \(\Gamma\) by minimizing a potential entropy defined as follows [33]:  
\[H=-\sum_{i=1}^{N}\frac{\rho_{i}}{z}\log(\frac{\rho_{i}}{z}), \tag{44}\]  
where \(z=\sum_{i=1}^{N}\rho_{i}\) is a normalization factor. The density peaks algorithm for the transportation network is given by Algorithm 4. ```
0:A set \(\Gamma=\{T_{1},\ldots,T_{N}\}\) with \(N\)Snakes. 1: Construct the mutual K-nearest-neighbor graph. 2:while Snake \(T_{i}\) is in \(\Gamma\)do
3: Compute \(\rho_{i}\) using Equations (38) and (44)
4:endwhile
5:while Snake \(T_{i}\) is in \(\Gamma\)do
6: Compute the distance \(\delta_{i}\) with respect to Equation (35). 7: Compute the value \(\tau_{i}\) according to the Equation (36). 8: Compute the quantity of \(\theta_{i}=\delta_{i}-\tau_{i}\) according to Equation (37). 9:endwhile
10: Construct the decision graph according to the values of \(\rho\) and \(\theta\) (Equations (38) and (37))
11: Identify the cluster centers from the decision graph automatically using the probability density function in the decision graph (Equation (40)). ```  
**Algorithm 4** Density Peaks for Detecting the Number of Partitions in a Transportation Network
## 3 Cross-Layer Adaptive Feedback Scheduling
### Analysis of Deadline Misses over WLAN  
Before designing the feedback scheduling algorithm, the temporal behaviour of WLAN in terms of DMR needs to be studied. In the following, the effects of the transmission rate \(r\) and the sampling period \(h\) on the DMR \(\rho\) are analysed through simulation experiments.  
Assume that there is no interfering signal in the system shown in Fig. 1, and the sizes of all data packets to be transmitted over the network are 1 KB. Fig. 3 depicts the DMR of the system with different transmission rates and different sampling periods. For each pair of transmission rate and  
Figure 3: Deadline miss ratio of the wireless control system.  
sampling period, the simulation is run 10 times, each for 3 seconds. Each value given in Fig. 3 is the arithmetical mean of the DMRs recorded separately in these 10 runs.  
Since the three control loops in the system are completely identical, every change in sampling period shown in Fig. 3 implies that the sampling periods of all three control loops are adjusted at the same time. With a given transmission rate, the DMR decreases as the sampling period increases, and the rate of the change also decrease gradually.  
When the sampling period is relatively small, the network traffic is heavy, resulting in frequent collisions among communication nodes. As a result, the communication delay of a data packet may become large, even go beyond the deadline, or the data packet may be dropped due to too many retransmission attempts. In such situations, the DMR will be large. Enlarging the sampling period can reduce the DMR. The reasons behind can be explained as follows.  
* Firstly, the increase of sampling period reduces the amount of network traffic and hence the probability of node collisions. Consequently, the communication delay of data packets decreases on average, and the possibility of data packets being dropped also decreases thanks to the reduction in the number of retransmission attempts.
* Secondly, as the sampling period increases, the deadlines of data packets to be transmitted increase accordingly. Consequently, longer communication delays are allowed. Both of these effects result in reduced DMRs.  
Comparing the results for \(r=5.5\) Mbps and \(r=11\) Mbps, it can be seen that larger transmission rate benefits the reduction of DMR, especially when the communication resources are scarce. For instance, when h = 12 ms, the DMRs for \(r=5.5\) and 11 Mbps are \(\rho=77.6\%\) and \(2.7\%\).
# QoS-Aware Scheduling Algorithm Enabling Video Services in LTE Networks
## 5 Result and Discussion
### Video Packet Loss Ratio  
Figure 2 presents the packet loss ratio PLR for video traffic for five different proposed algorithms. It clearly illustrates that the proposed PrOEXPrule algorithm achieved lower PLR than popular LTE DL algorithms. This is possible because packets reach their destination with an acceptable delay, maintaining the Quality of Service (QoS). Since PrOEXPrule achieved 0.00801 s with five users and EXP-Rule achieved 0.06268 s, the improved result of PrOEXPrule is clear. There was the same improvement with an increased number of users; at 25 users, the result for PrOEXPrule reached 0.19375, while EXPRule reached 0.61174 with the same number of users. Nevertheless, the PLR varied directly with the connected usercount; the proposed technique offers superior results compared to the other techniques. MLWDF, EXP/PF, and EXPRule demonstrate better results than PF because they consider resource allocation delay before decision-making.
## 4 Results
### Flow Experience: Level of Fun and Playfulness  
We employed four indices to measure flow experience: concentration, control, enjoyment, and sense of time distortion. These indices were measured on a 5-point Likert scale, with a higher score indicating a higher level of positivity. With regard to level of fun and playfulness, significant differences were observed between parametric CAD modeling and VR free-form modeling (\(p\) = 0.04 and \(p\) = 0.01, respectively). The mean values obtained for the four indices when using VR free-form modeling were higher than those when using parametric CAD modeling. The mean values of level of fun and playfulness were 4.00 (standard deviation = 1.12) and 3.95 (standard deviation = 1.06), respectively, when using VR free-form modeling and 3.38 (standard deviation = 0.80) and 3.19 (standard deviation = 0.98), respectively, when using parametric CAD modeling. In the statistical analysis, we present the mean values, line charts, standard deviations, and \(t\)-test results associated with the levels of fun and playfulness for both conventional parametric CAD modeling and VR free-form modeling, as illustrated in Figure 5 and detailed in Table 4.  
However, the data reveal only a marginally significant difference in the perceived level of fun, noted at 0.04. Given the close proximity of the \(p\)-value to the significance threshold, this necessitates a cautious interpretation. It is crucial to contextualize these findings and validate them against other evidence. Notably, when taking into account overall immersion and its indicators--focus, time distortion, and control--no significant differences emerge. These aspects, in conjunction with the borderline \(p\)-value for perceived interest, call for a comprehensive examination.  
Figure 5: Mean levels of fun and playfulness when performing conventional parametric CAD modeling and VR free-form modeling (unit, points; five-point Likert scale).
## 3 A Feasible Solution and the Mathematical Formulations
### A Feasible Solution to the Problem  
Figure 1 displays an output of a feasible solution to the problem. Indeed, it is the optimal solution. It corresponds to the instance known as Internet2 OS3E. In the left figure,  
the green lines indicate the arcs that can be used to connect network nodes. Switches are represented by purple circles, and network controllers are shown as blue squares. On the right side of Figure 1, the connections between switches and controllers are depicted with red lines. It is important to note that the entire network is connected, but for the sake of clarity, the communications between controllers in the backbone are not shown in the figure.
# The Viable but Non-Culturable (VBNC) State, a Poorly Explored Aspect of Beneficial Bacteria
###### Abstract  
Many bacteria have the ability to survive in challenging environments; however, they cannot all grow on standard culture media, a phenomenon known as the viable but non-culturable (VBNC) state. Bacteria commonly enter the VBNC state under nutrient-poor environments or under stressful conditions. This review explores the concept of the VBNC state, providing insights into the beneficial bacteria known to employ this strategy. The investigation covers different chemical and physical factors that can induce the latency state, cell features, and gene expression observed in cells in the VBNC state. The review also covers the significance and applications of beneficial bacteria, methods of evaluating bacterial viability, the ability of bacteria to persist in environments associated with higher organisms, and the factors that facilitate the return to the culturable state. Knowledge about beneficial bacteria capable of entering the VBNC state remains limited; however, beneficial bacteria in this state could face adverse environmental conditions and return to a culturable state when the conditions become suitable and continue to exert their beneficial effects. Likewise, this unique feature positions them as potential candidates for healthcare applications, such as the use of probiotic bacteria to enhance human health, applications in industrial microbiology for the production of prebiotics and functional foods, and in the beer and wine industry. Moreover, their use in formulations to increase crop yields and for bacterial bioremediation offers an alternative pathway to harness their beneficial attributes.  
Keywords:VBNC state; stress; beneficial bacteria; rhizosphere; latency; survival +
Footnote †: journal: Microorganisms
## 3 Sinusoidal Models in Simulation Studies
### Noise Variability with Trend  
This model extends phase shift dynamics by incorporating stochastic fluctuations in signal strength, often observed in telecommunications and environmental systems:  
\[y_{t}=A\sin(\omega t+f(t))+C+\beta t+\sigma(t)\varepsilon_{t}, \tag{14}\]  
where \(\sigma(t)\) modulates the variance of the noise component over time.  
Figure 2 illustrates the six sinusoidal models over a five-year period, each comprising 300 data points (one observation every six days). This resolution captures finer seasonal variations, allowing for a detailed analysis of trend and cyclic patterns. In practical applications, datasets are often aggregated to monthly intervals, aligning them with real-world use cases while preserving key patterns and trends.  
Figure 2: Simulated sinusoidal time series models over a five-year period, each showing synthetic data (blue lines) with distinct periodic patterns and a consistent upward trend.