esa-sceva/satcom-chunk-collection huggingface dataset:
# An Adaptive Control Scheme for Data-Driven Traffic Migration Engineering on 5G Network
## 4 Validation and Result Analysis
### The Residual Energy Resources and their Variance  
The simulation results in the previous section show that the network performance is optimal when SNR is 10 dm and the number of OVSs in the network is 250. Then, the overall energy resource consumption of the virtual network is simulated under the optimal state.  
Figure 9 shows the variation of the average residual energy resources of the network as the running time increases. The network will stop when the OVS is damaged or energy resources depletion occurs in the virtual layer. We can see from Figure 9 that the proposed TME algorithm has the most residual energy resources out of the HPR, TE and A* Search algorithms under the same run time. Figure 10 presents the variance of energy consumption as the run time increases. The TME algorithm has the smallest variance, which reflects the balance of energy resource consumption and indicates that the data traffic load of OVSs in the virtual network layer is relatively balanced. This result verifies the effectiveness of the TME algorithm in traffic prediction migration from the perspective of traffic balance, and the reasons are described in detail in Section 4.  
Figure 8: KPIs with 250 OVSs under different SNRs. (**a**) KPI (Run time). (**b**) KPI (Throughput). (**c**) KPI (Average delay).
## 2 Materials and Methods
### Digestion of Ma-B Flocs for Biogas Production
#### 2.4.2 Measurement of Biomethane Production  
The measurement of the volume of biogas produced, as well as its composition, was performed every day to monitor the evolution of biogas production. The biogas volume production was measured by the acidic water displacement method (a chloridic acid solution at pH \(\approx\) 4.0 prepared from a 37% concentrated one) in a test tube every 24 h. Indeed, the use of acidic water avoids the loss of CO\({}_{2}\) due to its high solubility in water. The composition of the biogas (CH\({}_{4}\) and CO\({}_{2}\)) was analyzed using a Varian 430-GC gas chromatograph, USA. The detector of this chromatography is a thermal conductivity detector (TCD), the column is the capillary column Varian@GC CP-Carboplot 25 m (L) \(\times\) 0.53 mm (inner diameter) \(\times\) 25 \(\upmu\)m (film thickness), coated with a layer of carbon particles of 0.2-1 \(\upmu\)m in size. Samples of 100 \(\upmu\)L of biogas gas phase were extracted from the bottles by syringe and injected into the gas chromatograph. The pressure in the injector was 15 psi, the carrier gas was argon (\(\geq\)99.999% of purity) at a flow rate of 6 mL.min\({}^{-1}\), and the temperature of the detector was set at 180 \({}^{\circ}\)C. Each analysis lasted 7.50 min, and the temperature increase program was as follows: 60 \({}^{\circ}\)C from 0 to 2 min, rise to 110 \({}^{\circ}\)C between 2 and 3 min at a rate of 20 \({}^{\circ}\)C min\({}^{-1}\), then 110 \({}^{\circ}\)C from 3 min to 7.5 min. Calibration was carried out beforehand with the following standards: Nitrogen, Oxygen, Methane, Carbon dioxide, and Sulfuric acid. All standards were from MESSER (Bad Soden am Taunus, Germany) and had a purity of 99.999%.  
Methane production over the incubation period was measured in terms of cumulative daily production, given by the quotient between the volume of gas produced in milliliters and grams of VS of the fed substrate.
# A Brief Review of Multipath TCP for Vehicular Networks
## 8 Conclusions  
MPTCP has attracted tremendous attention from both academia and industries. A discussion on the use of MPTCP in vehicular environments has become an urgent issue. In this paper, we discussed the existing studies, technical challenges, and open problems regarding MPTCP for vehicular networks. We first conducted a survey on MPTCP and then compared MPTCP with SPTCP. Then the related studies on MPTCP were discussed. After that, we reviewed the existing studies on the use of MPTCP for vehicular networks with detailed discussions on the benefits that MPTCP could bring. We also give a brief review on multipath routing technology for vehicular networks. Finally, we pointed out future research directions on MPTCP for vehicular networks.  
Conceptualization, L.C., and C.W.; Methodology, L.C.; Investigation, L.C., W.B. and C.W.; Writing-Original Draft Preparation, L.C.,and C.W.; Writing-Review & Editing, L.C.,T.Y., W.B. and C.W.; Supervision, Y.J.; Project Administration, C.W.; Funding Acquisition, W.B. and C.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported in part by ROIS NII Open Collaborative Research 21S0601, in part by the National Natural Science Foundation Program of China under grant number 62062031, in part by the Inner Mongolia Natural Science Foundation grant number 2019 MS06035 and Inner Mongolia Science and Technology Major Project, China, and in part by JSPS KAKENHI grant numbers 18 KK0279 and 21H03424, Japan.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analysis, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 3 Analytical Models of Lattice Structures Built with Polygon Hollow Shapes
### Analytical Models for Predicting the Behaviour of Selected Polygon Hollow Structures
#### 3.2.1 The Gibson-Ashby Model for a Regular Hexagonal Hollow Cell  
Figure 5 shows a unit planar hexagonal hollow cell with two mutually orthogonal planar in-plane directions of loading indicated. Equation (5) represents the analytical model describing the behaviour of a planar hexagonal polygon structure when subjected to loads in the in-plane directions [18; 20].  
The stiffness of the hexagonal hollow cell is seen from Equation (5) to be related directly to the third power of the ratio (\(t/L\)), which from Equation (1) implies a direct relationship to the third power of the relative density of the cell [18].  
\[E_{y}^{*}=E_{x}^{*}=\frac{4}{\sqrt{3}}\,E_{s}\bigg{(}\frac{t}{L}\bigg{)}^{3} \tag{5}\]  
The symbols \(E^{*}\) and \(E_{s}\) in this equation represent the elastic modulus of the polygon hollow structure and that of the solid material of the polygon walls, respectively. Subscripts \(y\) and \(x\) in the parameter \(E^{*}\) represent the \(y\)- and \(x\)-directions, respectively. It is garnered  
Figure 4: A typical hexagonal hollow structure showing all three mutually orthogonal directions in which the behaviour of polygonal hollow structures is analysed.  
from Equation (5) that the stiffness of the structure with respect to its elastic modulus grows with an exponential of order 3 of the ratio \(t/L\) for a hexagonal hollow structure loaded in the two planar in-plane directions, individually.
## 3 Theoretical Part
### XR Technologies in Education
#### 3.2.3 Mixed Reality (MR)  
MR (Mixed Reality) merges elements of both VR and AR to create immersive environments where the digital and physical worlds coexist and interact in real time. By allowing users to manipulate digital objects within the real world, MR opens new possibilities for education, especially in fields such as engineering, medicine, and design. This combination of real and virtual elements enables students to engage with concepts in a highly interactive manner, enriching their learning experience and making it more dynamic and practical [23].  
In the field of education, MR allows students to explore and manipulate complex systems and conceptual models in ways that traditional methods simply cannot. For instance, in medicine, MR can be used to simulate surgical procedures, giving students the opportunity to practice and refine their skills in a safe and controlled environment before performing real surgeries on patients. This offers a highly effective way of training medical professionals without the risks associated with practicing on actual patients [24]. In engineering, MR enables students to design, construct, and test prototypes in real spaces, drastically reducing development time and production costs. By virtually interacting with their designs, students can gain a better understanding of how their models work in the real world, providing valuable hands-on experience without the need for physical materials [25].  
Despite the vast potential of MR in education, there are challenges to its widespread implementation. One of the primary obstacles is the complexity involved in integrating MR technology into the curriculum, which requires highly specialized equipment and expertise. Additionally, more research is needed to understand the full impact of MR on student learning and cognitive development. Although further studies are essential, MR holds immense promise in transforming education by enabling students to learn through direct experience and real-time interaction in ways that were previously unimaginable [26].
# CD44 Depletion in Glioblastoma Cells Suppresses Growth and Stemness and Induces Senescence
## 2 Materials and Methods
### Ki67 Immunofluorescence Microscopy  
Immunofluorescence microscopy was carried out in parental or CD44 KO U251 MG cells. After seeding, cells were starved for 24 h and subsequently cultured in RPMI medium, including 10% FBS, for 2 days. Then, cells were fixed for 10 min in 3.7% (_w/v_) formaldehyde in PBS, followed by permeabilization with 0.1% Triton X-100 in PBS for 10 min, blocked for 60 min with IgG-free 1% BSA in PBS, and incubated overnight at 4 \({}^{\circ}\)C with 0.4 \(\upmu\)g/mL primary antibody against Ki67 (Santa Cruz Biotechnology, Dallas, USA, sc-23900) diluted in 1% BSA in PBS. Next day, coverslips were incubated with anti-mouse Alexa Fluor 546-conjugated secondary antibody (Invitrogen, Thermo Fisher Scientific) at a concentration of 1:1000 in 1% BSA in PBS for 1 h at room temperature in the dark. Extensive washes were performed between the aforementioned steps. Subsequently, coverslips were set onto glass slides and mounted by using 10 \(\upmu\)L of VectaShield HardSet mounting medium containing 4\({}^{\prime}\),6\({}^{\prime}\)-di-amidino-2-phenylindole (DAPI; Vector Laboratories, Burlingame, CA, USA) for nuclear visualization. A Zeiss Axioplan 2 fluorescence microscope was used with a Zeiss 20x objective lens. Images were acquired with a Hamamatsu C4742-95 CCD digital camera and the acquisition software QED Camera Plugin version 1.1.6 (QED Imaging Inc., Rockville, MD, USA) and Volocity 1 (PerkinElmer Life Sciences, Waltham, MA, USA). At least six random micrographs were retrieved per condition at the same exposure time and were quantified as a per cent of positive Ki67 nuclei using ImageJ software (v1.8.0_172).
# Modelling and Stability Analysis of Articulated Vehicles
## 3 Stability Analysis Model for Articulated Vehicles  
On the basis of [PERSON] stability analysis theory, the eigenvalue method is used to analyse the relationship between vehicle parameters and stability. For the analysis, the kinetic model developed in the previous section is linearised. The forward velocity is assumed to be constant. In the event of snaking in articulated vehicles, the longitudinal force is relatively small compared with the lateral force. Hence, the longitudinal force can be disregarded. Moreover, the hydraulic steering system can be equivalent to a torsion spring, as shown in Figure 5. It should be noted that we have enlarged the articulated angle in the figure to facilitate interpretation, i.e., the actual \(\beta\) is not as large as shown in the figure.  
The vehicle dynamics equation can be expressed as follows:  
\[\begin{cases}m_{1}\big{(}\dot{v}_{y1}+v_{x1}\omega_{1}\big{)}+m_{2}\big{(} \dot{v}_{y2}+v_{x2}\omega_{2}\big{)}\cos\beta=F_{y1}+F_{y2}+\big{(}F_{y3}+F_{y4 }\big{)}\cos\beta\\ \Big{(}I_{1}+m_{1}L_{2}^{2}\Big{)}\dot{\omega}_{1}=M+\big{(}F_{y1}+F_{y2}\big{)} (L_{1}+L_{2})\\ \Big{(}I_{2}+m_{2}L_{4}^{2}\Big{)}\dot{\omega}_{2}=-M-\big{(}F_{y3}+F_{y4}\big{)} (L_{3}+L_{4})\\ \dot{\beta}=\omega_{1}-\omega_{2}\end{cases} \tag{21}\]  
Since \(\beta\) is considerably small that it is acceptable to assume \(\cos\beta\approx 1\), then the first equation in Equation (21) can be simplified to  
\[m_{1}\big{(}\dot{v}_{y1}+v_{x1}\omega_{1}\big{)}+m_{2}\big{(}\dot{v}_{y2}+v_{x 2}\omega_{2}\big{)}=F_{y1}+F_{y2}+F_{y3}+F_{y4}\]  
The front and rear vehicle kinematics relationship is as follows:  
\[\begin{cases}v_{y1,2}=v_{y1}+L_{1}\omega_{1}\\ v_{y1,3}=v_{y2}-L_{3}\omega_{2}=v_{y1}-(L_{2}+L_{3}+L_{4})\omega_{1}+(L_{3}+L_ {4})\dot{\beta}+v_{x1}\dot{\beta}\end{cases} \tag{22}\]  
Tire forces can be expressed as follows:  
\[\big{\{}F_{y}=C_{x}\alpha \tag{23}\]  
Figure 5: Simplified model of vehicle dynamics.
## 2 Related Work
### Cooperative vs. Collaborative  
Cooperative communication solves the problem of communication interruption on multiple hops by introducing multiple intermediate relay nodes. However, it increases the complexity of the routing. Moreover, some relay nodes have to forward data from a neighbor just for cooperation. This function may not affect global communication [53]. Unlike cooperative communication, collaborative communication takes advantage of spatial diversity to produce a higher gain in received power. In addition, it combines the power of all the collaborative nodes in a way that reduces the transmitted power of each node. The advantage here is twofold: significant improvement in power consumption and gain in system capacity.  
Collaborative networks maintain energy and, in turn, transmission power, where the noise effect is considerably reduced thanks to the characteristics that the collaborative network presents to have efficiency in the network [50].  
Cooperative and collaborative wireless sensor networks are a type of wireless sensor network in which multiple sensor nodes work together to gather, process and transmit data [54]. These networks are characterized by their ability to share resources and collaborate in order to improve overall performance and efficiency. The nodes in a cooperative wireless sensor network can communicate with each other directly or through a central controller and they can share information about the environment, their own status and their energy levels. This allows for better coverage and accuracy of data collection, as well as improved fault tolerance and robustness. Cooperative wireless sensor networks areoften used in applications such as environmental monitoring, industrial automation, smart cities, healthcare, disaster management and surveillance.  
Within the WSN designs, the RSSI (Received Signal Strength Indicator) distance measurement is one of the most used parameters because it requires less implementation complexity and energy consumption. The RSSI distance measurement principle describes the relationship between the transmitted and received power of the wireless signal and the distance between nodes [55]. Another parameter that characterizes the quality of the link is the LQI (Link Quality Indicator) and the reception of Packet Reception Rate packets can be associated with both RSSI and LQI [56] under different power levels and in different propagation environments, managing to describe the behavior of a WSN in a given environment [57].  
This work aims to analyze two network management approaches used in wireless sensor networks: cooperative and collaborative. Each scheme provides advantages and disadvantages for the network and the nodes. The comparison is studied in human health monitoring applications in homes. We analyze scenarios such as detecting minor accidents and regular monitoring of people's main vital signs and sleep behavior. These approaches are measured with network performance metrics to discern their energy impact in the home and whether this type of health care network does not represent a high cost for people who do not need it.
# Improvement of Perception Layer Routing Protocol with Static Nodes in IoT-Based Microgrids
## 3 System Models
### Energy Model under Static Nodes  
In this paper, in consideration of the application scenarios of the routing protocol of the perception layer of the Internet of Things, the wireless communication energy consumption model as shown in Figure 3 was utilized:  
In this paper, a radio model was selected as the energy model: \(E=kd^{n}\), where \(E\) is the energy consumed, \(k\) is the number of bits of the data sent, \(d\) is the communication distance, and \(n\) is the exponent. The value of \(n\) is determined by the model fitting of the network. If the network model fits the multipath fading model in the channel model, then \(n=4\); otherwise, the network model fits the free space model in the channel model, and \(n=2\).  
The energy \(E_{TX}(m,d)\) consumed by a node to send information contains two parts: \(E_{AX}(m,d)\) and \(E_{TPX}(m)\).  
\[\begin{split} E_{TX}(m,d)&=E_{AX}(m,d)+E_{TPX}(m) \\ E_{AX}(m,d)&=m\epsilon d^{2}\\ E_{TPX}(m)&=mE_{PX}\\ E_{TX}(m,d)&=m\epsilon d^{2}+mE_{PX},\end{split} \tag{1}\]  
where \(E_{AX}\) refers to the energy consumption of the information sent by the node in the path; it can be seen that \(E_{AX}\) is proportional to the square of the distance \(d,m\) is the size of the data packet sent by the node, the unit is bit, \(\mathcal{E}\) is the energy consumption coefficient, and \(E_{PX}\) represents the energy consumption of a unit of data in the node hardware, \(E_{TPX}(m)\) refers to the energy consumption of data of \(m\) bits in the node hardware.  
The energy consumption \(E_{RX}(m)\) of receiving \(m\) bits of data is:  
\[E_{RX}(m)=mE_{RX}, \tag{2}\]  
where \(E_{RX}\) is the energy consumption of the node receiving a unit of data.
# Temperature State Awareness-Based Energy-Saving Routing Protocol for Wireless Body Area Network
## 4 TSAER: Protocol Description
### Route Benefit Description  
#### 4.2.1 Performance Parameter Selection  
Data reception and transmission between sensor nodes will cause the node temperature to rise, and the temperature rise not only affects the data transmission reliability but also causes damage to human tissues in severe cases. In order to reduce the problem of node temperature rise caused by frequent data transmission between sensor nodes, this paper proposes a method to adjust the probability of node data reception by sensing the temperature state of the node. The node dynamically adjusts the probability of receiving data according to its own temperature value, so that the data are more evenly distributed in the network, which is beneficial to delay the rise in the temperature of the node.  
The node temperature state is divided into three intervals, namely the normal temperature interval, the heating interval, and the high-temperature interval. t is the current temperature value, as shown in Figure 2 as the temperature state interval.  
Here, \(T_{\mathrm{in}}\) is the initial value of the temperature, \(T_{1}\) is the critical value of the heating range, and \(T_{\mathrm{th}}\) is the critical value of the high-temperature range.  
A node can adjust its reception probability according to different types of data and temperature state intervals. P is the data reception probability of the node, the probability of receiving urgent data is P1, and the probability of receiving normal data is P2. The detailed description is as follows.  
When the temperature of the node is in the normal temperature interval, the node can forward and receive any type of data; therefore, P1 = P2 = 1.  
When the temperature state of the node is in the heating interval, there are different receiving probability calculation methods for two different types of data to not affect the data transmission. Emergency data are still forwarded with probability 1, while normal data are forwarded with varying probability.  
In the heating interval, in order to achieve a more obvious effect of suppressing the temperature rise in the node, the data reception probability P2 of the node at this time should be reduced as the temperature of the node rises. Therefore, the change trend of P2 should be a decreasing concave function. Consider the simple quadratic function f(t) as the change function describing P2; the maximum value is at T\({}_{1}\), f(T\({}_{1}\)) = 1, and the minimum value is at T\({}_{th}\), f(T\({}_{th}\)) = 0. Its formula is expressed as follows.  
\[\text{P2}=\text{f(t)}=(\text{t}^{2}-2\text{ T}_{th}\text{t}+\text{ T}_{th}^{2})/(\text{T}_{1}-\text{T}_{th})^{2}. \tag{9}\]  
When the temperature of the node is in the high-temperature interval, it is easy to cause damage to human tissues. At this time, the node no longer receives any data. Therefore, P1 = P2 = 0.  
Sink nodes always receive data with probability 1, and the receiving probabilities of nodes in different temperature intervals are shown in Table 1.
## 3 Shadow Encoding Scheme for Power Spectrum Density
### Self-Synchronization  
The sender and receiver should follow the same clock interval to make the transmission useful. If the sender's clock is slower or faster than the receiver's, the receiver might not be able to receive the bits. The pattern of bits would change, and 0 s may be received as 1 s at the receiver's end or vice versa [23].
## 2 Performance Limitations of LS-MZS
### Optical Crosstalk  
Optical crosstalk occurs when a portion of the signal power leaks into an unwanted output, which could be generated in both MZI and waveguide crossing because of their design. We model these leakages by a factor \(m\) in MZI and \(x\) in waveguide crossing (see Figure 1). The values of \(m\) and \(x\) are usually less than 0.01. Crosstalk ratio is defined as the difference, in logarithmic scale, of the output power on the destructive port to the one on the constructive port. The crosstalk issue in MZI is largely due to phase errors or power imbalance inside the arms of the Mach-Zehnder. The phase error can be corrected by introducing heaters on both sides [11], whereas the power imbalance is produced by intrinsic absorptions in waveguide when using electro-optic phase-shifting diode junctions [12] and by the imbalance in 3-dB couplers. Optical crosstalk ratio in MZI and waveguide crossing, named \(Xtalk_{MZI}\) and \(Xtalk_{X}\), can be written as follows:  
\[Xtalk_{MZI}=10 log_{10}\bigg{(}\frac{mP_{in}l_{MZI}}{(1-m)P_{in}l_{MZI}}\bigg{)} \approx 10 log_{10}(m) \tag{3}\]  
\[Xtalk_{X}=10 log_{10}\bigg{(}\frac{xP_{in}l_{X}}{(1-x)P_{in}l_{X}}\bigg{)} \approx 10 log_{10}(x) \tag{4}\]  
In the rest of the paper, the unwanted power, written in blue in Figure 1, will be called crosstalk power. It is worth mentioning that there are generally two types of crosstalk, leakage from other signal sources and multi-path interference of the original signal. In this work, they will be treated as purely additive crosstalk.
# Adaptive Hierarchical Density-Based Spatial Clustering Algorithm for Streaming Applications
## 3 Methodology
### Adaptive HDBSCAN Implementation  
This section outlines the process of putting the suggested Adaptive HDBSCAN algorithm into practice. The canonical HDBSCAN can be broken down into five basic steps, which are the pairwise distance calculation, Euclidean to \(\lambda\)-space transformation, minimum spanning tree building, cluster hierarchy building, and the condensation of the cluster hierarchies and excess of mass calculation. For Adaptive HDBSCAN, the minimum spanning tree is built adaptively based on the number of points being clustered. For smaller numbers of points, [PERSON]'s algorithm [19] is used, whereas [PERSON]'s algorithm [20] is used for a larger number of data points.  
Figure 1 below shows the pseudocode of the Adaptive HDBSCAN algorithm. The dotted area demarcates the contribution of this paper. Canonically, HDBSCAN runs on [PERSON]'s algorithm regardless of the number of points. The contribution of this paper is the usage of [PERSON]'s algorithm as a method to speed up computation when the number of points is low.  
Figure 1: Workflow for implementing Adaptive HDBSCAN.