esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancements and Challenges in CoAP--A Survey
## 4 Enhancements in CoAP
### Congestion Control Mechanisms in CoAP
#### 4.1.1 Default CoAP  
Congestion Control in Default CoAP: The default CoAP employs a BEB method to cater the congestion control in the network [2]. In case of reliable transmission, a CON message is transmitted from a client node to a server node. If the message is not successfully transmitted in the first attempt, a retransmission is carried out. The CoAP choses a random value of RTO for the first transmission between 2-3 s. If the first retransmission is failed, the BEB doubles the RTO in order to avoid congestion. Hence the new RTO \((RTO_{new})\) value is twice the previous RTO \((RTO_{prev})\) value according to Equation (1).  
\[RTO_{new}=2*RTO_{prev} \tag{1}\]  
This method of congestion control is not very efficient as it causes long idle delays in the network and does not consider the dynamic network conditions. To make it efficient, an advance congestion control mechanism named as Congestion Control/Advance (CoCoA) was developed.
# Exploiting Data Analytics and Deep Learning Systems to Support Pavement Maintenance Decisions
## 1 Introduction
### Relationship of Factors and Features that Contribute to Pavement Maintenance Interventions  
The selection of potential sites for pavement maintenance is usually dependent on the pavement age and condition, frequency of maintenance interventions, traffic rates, accessibility and safety [18]. The preference between these sites can then be decided based on performance and distress surveys. In practice, there are a multitude of different road performance indicators that can be employed during the decision-making processes [3]. The use of these indicators is generally dependent on the road agency, their needs and  
Figure 1: How data are utilized in the PMS.  
the available data such as distress and surface texture [9]. Many PMS's will utilize top-down approaches wherein funding levels required to meet long-term performance goals are estimated but in these scenarios, further steps are still required to prioritize specific segments for maintenance interventions [19]. However, it has been found that issues have been identified within the methodologies utilized for the PMS in effectively considering multi-attribute condition information for modelling processes and with uncertainties linked primarily to pavement deterioration despite the establishment of the importance of environmental and economic conditions [20].  
Performance indicators are useful and important because they help to efficiently allocate resources amongst options based on the availability of resources [21]. It has been suggested that frameworks for performance indicators for road assets should be divided across two levels--general performance indicators which can provide an overview and can be ascertained from public statistics and detailed objective indicators of service quality and institutional effectiveness [21]. These overview characteristics are important as they can provide a summarized view of the network's situation. Concerning the overview performance indicators, there are typical feature groups that should be assessed to ascertain the summary of the road asset [21; 22] which include:  
1. Network parameters--geometric configuration and dimensions of roads
2. Asset values
3. Road users--types of users and trip purposes
4. Demography and economic circumstances--population and land area
5. The density of network and roads
6. Use of roads--travel by class
7. Safety--accidents and fatalities  
With regard to these indicators, it is possible to retrieve a lot of this data from local sources such as censuses, therefore making them a viable resource outlet for obtaining data for modelling scenarios for small or under-resourced cities. The data sourcing can be complicated based on specific circumstances but it is a necessary step to adequately understand particular situations and predict future trends.
# A Scheduling Scheme for Improving the Performance and Security of MU-MIMO Systems
###### Abstract
The proposed scheduling scheme is presented in Section 4, where its computational complexity is also analyzed. In addition, this section introduces some aspects related to other proposals from the literature. Then, Section 5 presents representative numerical results and discussions, where the performance of the proposed scheduling algorithm is also compared with the performance of other proposals from previous works. Finally, the main conclusions of this work are summarized in Section 6. In the following \(x\), \(\mathbf{x}\) and \(\mathbf{X}\) represent scalar, vector and matrix, respectively. Further, \((\cdot)^{\dagger}\) denotes pseudo-inverse, \((\cdot)^{-1}\) denotes matrix inversion, \((\cdot)^{H}\) represents conjugate-transpose, \(\mathrm{E}[\cdot]\) is the expectation operator, \(\mathrm{Var}[\cdot]\) is the variance operator, the function \(\max[\mathbf{x}]\) obtains the maximum value from \(\mathbf{x}\), \(\lceil\cdot\rceil\) denotes ceil operation, \(f_{x}(x)\) is the probability density function (PDF) of the random variable \(x\), \(\|\mathbf{x}\|_{F}\) is the Frobenius norm of \(\mathbf{x}\), \([\mathbf{X}]_{k,\ell}\) indicates the element in the \(k\)-th row and \(\ell\)-th column of \(\mathbf{X}\), and \(\mathrm{j}=\sqrt{-1}\) is the imaginary unit.
## 4 Combining ground data and VIs to scale biophysical processes over large areas
### What Flux Towers Measure and Sources of Error in Flux Towers
Net carbon fluxes are due to net uptake by plants in photosynthesis during the day, release of carbon dioxide by plants at night, and by soil respiration and litter decomposition. Net moisture fluxesare due to bare soil evaporation plus plant transpiration minus precipitation and condensation. Top-of canopy flux towers can be combined with under-canopy measurements to calculate soil and understory respiration [55] and plant transpiration can be measured with sap flow sensors [56], which measure the flow of water through plant stems. Natural isotopes of oxygen and hydrogen can also be used to distinguish between evaporation and transpiration in ET [57]. Hence, the individual components of carbon and moisture fluxes can be measured at tower stations. Flux tower and sap flow data can also test cause-and-effect relationships between meteorological and soil variables and plant performance that can inform ecophysiological studies. For example, it is possible to invert the Penman-Monteith equation for ET, to obtain frequent estimates of stomatal conductance (or resistance) in response to stress factors under natural conditions over the course of hours, days, weeks, seasons and years [58]. An example using three of the plant species shown in Figures 1-4 is in Figure 5. Sap flow was measured in plants for seven days under unstressed conditions and correlated with micrometeorological data to produce a stomatal resistance model for each species [36]. Then the plants were subjected to salt stress and dry-down, and measured stomatal resistance was compared to modeled values over three subsequent days. All three species had similar patterns of transpiration and stomatal resistance under unstressed conditions, but the two mesic trees, cottonwood and willow, showed increased stomatal resistance in response to stress, whereas saltcedar, which is drought- and salt-tolerant, did not increase its stomatal resistance. The type of experiments illustrated in [36] and [58] were formerly only possible over short intervals on individual leaves enclosed in leaf chambers, but they now can be performed unobtrusively on whole plants [36] and mixed stands of plants [58].
## 5 Experiments
### Orientation Error for 6-DOF Tasks  
In all the experiments, 6-DOF tasks were performed. For a 6-DOF task (\(m=6\)), where the position and orientation of the end-effector are considered, the expression \(\mathbf{r_{d}}-\mathbf{r}\) (mentioned in Section 3) has a specific definition that depends on the orientation representation, i.e., \(\mathbf{\tilde{r}}=\mathbf{r_{d}}-\mathbf{r}\) does not hold for all orientation representations [1]. In this work, unit quaternions are used to describe the end-effector's orientation because of their efficiency and nonsingular representation for all orientations [1; 19; 20; 21].  
A unit quaternion \(Q=[w+xi+y\mathbf{j}+z\mathbf{k}]\) is represented in scalar-vector form by \(Q=\{s,\mathbf{v}\}\) with \(s\in\mathbb{R}\) and \(\mathbf{v}\in\mathbb{R}^{3}\), where \(s\) and \(v\) are called the scalar and vector elements of \(Q\), respectively. The desired and current pose are defined using unit quaternions for orientation as \(\mathbf{r_{d}}=\begin{bmatrix}\mathbf{P_{d}}&\mathbf{Q_{d}}\end{bmatrix}^{T}\) and \(\mathbf{r_{c}}=\begin{bmatrix}\mathbf{P_{c}}&\mathbf{Q_{c}}\end{bmatrix}^{T}\), where \(\mathbf{P_{d}}=\begin{bmatrix}x_{d},&y_{d},&z_{d}\end{bmatrix}\) and \(\mathbf{P_{c}}=\begin{bmatrix}x_{c},&y_{c},&z_{c}\end{bmatrix}\) are the desired position and current position, respectively, and \(Q_{d}=\{s_{d},\mathbf{v_{d}}\}\) and \(Q_{c}=\{s_{c},\mathbf{v_{c}}\}\) are the desired orientation and current orientation, respectively. The position error \(\mathbf{e_{p}}\) is defined as \(\mathbf{e_{p}}=\mathbf{P_{d}}-\mathbf{P_{c}}\). The orientation error can be described in terms of the quaternion \(\Delta Q=\{\Delta s,\mathbf{\Delta v}\}\), where [1]:  
\[\Delta s =s_{c}s_{d}-\mathbf{v_{d}^{T}}\mathbf{v_{c}}, \tag{31}\] \[\mathbf{\Delta v} =s_{c}\mathbf{v_{d}}-s_{d}\mathbf{v_{c}}-\mathbf{v_{d}}\times\mathbf{v_{c}}.\]  
If the desired orientation and current orientation are aligned, i.e., with zero orientation error, then \(\Delta Q=\{1,0\}\). Consequently, it would be sufficient to define the orientation error to be \(\mathbf{\Delta v}\). It is also important to follow a convention for the sign of the quaternion because \(Q=\{s,\mathbf{v}\}\) and \(-Q=\{-s,-\mathbf{v}\}\) represent the same orientation. A common convention is to keep the scalar quaternion element positive. Take this into account and the orientation error is defined as follows:  
\[\mathbf{e_{O}}=\begin{cases}\mathbf{\Delta v},&\text{if }\Delta s\geq 0\\ -\mathbf{\Delta v},&\text{if }\Delta s<0.\end{cases} \tag{32}\]  
Separating the position and orientation errors, the motion planning control law (6) is rewritten as  
\[\dot{\mathbf{r}}=\dot{\mathbf{r_{d}}}+\begin{bmatrix}K_{P}&0\\ 0&K_{O}\end{bmatrix}\begin{bmatrix}e_{P}\\ e_{O}\end{bmatrix}, \tag{33}\]  
where \(K_{P}\) and \(K_{O}\) are positive definite diagonal \(3\times 3\) matrices. Note that \(\mathbf{e_{O}}\) in (32) is not an angle difference but its size represents the size of the orientation error that, as shown in [1; 22], can achieve the convergence of the orientation error.
## 4 Simulation Setup and Experimental Results
### Improved LEACH Protocol Experimental Results
In Figure 9, the links between the number of hops that are required to find CH are depicted. The most used hop in each round is done by the LEACH protocol, even though the percentage difference is too small. As the network in terms of size increases, the number of hops will also increase. The results of the simulation and analysis reveal that the proposed protocol is capable of achieving a high level of security, outperforming other schemes that had been offered earlier, and improving the effectiveness and efficiency of transmitted data among sensors in a network. Figure 8: Overall time consumption per round for a 100-node network. Figure 7: Dead nodes vs. rounds for a 100-node network. Figure 10 presents a performance comparison between our proposed enhanced LEACH protocol (E-LEACH), the original LEACH (LEACH), and another version of the improved LEACH protocol (E-DEEC) proposed in reference [26; 40]. The simulation was conducted using the same node parameters presented in Table 5 In each case, 100 sensors were placed randomly in the field and the positions of the nodes were maintained throughout the experiment. The simulation was set up to run for 1500 rounds and the corresponding number of dead nodes were recorded after each round as depicted in Figure 10. It can be seen from the figure that in the original LEACH protocol, instances of dead nodes started to appear at around 600 rounds and all the nodes died at about 1100 rounds. Meanwhile, the E-DEEC of reference [26; 40] has improved on the original LEACH by prolonging the instance of the appearance of the first dead node to around 900 rounds, but eventually all nodes die at about the same time with the original LEACH. However, the proposed E-LEACH has improved on both the initial incidence of dead nodes (around 904 rounds) and after 1496 rounds there are still nodes that are alive which is an indication of better energy management and extends the lifespan of the network. Figure 10: Performance comparison of Enhanced LEACH with other versions. Figure 9: Overall hop count per round for a network of 100 nodes.
## 3 Efficiency and Accuracy of the Proposed Simulation Method
### Wind Tunnel Test Performed by [PERSON] and [PERSON]
#### 3.2.1 FDS Model to Simulate the Experiment  
The FDS model used to simulate the experiment can be easily constructed through the method introduced in Section 2.1.1 based on the FDSGenerator. The wind speed profile of the FDS model was made consistent with that used in the wind tunnel test (see Figure 12). The computational domain covered a volume of \(18\)\(H\times\)\(12\)\(H\times 6\)\(H\). The grid size of the domain under the height of \(2\)\(H\) m was set to \(0.025\)\(H\times 0.0025\)\(H\times 0.0025\)\(H\). The grid size of the domain between the heights of \(2\)\(H\) and \(4\)\(H\) was set to \(0.05\)\(H\), whereas that of the remaining domain was set to \(0.2\)\(H\). The total number of cells was \(2.771\times 10^{6}\). According to the experiment setup, the mass fraction of the tracer gas on the pollutant source at the center of the street bottom surface was taken as \(100\times 10^{-6}\). The constant pollutant release rate of the pollutant source was set to \(1.14\times 10^{-5}\) m\({}^{3}\)/s, given that the area of the vent was set to \(2.50\times 10^{-5}\) m\({}^{2}\) and the pollutant release velocity was \(0.456\) m/s. According to the FDS user guide [33], the \"PERIODIC\" boundary condition was adopted for the inlet, outlet, and lateral boundaries, while the \"GROUND\" and \"OPEN\" boundary conditions were used for the ground and top boundary, respectively. The total simulation time was set to \(30\) s, of which the first half was used to  
Figure 11: Wind tunnel test performed by [PERSON] and [PERSON] [37].  
Figure 10: Experimental and numerical results of the pollutant concentration on the (**a**) rooftop and (**b**) leeward surface.  
achieve a stable flow field while the remaining half was used to record the pollutant concentration data around the buildings.
# Averting BER Floor with Iterative Source and Channel Decoding for Layered Steered Space-Time Codes
## 3 Source Encoding and Soft-Bit Source Decoding
In the second step, we calculate the last set of \(K\) redundant bits \(r_{t}(k)\) with the XOR operation of the \(K\) source bits, while setting \(b_{t}(k)=0\) for \(k=1,2,\ldots K\). Mathematically,  
\[r_{t}(k)=[b_{t}(1)\oplus b_{t}(2)\ldots\oplus b_{t}(K)] \tag{3}\]  
for \(k=1,2,\ldots K\), while setting \(b_{t}(k)=0\) and \(\oplus\) represents the XOR operation in Equation (3). With this algorithm, we are able to generate specific \(N\)_-bit_ codewords with \(d_{H,\text{min}}\geq 2\). The symbols are categorized according to their \(d_{H,\text{min}}\) into various LSSTC-SP-URC-SBC schemes until the highest \(d_{H,\text{min}}\) is attained for the specific rate SBC. [PERSON] et al. [50] proved that for the convergence of an iteratively decoded system, the legitimate codewords should have a \(d_{H,\text{min}}\) of at least 2. As all the LSSTC-SP-URC-SBC schemes generated after the above two steps fulfil the Kliewer criterion, hence we can clearly forecast the effectiveness of these SBC schemes. Other system parameters deployed in our proposed three-stage LSSTC-SP setup are given in Table 3.
# SLMAS: A Secure and Light Weight Mutual Authentication Scheme for the Smart Wheelchair
### Key Terms Explanation
#### 1.1.4 Symmetric Cryptography  
Data may be encrypted and decrypted employing a single secret key with symmetric cryptography. Therefore, this means the sending party and the person who receives the encrypted data must have access to the private key. Other terms used for symmetric cryptography include shared-secret and secret-key.
## 3 Proposed Methodology  
This research introduces a model that utilizes a parallel fuzzy cognitive map approach for the purpose of identifying the most prominent nodes within a designated network. Though an FCM has been implemented in a variety of domains, the use of an FCM in network science is limited. A focal node detection mechanism based on a fuzzy cognitive map was proposed in [58]. Focal nodes have a profound influence on community formation within a network. Focal nodes tend to attract other nodes due to their property of a high degree of centrality. Nodes seeking connectivity, influence, or access to resources are more likely to gravitate towards focal nodes. As nodes join the network, they often form communities around these central figures. Focal nodes often become the core of a community, around which other nodes cluster. They provide a focal point of connectivity and influence, shaping the community's structure and dynamics. Peripheral nodes in the community are connected to the focal node but may have fewer connections with each other. Focal nodes also act as bridges or connectors between communities. Their connections to multiple communities facilitate the exchange of information, resources, or influence across otherwise separate groups. Focal nodes enable the formation of cross-community interactions and integration, contributing to a more cohesive network. Focalnodes also influence the boundaries of communities within a network. Their connections and interactions with nodes from different communities can determine the extent of overlap or separation between these communities. Focal nodes may attract nodes from different communities, leading to the merging or expansion of communities. Alternatively, they may repel nodes from certain communities, resulting in the formation of distinct isolated groups. Focal nodes play a crucial role in maintaining community cohesion. Their high connectivity ensures efficient communication and information flow within the community. Focal nodes often possess a greater influence over decision-making processes and can shape the community's shared goals, norms, and values.  
To uncover the community structure of the network, the identified focal nodes are provided to a distributed genetic algorithm. The overall framework of the proposed algorithm is depicted in Figure 1. To identify the focal nodes within a network, the fuzzy cognitive map is provided with the network as input. The focal nodes, thus detected, aid the genetic algorithm in detecting the community structure of the network.
# A Versatile Shared-Aperture Antenna for Vehicle Communications
## 2 Antenna Configuration
### S-Band Circularly Polarized Antenna  
The circular polarization dipole structure adopts the shape of a clover, and the two polarized dipoles are connected by the circular phase shift line to realize the circular polarization characteristic of the antenna. The specific parameters of the antenna structure are provided in Figure 6 and Table 2. The substrate used is Taconic RF35 with a thickness of 1 mm.  
To further widen the bandwidth, four parasitic patches and ground pins are introduced around the antenna. The parasitic patch can be divided into radiating edge or non-radiating edge coupling. The bandwidth generated by the former is larger than that of the latter, and its basic principle is based on capacitive coupling. The induced current generated on the parasitic patch can add a new resonant point to the antenna, which can improve the overall bandwidth when it is different from the main resonant point and they are close to each other. In addition, the ground pin further improves the antenna's lateral radiation.  
As shown in Figure 7, in order to ensure the upward radiation of the dipole, a metal plane is introduced at a quarter wavelength below the dipole structure. This causes the electromagnetic waves radiating upward from the dipole to constructively interfere with the electromagnetic waves reflected downward from the metal plate.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Para.** & \(W_{1}\) & \(W_{2}\) & \(L_{1}\) & \(A\) & \(P\) & \(R_{0}\) & \(R_{1}\) & \(R_{A}\) & \(H\) \\ \hline Value & 2 & 12 & 10 & 10 & 40 & 2.1 & 2.4 & 4 & 13 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Cross-dipole antenna dimensions (mm).  
Figure 6: Geometry of proposed cross dipole antenna. (**a**) Top view; (**b**) side view.  
Figure 7: Wide beam principle of proposed cross dipole.  
The current distribution of the antenna at 3 GHz is shown in Figure 8. When t = 0 and t = T/2, the current on the dipole arm is along the X axis, and when t = T/4 and t = 3T/4, the current on the dipole arm is along the Y axis, indicating that the antenna has good circular polarization characteristics.
# Antisense-Mediated Down-Regulation of Factor V-Short Splicing in a Liver Cell Line Model
## 2 Materials and Methods
### F5 Transcript Analysis  
#### 2.5.1 RNA Isolation and Reverse Transcription  
Total RNA was isolated from HepG2 cells using TRIzol Reagent (Invitrogen) according to the manufacturer's instructions and quantified with a NanoDrop 2000. Total RNA from primary human hepatocytes, which was used as control in some experiments, was purchased from 3H Biomedical AB (Uppsala, Sweden).  
RNA (2 \(\upmu\)g) was reverse-transcribed with MultiScribe(tm) Reverse Transcriptase (Applied Biosystems) using a \(F5\)-specific primer (5\({}^{\prime}\)-ATTCCGAGATGCCTTCATAC-3\({}^{\prime}\)) located in exon 14.
## 3 Data Routing Approaches in WSNs
### Energy-Efficient Approaches  
In general, energy-efficiency is improved by utilization of two methods: first, _Energy balancing_ in which overloading and multi-functioning of some specific nodes is prevented and the residual energy of the sensor nodes is monitored. Second, _Energy consumption reduction_ to mitigate the average energy consumption of network by using different methods such as clustering, reducing the number of messages, reducing overheads and cooperative communications. In the following, we provide an overview of various approaches proposed in the literature under each category.