esa-sceva/satcom-chunk-collection huggingface dataset:
# Stream-DBSCAN: A Streaming Distributed Clustering Model for Water Quality Monitoring
## 2 Method Principle
### Distributed Streaming Data Processing  
The key challenge in processing big data is to discover new knowledge and laws from data that have low value density, irregular distribution, and deeply hidden information. This represents a significant shift from big data to big knowledge to big decision-making [17]. In the era of big data, new technologies and methods have been developed to improve the ability to use vast amounts of data for decision-making and knowledge discovery [18]. The most common method for data analysis is called data mining [19]. Data mining uses efficient data analysis and mining rules to process vast volumes of data and uncover potential connections and patterns between data attributes. In the 4V attributes of big data, data timeliness is given top priority in the processing of big data. With the sheer volume of data available, finding hidden knowledge in a short period is crucial for big data processing. Scholars have developed mining algorithms for massive data that have approached optimal tempo-spatial complexity after several iterations. However, the computing power of computers is still inadequate to meet the requirements of processing massive data within a short period. The main reasons for this are summarized as follows:  
The first issue is that batch mode processing renders data generated earlier meaningless because it requires reading, calculating, and outputting data in batches, bundling data at different time scales for input and output. To address these problems, stream data processing was proposed to overcome the shortcomings of batch processing. In stream data processing, data is continuously transmitted into memory in the form of streams and processing results are returned immediately after being processed. As a result, stream data processing provides more timely processing results [20].  
The second issue is that due to the large amount of data and high data dimensionality, the computing power of a single computer is insufficient to process massive data. Not only does the calculation take a longer time, but the memory also cannot store such a huge amount of data. However, stream processing reads data only once, and the memory does not have to load the entire batch of data, enabling the computer to process a large amount of data without overflowing the memory. The problem of insufficient computing power can be solved using distributed computing, where the amount of computation is allocated to each computer node, and the data results can be obtained at a very low cost through merging rules.  
In recent years, numerous clustering algorithms have been introduced in data mining to enhance the speed and scalability of processing big data. These clustering algorithms can be broadly divided into two categories [21]: single-machine-based clustering and distributed multi-machine clustering. As distributed clustering can fundamentally overcome the limitation of computing capability, the research of some clustering algorithms has gradually shifted from a single-machine approach to a distributed approach.
# A Self-Powered UHF Passive Tag for Biomedical Temperature Monitoring
## 3 Design Methodology, Layout and Circuit Simulation
### Layout and Circuit Simulation
#### 3.2.2 Temperature Sensor and Voltage Buffer  
Figure 14 shows the location of the proposed temperature sensor inside the microphotography of the whole self-powered tag. In addition, a zoomed-in view of its layout is included. Its used silicon area is 170 \(\upmu\)m \(\times\) 175 \(\upmu\)m. The oscillation frequency was set at 250 kHz for a temperature of 25 \({}^{\circ}\)C. The dimensions of the transistors that compose the temperature sensor are listed in Table 4.  
The post-layout transient response simulation is drawn in Figure 15a, where an oscillation frequency \(f_{osc}=210\) kHz at 25 \({}^{\circ}\)C can be noticed. Figure 15b shows a positive dependence of the oscillation frequency \(f_{osc}\) on temperature. The temperature sensitivity of the sensor is 858.46 Hz/\({}^{\circ}\)C with a standard error of 6.46 Hz/\({}^{\circ}\)C over \(-40\) \({}^{\circ}\)C to 60 \({}^{\circ}\)C. The power consumption of this block is 852 nW, sinking a total current \(I_{72}+I_{73}=1.42\)\(\upmu\)A for a voltage supply of \(V_{DD_{2}}=0.6\) V.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Element** & **Value** & **Element** & **Value** & **Element** & **Value** \\ \hline \(m_{p_{1}}\) & (6.24 \(\upmu\)m/1.8 \(\upmu\)m) \(\times\) 8 & \(m_{1},m_{4}\) & (3.265 \(\upmu\)m/3.6 \(\upmu\)m) \(\times\) 4 & \(m_{3}\) & (7.19 \(\upmu\)m/3.6 \(\upmu\)m) \(\times\) 4 \\ \(m_{n_{1}}\) & (16 \(\upmu\)m/1.8 \(\upmu\)m) \(\times\) 8 & \(m_{2}\) & (3.265 \(\upmu\)m/3.6 \(\upmu\)m) \(\times\) 6 & \(m_{5},m_{6}\) & (0.9 \(\upmu\)m/0.36 \(\upmu\)m) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Elements and transistor sizes of the temperature sensor.  
Figure 14: Location of the proposed temperature sensor inside the microphotography of the whole self-powered tag and a zoomed-in view of its layout.
## 2 Open-Loop Tracking and Inversion Algorithm
### Open Open-Loop Data Processing  
In the OL mode, the phase used to retrieve for atmosphere profiles can be expressed as sum of the modeled phase (MP) and the recovered phase (RP). The MP is obtained from the occultation geometry and climate models. The RP is restored from observations of the In-Phase(I)/Quadra-Phase(Q) components through three steps: frequency reduction conversion; NDM removal; and adjacent phase connection [28; 31; 32; 33; 34]. The ways to eliminate the influence of NDM in the OL mode are the internal and external methods, and the biggest difference between them is that the external mode requires GPS bit data. Due to geographical restrictions, however, only about 70% of the COSMIC occultation data has corresponding GPS bit data [31; 32; 33; 34]. Due to a lack of information in determining the initial sign of RP, there may be a different sign between the initial I/Q components and the real phase, which will negatively correct the MP. We adopt the strategy of the full OL data recovery process, assisted with closed-loop observations to avoid the above-mentioned problems. Through MPs adding or subtracting RPs, we can obtain two series phases, named corrected phases A/B(CPA/CPB). We use the correlation between the mountain-based closed-loop observation data of an elevation angle above 3 degrees and CPA/CPB to determine the initial sign of RP, and obtain a high-consistency corrected OL phase for subsequent atmospheric parameter inversion.
# Research on Characteristics of Flow Noise and Flow-Induced Noise
## 3 Validation of Calculation Method
### Computational Results and Analysis  
The pressure coefficient distribution of the computational model is shown in Figure 2. The pressure coefficients at the front and rear ends of the model surface were large. Because the water flow collides with the submarine hull at these positions, there was a sharp decrease in fluid velocity. According to the Bernoulli equation, the pressure at these  
Figure 1: Mesh of the computational domain.  
positions will increase, thus generating a higher-pressure coefficient. The velocity in the middle section was larger, so the pressure coefficient was smaller.  
As shown in Figure 3, the velocity on the wall surface was zero due to the wall boundary condition. We can observe the influence of the model's stagnation point on the velocity field, as well as the impact of the wake and trailing vortices on the flow field, which is consistent with the theory. The teardrop shape minimized shape drag to the maximum extent. Towards the rear, the streamlined design gradually narrowed, making it easier for the fluid to re-converge. This allowed water to flow more smoothly around the submarine, reducing turbulence and vortices in the water, resulting in a smaller wake. An increase in fluid velocity caused the wake to elongate and narrow. High-speed fluid would produce a longer wake, while low-speed fluid would generate a shorter wake.  
Compare the numerical simulation results with the experimental data. According to the experimental data provided in reference [23], the layout of the hydrophones in the experiment is shown in Table 1.  
As shown in Figure 4, we compared the simulation results of hydrophones numbered 1, 3, 5, and 8 with the experimental results of the submarine's sound pressure level.  
\begin{table}
\begin{tabular}{c c c} \hline
**Hydrophone** & **Axial (m)** & **Circumferential (\({}^{\circ}\))** \\ \hline A1 & 0.48 & 180 \\ A2 & 1.28 & 180 \\ A3 & 1.6 & 180 \\ A4 & 2.08 & 180 \\ A5 & 2.72 & 180 \\ A6 & 1.6 & 95 \\ A7 & 1.6 & 120 \\ A8 & 1.6 & 150 \\ \hline \end{tabular} The circumferential position refers to the angle between the hydrophone and the positive Y axis.  
\end{table}
Table 1: Experimental hydrophone layout.  
Figure 3: Cross-sectional velocity distribution of the computational model.  
Figure 2: Pressure coefficient distribution of the computational model.  
When the frequency was less than 1000 Hz, the simulation results were slightly smaller than the experimental values, with an error of about 8%, and the variation trend was consistent with the experiment. When the frequency was greater than 1000 Hz, the simulation results were basically the same as the experimental results with the same trend. This also validated the accuracy of the numerical simulations in this study, confirming the viability of using this method for flow field calculations.
# On Alpha-Expansion-Based Graph-Cut Optimization for Decoder-Side Depth Estimation
## 3 The Proposed Method
### Neighboring Segments Label Examination  
One of the main issues observed in depth maps, which heavily influences the perceived quality of final virtual view synthesis, is the displacement of the object boundaries [5]. This issue is addressed in IVDE by using superpixels [15] in graph-cut optimization instead of pixels. Superpixels tend to follow the border of objects in encoder-side depth estimation [21].  
Nevertheless, video compression can decrease their sharpness in decoder-side schemes, making it harder to estimate the proper shape of objects. Depth errors produced at this stage can propagate throughout the entire sequence, e.g., due to temporal consistency improvements based on utilizing depth values from previous frames [14]. It significantly reduces the final quality of virtual views presented to a viewer.  
Let us assume that as a consequence of a too-large value of \(n\) in the first two coarse graph-cut cycles, a segment on the edge of the object was incorrectly labeled as having the depth of the foreground. If the unmodified hierarchical alpha-expansion is used, the error will be hard to correct in the optimization cycles, as for this segment, only the closest values of depth will be examined, so the values that represent background will not be proposed. On the other hand, there is a high probability that, among the neighboring segments, we can find depth values closer to correct one. Therefore, we propose to also examine depth values from the neighboring segments in graph-cut refinement cycles thusly:  
\[P_{on}\left(\overline{d}_{p}\right)=\Big{\{}r:\left|\overline{d}_{p}-r\right|< n\bigwedge r=\overline{d}_{q}\Big{\}}. \tag{6}\]  
At the expense of slight increase in the number of depth values to be examined, the proposal can be used to remove some of incorrectly estimated depth values resulting from, among other things, object boundary displacements. The basic idea of using the depth of neighboring segments has already been researched and found to be useful in depth refinement [22], as it was proven to highly increase the final quality of depth maps. In our proposal, we incorporate this idea directly into the optimization itself. Such an approach removes the unnecessary computational overhead required to run additional refinement after the depth estimation. Reducing this overhead aligns with the requirements of immersive video coding based on decoder-side depth estimation.
## 5 Discussion
### Limitations  
While our study results reveal valuable findings, it is necessary to recognize its limitations. First, the proposed model was tested by using the data collected from social media users in Taiwan. Further studies should examine our findings in different countries, to test the generalizability of this study. Second, there are some factors, such as reciprocity (receiving and giving equal amounts of support) and mutual interdependence [16] that may affect social-support activities, which, in turn, affect users' perceptions of well-being. Further studies should be conducted to explore the antecedents of subjective well-being by simultaneously integrating various standpoints (e.g., equity theory and social exchange theory). Future studies should also employ different aspects of social capital (e.g., structural capital and cognitive capital), to identify the antecedents of online support activities. Finally, while usingexperience to test the influences of different types of social-support activities on subjective well-being may advance our understanding about the change in users' behaviors and beliefs as online social contacts increase over time, a longitudinal investigation is needed to examine how social networks develop over time and how users deal with stressful events by using social media [25]. Further studies should also investigate the effect of offline relationships on the link between social-support activities and subjective well-being, to examine whether offline relationships impact online support activities.
## 2 Materials and Methods  
### Materials and Reagents  
Carboxylated MNPs (180 nm) were purchased from Allrun Nano Science & Technology Co., Ltd. (Shanghai, China). Brain heart infusion (BHI) broth and Luria - Bertani (LB) broth were bought from Hopebio Co., Ltd. (Qingdao, China). Ethyl(dimethylaminopropyl) carbodiimide (EDC) and N-Hydroxysulfosuccinimide sodium salt (NHSS) obtained from
## 1 Introduction  
One of the main questions a designer needs to answer while developing a new system, being it a program, a protocol, or some hardware, is: _how can I be sure my system works as expected?_ There are several ways to answer this question.  
* One can test the system, by feeding it with several inputs and looking at the correctness of the generated output. However, as [PERSON] said in his Turing Award lecture [1],  
\"program testing can be a very effective way to show the presence of bugs, but is hopelessly inadequate for showing their absence.\"  
In fact, if the testing inputs do not trigger the bug, it remains undetected, ready to cause damages once the system is deployed to production.  
One can manually prove that the system is correct by applying the techniques learned in programming and cryptography courses, such as by using Hoare logic/triples to prove properties of programs or by showing that the cryptographic protocol is provably secure. This approach provides the desired guarantees, but it is tedious, error prone, and reasonably applicable only to very small systems.  
One can apply one of the several techniques developed by researches specifically for this purpose, such as model checking, abstract interpretation, and high-order theorem proving. These techniques can be usually applied automatically to the system and are able to scale to large systems.  
This latter approach provides the positive answer to our initial question.
## 3 MAC Protocols for CRBANs
### Dynamic Channel Adjustable Asynchronous Cognitive Radio MAC Protocol  
Because of the fast channel switching capability of asynchronous MAC, the dynamic channel adjustable asynchronous cognitive radio MAC (DCAA-MAC) protocol [16] provides low latency, energy efficiency, configurability and no need for synchronization in wireless medical body area networks.  
At initialization, each node scans and selects a channel with the best conditions (e.g., low signal-to-noise ratio (SNR), least frequently used, _etc._). Each node in the network goes to sleep and wakes up periodically and independently. A node first sends a preamble; then, after receiving the acknowledgment (ACK) message from the destination node, it sends the data packets. On the receiver side, the receiver detects the preamble and remains woken up in order to receive the data. Once transmission is complete, both the sender and receiver nodes go to sleep mode. If the node detects interference (appearance of noise or PUs) on its channel, it switches to another available channel for effective communication.  
Multiple channels allow the maintenance of effective communication even upon the appearance of PUs. Nodes detect signals with greater frequency than the threshold during clear channel assessment (CCA) with a fast sensing period on the current channel and listen to signals to find the address of the destination periodically. If the address of the destination matches its own address, the node sends the ACK message; otherwise, it goes to sleep mode. If the address of the destination is not decoded, it determines that there is interference or that PUs have appeared and switches to another channel for PU protection and QoS provision. The node switches to a control channel and listens to find another node that is broadcasting a channel switching preamble (CSPreamble). After receiving the preamble, the node transmits the channel switching ACK (CSACK) message to the source node and switches to the channel.  
Spectrum sensing is one of the major requirements of cognitive radio. Energy detection and feature detection are the most commonly used techniques for spectrum sensing in the CR environment. In feature detection, the presence of primary users is determined by extracting specific features. The feature detection technique is most effective for CR networks, but requires significantly long sensing times and is computationally complex. Energy detection is the optimal solution to sense the presence/absence of PUs. In DCAA-MAC, channel switching is performed on the basis of energy detection. The fast channel switching mechanism provides QoS for medical body area sensor networks.  
In DCAA-MAC, PU protection and QoS provision are considered, that is, a node switches to another channel if the address of the destination is not decoded, by assuming that PUs have appeared. Channel switching is performed based on energy detection to provide QoS. Each node goes to sleep mode and wakes up periodically and independently of others. DCAA-MAC has low energy consumption and low latency and allows coexistence with simultaneously operating and independent networks.
## 4 Analysis and Comparison of the Model Performance
### Analysis of the Prediction Model  
All experiments were conducted via the toolbox Keras Sklearn or Pytorch in a computer equipped with Intel Xeon E5-2696v4 CPU at 2.20 GHz, 128 GB RAM, and NVIDIA GeForce RTX 3060 Ti GPU. The change in loss functions during training is shown in Figure 10. It is evident that with an increase in the number of training iterations, the loss function gradually decreases. During the initial stages of training, the loss on the training set is lower than that on the test set. However, as the number of training iterations increases, the difference between the two gradually diminishes.  
The results of the model on the training and test sets are presented in Figure 11. The \(R^{2}\) values for the training and test sets are 0.927 and 0.891, respectively. The model performs better on the training set compared to the test set. The distribution of data within the deformation range does not exhibit a uniform pattern across intervals. For the same measured deformation value at a given site, the predicted results show a fluctuation range. This phenomenon may be attributed to certain construction-related factors that were not incorporated into the model training, such as face mapping information, the quality of blasting excavation, and the quality of initial support. These factors, to some extent, determine the final deformation value.
## 3 Results
### Tests Using Verified Numerical Model  
Using the numerical model, verified according to the method described above, tests were conducted under a load of the same order as used in other FEM studies [24], amounting to 30 N, applied at point A and consistent with the physiological loading. The distribution of von Mises stress was obtained, as illustrated in Figure 7. As well as sites with a relatively small area, at which the supports and force of loading were applied, the stresses in the majority of the orthosis were below 4.4 MPa (Figure 7) and fit in the linear region of the stress-strain curve of polypropylene [25], confirming the assumptions made earlier regarding the orthosis model.  
It can be seen from Figure 7 that it is hard to identify the working scheme of the orthosis. To more clearly present the method by which the orthosis's mechanical structure transmits loads, Figure 8 presents views of the front part of the brace that are shown to illustrate the principal stress vectors on the inside and outside of the front face of the orthosis.  
Figure 8: The principal stress distribution \(\sigma_{1}\) (red) together with stress \(\sigma_{3}\) (blue) in the region of the  
Figure 7: View of orthosis with values of von Mises stress: (**a**) left side—front from exterior, (**b**) right side—front from interior.  
orthosis's front part indicated in the illustration: (**a**) front part of the brace, (**b**) distribution of stresses on the outer face of selected region, (**c**) distribution of stresses in the cross-section of the front part of the brace; the inner wall layer is seen when \"removing\" some of the material of the outer wall.  
Figure 9 shows the vectors of principal stresses solely for tensile stresses \(\sigma_{1}\). In this illustration, the maximum principal stress trajectories \(\sigma_{1}\) are plotted. Lines were laid parallel to the stress vector directions, starting and ending at the points of application of the applied external forces. The trajectories formed splines with mild changes in direction, tangent to \(\sigma_{1}\) vectors.