esa-sceva/satcom-chunk-collection huggingface dataset:
# A Reinforcement Learning Based Intercell Interference Coordination in LTE Networks
## 5 The Proposed ICIC Scheme
### Packet to Retransmit Identification Step  
In this step, we identify which packets to retransmit. In the previous step, transmit power of each encodeB on each RB have been set. A best solution solution may have some users still experiencing high interference by getting low SINR. We assume a packet sent on the corresponding RBs might not be correctly decoded at the receiver side and thus will be rejected. These packets should be transferred in the retransmission queue of the corresponding user. The retransmission is handled by the HARQ manager. There are eight parallel stop and wait (SAW) HARQ processes. The retransmission of packets can occur at any time relative to the initial transmission since HARQ processes are asynchronous in the LTE downlink. The step is summarized in Algorithm 7.
```
1 for\(t\gets 1\)toSimulation_timedo
2 for\(p\gets 1\)toPopulation_sizedo
3 for\(c\gets 1\)toNumber_of_cellsdo
4 for\(u\gets 1\)toNumber_of_users_in_the_celldo
5 for\(r\gets 1\)toNumber_of_Resource_Blocksdo
6 chromosome\([p][c][u][r]\leftarrow\)random number betweenMinimum_transmit_Power andMaximum_transmit_Power
7 end for
8
9 end for
10
11 end for
12
13 end for
14
15 end for
16
17 for\(g\gets 1\)toNumber_of_generationsdo
18\(Number\_of\_chromosomes\_to\_be_crossed\gets crossover\_rate*Population\_size\)
19 for\(n\gets 1\)toNumber_of_chromosomes_to_be_crosseddo
20\(n_{c}\leftarrow\)random number between 1 andPopulation_size Addchromosome\([n_{c}]\) to chromosomes to becrossed
21 end for
22 for\(n\gets 1\)toNumber_of_chromosomes_to_be_crossed/2 do
23 Cross chromosome\([n]\)withchromosome\([N_{c}/2+n]\)
24 end for
25 Add new \(Number\_of\_chromosomes\_to\_be_crossed\) children obtained to population\(Number\_of\_chromosomes\_to\_be_mutate\gets mutation\_rate*Population\_size\)
26 for\(n\gets 1\)toNumber_of_chromosomes_to_be_mutatedo
27\(n_{m}\leftarrow\)random number between 1 and \(P\) Addchromosome\([n_{m}]\) to chromosomes to be mutate
28
29 end for
30 for\(n\gets 1\)to\(N_{m}\)do
31 Mutate chromosome\([n]\)
32 end for
33 Add new \(Number\_of\_chromosomes\_to\_be_mutate\) mutated chromosomes obtained to population\(p\gets 1\)toPopulation_sizedo
34 for\(c\gets 1\)toNumber_of_cellsdo
35 for\(u\gets 1\)toNumber_of_users_in_the_celldo
36 for\(r\gets 1\)toNumber_of_Resource_Blocksdo
37 Calculate \(SINR[c][u][r]\) according to Formula \((1)\)
38 end for
39 end for
40 end for
41
42 end for
43
44 end for
45
46 end for
47 Sortchromosome according to \(SINR\) descending
48 Keep chromosome\([1]\) tochromosome\([Population\_size]\) for next generation population
49 end for
50 for\(c\gets 1\)to\(Number\_of\_cells\)do
51 for\(u\gets 1\)to\(Number\_of\_cells\)do
52 for\(r\gets 1\)to\(Number\_of\_Resource\_Blocks\)do
53\(Transmit\_Power[c][u][r]\gets chromosome[1][c][u][r]\)
54 end for
55
56 end for
57
58 end for
59
60 end for
61
62 end for
63
64 end for
65
66 end for
67
68 end for
```  
**Algorithm 6**Power allocation algorithm details
## 2 Research Program and Methodology
### Mechanism of Action of Electrochemically Modified Mudstone
#### 2.2.2 Chemical Reaction Cementation  
The migration of cations from the mudstone to the cathode forms particle agglomerates with the soil particles in their path. This phenomenon modifies the plasticity of clay minerals and increases cohesion. It also enhances the frictional properties of soil particles, thereby increasing the internal friction angle [26].  
Using iron electrodes as an example, during the electrochemical stabilization of mudstone, iron electrodes undergo redox reactions to introduce iron ions into the clay minerals and produce new cementation materials through hydration reactions, achieving the cement tation of mineral particles. The cementation reaction process when using iron electrodes for the electrochemical stabilization of mudstone is as follows.  
Iron electrodes form large amounts of divalent iron ions when energized:  
\[\text{Fe}-2e^{-}=\text{Fe}^{2+} \tag{1}\]  
Fe\({}^{2+}\) will hydrolyze with OH\({}^{-}\) in water to form the transiently present compound Fe(OH)\({}_{2}\):  
\[\text{Fe}^{2+}+2\text{H}_{2}\text{O}=\text{Fe}\big{(}\text{OH}\big{)}_{2}+2 \text{H}^{+} \tag{2}\]  
Fe(OH)\({}_{2}\) is extremely unstable in air and will all react with the combined action of oxygen and water to form Fe(OH)\({}_{3}\):  
\[4\text{Fe}(\text{OH})_{2}+\text{O}_{2}+2\text{H}_{2}\text{O}=4\text{Fe}(\text{ OH})_{3} \tag{3}\]  
Some of the Fe(OH)\({}_{3}\) will weather in the air and undergo a dehydration reaction to form ferric trioxide:  
\[2\text{Fe}(\text{OH})_{3}=\text{Fe}_{2}\text{O}_{3}+3\text{H}_{2}\text{O} \tag{4}\]  
The electrochemical consolidation process described above effectively consolidates mudstone by converting its natural cementation to iron cementation, a method widely accepted by field engineers. The choice of different electrode materials or electrolyte compositions can result in various cementation states, significantly influencing the effectiveness of electrochemical reinforcement.  
Mudstone, a widely occurring rock in nature, often appears on slopes. The shear strength of mudstone is a critical factor affecting slope stability. Electrochemical modification can achieve drainage consolidation of mudstone and improve its shear strength parameters, significantly enhancing the stability of slopes containing mudstone. Therefore, this study used the shear strength of mudstone after electrochemical modification as a comparative indicator to select the optimal electrode material, electrolyte solution, and concentration. This achievement has important value for controlling mudstone deformation and improving slope stability.
# A Dynamic Service Placement Based on Deep Reinforcement Learning in Mobile Edge Computing
## 1 Introduction
### Contributions and Paper Organization  
In this paper, we introduce a novel dynamic placement framework based on deep reinforcement learning (DSP-DRL) to optimize the QoS for users under the constraints on physical resources and operational costs. Our contributions can be summarized as follows:  
* We investigate the service placement problem in mobile edge computing with multiple users, and we propose to minimize the total delay of users by considering the limitation on physical resources and cost.
* We propose a decentralized dynamic placement framework based on the deep reinforcement learning (DSP-DRL) by introducing the migration conflict resolution mechanism during the learning process to maintain the service performance for users. We formulate the service placement under the migration conflict into a mixed-integer linear programming (MILP) problem. Then, we propose a migration conflict resolution mechanism to avoid the invalid state and approximate the policy in the decision modular according to the migration feasibility factor.
* Extensive evaluations demonstrate that the proposed dynamic service placement framework outperforms baselines in terms of efficiency and overall latency.  
Figure 1: An illustration of the dynamic service placement in mobile edge computing.  
The remainder of this paper is organized as follows. Section 2 surveys related works. Section 3 describes the model and then formulates the problem. Section 4 investigates the dynamic service placement framework based on deep reinforcement learning. Section 5 includes the experiments. Finally, Section 6 concludes the paper.
###### Abstract  
This paper investigates the problem of RF energy harvesting in wireless sensor networks, with the aim of finding a suitable communication protocol by comparing the performance of the system under different protocols. The network is made up of two parts: first, at the beginning of each timeslot, the sensor nodes harvest energy from the base station (BS) and then send packets to the BS using the harvested energy. For the energy-harvesting part of the wireless sensor network, we consider two methods: point-to-point and multi-point-to-point energy harvesting. For each method, we use two independent control protocols, namely head harvesting energy of each timeslot (HHT) and head harvesting energy of dedicated timeslot (HDT). Additionally, for complex channel states, we derive the cumulative distribution function (CDF) of packet transmission time using selective combining (SC) and maximum ratio combining (MRC) techniques. Analytical expressions for system reliability and packet timeout probability are obtained. At the same time, we also utilize the Monte Carlo simulation method to simulate our system and have analyzed both the numerical and simulation solutions. Results show that the performance of the HHT protocol is better than that of the HDT protocol, and the MRC technology outperforms the SC technology for the HHT protocol in terms of the energy-harvesting efficiency coefficient, sensor positions, transmit signal-to-noise ratio (SNR), and length of energy harvesting time.  
energy harvesting; wireless sensor networks; system reliability; packet timeout probability; outage probability +
Footnote †: journal:  
[PERSON]; [PERSON] System Performance Analysis of Sensor Networks for RF Energy Harvesting and Information Transmission  
_Future Internet_**2023**, 15, 172. [[https://doi.org/10.3390/615050172](https://doi.org/10.3390/615050172)]([https://doi.org/10.3390/615050172](https://doi.org/10.3390/615050172))
###### Abstract  
The objective of this study is to examine the effect of the poor anchorage length of glass fiber reinforced polymer (GFRP) bars used for longitudinal reinforcement on the flexural and shear performances of beam elements in exterior beam-column connections made using high-strength materials. Six exterior beam-column connection specimens were tested under reversal cyclic loads applied at the free-end of the beam. The selected strength categories of materials in the beam element were as follows: 35 MPa and 70 MPa for the design compressive strength of concrete, 400 MPa and 600 MPa for the yield strength of conventional longitudinal steel bars, and 800 MPa for the tensile strength of the GFRP bar. All the longitudinal steel bars of the beams satisfied the minimum requirements of the provisions of ACI 318-14, whereas all the longitudinal GFRP bars of the beam were linearly anchored into the column section, resulting in poor anchorage length, especially for the beam with the concrete compressive strength of 35 MPa. The flexure-governed beams with GFRP bars exhibited a greater increasing rate in displacement at the pre-peak state and did not display the plastic flow characteristic after the peak load when compared with companion beams with steel bars. The beams with GFRP bars possessed lower diagonal cracking strengths and shear capacities than the companion beams with steel bars although the shear capacities of the beams with GFRP bars could be conservatively predicted using the design equation of ACI 440.1R-15 provision. The low elastic modulus and elongation capacity of GFRP bars resulted in large displacements and brittle post-peak beam performances. Furthermore, the lack of anchorage length of GFRP bars in exterior beam-column connection significantly reduced the flexural strength and ductility of the beam element.  
GFRP bar; anchorage length; ductility; flexural strength; shear strength Article
## 3 Results and Discussion
### Material Properties Probing
As PNA signal variations with bias are expected to be the signature of polarons with high mobility, the above analysis is also consistent with identical sharp transient response in amplitude and phase time profiles (see Figure 2b and Figure S2b). Meanwhile, the contribution of traps could also be observed. This requires lower frequency operation to probe a deeper volume and a large tip bias. The SMM probing depth and the variations of the material electrical properties upon bias are depicted in Figure S5c,d. Associated experimental results are displayed in Figure S5. Figure S5a shows the interferometric response across a fringe with minimum frequency \(f_{0}\) chosen around 3.5709 GHz with an operating set up \(f_{\text{OP}}>f_{0}\) to minimize drift induced perturbations during measurement. Figure S5b displays the corresponding PNA amplitude and phase time profiles and their variations with bias. The phase signal decreases with bias consistently, with variations of the interferometric profile towards a +\(\pi\) shift of the signal at \(f_{0}\). Like previous phase profiles, sharp transient variations are also observed with bias. This suggests again a charge increase in P3 HT upon bias forming polaron in the probed volume and whose fast dynamics cannot be resolved by the VNA controller. On the contrary, transient mechanisms can clearly be observed in the PNA amplitude time profile. These transient discrepancies between phase and amplitude profiles confirm that different mechanisms can be probed simultaneously with SMM and be discriminated between the two signals. The amplitude decreases with more negative bias, indicating now a frequency shift towards higher values. Therefore, the origin of the drift can no longer attributed to contractions of the probed volume upon conductivity increase. The observed transient with characteristic time around 10 s upon bias variations would rather be attributed to carrier trapping/detrapping mechanisms. As illustrated in Figure S4c,d, the low frequency operating microwave is now probing the whole semiconducting film. Upon the bias, no variations of the penetration depth can occur. However, the large electric field across the field is likely to empty hole deep traps in the P3 HT at the back contact. This can explain the shift towards higher frequencies of the PNA amplitude, corresponding to a decrease in probed capacitance, while the slow transient characteristics describe their peculiar dynamics.
## 2 Materials and Methods
### Data Organization  
To increase the number of 1D-CNN input data, segmentation was applied according to [25; 26]. For that purpose, rotation speed (RPM) was firstly converted into revolutions per second (_rps_) by using Equation (1). Afterward, segment length (\(t_{d}\)) was estimated, considering the number of revolutions of interest (_# revol_) and previously calculated revolutions per second (Equation (2)).  
\[rps=\frac{RPM}{60} \tag{1}\]  
\[t_{d}=\frac{\#\ revol}{rps} \tag{2}\]  
Once the segments were obtained, they were organized into seven categories in accordance with a column entitled \"Failure Level\" in Table 2, due to the compatibility among datasets; in other words, the machine had to have had three machinery defects and two failure levels in order to test the network's robustness. Those categories were: (1) moderate and (2) severe IR failure, (3) moderate and (4) severe OR failure, (5) moderate and (6) severe RE failure and (7) no failure. As each category was different in size (seeFigure 1), the overlapping at 12.5% or 25% (depending on category size) was applied in order to balance all the categories [27,28]. Lastly, each database was divided into two sets: training (80% of data) and testing (20% of data) [29]. Training datasets were subdivided further into two parts: training (75% of data) and validation (25% of data). Data organization is depicted in Figure 1.  
Having organized and balanced the three databases, CWRU, NSF-IMS and T-Y Wu databases were, respectively, used to (1) determine configuration parameters, (2) train a model and (3) generalize such a model.
## 4 Proposed Algorithm (NCRA-DP-ACO)
### Pheromone Management
#### 4.2.6 Objective Function  
When placing VMs in datacenters, choosing the best setup is crucial. In this document, this study focuses on reducing energy consumption, reducing the carbon footprint, and lowering network costs. Our goal is to find the most cost-effective way to run the workload in the cloud, being mindful of the environment and money. We want to prioritize using less energy and reducing carbon emissions for the cloud provider while also controlling network costs through smart VM placement.  
The cost of running all VMs at different sites is formulated in Equation (20).  
\[C_{T}=\sum_{j\in VM}\sum_{i\in PM}C\left(VM_{ij}\right)\times x_{ij} \tag{20}\]  
\(C_{T}\) is composed of the cost of energy used by the VMs in datacenters in addition to the cost of the carbon footprint at each site according to the associated tax, as shown in Equation (21).  
\[C_{T}=C_{EC}+C_{CF} \tag{21}\]  
where \(C_{EC}\) is the cost of energy consumption modeled in Equation (22), and \(C_{CF}\) is the cost of the carbon footprint determined using Equation (24).  
\[C_{EC}=\sum_{i=1}^{PM}\left[\left(PM_{i,t+}^{power}-PM_{i,t}^{power}\right)+PM_{ i,t+}{}^{overhead}\ \right]\times z_{i} \tag{22}\]  
where \(PM_{i,t}^{power}\) is the server power consumption at time \(t\), and \(PM_{i,t+}^{power}\) is the server power consumption after running the new VM calculated according to Equation (23). \(z_{i}\) shows whether the PM belongs to the ant's solution, and the overhead power is expressed as  
\[PM_{i,t+}{}^{overhead}=\left(PM_{i,t+}^{power}-PM_{i,t}^{power}\right)\times \left(PUE(U_{t},H_{t})-1\right) \tag{23}\]  
Equation (7) can be used to calculate the future overhead power of the datacenter (\(PUE(U_{t},H_{t})-1\)).  
\[C_{CF}=C_{EC}\times R_{d}^{Carbon}\times R_{d}^{Tax} \tag{24}\]  
where \(R_{d}^{Carbon}\) and \(R_{d}^{Tax}\) are the carbon footprint and carbon tax in datacenter d, respectively.  
With all the definitions in the previous sections, the overall multi-objective optimization goal integrating all aspects is formulated in Equation (25):  
\[\begin{split}\text{min }C_{T}\\ \text{Subject to }(1)-(4)\end{split} \tag{25}\]  
The objective function is a fitness measure for evaluating solution suitability. Solutions are primarily ordered with respect to their green energy use to choose the datacenter. Within a datacenter, the solutions are sorted according to their overall fitness value per objective function. The solution with the best overall fitness is selected.
## 4 Simulation Results and Analysis
### Continuous Step Simulation
Table 3 shows the statistical characteristics of calculate time consumption in each step. For the MPC controller, the mean of all steps is 20.916 ms, the mean for the explicit MPC controller based on a sequence search is 1.671 ms, and the mean for the explicit MPC controller based on a binary search tree is 1.389 ms. The real-time performance of the explicit MPC controller based on a binary search tree is the best. Moreover, compared with the explicit MPC based on a sequential search, the maximum of all steps and the minimum of all steps decrease 25.6% and 65.8%, respectively, for the explicit MPC controller based on a binary search tree. At each iteration, the method based on a binary search tree needs to complete a comparation, judging where \(d_{h_{k}}\leq 0\) holds, while an iteration implies many comparations for the sequence search. \(H_{i}\theta\leq K_{i}\) holds when each inequation corresponding to each row holds. This is why the explicit MPC controller based on a binary search tree consumes less time. Through the comparison experiments, choosing the explicit MPC controller based on a binary search as the active jet noise controller is feasible.
# Investigation of Pellet Properties Produced from a Mix of Straw and Paper Sludge
## 2 Materials and Methods
### Proximate Analysis of Samples  
The TGA-701 thermogravimetric analyzer, which determines material weight loss as a function of temperature in a controlled environment, was used to analyze the composition of the produced samples and the results of measurement are summarized in Table 4. The results of dry mass are given. The first measured quantity is moisture and the highest humidity is shown by pure straw pellets (S100).  
The paper sludge contains a negligible amount of fixed carbon, despite the relatively high proportion of volatile combustible material. In this case, the proportion of volatiles is highest for straw pellets, the value being close to that of wood pellets' volatile matter (75%) [26]. As the amount of sludge in the samples increases, a gradual decrease in the volatiles content can be observed.  
The large proportion of non-combustible components in the paper sludge causes high ash concentration and it can be noticed that sludge pellets show the highest value of ash. For the sample SPS-80/20 and SPS-70/30, the ash content is up to 2.5 times higher than that of S100 pellets. Such high values can lead to burner clogging and other operational problems.  
In the study conducted by [PERSON] et al., the effect of various proportions of papermaking sludge mixed with wheat straw on the quality of this fuel is presented. Comparing some of the results, the deviation in the range of 1-2% was noticed in the case of carbon, hydrogen and ash content. Comparison of nitrogen values shows content fluctuations in the range of 7-16% [16]. However, this may be due to the type of straw, different composition of soil on which the straw was grown, or the harvesting period.
## 4 Theoretical Calculation and Analysis
### Defining Boundary Conditions
#### 4.4.1 Convective Heat Transfer Coefficient Function for the Top Surface  
The convective heat transfer coefficient is defined as the heat transfer capability of an interface between a fluid and a solid surface. The unit of the convective heat transfer coefficient is \(\mathrm{kJ}/\left(\mathrm{m^{2}\cdot h\cdot{}^{\circ}C}\right)\) in this paper. In order to simplify the circulation process of water, a layer of water was considered as a thermal insulation layer. Based on the literature [22], if a concrete surface has a thermal insulation layer, the heat transfer coefficient of the thermal insulation layer can be considered same as the convective heat transfer coefficient. According to the time and depth of water circulation during the construction process, the convective heat transfer coefficient during the water storing process was calculated by using Equations (11) and (12) as shown below. At the same time, the corresponding enhancement coefficient [22] was considered according to the interval time. The convective heat transfer coefficient of the top surface of the foundation at each step was obtained through an interpolation. A curve was fitted as a function of the top surface temperature as shown in Figure 11.  
\[\beta=23.9+14.5\
u \tag{11}\] \[\beta_{\mathrm{s}}=\frac{1}{R_{\mathrm{S}}}=\frac{1}{(1/\beta)+ \sum\left(\mathrm{h_{i}}/\lambda_{\mathrm{i}}\right)} \tag{12}\]  
In Equation (11), \(\
u\) is the wind speed of air. In Equation (12), \(\beta_{s}\) is the convective heat transfer coefficient of the top surface of the foundation; \(R_{s}\) is the total thermal resistance of the circulating water layer; \(\beta\) is the convective heat transfer coefficient of the air layer and can be calculated by Equation (11); \(h_{i}\) is the depth of circulating water; and \(\lambda_{i}\) is the thermal conductivity of circulating water at different depths.  
Figure 10: Model of 1/4 foundation.