esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive Traffic Signal Control Method Based on Offline Reinforcement Learning
## 1 Introduction  
Traffic signal control, a critical tool for alleviating traffic pressure and improving road efficiency, plays a key role in this context. However, with the rapid growth in vehicle numbers and the diversification of traffic patterns, traditional fixed-time signal control methods [1] can no longer meet the demands of dynamic traffic, exacerbating congestion. This makes the study of adaptive traffic signal control (ATSC) methods particularly urgent.  
ATSC dynamically adjusts signal phases and durations by analyzing real-time data such as traffic flow and queue length, allowing for a better response to rapidly changing traffic conditions. This paper proposes an intelligent signal control strategy based on Offline RL, with the following contributions:  
(1). This paper introduces a novel strategy for modeling and predicting traffic signal phases and durations as independent variables. The determination of the phase is based on a comparative analysis of the q-values for each phase, while the phase duration is ascertained by multiplying the model's output of a continuous decimal with a preset maximum duration. This approach significantly enhances the accuracy of phase and duration predictions.  
(2). We propose a method called SD3-Light, an adaptive traffic signal control approach that integrates advanced Offline RL techniques. The model can accurately predict the optimal sequence of signal phases and their durations based on multidimensional intersection state features, ensuring that the control strategy effectively adapts to complex and variable traffic environments.  
(3). We designed and constructed a comprehensive offline dataset to support model training and validation. This dataset incorporates diverse intersection characteristics from a specific traffic network and provides detailed records of various phase configuration schemes. By leveraging these rich data samples, the proposed method can train an efficient model that adapts to various traffic conditions without relying on real-time traffic data.
## 3 Methodology and Model
### Three-Dimensional PSA-DV-Hop Algorithm Based on Optimal Subset of Nodes
#### 3.3.1 Node Correlation Degree  
To address the aforementioned issues, a subset strategy based on correlation degree is proposed. The correlation degree of beacon nodes integrates multiple similarity metrics to assess the influence and importance of each beacon node. For a beacon node, its distance similarity is defined as the reciprocal of the difference in the number of hops to the unknown node:  
\[DS_{xy}=\frac{1}{hops_{x}-hops_{y}} \tag{32}\]  
Local path index (LPI) [21] quantifies the similarity of topological relationships between nodes and is expressed as Equation (33):  
\[LPI_{xy}=A^{2}+\lambda A^{3}+\cdots+\lambda^{n-2}A^{n} \tag{33}\]  
where \(A^{n}\) represents the set of distinct connectivity paths of length \(n\) between beacon node \(x\) and \(y\). \(\lambda\) is a damping factor that controls the path weights. In this paper, \(\lambda\) is set to 0.5, meaning the weight of each order path is half that of the previous order path. This setting balances the contributions of different orders of paths to the LPI while preventing any single order of paths from excessively influencing the result.  
Figure 2: Flowchart of the 2D algorithm.  
The degree of similarity between neighbor nodes can be determined by resource allocation (RA) [22]. The RA between two nodes is defined as the number of resources received by node \(y\) from \(x\):  
\[RA_{xy}=\sum_{u\in N(x)\cap N(y)}\frac{1}{|N(u)|} \tag{34}\]  
The correlation of a beacon node, combining the above multiple parameters and weighted by normalization, can be expressed as follows:  
\[S_{xy}=\alpha\cdot\frac{DS_{xy}-DS_{min}}{DS_{max}-DS_{min}}+\beta\cdot\frac{ LPI_{xy}-LPI_{min}}{LPI_{max}-LPI_{min}}+\gamma\cdot\frac{RA_{xy}-RA_{min}}{ RA_{max}-RA_{min}} \tag{35}\]  
where \(\alpha\), \(\beta\), and \(\gamma\) are weighting factors set to 0.5, 0.2, and 0.3, respectively.
# Link Connectivity and Coverage of Underwater Cognitive Acoustic Networks under Spectrum Constraint
## 4 Analysis of Link Connectivity and Probability of Coverage
### Probability of Coverage  
In traditional terrestrial cellular networks, the probability of coverage is defined as the probability that the received signal-to-interference-plus-noise ratio (SINR) is higher than a threshold [20]. Specifically, for cellular networks with a distribution of distance between a user and a base station, the probability of coverage is the expectation (in terms of the distance between a user and a base station) of the probability that the received SINR is higher than a threshold [20]. However, for SUs in UCANs, if signal from an SU can cover another SU (i.e., an SU can connect with another SU), it needs two conditions: (1) SUs can topologically connect to each other (refer to Section 4.1); (2) both SUs have the spectrum (refer to Section 4.2). Therefore, in contrast to terrestrial cellular networks, we originally define the probability of coverage of SUs in UCANs as the expectation (in terms of the distance between two SUs) of the probability that an SU can connect with another SU. Then, the probability of coverage, denoted by \(p_{cov}\), can be expressed as follows:  
\[p_{cov}=E_{r}[p_{con}], \tag{14}\]  
where \(E_{r}[*]\) is the expectation of \(*\) in terms of the distance \(r\).  
We assume that the distance between a pair of SUs \(r\) follows the uniform distribution in \((0,R_{d}]\), where \(R_{d}\) is the maximum distance of \(r\). Then, Equation (14) can be expressed by  
\[p_{cov}=\int_{0}^{R_{d}}p_{con}\cdot f_{r}(r)dr=\int_{0}^{R_{d}}p_{con}\frac{1 }{R_{d}}dr, \tag{15}\]  
where \(f_{r}(r)\) is the probability distribution function (PDF) of \(r\) and \(p_{con}\) is given by Equations (12) and (13).
# ITFDS: Channel-Aware Integrated Time and Frequency-Based Downlink LTE Scheduling in MANET
###### Abstract  
One of the crucial problems in Industry 4.0 is how to strengthen the performance of mobile communication within mobile ad-hoc networks (MANETs) and mobile computational grids (MCGs). In communication, Industry 4.0 needs dynamic network connectivity with higher amounts of speed and bandwidth. In order to support multiple users for video calling or conferencing with high-speed transmission rates and low packet loss, 4G technology was introduced by the 3G Partnership Program (3 GPP). 4G LTE is a type of 4G technology in which LTE stands for Long Term Evolution, followed to achieve 4G speeds. 4G LTE supports multiple users for downlink with higher-order modulation up to 64 quadrature amplitude modulation (QAM). With wide coverage, high reliability and large capacity, LTE networks are widely used in Industry 4.0. However, there are many kinds of equipment with different quality of service (QoS) requirements. In the existing LTE scheduling methods, the scheduler in frequency domain packet scheduling exploits the spatial, frequency, and multi-user diversity to achieve larger MIMO for the required QoS level. On the contrary, time-frequency LTE scheduling pays attention to temporal and utility fairness. It is desirable to have a new solution that combines both the time and frequency domains for real-time applications with fairness among users. In this paper, we propose a channel-aware Integrated Time and Frequency-based Downlink LTE Scheduling (ITFDS) algorithm, which is suitable for both real-time and non-real-time applications. Firstly, it calculates the channel capacity and quality using the channel quality indicator (CQI). Additionally, data broadcasting is maintained by using the dynamic class-based establishment (DCE). In the time domain, we calculate the queue length before transmitting the next packets. In the frequency domain, we use the largest weight delay first (LWDF) scheduling algorithm to allocate resources to all users. All the allocations would be taken placed in the same transmission time interval (TTI). The new method is compared against the largest weighted delay first (LWDF), proportional fair (PF), maximum throughput (MT), and exponential/proportional fair (EXP/PF) methods. Experimental results show that the performance improves by around 12% compared with those other algorithms.  
**Keywords:** mobile ad-hoc networks (MANETs); mobile computational grids (MCGs); frequency domain scheduling; long term evolution (LTE); largest weight delay first (LWDF) +
Footnote †: journal: Journal of Computer Science
# Optimizing Weighted Fair Queuing with Deep Reinforcement Learning for Dynamic Bandwidth Allocation
## 4 Proposed Model  
### Model Definition  
Using the WFQ discipline as a scheduling algorithm, the information system can be described as a G/G/1/K parallel queueing system [22], where routers are modeled as such. Differential arrival flows arrive at each queue independently and are then sent at a rate determined by the appropriate bandwidth allocation. The arrival rates and patterns of incoming packets are erratic and change over time, whereas the router's overall bandwidth resources are constant. To enhance the system's performance, allocating limited bandwidth resources across queues with varying arrival attributes is crucial. The proposed system consists of n G/G/1/K queueing systems that share standard bandwidths in parallel, as shown in Figure 1.  
In this study, it is assumed that the packets enter a matching buffer to wait for service after arriving at the system via a process with commonly dispersed interarrival periods, where \(\lambda i\) represents the flow and \(i\) is the arrival rate. The flow \(i\) locates service times for packets from the associated buffer broadly and dispersedly. The service rate is \(\mu_{i}=w_{i}\times b\), where b is the system's total bandwidth and \(w_{i}\) is the server's allocated weight. \(K_{i}\) is the buffer size for the queue\({}_{1}\). If the buffer is full, newly arriving packets will be discarded. The goal is to find a control policy that can effectively decrease the average latency and packet loss rate in a WFQ system.
## 4 Simulation Results and Discussion  
### Performance Evaluation of JS-MFO for Benchmark Functions  
The numerical efficiency of JS-FO was analyzed on eight popular benchmark functions to extensively evaluate its performance. These functions are divided into four unimodal functions and four multimodal functions, which are listed in Table 1[28]. The unimodal functions were used to verify the exploitation of algorithms. The multimodal functions were used to check the exploration of algorithms. Experimental tests were performed for JS-MFO, MFO, and JS separately in order to verify the effectiveness of the hybrid approach. This task was accomplished for 30 trials, with the maximum number of iterations (MaxIt) = 500 and the population size equals 50 (N\({}_{\text{{\em POP}}}\) = 50). Statistical indices such as the average (mean), standard deviation (SD), and minimum (best) of 30 trials were determined for each benchmark function and are shown in Table 2. The findings in the previous table distinctly exhibit the capacity of the proposed JS-MFO in overcoming the basic algorithms JS and MFO in terms of solution quality (in reaching the near-global solution with slightly more execution time). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Fun**} & \multicolumn{3}{c}{**Equation**} & **B** & **Min** & **D** & **T** \\ \hline \(f_{1}\) & \(f(x)=\sum_{i=1}^{n}x_{i}^{2}\) & & \([-100,100]\) & 0 & 30 & Unimodal \\ \(f_{2}\) & \(f(x)=\sum_{i=1}^{n}x_{i}^{2}\) & & \([-10,10]\) & 0 & 30 & Unimodal \\ \(f_{3}\) & \(f(x)=\sum_{i=1}^{n}[100(x_{i+1}-x_{i})^{2}+(x_{i}-1)^{2}]\) & & \([-30,30]\) & 0 & 30 & Unimodal \\ \(f_{4}\) & \(f(x)=\sum_{i=1}^{n}[x_{i}+\prod_{i=1}^{n}|x|\) & & \([-10,10]\) & 0 & 30 & Unimodal \\ \(f_{5}\) & \(f(x)=-20\varepsilon p\left(-0.2\sqrt{\frac{1}{2}\sum_{i=1}^{n}x_{i}^{2}} \right)-c\varepsilon p\left(\frac{1}{2}\sum_{i=1}^{n}\cos(2\pi x_{i})\right)+20+e\) & \([-32,32]\) & 0 & 30 & Multimodal \\ \(f_{6}\) & \(f(x)=\sum_{i=1}^{n}[x_{i}^{2}-10\cos(2\pi x_{i})+10]^{2}\) & & \([-5.12,5.12]\) & 0 & 30 & Multimodal \\ \(f_{7}\) & \(f(x)=\frac{\pi}{n}\{10 sin^{2}(\pi y_{1})+\sum_{i=1}^{n}(y_{i}-1)^{2}[1+10\sin^{ 2}(\pi y_{i+1})]+(y_{n}-1)^{2}\}\) & & \([-50,50]\) & 0 & 30 & Multimodal \\  & & \(+\sum_{i=1}^{n}u(x_{i},10,100,4)\), \(y_{i}=1+\frac{x_{i}+1}{4}\) & & & & & \\ \(f_{8}\) & \(f(x)=0.1\left\{\sin^{2}(3\pi x_{1})+\sum_{i=1}^{n}(x_{i}-1)^{2}[1+\sin^{2}(3 \pi x_{i+1})]+(x_{n}-1)^{2}[1+\sin^{2}(2\pi x_{n})]\right\}\) & \([-50,50]\) & 0 & 30 & Multimodal \\  & & \(+\sum_{i=1}^{n}u(x_{i},\,5,\,100,\,4)\) & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Benchmark function characteristics.
## 2 Preliminaries  
In this paper, we assume that all of the wireless sensor nodes (referred to sensor nodes or nodes) and the sink node (sink) are stationary after deployment. These nodes are assumed to be homogenous, with the same communication capacity, sensing range, and data processing capacity. In addition, we assume that the position of every sensor node is known _a priori_, and the sink is aware of the positions of all sensor nodes in this study. The sensing area for a sensor node is assumed a disk-shaped area with a specific diameter (\(r_{s}\)). In fact, the CoCMA can be applied to WSNs with irregular-shaped sensing areas as long as their sensing coverage models are given.  
The proposed approach considers the point coverage problem in a given area. The POI is the place where the event occurs. We assume that an event signal is always generated at each POI. As shown in Figure 1, each POI is surrounded by a number of sensor nodes. If the POI is located within the sensing range \(r_{s}\) of a given sensor node, the sensor node will detect the event signal and transmit the measured data to the sink by a multi-hop manner.
## 3 HIPERLAN/2 System  
HIPERLAN is a standard on digital high-speed wireless communication, developed by ETSI [10; 14; 15; 16]. Orthogonal frequency-division multiplexing (OFDM) is employed to operate in the 5 GHz band [17]. Figure 2 shows the HIPERLAN/2 system.  
Figure 2: Classical HIPERLAN system.  
The model of HIPERLN/2 that exists in the MATLAB consists of several stages; the first stage is the Bernoulli random generator that generates the binary data depending on the probability of zero, followed by the convolutional coder, which codes the data by using one of the polynomials that can be written by using the command (poly2 trellis(3, [7 5])). At the same time, the encoder can do the puncturing of encoded data. The third part is the interleaver, which interleaves the data using two types of interleavers (matrix interleaver and equation interleaver). The interleaving perform of the matrix interleaver is done by filling a matrix with symbols row by row, then sending the filled data column by column, whereas equation interleaver rearranges the data depends on specific equation that determine the symbol position without omitting any symbols. The fourth stage is the modulator, which modulates the data using m-modulation scheme, then the output symbols from the modulator will be normalized before sending it by using OFDM transmitter [14; 15]. These stages represent the transmitting part. There is a channel of Additive white Gaussian noise (AWGN) type between the transmitter and the receiver. The receiver consists of the OFDM receiver, demodulator, two stages of deinterleavers, and Viterbi decoder. The receiver components work opposite to those components in the transmitter side.
## 4 Statistical Results
### Whole-Period Region  
To comprehensively investigate the influence of scaling ground motion on the occurrence of further damage in systems characterized by different periods and lateral strengths, the biases arising from the scaling of aftershock ground motion are averaged over a span of 30 periods and three values of \(R\). The average bias for systems exhibiting energy proportional to plastic deformation (EPP) across the complete spectrum of periods is illustrated in Figure 10. Furthermore, Figure 10 also presents the corresponding outcomes for systems with modified Clough (MC), pinching (PH), and strength softening and deterioration (SSD) hysteretic behaviors, in order to assess the effects of aftershock ground motion scaling on the occurrence of additional damage in systems exhibiting different hysteretic behavior.  
The findings presented in Figure 10 demonstrate that the scaling of aftershock ground motion has a greater impact on the additional damage incurred by EPP systems, with the exception of cases where the scale indicators used are \(\
abla PGA\) and \(\
abla PGV\). This is due to the fact that the biases observed in EPP systems are generally higher compared to systems that exhibit degradation or pinching behavior, such as MC systems, PH systems, or SSD systems. In the context of scale indicators, it is observed that biases within 20% are evident in degrading systems, whereas biases associated with EPP systems surpass this threshold.  
Figure 8: Comparison between the benchmark model and scaled model for the EPP system with \(R=4\) and \(T=2.0\) s: (**a**) \(\
abla S_{\text{a}}\), \(SF=1.0\); (**b**) \(\
abla S_{\text{a}}\), \(SF=6.0\); (**c**) \(\
abla PGD\), \(SF=1.0\); (**d**) \(\
abla PGD\), \(SF=6.0\).  
Furthermore, the relationship between bias and hysteretic systems varies depending on the specific scale indicators employed. For example, the differences among the biases of four hysteretic systems are large for \(\
abla PGD\), while the corresponding differences are small for \(\
abla PGV\). The scale indicators of \(\
abla SI\), \(\
abla PGV\), \(\
abla Ic\), and \(\
abla Sa\) exhibit superior performance compared to the other four scale indicators, effectively managing bias within a range of 30%, even for large \(SF\). In different engineering contexts, the permissible bias varies. Based on the results from Figure 10, and considering the allowed bias, the maximum allowable \(SF\) can be determined for different structures and \(IM\).  
Figure 9: Potential distortion caused by the scaling of ground motion for the EPP in the long-period range.
## 3 Research Results
### Questionnaire Survey Results  
#### 3.2.1 Analysis of Basis Data  
During the field experiments, a total of 72 visitors were invited to use the proposed system and filled out the questionnaire survey forms afterward. Two of them were excluded because their questionnaire data were incomplete and regarded as invalid. The statistics of the remaining 70 visitors' basic data are shown in Table 6, from which it can be seen that there are 30 males and 40 females, and the younger group aged 19-30 accounts for 43 with a percentage of 61.4%. Regarding educational backgrounds, the largest group is the one with bachelor's degrees or above, accounting for 65.7% with 46 persons. In terms of the job category, students account for 61.4%, and the other categories account for 27.1%, of which most are retired elderly people. In short, the sample visitors in this survey are mainly young students, as desired for the aim of promoting the puppet culture among younger people.  
In terms of the media source, 47.1% and 22.9% of the audience watch puppet shows on the spot and on TV, followed by 21.4% watching on the Internet. It can be seen that the majority of the audience watches puppet shows in the traditional way. In addition, 40% of the audience watched puppet shows in the past two weeks, 20% in the past two months, and 20% in the past year. This reveals that 80% of people watch puppet shows in a year, which is a very high proportion. When asked whether they have experience in operating devices similar to the proposed system, 65% of the respondents said no, suggesting that the design of this study is innovative and meaningful.
# A Survey on Energy-Aware Security Mechanisms for the Internet of Things
## 3 Security Protections in IoT Environments
### Defense at the Device Layer
#### 3.3.5 Secure Enclaves  
Secure enclaves use specialized hardware controls used to securely isolate data and instructions at the CPU level [52]. Such an isolation is furnished by strong cryptography that verifies the authenticity of the enclave while offering privacy and integrity of the enclave code and the sensitive data being processed.
###### Abstract  
Underwater wireless sensor networks (UWSNs) have become highly efficient in performing different operations in oceanic environments. Compared to terrestrial wireless sensor networks (TWSNs), MAC and routing protocols in UWSNs are prone to low bandwidth, low throughput, high energy consumption, and high propagation delay. UWSNs are located remotely and do not need to operate with any human involvement. In UWSNs, the majority of sensor batteries have limited energy and very difficult to replace. The uneven use of energy resources is one of the main problems for UWSNs, which reduce the lifetime of the network. Therefore, an energy-efficient MAC and routing techniques are required to address the aforementioned challenges. Several important research projects have been tried to realize this objective by designing energy-efficient MAC and routing protocols to improve efficient data packet routing from Tx anchor node to sensor Rx node. In this article, we concentrate on discussing about different energy-efficient MAC and routing protocols which are presently accessible for UWSNs, categorize both MAC and routing protocols with a new taxonomy, as well as provide a comparative discussion. Finally, we conclude by presenting various current problems and research difficulties for future research.  
energy-efficiency; MAC protocols; ALOHA; TDMA; FDMA; bio-inspired E2 RPs; cluster based EPRPs; reinforcement learning based E2 RPs +
Footnote †: journal: Electronics Letters
# Performance Evaluation of Laetitic Subgrade Soil Treated with Lime and Coir Fibre-Activated Carbon
## 3 Test Results and Discussion  
### pH  
Figure 3 indicates plots of pH values versus days for various lime and activated carbon contents. The pH of the investigated lateritic soil is 4.05, which indicates that it is acidic. The pH value of 12% lime-treated soil is 12.42, but it drops to 11.13 after more than 150 days. In this study, the results for pH values with rising lime and curing time were consistent with [57] for cement and [58] for lime, in that the authors reported that pH declined during curing time. Likewise, the pH decreased by 3%, 6%, 9%, and 12% Lime during curing time in the current study. At 12% lime, the alumina and silica components in the soil are dissolved out of the soil, making it accessible to combine with Ca\({}^{2+}\) to develop calcium silicates and aluminates as cementing products. Based on the pH test results, 12% lime is the initial lime consumption. When a soil has a pH value smaller, it needs more lime content to improve soil strength [57] due to significant hydration products formed (such as CAH, CSH, and hydrated lime). Lime stabilisation decreases volume change and Afterberg limits of soils but increases the strength and shrinkage limit of the soil-cement matrix [59]. However, in this study, the four percentages of the lime were employed (3%, 6%, 9%, and 12%) to treat lateritic soil. AC also increased pH to 6.6, 6.8, and 7 for 1%, 2%, and 3% AC, respectively. It is evident that 3% lime increases pH value significantly in comparison with AC because lime is an alkali material, while AC is neutral in this study.