esa-sceva/satcom-chunk-collection huggingface dataset:
# QoS-Guaranteed Radio Resource Management in LTE-A Co-Channel Networks with Dual Connectivity
## 3 Proposed Schemes
### Max-Min Threshold Scheduler
#### 3.1.2 Threshold Restrictions  
Recall that there are two types of UEs in the system, namely MUE and PUE. Thus, we let \(U=U^{M}\cup U^{P}\), where \(U^{M}\) and \(U^{P}\) are the set of MUEs and PUEs respectively. Since MUE can only access RBs of the Macro BS, the proposed MTS scheme gives higher priority to MUE when allocating RBs of the Macro BS, as illustrated in Figure 4. The MTS adopts a threshold-based scheme which allocates a certain number of RBs to MUEs before it exercises the max-min scheduling. The basic idea is to guarantee the QoS of MUEs first. It then adopts the max-min scheduling to allocate the remaining RBs to both MUEs and PUEs. On the other hand, the pico BS exercises the max-min scheduling to allocate the RBs to PUEs only.  
Figure 3: Table of user equipment (UE)’s CQI at each resource block (RB).  
The number of RBs reserved for MUEs is based on solving following optimization problem, similar to the problem defined in Section 2, but two new constraints are added, as shown in the following.  
\[\max_{X_{u,k}}\sum\
olimits_{u\in\mathbf{U}^{M}}\sum\
olimits_{k=1}^{K}CQI_{u,k }\;*\;X_{u,k}. \tag{7}\]  
Subject to:  
\[\sum\limits_{u\in\mathbf{U}^{M}}X_{u,k}\;\leq 1\;\;\;\;\;\forall k \tag{8}\]  
\[\sum\limits_{k=1}^{K}X_{u,k}\;\leq\;NRB_{u}\;\;\;\;\;\;\forall u\in\mathbf{U} ^{M} \tag{9}\]  
\[CQI_{u,k}\geq 7\;\;\;\;\;\forall k \tag{10}\]  
\[X_{u,k}\;\in\{\,0,\;1\}\;\;\;\;\;\forall u\in\mathbf{U}^{M},k \tag{11}\]  
where Equation (9) means that the number of allocated RBs cannot exceed the QoS requirement of the MUE, and Equation (10) means that the channel quality must be higher than CQI 7 to avoid wasting RBs on low CQI MUEs. In summary, the threshold-based mechanism is expected to achieve similar performance of the max-min scheduling because both methods are based on similar optimization objectives. The purpose of the threshold-based mechanism is to protect MUEs to access enough resources before competing with PUEs.
## 4 Research Methodology  
ETABS and STRAP software [20; 21] were used to perform numerical analyses of the real building and to calculate its dynamic parameters like mode shapes, modal frequencies, damping ratio, etc., according to modern design codes [1; 3]. The experimental part of this research is based on measuring the dynamic behavior of the real building in order to obtain the above-mentioned parameters.  
The analytical and experimental evaluation of the building's dynamic parameters was performed for 11 different stages corresponding to the phases of the building's strengthening and upgrading (Table 1). The analytical and experimental results obtained for each of the phases were compared in order to validate the desired strengthening effect for improving the design procedure.  
The analysis of the existing building and evaluation of its seismic resistance were performed using the material strength values of the existing structure that were obtained by non-destructive testing methods [5]. For the analysis of the strengthened and upgraded building, the mechanical properties of the existing building parts and the strengthening components in their current state were used. The properties were obtained by both non-destructive and destructive testing. With this aim, samples of concrete were taken from the existing building before strengthening [5].  
The building analysis process included the following steps:  
* Initial 3D linear elastic analysis under vertical static loads only;
* Modal analysis according to the modern seismic design code requirements [1; 3];
* Comparing the results obtained by ETABS [20] and STRAP [21].  
Figure 2: Strengthening and upgrading of the building: black—existing part, blue—new part, following [5]. (**a**) Plan, (**b**) foundations before strengthening, and (**c**) section view.  
The building's dynamic behavior was measured using a system of acceleration and velocity sensors, connected to a computer for data logging and processing. The building's dynamic behavior was recorded under such loads as wind, traffic in the building's vicinity, etc. The dynamic parameters of the investigated building were obtained experimentally after each strengthening or upgrading stage when the concrete full load-bearing capacity was achieved. The formwork was removed before measurements to avoid the effect on the dynamic parameters of the structure.  
It should be mentioned that the dynamic response of a building in the event of a strong earthquake is significantly different quantitatively and qualitatively from that of moderate wind, traffic-induced, and other similar forces. This is because under a strong earthquake, the response of a building is non-linear, which leads to crack formation and corresponding changes in the building's dynamic parameters. However, the design of strengthening is based on the spectrum, corresponding to the elastic stage of the building before the earthquake (as is). Hence, the dynamic parameters obtained from records of the building's response to traffic and wind loads correspond to the above-mentioned elastic stage (as is) and may be used for design purposes.
## Materials and Methods
### Membrane Performance Testing
#### 2.6.2 Antibioticing Properties of the Developed Membranes  
The antib biofouling properties of the developed membranes were studied with model suspensions both Gram-positive and Gram-negative bacteria (_E. coli_ and _Bacillus subtilis_ (_B. subtilis_) suspensions, respectively). Colonies for each species were grown on Luria-Bertani (LB) agar with overnight incubation at 37 \({}^{\circ}\)C. Single colonies were then selected and cultured in LB broth overnight shaking at 37 \({}^{\circ}\)C and 200 rpm. The next day, serial dilutions were prepared in order to obtain bacterial suspensions with 0.025 optical density (OD) at a wavelength of 600 nm. Membrane samples were placed in the bacterial suspensions for 10 min before being transferred onto agar plates for overnight incubation at 37 \({}^{\circ}\)C. The next day, the membranes were fixed with 4% paraformaldehyde before being washed with phosphate buffer solution. Next, the membranes were subjected to sputtering with gold before being viewed under the SEM.  
Another set of experiments tested the biofouling resistance of our membranes after contact with bacterial suspensions over a duration of one, three and seven days. To that end, membranes were incubated with _E. coli_ suspension (0.025 OD) for different time periods as stated above before the flux was measured. These experiments were repeated with TSE in place of bacterial suspension. The obtained TSE ranged in pH from 6.7 to 7.3 and had a biochemical oxygen demand of 1.6 mg/L. The measured chemical oxygen demand was 10 mg O\({}_{2}\)/L, whilst the total organic carbon was 1.5 mg/L.  
In an alternative experiment, the membranes were tested for their biofouling resistance upon having _E. coli_ suspension (0.025 OD) pipetted on the active surface of the membranes and left incubated overnight at 37 \({}^{\circ}\)C. The next day, the membranes' normalized flux was determined. For comparison, two commercial membranes, namely PM UP150 (Microdyn-Nadir, Germany) and UE50 PES membrane (TriSep, USA) were used.
## 2 Complex-Image Layout Analysis Methods
### Layout Analysis Method Based on Machine Learning
#### 2.3.2 Layout Analysis Based on Neural Networks
Two innovations of DeepLabv1 are the proposed Atrous Convolution and the Fully Connected CRF-based field. The difference between these innovations and DeepLabv2 is that Atrous Spatial Pyramid Pooling (ASPP) has been proposed [44]. DeepLabv3 further optimizes ASPP, including adding \(1\times 1\) convolution, batch normalization (BN) operation, etc. [45]. DeepLabv3+ adds an upsampling decoder module modeled on the U-Net structure to optimize edge accuracy [46]. [PERSON] [47] solves the scene-segmentation task by acquiring rich contextual dependencies based on a self-attentive mechanism, and proposes a Dual Attention Network (DANet) which adaptively integrates local features and global dependencies. Two types of attention modules are added to the traditional extended FCN to model semantic interdependencies in spatial and channel dimensions, respectively. [PERSON] [48] proposed a novel and efficient Short-Term Dense Connection (STDC) network architecture, which constituted the basic module of the network by gradually reducing the dimension of feature images and using the aggregation of feature graphs for image representation. A detail aggregation module is proposed in the decoder, which integrates the learning of spatial information into the lower layer using a single-stream method. Finally, the bottom and deep features are fused to predict the final segmentation result. [PERSON] [49], in order to perform high-quality real-time semantic segmentation, thus proposes the Feature Pyramid Aggregation Network (FPANet). This network can be considered as an encoder-decoder model, where, in the encoder phase, ResNet and Atrous Space Pyramid Pooling (ASPP) are used to extract more advanced semantic information. In the decoder stage, a bilateral-oriented feature pyramid network for semantic segmentation is proposed to simultaneously acquire semantic and spatial information of the image and to fuse different levels of features. [PERSON] [50] proposed an image semantic segmentation method (DECANet), which introduces a channel attention module to model the dependencies of all channels, improves the expressive ability of the network, selectively learns and strengthens channel features, and utilizes the Atrous Spatial Pyramid Pooling (ASPP) structure. The multi-scale fusion of the extracted image convolution features can reduce the loss of image detail information, and the semantic pixel position information can be extracted without changing the weight parameters, to accelerate the convergence speed of the model. As shown in Table 3, the above neural network-based layout analysis methods are compared.
# Impacts of Thermal Maturity on the Carbon Isotopes of
###### Abstract  
The carbon isotopic behavior of propane compounds during thermal maturation remains ambiguous due to limitations in current detection techniques. In this study, a low-maturity lacustrine shale sample was pyrolyzed in a hydrous semi-open pyrolysis system. The propane compounds from the artificially matured samples (\(R_{\rm o}=0.72\)-\(1.28\%\)) have been separated and enriched for the test of their carbon isotopes (\(\xi^{13}\)C). The results show that thermal maturity can somewhat affect the carbon isotopes of monomeric propane compounds, with a maximum difference value over \(21\%\). However, thermal maturity has different effects on the \(\xi^{13}\)C values for different monomeric propane compounds. For example, the carbon isotopic values of \(228\)-homohopane at different thermal stages can vary up to \(21\%\), while only \(3\%\) for C\({}_{29}\)\(\updelta\upalpha\). In addition, the carbon isotopes of different monomeric propane compounds show distinct evolution trends. For C\({}_{29}\)\(\updelta\upbeta\) and C\({}_{29}\) Ts, their carbon isotopes first become slightly heavier and then become lighter, reaching the lightest value at \(350~{}^{\circ}\)C. When the pyrolysis temperature continues to increase, the \(\delta^{13}\)C values become heavier and finally become lighter. However, the \(\delta^{13}\)C values of Ts, Tm, \(225\)-homohopane, and \(226\)-homohopane show a completely reversed trend. They initially become slightly lighter and then become heavier, reaching the maximum value at \(350~{}^{\circ}\)C. When the pyrolysis temperature continues to increase, the \(\xi^{13}\)C values become lighter and finally become heavier. Meanwhile, the carbon isotopes of C\({}_{29}\)\(\updelta\upalpha\), C\({}_{30}\)\(\updelta\upbeta\), C\({}_{30}\)\(\updelta\upalpha\), and non-hopane gammacerane almost remain constant at different thermal stages. When the carbon isotopes of propane compounds are used in the studies of oil-source correlation, it is prudent to consider the effects of thermal maturity on these values.  
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# TOAR: Toward Resisting AS-Level Adversary Correlation Attacks Optimal Anonymous Routing
## 6 Evaluation
### Effectiveness Analysis  
The first part of the experiment involves a MATLAB code and data to evaluate the effectiveness of the algorithm. The CAIDA AS relationship dataset is used, which contains 63,361 nodes and 320,978 edges. For each AS, a routing policy is configured. Based on the topology and policy settings from the dataset, the top 10 ASes with the highest traffic are chosen as destinations. Additionally, for each selected destination AS, the top 200 ASes sending the most traffic to that destination are identified, generating 2000 end-to-end routing intents. By mapping Tor nodes to Internet AS nodes using the Tor Metric published relay node information, 1000 valid end-to-end intents are filtered out. In the following step, TOAR's effectiveness is analyzed in terms of the security policy function, the expected improvement function, and the routing search results.  
* Effectiveness of Security Policy Functions:  
The security policy function \(d(p,k,m,n)\), as defined in Equation (3) in the system model, portrays the probability that an inter-domain route \(r\) will be compromised, and Figure 7 shows an example of this function, with the security policy function values on the vertical axis and the \(k\) values on the horizontal axis. The function grows by nearly 100% in the case of \(p=0.1,k_{max}\)\(0\to 7\), \(max\)\((m,n)<10\). From the intuition of reducing the number of shared points and the change in the modeled security function, deploying a two-stage route optimization algorithm improves the anonymity of the system.  
Figure 6: Impact of path length \(n\) on the probability \(d\).  
* Effectiveness of Expectation Improvement Function:  
The second stage of the algorithm is based on Equation (16) which defines the unit cost of routing security improvement, \(C=c\big{(}r,r^{*}\big{)}=\frac{b\cdot I_{\text{path}}(r,r^{*})}{I_{I}(r,r^{*})}\), to decide whether it can be further iteratively optimized or not, so the absolute magnitude of the difference between \(C^{*}\) and \(C\), \(\Delta C\), can be a measure of how much the algorithm has improved the security. Therefore, the absolute value of the difference between \(C^{*}\) and \(C\), \(\Delta C\), can be used to measure the improvement of the algorithm in terms of security. Other metrics can also be used; for example, the magnitude of the quotient between \(C^{*}\) and \(C\) can be used to indicate the degree of improvement in the second stage. Based on the experimental result data, the enhancement of the optimization algorithm in the second stage is counted, as shown in Figure 8.  
* Effectiveness of search results:  
Within a given number of cycles, TOAR can dramatically increase the chances of finding an end-to-end route that matches the policy by about 60%, which is nearly 2.5 times higher compared to the 25% success rate of a naive enumeration search, as shown in Figure 9.  
Figure 8: The \(\Delta C\) statistic of the results.  
Figure 7: Trend of security policy function \(d(p,k,m,n)\).
## 3 Proposed Approach
### Segmentation
#### 3.1.4 Particle Swarm Optimisation (PSO)  
PSO has three major steps, including generating the particles' positions and velocities, velocity update, and position update. This algorithm has five important parameters, as presented in Table 2.  
The random velocities and positions of all particles are initialised within the specific ranges. The values of \(\overrightarrow{p}_{i,best}\) and \(\overrightarrow{g}_{i,best}\) can be updated based on the fitness function \(f\) when the following conditions are satisfied:  
\[\begin{split}\overrightarrow{p}_{i,best}&= \overrightarrow{p}_{i}\,if\,f\left(\overrightarrow{p}_{i}\,\right)>f\left( \overrightarrow{p}_{i,best}\right)\\ \overrightarrow{g}_{i,best}&=\overrightarrow{g}_{i} \,if\,f\left(\overrightarrow{g}_{i}\,\right)>f\left(\overrightarrow{g}_{i, best}\right)\end{split} \tag{4}\]  
The fitness of a particle is calculated as the quantisation error [71]:  
\[f=\frac{\sum_{j=1}^{k}[\sum_{\forall p\,\,\,c_{ij}}d\left(p,v_{j}\right)/ \left|C_{ij}\right|]}{k} \tag{5}\]  
where \(d\) is defined as the distance between a particle \(p\) and a centroid \(v_{j}\), \(\left|C_{ij}\right|\) represents the number of particles belonging to the cluster \(C_{ij}\), and \(k\) is the number of clusters. During the iteration, velocities of all particles are updated by:  
\[\overrightarrow{v}_{i}=w\overrightarrow{v}_{i}+c_{1}R_{1}\left(\overrightarrow {p}_{i,best}-\overrightarrow{p}_{i}\right)+c_{2}R_{2}\left(\overrightarrow {g}_{i,best}-\overrightarrow{p}_{i}\right) \tag{6}\]  
where \(\overrightarrow{v}_{i}\), \(\overrightarrow{p}_{i}\), \(\overrightarrow{p}_{i,best}\) are the velocity, position, and the local best position of particle \(i\), respectively. \(\overrightarrow{g}_{i,best}\) shows the global best position for the whole population. The \(w\) is used to control the convergence behaviour of the PSO algorithm, \(R_{1}\) and \(R_{2}\) are random variables between \([0,1]\), and \(c_{1}\) and \(c_{2}\) are used to control particle movement during each iteration. The particles' positions can be updated by the following equation:  
\[\overrightarrow{p}_{i}=\overrightarrow{p}_{i}+\overrightarrow{v}_{i} \tag{7}\]
# An Adaptive Symmetrical Load Balancing Scheme for Next Generation Wireless Networks
## 2 Related Work
### Load Balancing in Control Plane
The WRR scheme allocates fixed weight values to servers based on their capability; as a result, the distribution of load using these weighted values is not effective for managing a large number of flow requests. Our proposed scheme, on the other hand, is a flexible algorithm architecture that uses ASLB and clustering to balance the load in the control plane.
# An Analysis of MPTCP Congestion Control
## 6 MPTCP Schedulers
The existing literature mainly focuses on individual performance evaluation of schedulers or CC algorithms. However, the relationship between CC and the scheduler is vital. As discussed, the default scheduler of MPTCP (minRTT), prefers the subflow with the smallest RTT as long as their is capacity in the send window to transmit further packets. Detection of when the window capacity is exhausted is performed by the CC algorithms. MPTCP CC adjusts the send rate and balances the degree of congestion within each subflow to improve throughput/resource utilisation. Path scheduling is used to distribute data to different subflows to improve transmission efficiency [1]. As the default scheduler is most likely to be deployed on the Internet, we will limit discussion to the interaction and performance of CC algorithms when used with minRTT.
# Optimization of Submodularity and BBO-Based Routing Protocol for Wireless Sensor Deployment
## 2 Wireless Sensor Deployment Optimization
### IHACA-COpSPIEL Deployment Method
#### 2.2.2 Improved Heuristic Ant Colony Algorithm  
The ant colony algorithm was inspired by the research on real ant colony behavior, and has been applied to the optimization of communication networks and others. The essence of the ant colony algorithm is to use pheromone as a medium for ants in an ant colony to communicate. In the sensor layout, the ants must be moved toward the sensor node with a large submodular gain. The traditional ACA algorithm has the problem of blind search and it is easy to fall into local optimal solutions. To improve the heuristic function and pheromone, a new mechanism, improved heuristic ant colony algorithm, IHACA is proposed.  
The heuristic function of the traditional ant colony algorithm takes no consideration of the distance relationship between the next node \(j\) and the adjacent cluster head, and the search is blind. Therefore,this paper adds the Euclidean distance between the next node j and the cluster head of the adjacent cluster. The improved heuristic function is as follows.  
\[\eta_{ij}=\frac{1}{wdist(i,j)+(1-w)dist(j,g_{i1})},w\in(0,1). \tag{10}\]  
where \(g_{i1}\) is the first node of cluster \(i\) and \(w\) is the weight.  
In order to avoid premature, stagnation or local optimization problems due to excessive pheromone concentration [27], this paper introduces a local and global pheromone update mechanism. Local update of pheromone helps ants to select unselected points, and a full update of pheromone helps to enhance the global search ability of the algorithm.  
Each ant moves from node \(i\) to node \(j\), and needs to update the pheromone on the path \((i,j)\) that it just walked.  
\[\tau_{ij}(n+1)=\xi\tau_{ij}(n)+\varepsilon\tau_{0}. \tag{11}\]  
In Equation (11), \(n\) is the number of iterations, \(\xi\) is a local pheromone evaporation coefficient, \(\tau_{0}\) is a pheromone under initial conditions, and \(\varepsilon\) is a constant.  
When all the ants complete this iteration, we select the shortest path and the longest path in this iteration to globally update the pheromone on the path.  
\[\tau_{ij}(n+1)=(1-\rho)\tau_{ij}(n)+\rho\sum_{k=1}^{m}\Delta\tau_{ij}^{k}. \tag{12}\]  
\[\Delta\tau_{ij}^{k}=\left\{\begin{array}{l}\frac{Q}{L_{best}},L\in L_{best} \\ -\frac{Q}{L_{worst}},L\in L_{worst}\\ 0,\mathit{other}\end{array}\right. \tag{13}\]  
In Equations (12) and (13), \(m\) is the number of ants, \(\rho\) is the evaporation coefficient of the global information system, \(\tau_{ij}^{k}\) is the pheromone left by ant \(k\) on \((i,j)\), \(Q\) is the pheromone quality coefficient, \(L_{best}\) is the shortest path, and \(L_{worst}\) is the longest path.
## 5 Results and Discussion
### Implications of Regulation and Certification  
#### 5.2.1 Regulation Regarding the Application Layer  
The BSI TR-03109 was mainly designed from a security and technical perspective. Value-added services based on the usage of the bidirectional communication channel have not yet been specified; i.e., so far, there was no special focus on the specification of the application layer. However, aiming at interoperability and maximum security, it is unclear whether the BSI will regulate interfaces and processes for further use cases in the future. For controlling CLSs, such as DERs and EMSs, mandatory application protocol requirements are explicitly not included in the technical guidelines. This means that apart from the mandatory utilization of an encrypted TLS connection, application protocols may freely be selected and specified. There are no regulatory application layer restrictions concerning the communicated data or the interfaces provided by CLSs. This could provide opportunities in which the market itself rather than the regulator comes up with innovative solutions and new business models involving the usage of the bidirectional communication channel for application layer protocols. The competition between innovative solution providers will emerge in new economically useful business models. Therefore, binding restrictions regarding the communication on the application layer would hinder the development of innovative solutions and business models required for the economically sensible integration of DERs. To some extent this is also visible in our prototype implementation, which shows that there are different meaningful options of how the communication between EMSs and aEMPs can be implemented in an effective and efficient way. Furthermore, application layer restrictions would have to be use case specific, as the communication channel is explicitly intended to be used not only for smart-grid-related control functionalities, such as the control of DERs or EMSs, but also for further value-added services in the context of smart homes and smart buildings, such as services for assisted living (cf. [41]).