esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Dynamic Resource Allocation and ACB Scheme for Delay-Sensitive Devices
### Proposed Optimization Scheme
\(N>L_{avail\_DS}\)  
In this situation, the amount of preamble resources is not sufficient for DS devices that attempt to access the network, thus we take \(L_{DS}^{*}\) as the maximum value \(L_{avail\_DS}\) to serve more devices. Our principle is to make use of the ACB mechanism for the reason that the number of contending devices can be controlled appropriately by adjusting the ACB factor. Thus, the problem can be formulated as:  
\[ \begin{split} p_{ACB}^{*}=\arg\max_{0\leq p_{ACB}\leq 1}P_{L\_succ }\\ s.t.:& T_{delay}\leq D_{req},\end{split}  \tag{11}\]  
where \(p_{ACB}^{*}\) is the optimal value of \(p_{ACB}\). We know the average number of DS devices that pass the ACB mechanism is \(N_{pa}=N\cdot p_{ACB}\). Therefore, reviewing the expression of access delay in Equation (7), we are able to obtain the set of \(p_{ACB}\) to meet the delay requirement as follows:  
\[p_{set}=\left\{p_{ACB}|\frac{T_{slot}}{p_{ACB}\cdot\left(1-\frac{1}{L_{avail \_DS}}\right)^{N\cdot p_{ACB}-1}}\leq D_{req},0<p_{ACB}\leq 1\right\} \tag{12}\]  
by substituting Equation (12) into Equation (11), the optimal value of \(p_{ACB}\) can be written as:  
\[p_{ACB}^{*}=\arg\max_{p_{ACB}\in P_{Pat}}P_{L\_succ} \tag{13}\]  
Similar to the first situation, for the special case when \(p_{set}\) obtained by Equation (12) is \(\varnothing\), we take \(p_{ACB}^{*}=\frac{L_{avail\_DS}}{N}\) to maximum the preamble utilization rate. In order to facilitate the understanding of the proposed control scheme which has been discussed in the previous paragraph, our algorithm to obtain \(L_{DS}^{*}\) and \(p_{ACB}^{*}\) when the number of attempting DS devices in a certain RA slot is \(N\) is summarized in Algorithm 1. ```
1:\(N\): number of DS devices attempt to access
2:\(p_{ACB}\): ACB factor
3:\(L_{DS}^{*}\): optimal number of preambles allocated to DS devices
4:\(p_{ACB}^{*}\): optimal value of ACB factor for DS devices
5:\(L_{avail\_}\): number of available preambles
6:if\(N\leq L_{avail\_DS}\)then
7: Set \(p_{ACB}^{*}=1\);
8: Compute \(L_{set}\) through Equation (9);
9:if\(L_{sat}=\varnothing\)then
10:\(L_{DS}^{*}=N\);
11:else
12: Compute \(L_{DS}^{*}\) through Equation (10);
13:endif
14:else if\(N>L_{avail\_DS}\)then
15: Set \(L_{DS}^{*}=L_{avail\_DS}\)
16: Compute \(p_{set}\) through Equation (12);
17:if\(p_{set}=\varnothing\)then
18:\(p_{ACB}^{*}=\frac{L_{avail\_DS}}{N}\);
19:else
20: Compute \(p_{ACB}^{*}\) through Equation (13);
21:endif
22:endif
```  
**Algorithm 1** Proposed dynamic resource allocation and ACB scheme in a slot
## 2 Materials and Methods
### Configuration of the ANN Model  
In this study, a multilayer perceptron class of ANN is used. The network comprises two hidden layers, and each layer consists of 40 and 30 artificial neurons, respectively. Cross-validation technique is employed to validate the network model and to prevent overfitting issues [50; 60]. As an activation function, LeakyReLU (Leaky Rectified Linear Unit) is chosen for hidden layers. It is usually used to overcome the vanishing gradient issues. This problem could face other activation functions, such as tan or sigmoid [61]. During the training phase, Adam optimizer [62] is employed, which is one of several optimization algorithms used to optimize neural networks [63]. It is based on the gradient descent method to minimize errors that are made by a prediction model during the training phase [64].  
Three subsets of the data are used by splitting the entire measurements called: training, validation, and test set. 80% of the total number of measurements is used for training set, while 10% for each validation and testing (Table 1). Data used for training is employed to initiate network patterns or features, such as bias, weights, etc. However, the validation data provides a way to optimize the model's hyper-parameters, i.e., number of hidden layers and neurons. The term \"cost function\" is an expression gives indication of the predictive power of the model. Since the predictions are continuous values (insulation curves), the root-mean-square error (\(RMSE\)) function can be used as a cost function,  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(\hat{y}_{i}-y_{i})^{2}}, \tag{3}\]  
where \(n\) presents the number of observations (measurements) that are used in the training phase. Output (predicted) and input (measured) values are donated by \(\hat{y}_{i}\) and \(y_{i}\), respectively. In this study, the developed model illustrates an overall prediction accuracy of 4.44 dB for all predicted and measured curves. However, for a better judgment of the results and considering various frequency bands, each estimated insulation curve is analyzed with comparison to measured one using the \(RMSE\) function considering values in each one-third-octave band from 50 Hz to 5 kHz.
# KCFS-YOLOv5: A High-Precision Detection Method for Object Detection in Aerial Remote Sensing Images
## 2 Related Works  
### Current Mainstream Object Detection Algorithm  
Many studies have been investigated to address these tricky challenges. For example, before 2010, although traditional object detection methods such as template matching [7] and machine learning [8] are convenient to implement, the detection performance was poor. In 2012, [PERSON] et al. [25] proposed a Deep Convolutional Neural Network (D-CNN) called AlexNet to bring object detection into the era of deep learning. [PERSON] [9] proposed a two-stage object detection algorithm called the region convolutional neural network (R-CNN), it surpasses other algorithms in detection accuracy, but the detection speed is slower. On this basis, [PERSON] et al. [10] and [PERSON] et al. [11] proposed the Fast Region-Based Convolutional Neural Network (Fast R-CNN) and the Faster Region-Based Convolutional Neural Network (Faster R-CNN) which improve the speed and accuracy of the R-CNN algorithm. Although the improved two-stage algorithm has improved the detection efficiency, it is still difficult to meet the needs of practical detection. Therefore, to intensify the practicability of the algorithm, [PERSON] et al. [18] proposed a single-stage detection (SSD) algorithm; SSD serial algorithms have high detection accuracy and speed, but a disadvantage is that they require many manual parameters, which causes difficulties in practical applications. Since 2016, [PERSON] et al. [12] introduced the YOLO module (You Only Look Once), and YOLOv2 [13]. They balance the detection accuracy and detection speed to realize real-time detection of ordinary-sized objects. Meanwhile, because of its unique grid cell framework, the detection performance of tiny objects is relatively poor. In 2018, [PERSON] et al. [14] proposed YOLOv3, by adding Mosaic data augmentation to strengthen the network's ability to surpass the two-stage model in accuracy and YOLOv4 [15] was born in 2020. YOLOv5 is the most recognized sequel to the YOLO serial algorithms. Although the average detection accuracy and the detection efficiency have been improved compared to the previous works, the detection effect of multi-scale objects with complex backgrounds still needs to be improved.
## 1 Introduction
[PERSON] et al., constructed Pb/PbO/\(\beta\)-Bi\({}_{2}\)O\({}_{3}\) microspheres for bisphenol A degradation and NO removal [53] and [PERSON] et al., obtained three types of supported Cobalt catalysts (CoO\({}_{x}\)/SiO\({}_{2}\), CoO\({}_{x}\)/Al\({}_{2}\)O\({}_{3}\), CoO\({}_{x}\)/TiO\({}_{2}\)) for CO oxidation [54]. There are limited reports for metal titanates in degrading gaseous pollutants. More specifically SrTiO\({}_{3}\) is the main perovskite material associated with degradation of VOCs like isopropyl alcohol (IPA) [55], 2-propanol [56; 57], and toluene [58; 59]. [PERSON] et al., [60] synthesized SrTiO\({}_{3}\) perovskites doped by two transition metals (Cu and Mn) in B-site by one pot hydrothermal method with the Mn-doped catalyst exhibiting the highest catalytic activity as toluene is completely converted to CO\({}_{2}\) at temperatures below 350 \({}^{\circ}\)C.  
In the present study metal titanate nanorods ATO\({}_{3}\), A = Mg, Ni, Co, Zn were successfully synthesized in order to be used as photocatalysts for toluene photooxidation (in the gas phase), activated by light emitting diodes (LEDs), a low powered and cost irradiation source. Hence, the impact of perovskite type materials on photocatalysis expands in gaseous pollutants and the effect of A-site metal on optical and structural properties are examined. To the best of our knowledge, this is the first study on the photocatalytic activity of metal titanate nanorods, in gas phase treatment.
###### Abstract  
This research investigated the effects of types of cohesive-frictional soil and geotextile reinforcement configurations on the bearing capacity of reinforced soil foundation (RSF) structures, via laboratory test and numerical simulation. The four reinforcement configurations studied for the RSF included: (i) horizontal planar form of geotextile, (ii) full-wraparound ends of geotextile, (iii) full-wraparound ends of geotextile with filled-in sand, and (iv) full-wraparound ends of geotextile with filled-in sand and sand backfill. The foundation soils studied were mixtures of fine sand and sodium bentonite at replacement ratios of 0, 20, 40, 60, 80, and 100% by dry weight of sand to have various values of plasticity index (PI). The numerical analysis of RSF structures was performed using PLAXIS 2D software. Several factors were studied, which included: embedment depth of the top reinforcement layer (U), width of horizontal planar form of the reinforcement (W), and spacing between geotextile reinforcement layers (H). Number of reinforcement layers (N) was varied to determine the optimum parameters of U/B, W/B, H/B, and N, where B is the footing width. The most effective improvement technique was found for the full wraparound ends of geotextile with filled-in sand and sand backfill. The outcome of this research will provide a preliminary guideline in a design of RSF structure with different ground soils and other RSF structures with different geosynthetic types.  
ground improvement; earth reinforcement; geotextiles; numerical analysis +
Footnote †: journal: Applied Sciences
# A Review of Current Trends, Techniques, and Challenges in Large Language Models (LLMs)
## 9 Future Directions and Development Trends
### Continual Learning and Adaptability  
For LLMs to have excellent utility, they must continually learn from new data, adapt to changing contexts, and retain previously learned knowledge. Approaches to accomplishing these goals require research investigations along multiple directions. First, the development of algorithms and methodologies for incremental learning is needed to enable models to learn new information without forgetting already learned information. Replay-based methods, regularization, and parameter isolation are some techniques that need further investigation.  
LLMs with external memory components like _attentional interfaces_ help to retain previously learned information. These are referred to as memory-augmented architectures. LLMs need a mechanism to prioritize new information while preserving old knowledge to realize continual learning. Using _adaptive learning rate schedules_, a model can dynamically adjust learning rates for different parts of the model or specific examples. Task-agnostic representations help LLMs learn more generalized features that transfer across different tasks. Learning task-agnostic representations helps in continual learning as models can adapt to new tasks without drastic retraining.  
Regularization methods encourage model parameters to remain stable and selectively update them for new information, which aids in continual learning. For example, elastic weight consolidation (EWC) and synaptic intelligence help models to retain learned information. As noted earlier, meta-learning and few-shot learning approaches enable models to adapt quickly to new tasks or domains with minimal data. Finetuning the models on new data while leveraging pretrained representations helps in adaptation. Another approach to adaptation is through ensemble models, which combine learning paradigms such as episodic memory systems and continual learning techniques.
# A Multi-Gateway Behaviour Study for Traffic-Oriented LoRaWAN Deployment
## 4 Simulations and Results Analysis
### Studied Scenarios  
In this subsection, we study three scenarios to show how the payload length impacts the A-PDR and how it interacts when varying the sending packet frequency, the packet payload, the number of gateways, and the number of nodes, with and without confirmed traffic.
## 4 Implementation
### Simulation BER of COOK Scheme  
In the receiver side of the communication system, the electrical amplitude of the received signal is illustrated as:  
\[r(t)=I(t)+\sum_{i=-\infty}^{+\infty}I(t)a_{i}g(t-iT_{symbol})+n(t) \tag{3}\]  
where \(a_{i}\) is the level of the \(i^{th}\) OOK symbol and \(a_{i}\)\(\in\) {0,1}; we assumed that the probabilities of bit 0 and 1 are \(P_{0}\) and \(P_{1}\), \(g(t)\) presents as the rectangular function, and \(T_{symbol}\) is the symbol duration. Because of the only additive white Gaussian noise (AWGN) in channel model, the BER can be presented [26] in Equation (4).  
\[P_{e}=\frac{1}{2}erfc(\sqrt{\frac{E_{b}}{2\sigma_{n}^{2}}}) \tag{4}\]  
Because of the only AWGN channel, the received signal in the receiver side for bits \"0\" and \"1\" is shown as follows:  
\[r(t)=\left\{\begin{array}{c}n(t),a_{i}=0\\ 2I(t)+n(t),a_{i}=1\end{array}\right. \tag{5}\]  
Figure 10: SNR measurement.  
Figure 11 shows the estimated curve of the COOK scheme versus the SNR with the matched filter and zero-crossing filter compared with the theoretical bit error probability of OOK modulation based on the additive white Gaussian noise channel. The figure demonstrates that the use of a matched filter to decode the COOK scheme is better than the zero-crossing filter. It illustrates that the performance of our proposed scheme is better than that of the COOK proposed in [22]. From the SNR measurements of Figures 10 and 11, we can gain a BER of \(10^{-4}\) at 20 m (SNR of 12.5 dB, exposure time of 50 us) using the matched filter. However, with the zero-crossing filter, we need an SNR of 15 dB corresponding to the distance of 15 m and an exposure time of 50 us. The exposure time has a significant role in the SNR measurement because the SNR improves with an increase in the exposure time. However, a long exposure will reduce the communication bandwidth. To achieve the best performance, it is important to set the exposure time carefully depending on the application. Besides that, the COOK using matched filter improved the performance significantly, which is almost the same with the theoretical bit error probability of OOK modulation in \(E_{b}/N_{0}\) values of 0-8 dB. This facility to improve the SNR values of the system without changing the exposure time, from that increase the distance communication without reducing the system performance.
## 2 RIGHTrack Test
### Measured Parameters of Full-Scale Track  
Table 2 shows the test methods that can be used to obtain the skid resistance indicators. The test data from the RIGHTrack include the sideways force coefficient (SFC), the British pendulum number (BPN), the mean texture depth (MTD) measured by the sand patch method, and the mean texture depth measured by a laser method (SMTD) [15]. The current study focuses on the surface texture depth measured using vehicle-carried lasers, following the Chinese standard procedures (JTG/T E61-2014). The laser texture depth meter is composed of a range sensor, a longitudinal distance sensor, and a high-speed signal acquisition and data processing system. By using a multi-functional pavement condition measuring vehicle as shown in Figure 3, the sensor measured texture depth data can be collected very efficiently [16].  
The vehicle-carried laser texture depth meter is an instrument that uses the laser range and distance sensors to automatically and quickly detect the pavement surface texture [22].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Indicator** & **Abbreviation** & **Test Method** & **Specification** \\ \hline Skid Number & SN & Locked-wheel skid tester & ASTM E274-97-2006 [17] \\ Sideways Force Coefficient & SFC & Lateral force coefficient test & BS DD CEN/TS 15901-6-2009 [18] \\ Mean Summer SCRIM Coefficient & MSSC & SCRIM device & BS DD CEN/TS 15901-6-2009 [18] \\ British Pendulum Number & BPN & British pendulum tester & ASTM E303-93-2018 [19] \\ Mean Texture Depth & MTD & Sand patch method & ASTM E965-15-2019 [20] \\ Mean Texture Depth & SMTD & Vehicle bearing road laser & JTG/T E61-2014 [21] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Test methods for the characterization of skid resistance.  
Figure 3: Multifunctional vehicle equipped with a laser texture depth tester.  
Figure 2: Cumulative standard axle loads (CSAL) applied during RIGHTrack testing.  
It projects a beam of infrared laser onto the pavement surface, and the scattered signal is received by a light collector soon afterwards. The elevation data of the pavement texture are saved and then used to calculate the sensor measured texture depth (in the unit of mm). Assuming that the length of road where the SMTD is measured is \(D\), the calculation formula of SMTD is [23]:  
\[\text{SMTD}_{D} =\sqrt{\frac{n\sum\limits_{i=1}^{n}y_{i}^{2}-\left(\sum\limits_{i =1}^{n}y_{i}\right)^{2}-\frac{12\left(\sum\limits_{i=1}^{n}x_{i}y_{i}\right)^ {2}+p}{n^{2}-1}}{n^{2}}} \tag{1}\] \[p =\frac{5\left[\left(n^{2}-1\right)\sum\limits_{i=1}^{n}y_{i}-12 \sum\limits_{i=1}^{n}x_{i}^{2}y_{i}\right]^{2}}{4(n^{2}-4)}\] (2) \[n =\frac{D}{l} \tag{3}\]  
where, \(\text{SMTD}_{D}\) is the SMTD value corresponding to the reference calculation length \(D\), \(D\) = 300 mm; \(n\) is the number of locations where the profile elevations are measured; \(x_{i}\) is the nominal distance at point \(i\), \(x_{1}=-\frac{(n-1)}{2}\), \(x_{n}=\frac{(n-1)}{2}\); \(y_{i}\) is the measured profile elevation value at point \(i\); \(l\) is the spacing between the measuring points, \(l\leq 5\) mm.
# Enhanced Phase Retrieval Method Based on Random Phase Modulation
## 5 Conclusions  
In summary, two frameworks were used to reconstruct the beam-propagation-based random phase modulation method: the serial algorithm from SSPR and the parallel algorithm. Although the traditional serial algorithm can converge with a small number of iterations, the anti-noise ability is poor; thus, the reconstruction quality is seriously affected in practical application. On the other hand, the parallel framework has the ability to suppress noise, but the convergence speed is decreased. In this work, we proposed a serial-parallel cascaded framework to achieve quality imaging and rapid convergence of the beam-propagation-based random phase modulated method. The proposed cascaded framework uses an iterative result from the serial algorithm as initialization of the parallel algorithm to simultaneously ensure noise immunity and convergence speed. The simulation and experiment demonstrated the performance of the cascaded method, which inherited the merits of the serial and parallel methods. Furthermore, we analyzed the impact of the initial result from the serial process with different iteration numbers on the reconstruction performance to find the optimum number of iterations of the serial process. The compactness, quick convergence, robustness, and anti-noise properties grant the proposed serial-parallel cascaded random phase modulation method with good potential as an effective tool for wavefront detection and lensless imaging techniques.  
[PERSON] developed the algorithm; [PERSON] and [PERSON] conceptualized and designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON], [PERSON], and [PERSON] analyzed the data and wrote the paper; [PERSON] applied for the funding and managed the project. All authors read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China (61705232) and the Instrument Developing Project of the Chinese Academy of Sciences (YA17K014). The authors declare no conflicts of interest.
# FL-MD3 QN-Based IoT Intelligent Access Algorithm for Smart Construction Sites
## 5 Conclusions and Future Work  
### Conclusions  
This study addresses the IoT intelligent access problem for multi-site, multi-modal, and multi-user scenarios under a shared BS. The problem is modeled as a triple-objective optimization task: maximizing data transmission rate, minimizing access latency, and minimizing BER under bandwidth, rate, and privacy-security constraints. The FL-MD3 QN-based intelligent access algorithm is proposed. Simulation results demonstrate that FL-MD3 QN outperforms other algorithms in training time, latency, throughput, BER, average access success rate, and reward value. FL-MD3 QN reduces training time by 23% and 18.5% compared to DQN and MD3 QN, respectively. For latency, FL-MD3 QN achieves significant reductions across all service types, particularly 55.9% for video services. In throughput, FL-MD3 QN reaches 3.8 Gbps for video transmission, marking improvements of 72.7% and 58.3% over DQN and MD3 QN. Under high SNR, FL-MD3 QN achieves an ultra-low BER of 0.00045. It also attains high levels in average access success rate and reward value. These results validate FL-MD3 QN as an efficient and reliable IoT access solution for smart construction sites.
# Application of Collision Warning Algorithm Alarm in Fishing Vessel's Waterway
## 3 Application of Collision Warning Algorithm to Real Ship Test  
### Overview of the Real Ship Test  
To verify whether the developed algorithm generates alarms adequately in real seas, a real ship test was conducted for three small fishing vessels at sea near Yeongheung Island, Incheon, South Korea, between 7 a.m. and 4 p.m. on 17 October 2019. The weather was cloudy, and the daily average wind speed was 3.5 kts, which is considered to be relatively calm according to the Beaufort Scale 2 [28]. The three target ships were small, weighing \(\leq\)10 t, and had an LOA of approximately 10 m. The selection of a target ship was based on the fact that ships having a weight <20 t account for 69.65% of the other ships, including other fishing ships among registered ships in Korea [29]. Table 2 lists the specifications of each ship.  
For the test, we installed an omnidirectional antenna (9 dBi) and an exclusive millimeter-wave communication terminal in the three small fishing vessels that sailed according to real ship test scenarios. We verified the generation of collision alarms in accordance with the developed algorithm. Figure 3 shows the images of the front portion of each ship and the installed antennas.  
There are limitations to constructing a scenario that reflects actual maritime traffic conditions. Therefore, the scenarios were designed to include two situations: head-on and crossing situations, as classified by COLREGs [10]. The first scenario included two ships sailing in parallel while another ship sails toward them from the opposite side. The second scenario involved all three ships approaching a single location from different directions. In scenario 1, ships A and C and ships B and C are applied to the head-on situation, and in scenario 2, the crossing situation is applied to each ship. Figure 4 illustrates the two scenarios.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **Ship Type** & **Length (m)** & **Width (m)** & **Depth (m)** & **Gross Tonnage (G/T)** \\ \hline A & Fishing & 10.95 & 3.31 & 0.83 & 4.97 \\ B & Fishing & 10.00 & 3.27 & 0.82 & 6.63 \\ C & Fishing & 10.06 & 4.12 & 0.99 & 9.77 \\ \hline \end{tabular}
\end{table}
Table 2: Detailed specifications of target ships for the real ship test.  
Figure 3: Images of the front portion of each ship used in the real ship test and installed antennas: (**a**) ship A (Gyeonginho), (**b**) ship B (Yeonjaeho), and (**c**) ship C (Ara-2 ho).