esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Scaling and Reliability
#### 3.4.3 Analysis of Stress Testing Results  
Table 1 highlights the differences between single-instance (above) and multiple-instance (below) Ray-based model deployments. Single-instance deployments represent models run directly by VLMs without replication, while multiple-instance deployments leverage Ray to orchestrate and dynamically scale the number of VLM replicas across all available GPUs. By utilizing Ray's ability to create as many replicas as requested (in this case, up to 20 replicas for models under 7B parameters and up to 12 replicas for models above 7B), our system efficiently employs all four A6000 GPUs to handle increasing user loads. As user concurrency grows (from 10 to 20, 30, 40, 50, and beyond), Ray automatically scales the instances accordingly. The comparison in Table 1 consistently shows that multiple-instance models outperform their single-instance counterparts at every tested concurrency level. This performance gap widens as the number of concurrent users rises, demonstrating that distributing the workload across multiple replicas not only improves latency but also stabilizes response times under heavy load. To illustrate this further, consider the case of the LLaMA 8B model at different user loads: 10, 30, 50, and 100 concurrent users. Under single-instance deployment, the model experiences significant latency increases as the user load intensifies. For example, at 10 concurrent users, a single-instance LLaMA 8B model averages about 20.6 s. At 50 users, it jumps to an average of 102.5 s. In contrast, when employing multiple Ray-managed replicas of the LLaMA 8B model, the response times are dramatically lower and much more stable across these conditions. At 10 concurrent users, multiple instances respond in roughly 5.9 s on average; even at 50 concurrent users, they maintain a reasonable 16.9 s. The disparity becomes even clearer at 100 users, where single-instance LLaMA 8B models exceed 146 s while multiple-instance models hold steady at around 35.6 s, showcasing Ray's ability to dynamically adjust resources and sustain performance as concurrency grows. This disparity is depicted in Figure 4, where the rate of growth of single-instance models accelerates as the user number grows, and the rate of multiple instances remains constant, even under heavy loads.
# Extreme Learning Machines to Combat Phase Noise in RoF-OFDM Schemes
## 6 Discussion
For instance, given a 60-GHz application along 1250 km that imposes 14-dB SNR, the laser linewidth can be extended from 0.48 MHz to 0.75 MHz, by replacing the default pilot-based channel correction by the ELM algorithm in the complex domain. Hence, the superiority of the complex ELM among the others of techniques in terms of fighting amplitude and phase noises was confirmed. This advantage was more relevant for long distances and/or high frequencies. For low laser linewidths and high SNRs, the system performance was limited by AWGN and the phase noise of the laser oscillator, respectively. For a product of the fiber length and radio frequency less than 750 km-GHz, the successful OFDM reception depended on the SNR value; the laser linewidth contribution was negligible. Instead, for time delays greater than the OFDM symbol period, the BER limit was unattainable even in the absence of AWGN; see Figure 7. In the design of optical communication schemes, nevertheless, the maximum range was limited by the loss and nonlinearity of the fiber [21], which should be taken into account for a complete analysis. Note that since the bandwidth of the transmitted OFDM signal corresponded to 2 GHz, the radio frequency for this result must be greater than or equal to 20 MHz to properly model the chromatic dispersion effect. Figure 9: Considering the implemented phase noise reduction mechanisms, the BER against SNR with the product of the fiber length and radio frequency as the parameter. The laser linewidth and OFDM symbol period correspond to (**a**) 0.5 MHz and 32 ns, (**b**) 0.5 MHz and 64 ns, (**c**) 0.5 MHz and 128 ns, (**d**) 1 MHz and 32 ns, (**e**) 1 MHz and 64 ns, and (**f**) 1 MHz and 128 ns, respectively. Figure 11: Taking into account various products between the fiber length and radio frequency (time delays), the SNR and laser linewidth requirements at the BER limit for the considered phase noise reduction mechanisms. Figure 10: For the diverse phase error compensation methods, the BER vs. SNR with the relationship between the transmission distance and millimeter wave frequency as the parameter. The laser linewidth and OFDM symbol period equal to (**a**) 5 MHz and 3.2 ns, (**b**) 5 MHz and 6.4 ns, (**c**) 5 MHz and 12.8 ns, (**d**) 10 MHz and 3.2 ns, (**e**) 10 MHz and 6.4 ns, and (**f**) 10 MHz and 12.8 ns, respectively.
# Bangla Character Detection Using Enhanced YOLOv11 Models: A Deep Learning Approach
## 4 Analysis and Findings from Experiments
### Result Analysis
#### 4.4.1 Classification of Letters or Characters  
After completing model training, we employed the trained model to perform character prediction. The model's primary task is to accurately identify letters within an image. Once characters are detected, the model generates predictions in the form of test words and images based on the recognized letters. During the detection phase, predicted bounding boxes are overlaid onto the input image, visually highlighting the identified characters.  
Initially, the model was trained using a classification-based approach on individual characters. Following this, various test images were used to evaluate its performance. The model generates output based on the detected characters, and these are visually marked within the image using bounding boxes. The resulting annotated images are saved in the same directory as the training outputs. Additionally, the model's weight parameters are automatically stored in the train folder after each training session. Each time the model is retrained, a new \"exp\" folder is created to log the corresponding results.  
For testing, either a single image or a folder containing multiple images can be provided as input. If a folder path is specified, the model processes and predicts all images within that directory. By default, the output is stored in a designated results folder. This setup supports character predictions on both static images and video frames. Notably, the model achieved a character recognition accuracy of 99% on the test images.  
To facilitate accurate predictions, each Bangla character was labeled to form distinct classes. This labeling allows the model to effectively identify characters in both images and videos during inference. As shown in Figures 5-7, a new detection folder was created to store the model's outputs, including predicted labels and their associated confidence scores. Once the model successfully recognizes a character, it annotates the corresponding image and displays the output accordingly.  
Figure 4: Training and test accuracy curve for different backbones.  
Figure 5: Bengali letter or character (vowel) detection using an improved YOLOv11.  
Figure 6: Bengali letter or character (consonant) detection using an improved YOLOv11.  
Figure 7: Bengali letter (number) detection using an improved YOLOv11.
# Deep Reinforcement Learning for the Agile Earth Observation Satellite Scheduling Problem
[19] first proposed using pointer networks (PN) to solve the TSP, and their model refers to the traditional sequence-to-sequence (Seq2 Seq) structure. [PERSON] et al. [20] then used the policy gradient and actor-critic algorithms to train the PN model, which can obtain approximate optimal solutions for TSP with 100 tasks. [PERSON] et al. [21] proposed an end-to-end framework based on DRL, which divides the problem input into static input and dynamic input to solve the VRP with dynamic characteristics. [PERSON] et al. [22] proposed a graph pointer network (GPN) to solve the TSP. Additionally, DRL methods have been used in several classical real-world problems [23; 24]. The literature highlights the strong potential of DRL to solve combinatorial optimization problems, and some scholars have performed related studies. [PERSON] et al. [25] proposed an end-to-end framework based on DRL for the AEOSSP as first attempt to apply DRL to AEOSSP. [PERSON] et al. [26] proposed a two-phase neural combination optimization method with reinforcement learning and used a neural combination optimization with the reinforcement learning method to determine the observation sequence and a reinforcement learning algorithm based on a deep deterministic policy gradient to determine the start time of the tasks. [PERSON] et al. [27] proposed a deep reinforcement learning and parameter transfer-based approach (RLPT) to solve a multiobjective AEOSSP. All these methods simplify the time-dependent attitude transition time constraint, but this constraint is one of the important constraints of the AEOSSP. To better represent this constraint, we propose to model the AEOSSP as a graph, with edges representing the pose transition time. On this basis, we propose to use GNN to solve the problem. In this paper, to solve the AEOSSP, we present a graph-based DRL method that is different from existing methods. The primary contributions of this study are as follows:  
(1) We model the AEOSSP with the time-dependent attitude transition time as a graph, which can more accurately represent tasks and their relationships through nodes and edges. (2) Based on the graph model of the AEOSSP, we design its MDP solution process and propose a graph attention network (GAT)-based decision neural network (GDNN) to represent the policy, which is trained by an RL method. (3) We design extensive experiments to demonstrate the effectiveness and timeliness of the proposed method by comparing it with specific competitors. In addition, we perform a model study to verify the structure and generalization of GDNN. The remainder of this paper is organized as follows. Section 2 models the graph formulation of the AEOSSP, the attitude transition time constraint, and the reformulation of the AEOSSP. Section 3 describes the proposed method, including the GDNN and the training method. Section 4 provides the computational experiments and analysis. Finally, Section 5 concludes the article and presents suggestions for future research.
## 4 Discussion
### Innovation, Limitations, and Further Research  
A method for accurately quantifying and qualitatively measuring the stability of the model was developed. The stability of the model was measured for the first time by changing the data structure and introducing the quantitative calculations of IHA-RVA, which has also been used in previous studies, but has never been quantified for the stability of runoff prediction models and has only been applied to measure the degree of variability in hydrological conditions [33]. In this study, the overall variability of the model indicators is 10.52%, which is a low variability, meaning that the model is highly stable.  
All three models showed negative values when predicting low values, and further model improvements are needed in this area to ensure that all data are positive.  
Due to the data, the results and conclusions of this study cannot be directly transferred to other watersheds, and more watersheds are needed to obtain more generalized results. Furthermore, in future studies, replication and validation in more watersheds could be attempted.  
The stability analysis of the model is the key to the preferred model. However, the factors affecting the stability of the model are complex and diverse. This study attempts to analyze the stability of the model only from the perspective of data structure, and the stability of the model performance needs to be carried out in depth. Due to the influence of climate change and human activities, the change of runoff becomes more complicated. However, the prediction of runoff by machine learning models cannot fully consider the influence of climate change and human activities. Therefore, a runoff prediction model that can fully combine the factors of climate and human activities needs to be further proposed.
# A Study on the Applicability of Bio-Sulfur as a Cement Admixture
## 3 Discussion  
As a result of a performance evaluation of cement composites made using bio-sulfur generated from power generation facilities in SML in Korea, the following conclusions can be drawn:  
When bio-sulfur was incorporated into the cement composite, a decrease in strength was observed. At a rate of 2.5%, the smallest amount used here, the degree of strength reduction was relatively low and surface discoloration (turbidity) was observed due to the incorporation of bio-sulfur. However, the amount of discoloration was insignificant when a rate of 2.5% was used.  
Overall, it was found that the sulfuric acid resistance of cement and concrete was improved by the incorporation of bio-sulfur and that the weight reduction rate was lowest when 7.5% of bio-sulfur was incorporated, leading to the best sulfuric acid resistance.  
Regarding the addition of bio-sulfur, ettrinigite (3 CaO-Al\({}_{2}\)O\({}_{3}\) 3 CaSO\({}_{4}\) 32H\({}_{2}\)O) hydrate was observed on the 28 th day, suggesting that sulfur contributes to hydrate formation to some extent.  
Therefore, when using bio-sulfur mixed into cement composite materials, addition of less than 2% is appropriate; a major strength improvement is unlikely, but this amount may improve the sulfuric acid resistance properties. It is suggested that it is necessary to closely review and analyze the economic feasibility and commercialization potential through additional research on prototype production and other factors with concrete producers and other stakeholders.
# An Extended Car-Following Model Considering Generalized Preceding Vehicles in V2X Environment
## 5 Numerical Simulation
### Simulation of Disturbance Propagation Process  
The neutral stability curves of the GPV model and the FVD model are obtained in Section 4. According to the conclusion of the section, the headway-sensitivity phase diagram is divided into two regions. The region above neutral stability curves is the stable region, in which a small disturbance can be suppressed or absorbed. Simulation of the disturbance propagation process can represent the operation characteristics of traffic flow when an incident or accident occurs in a realistic traffic system and thus is employed to verify the above theoretical analysis results. The simulation scenario on propagation process of disturbance with the GPV model and the FVD model is set as following: 100 identical vehicles with a length of 5 m in each lane of three are moving towards the same direction on a 1500 m circular road with a constant velocity of 2 m/s and the same headway of 10 m. Then, a small disturbance of 1 m/s (half of the initial velocity) and 2 m (one-fifth of initial headway) is exerted on the vehicles, and the propagation process of this disturbance in the vehicle fleet is simulated as shown in Figure 10.  
From Figure 10a, one can see that the same disturbance is rapidly suppressed to a small amplitude and finally absorbed with the GPV model. As shown in Figure 10b, this disturbance can be absorbed eventually with the FVD model. However, both the amplitude of the disturbance during the propagation process and the time for the disturbance to be absorbed are significantly greater than those of the GPV model, which has good agreement with the theoretical analysis results in Section 4.
## 2 Related Work
### Elaboration of Problems  
During the missions of on-orbit target tracking observation at Earth stations, on the one hand, the time window in which the target satellite is observed is limited to less than 10 min because of the zenith angle and observational environments. If the target satellite misses, the time window is wasted for recapture. The mentioned situation indicated that on-orbit target tracking systems required higher robustness. On the other hand, the echo signals only occupied an extremely small portion of the detection target surface in a large detector, and a large amount of computing resources were wasted in repetition of collecting, transmitting and processing redundant information, which seriously affected the improvement in the bandwidth of the tracking system. Therefore, there is a great engineering significance in developing a tracking algorithm with high robustness and high bandwidth for the target tracking mission of on-orbit satellites.
## 4 Simulation Setup and Experimental Results  
In this section, it is important to note that, traditionally, the relationship between the security mechanisms applied to the cluster of nodes is inversely proportional to the energy savings in each node of the network. The use of AES has been almost constant in these types of configurations, so using the proposed cryptographic methods not only represents an advantage in processing time, as shown in the experimental results, but also represents a clear energy saving in each node and the assurance that an additional layer of security is applied to protect the CH.  
To test the rekeying environment, we have configured physical nodes following the specifications described in Table 2.  
The scope of the paper is not the implementation of an efficient node, but instead a node to test the rekeying and strength of the protocol. To this end, we have used the physical nodes described in Table 2, on which we have installed Ubuntu 18.04 LTS and all the custom drivers. We are aware that by installing a kernel designed using YOCTO [23; 24; 37; 38], the savings in power consumption are close to the real environment of an embedded sensor device. To test the strength of the key management protocol, it needed the deployment of a higher number of nodes, such as the one described in Table 2, so we have emulated them in the physical devices. To test the rekeying speed operation, we have used up to eight physical nodes. In addition, we have scaled up to 1024, but from eight we have emulated the processors using threads. The results of the rekeying process are described in the results section. To test the proposed protocol and the energy consumption fingerprint, we have emulated a complete node using MATLAB. One of the challenges in evaluating the performance of modified routing protocols and the security properties of the methods used in this paper in contrast to other relevant algorithms is simulator selection. To emulate wireless networks, many simulation systems do exist and all of them are equally valid, such as Objective Modular Network Testbed in C++ (OMNET++), Network Simulator2 (NS2), and MATLAB [25; 39]. As previously mentioned, the cryptographic foundations were tested on real nodes. Deploying an extensive infrastructure of communicating nodes needs the support of emulation tools which provide information of interest to evaluate the approach being presented. In our work, we used MATLAB R2022a to test the performance of the WSN under various scenarios and then compare the results with other relevant works that use similar tools. The configuration used for the extension of the proposal (using DLT to make communications restricted to a closed group) is described below in Table 3:  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Component** & **Value** \\ \hline Processor & Quad Core 1.2 GHz Broadband BCM2837 64-bit CPU \\ Internal RAM & 1 GB \\ Antenna & BCM43438 wireless LAN and Bluetooth Low Energy (BLE) on board. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameters.  
Figure 5: Device smart contract.
# Hybrid Quantum Neural Network Image Anti-Noise Classification Model Combined with Error Mitigation
###### Abstract  
In this study, we present an innovative approach to quantum image classification, specifically designed to mitigate the impact of noise interference. Our proposed method integrates key technologies within a hybrid variational quantum neural network architecture, aiming to enhance image classification performance and bolster robustness in noisy environments. We utilize a convolutional autoencoder (CAE) for feature extraction from classical images, capturing essential characteristics. The image information undergoes transformation into a quantum state through amplitude coding, replacing the coding layer of a traditional quantum neural network (QNN). Within the quantum circuit, a variational quantum neural network optimizes model parameters using parameterized quantum gate operations and classical-quantum hybrid training methods. To enhance the system's resilience to noise, we introduce a quantum autoencoder for error mitigation. Experiments conducted on FashionMNIST datasets demonstrate the efficacy of our classification model, achieving an accuracy of 92%, and it performs well in noisy environments. Comparative analysis with other quantum algorithms reveals superior performance under noise interference, substantiating the effectiveness of our method in addressing noise challenges in image classification tasks. The results highlight the potential advantages of our proposed quantum image classification model over existing alternatives, particularly in noisy environments.  
quantum neural network; variational quantum algorithm; image classification; error mitigation +
Footnote †: journal: Applied  
**Citation:** [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Hybrid Quantum Neural Network Image Anti-Noise Classification Model Combined with Error Mitigation. _Appl. Sci._**2024, _14_, 1392. [[https://doi.org/10.3390/app14041392](https://doi.org/10.3390/app14041392)]([https://doi.org/10.3390/app14041392](https://doi.org/10.3390/app14041392))