esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
It is also worth mentioning that, during the burst release phase when poorly encapsulated and surface bound drugs are released, a higher level of burst release was observed from PTX-loaded microspheres than that observed from PGS-loaded microspheres. In addition, at the set temperatures (37, 41, and 44 \({}^{\circ}\)C) similar drug release profiles were seen. The values obtained for the thermodynamic parameters such as enthalpy (\(\Delta H\)), entropy (\(\Delta S\)), and Gibbs free energy (\(\Delta G\)) showed that the drug release was feasible at 37, 41, and 44 \({}^{\circ}\)C. They also revealed that the drug release process was orderly, endothermic, and nonspontaneous. Finally, it is important to note that the therapeutic potency of the various drug-loaded PLGA-CS-PEG microsphere formulations was assessed using Alamar blue assay. The results confirmed that the MDA-MB-231 breast cancer cell growth was highly repressed by PLGA-CS-PEG_PTX8 microspheres, with PLGA-CS-PEG_PGS5 microspheres being the least efficient. In any case, PLGA-CS-PEG microspheres loaded with drugs exhibited drug release and MDA-MB-231 cell growth inhibition that suggest that they are promising candidates for the controlled release of drugs (paclitaxel and prodigiosin) for triple negative breast cancer treatment. Before concluding, it is important to compare the results from this study to the results from a prior study by [PERSON] et al. (2020) in which similar materials (PLGA-PEG) were used to encapsulate anticancer drugs. As in prior work by [PERSON] et al. (2020) in which PLGA-PEG microspheres were studied, the current study in which we introduced chitosan into the polymer matrix resulted in the anomalous non-Fickian release of cancer drugs that was well characterized by the Korsmeyer-Peppas model. The drug release at the end of day 30 was also slightly lower for PLGA-CS-PEG microspheres than for PLGA-PEG microspheres at the same time duration. The slower rate of drug release suggests that extended release of cancer drugs due to the chitosan-PEG blend in the PLGA-CS-PEG microparticles could give rise to further extensions of the drug release durations than those associated with drug release from only PEG blend with PLGA. Finally, it is important to note here that prior in vivo studies of targeted drug release from implanted drug-loaded PLGAPEG microparticles [PERSON] et al. (2020) did not reveal any additional cytotoxicity effects after the extended elution of targeted cancer drugs (PGS and PTX) from the microparticles. This suggests that the PLGA-CS-PEG microspheres examined in this study are likely to elute targeted and untargeted drugs for longer durations at lower rates that are not likely to induce any additional cytotoxicity effects.
## 1 Introduction  
Bone age (BA) assessment is a critical tool for evaluating skeletal maturation in children and adolescents [1, 2]. It plays an essential role in pediatric endocrinology for diagnosing growth disorders [3], monitoring pubertal development [4], and guiding treatment strategies [5]. In forensic medicine, BA evaluation is important for estimating the chronological age (CA) of undocumented individuals, such as unaccompanied minors and asylum seekers [6, 7], affecting their access to vital services such as healthcare and education [8].  
Traditionally, BA assessment relies on posteroanterior radiographs of the left hand and wrist (PA-HW), applying qualitative manual scoring systems--such as the Greulich-Pyle (GPA) [9] and Gilsanz-Ratib [10] atlases--and quantitative approaches like the Tanner-Whitehouse 3 (TW3) method [11]. While these techniques remain foundational, they are inherently laborious, demand substantial expertise, and exhibit notable inter- and intra-observer variability. Furthermore, they complicate the accurate monitoring of longitudinal growth and frequently lack validation across diverse ethnic and demographic cohorts, accentuating the need for tailored, region-specific atlases to enhance precision [12; 13; 14].  
In order to address these challenges, efforts to automate BA assessment began as early as the late 1980s, with the HANDX system by [PERSON] [PERSON]'s (1989) [15], followed by the PROI-based framework by [PERSON] et al., (1991) [16], and [PERSON] et al.'s Computer-based Skeletal Aging Scoring System (CASAS) in 1994 [17]. While these pioneering platforms marked significant progress, they were still hindered by inefficiencies and lengthy processing times, underscoring the imperative for more advanced, truly automated solutions.  
Today, artificial intelligence (AI) has emerged as a compelling alternative to classical BA assessment methods. AI-powered platforms such as _BoneXpert(r)_ can generate BA estimates based on GPA and TW3 standards in under 15 s per PA-HW radiograph [18]. Acting as a local DICOM node that neither stores nor transmits patient data externally, _BoneXpert(r)_ complies with stringent privacy regulations while employing classic machine-learning techniques. At the same time, deep-learning architectures--most notably convolutional neural networks (CNNs)--have recently shown even greater speed, accuracy, and consistency in automating BA evaluation [19].  
Despite these advances, no comprehensive synthesis studies have yet evaluated AI-based BA models for accuracy, precision, and clinical applicability using PA-HW X-rays. Therefore, this systematic review aims to benchmark AI-based models for automated BA estimation from left-hand PA-HW radiographs against traditional methods. Additionally, this review explores the challenges and opportunities AI presents for improving BA assessment, while providing insights into future research directions in this rapidly evolving field.
# Research on Multi-Port Ship Traffic Prediction Method Based on Spatiotemporal Graph Neural Networks
## 4 Results
### Experimental Results  
In the experiment, we extracted the consecutive 21-day inflow and outflow data for each port from the test set as input and expected the model to generate predictions for the next 7 days of each port in a single output. Taking the Rotterdam sub-scenario as an example, which comprises 251 port nodes, the input should include an adjacency matrix with 251 nodes and an initial tensor of shape [32; 251; 21; 2]. Here, \"32\" represents the specified batch size, and \"2\" indicates that the initial features only include inflow and outflow. Correspondingly, the model's output should be a tensor of shape [32; 251; 7; 2]. By comparing the predicted 7-day traffic variations with the ground truth values, the predictive capability of the model can be evaluated. As presented in Table 2, cross-validation experiments were conducted across seven major multi-port network scenarios centered around the seven central ports. The GAGW model proposed in this study incorporates a weighted relationship to address the imbalance between spatial and temporal features. Consequently, the model demonstrates consistent and superior predictive performance across all scenarios, achieving the best results in the majority of experimental scenarios. Through a comprehensive comparison of the experimental results, it has been observed that the majority of the existing spatiotemporal graph models outperform the temporal prediction models. This indicates that the incorporation of graph neural networks indeed enhances the performance of predictive models in various scenarios and effectively captures intricate traffic flow patterns. Among the temporal prediction models, Transformer and WaveNet exhibit significantly superior performance compared to traditional temporal prediction models and even outperform certain spatiotemporal graph models in specific scenarios. This highlights the significance of capturing temporal patterns in maritime traffic flow prediction scenarios, thereby affirming the importance of time features in the field of traffic prediction.
# Adaptive Path Planning for UAV-Based Pollution Sampling
## 4 Experiments and Results
### Evaluation Criteria--Path Quality Metrics  
A set of path quality metrics were established to quantitatively evaluate the experimental results. These metrics are tailored to address the challenges associated with global path optimization.
## 4 Simulation Experiment  
The flow chart of the proposed method is illustrated in Figure 1. In this section, the proposed method will be applied to simulation data, including periodic impulse component, mixed AM-FM component and random noise. The simulated signal can be expressed as:  
\[\begin{array}{l}x(t)=H(t)+s(t)+n(t)\\ H(t)=\sum\limits_{k}A_{k}h(t-1/f-\lambda_{i})\\ h(t)=e^{-40t}\sin(2\pi 500t)\\ s(t)=(1+\sin(2\pi 15t))\cos(2\pi 35t)+\sin(2\pi 750t+\sin(2\pi 15t))\\ n(t)=0.6\cdot rand(n)\end{array} \tag{26}\]  
where \(f=10\) Hz is the impulse interval frequency. \(A_{k}\) and \(\lambda_{i}\), respectively, represent the amplitude and phase of the impulse signal, and the sampling frequency is set to 2000 Hz.  
As the massive implantation of noise interference, the sparseness and characteristic frequency of signal impulse are gradually submerged in the noise and are becoming less observable in Figure 2. After the optimal VMD processing with \(k=3\) and \(\alpha=500\), it retains a large amount of signal amplitude and highlights the periodic impulse waveform, which makes it easier to identify the real impulse in the time domain and frequency domain, as displayed in Figure 3a,b. Although the deterministic interference component is eliminated, the impulse characteristics are still ambiguous due to the serious noise pollution, and the high-order harmonic frequency of the envelope spectrum is partially lost. We further use the NSD method to extract useful signals, and the results are shown in Figure 3c,d. It can be seen that only a few distinguishable fake impulses remain in the de-noising results, and the higher harmonic frequencies of the envelope spectrum are significantly enhanced, which is very valuable for practical engineering diagnosis. For comparison, the same simulation signal is analyzed by Minimum Entropy Deconvolution (MED) methods [32]. The results of MED are shown in Figure 4. Due to the interference of AM-FM and random noise, the periodicity can hardly be observed in the MED-filtered signal, and the characteristic impulse frequencies of the envelope spectrum are also extremely weak.  
The above results preliminarily prove the reliability of the proposed method, which also shows that it is feasible to measure fault information from the perspective of impulse fluctuation. From the results, the integrity of the time-domain periodic impulse and envelope spectrum is well reflected. Especially, large noise is added in the simulation experiment, and the impulse is very weak.  
Figure 1: Flow chart of the proposed method.  
Figure 4: Filtering results of MED. (**a**) Time domain signal. (**b**) Envelope spectrum.  
Figure 3: (**a**) Extracted feature signal by optimal VMD. (**b**) Envelope spectrum of extracted feature signal. (**c**) De-noising signal. (**d**) Envelope spectrum of the de-noising signal.  
Figure 2: Simulation signal. (**a**) Impulse component. (**b**) Envelope spectrum of impulse component. (**c**) Synthetic signal. (**d**) Envelope spectrum of the synthetic signal.
# All about Delay-Tolerant Networking (DTN) Contributions to Future Internet
## 3 Related Work
### Space  
This category focuses on the initial concept of DTN, particularly its application in space-related contexts. Within this domain, significant advancements in protocol implementations and the utilization of satellites for terrestrial communications are identified and explored. A summary of the space-related works is illustrated in Table 1.  
While several studies have experimented with DTN-related protocols and solutions, the majority of them are conducted on a small scale. The mature and extensive DTN Engineering Network (DEN) testbed [10] is a worth-mentioning exception consisting of labs at NASA Centers, which are connected through VPNs for experimentation. In addition, SPICE, a state-of-the-art DTN testbed for developing and evaluating DTN implementations, architectures, and protocols (underlying or overlying) for satellite and space communications, is described in [11]. SPICE incorporates components such as a Portable Satellite Simulator (PSS) and CORTEX CRT and Satellite Tool Kit (STK), while it provides a link with a GEO satellite (HellasSat 2) to provide real satellite link characteristics.  
Furthermore, some works attempt to incorporate novel computing technologies to the DTN experimentation process. For instance, the authors in [12; 13; 14] followed microservice-based and distributed approaches utilizing cloud-based computing and containerization in an attempt to enhance interconnectivity and scalability while lowering operating costs. The latter work also proposed the interconnection of the DEN with AWS cloud-based Ground Stations (GSs).
## 3 Results and Discussion
### Measurement of the Tensile Strength
The violation shown in Figure 4a was recorded in a total of 2 out of 5 possible cases (after the degradation test), and in no case did the tensile strength exceed the value of 40 MPa (in contrast to the other post-processing technologies). The critical point here could realistically be created by the smoothing chemical flowing down the surface of the sample, which more precisely etches at the transition to a larger cross-section (the samples were chemically smoothed in a position identical to the position in Figure 4). This effect leads to the creation of micropores through which the degradation medium can penetrate into the core of the sample. Therefore, in chemical smoothing, it is most important to remove the sample from the closed container at the right moment [53]. The formation of micropores, or slightly exceeding the exposure time in the environment of dichloromethane, also indicates the highest level of absorbed moisture (of all the post-processing treatments implemented). Considering the level of relative moisture absorption of both standards, however, the benefit of chemical smoothing can be assessed as positive.
## 5 Results and Discussion
### Results of Approach 2  
The top 10 CIs and their accuracies are presented in Tables 9 and 10 by using a Data Fusion approach, for all three data sets. The set of fused CIs from the left-side accelerometers (LV, LA, and LL) named CI_DFL, the set of fused CIs from the right-side accelerometers (RV, RA, and RL) named CI_DFR as presented in Table 9, and the third set of fused CIs from all six accelerometers named CI_DFRL as presented in Table 10. Figure 5 shows the accuracy results for each data set. Here, the X-axis represents the number of \(k\) first CIs most important selected by RF. The Y-axis represents the average accuracy of the \(k\) first CIs. Regarding the 10 top CIs presented in the Tables 9 and 10, five of the 10 are common, four belong to the frequency domain, i.e., PKF, FSK, VCF, skewnessf, and one to the time domain, i.e. SCC.  
The highest accuracy (acc) using 10 condition indicators in CI_DFL was 97.56% with a std of 0.88, for CI_DFR was 98.01% with std of 0.88, and for CI_DFRL was 98.37% with std of 0.76. Of the latter, its accuracy by class is presented in the Table 11, where the class with the highest accuracy was number 4 obtaining 100% accuracy with 9 CIs.  
In order to illustrate the importance of feature selection and data fusion in the approach 2, a scatter plot obtained from the best case, related to the CI_DFRL set, is presented in Figure 7. This set provides the highest accuracy (98.37%) and lowest \(std\) (0.76) using 3 CIs. Best small clusters can be observed for each class in contrast to Figure 6. The accuracy using 3 CIs was of 88.24%. These features were **MDF**-Freq-LV, **VCF**-Freq-RL, and **CP2**-Freq-RA.
## 5 Conclusions  
In this work, AWOA was used to solve the energy consumption problem in WSN. The CH and SCH selection processes were successfully established and tested using AWOA and FIS. The multi-objective function was also designed for the CH selection process. Hop-count-based routing was developed. An Oppositional-based Whale optimization algorithm (OWOA) was developed for multi-constrained QoS routing with the help of AWOA. The performance of the proposed OWOA methodology was analyzed according to the following metrics: delay, delivery ratio, energy, NLT, and throughput. The OWOA was compared with conventional techniques such as PSO, GA, and WOA to demonstrate the efficacy of the proposed methodology. Compared to the existing methods, the proposed method attained an efficient outcome. WSNs are autonomous in nature and are distributed randomly in space. There is no central authority and the deployment of nodes is random, making the process prone to various security threats such as a malicious attack where a compromised node misleads other nodes by imitation. In future, security-aware data transfer using the updated version of AWOA could be implemented.  
Conceptualization, H.B., A.G. and A.C.; methodology, H.B., A.G., A.C. and D.A.; validation, A.G., D.A. and J.L.V.M.; formal analysis, F.S.A., S.M.A. and J.L.V.M.; investigation, F.S.A., S.M.A. and J.L.V.M.; resources, F.S.A., S.M.A. and J.L.V.M.; data curation, H.B., A.G., A.C. and D.A.; writing--original draft preparation, H.B., A.G., A.C. and D.A. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Taif University Researchers Supporting Project No. (TURSP-2020/347), Taif University, Taif, Saudi Arabia.  
The authors declare no conflict of interest.  
Figure 14: Node vs. Throughput.
## 2 System Model
We employ a banded channel matrix \(\mathbf{H}_{D}\in\mathbb{C}_{N_{s}\times N_{s}}\) for OFDM systems over doubly-selective channels, as shown in Figure 2 and [4], so the truncated system model for the \(k\)-th subcarrier can be approximated as below:  
\[ \begin{split}\mathbf{y}_{k}&=\mathbf{H}_{k}\mathbf{ s}_{k}+\mathbf{v}_{k}\\ &=\mathbf{h}_{k}s_{k}+\sum_{\bar{k}=k-Q,\bar{k}\
eq k}^{k+Q} \mathbf{h}_{\bar{k}}s_{\bar{k}}+\sum_{\bar{k}\
ot\in[k-Q,k+Q]}\mathbf{h}_{ \bar{k}}s_{\bar{k}}+\mathbf{v}_{k}\end{split}  \tag{5}\]  
Figure 1: System model of multiple feedback matched filter (MBMF)-successive interference cancellation (SIC) incorporating multi-segment channel estimation (MSCE). where the truncated received signal is given by \(\mathbf{y}_{k}=[y_{k-D},\ldots,y_{k},\ \ldots,y_{k+D}]^{T}\), \(\mathbf{h}_{k}\) denotes the \(k\)-th column vector of the truncated channel matrix, the truncated transmit symbol vector for the \(k\)-th subcarrier is \(\mathbf{s}_{k}=[s_{k-2D},\ldots,s_{k},\ldots,s_{k+2D}]^{T}\), the truncated noise vector is expressed by \(\mathbf{v}_{k}=[v_{k-D},\ldots,v_{k},\ldots,v_{k+D}]\) and the truncated channel matrix \(\mathbf{H}_{k}\) has a size \(Q=2D\), as illustrated in Figure 2.
# Broadband Power Line Communication for Integration of Energy Sensors within a Smart City Ecosystem
## 2 State of the Art
### Related Works
#### 2.3.1 Simulation in General
Reference [23] describes modelling and calculation of the CTF of the PLC technology in NS-3, where the mathematical definitions of the PLC channel are described. Ref. [50] deals with analysis of the channel transfer function of the PLC module for NS-3. Simulations of several topologies are provided considering BPLC technology. Similar work in [51] provides modelling of BPLC technology based on the PLC HomePlug AV (HPAV) specification, but it is erroneously stated as a standard. The paper describes, inter alia, the communication layers and medium access types of the specification. In the NS-2 environment, an HPAV model was created. This was verified by simulation, resulting in almost the same values as a real HPAV system. On the other hand, a real HPAV system and tested topology were not described as a simulated model. The aim of the analysis of these articles was to find out how PLC channel analyses are performed, for what purpose, and what is the relevance of the results. It can be stated that these works demonstrate the functionality of the simulator in the given technology by creating a model of the PLC system or describing a real model of the PLC system. When these works deal with the usability of simulators, they always achieve very specific results, especially by setting specific required conditions. Table 2 shows an overview of papers where the topology design considers a specific environment according to the type of communication line.
# Low-Cost Software Evaluation of Communication Delays in Networked Industrial Systems
## 5 Packet Generators
### In-House Packet Generator (Rtgen)  
The architecture of the in-house packet generator is relatively simple and has been optimized for performance based on the inner workings of the kernel scheduler and protocol stack. The main loop runs in user space as a high-priority, real-time SCHED_FIFO thread. Its timing source is the clock_nanosleep() system call, invoked immediately before scheduling the transmission of each test packet. To avoid the accumulation of timing errors, the system call is invoked with the TIMER_ABTIME flag set and given an absolute time reference based on the CLOCK_MONOTONIC clock. When the main loop detects an overload that led the generator to miss a transmission deadline, one of two possible behaviors can be selected by means of a runtime option as follows:  
1. The generator may try to maintain the nominal average packet transmission rate by transmitting a burst of packets after the overload interval.
2. Otherwise, it may skip the transmissions that could not be scheduled on time. In this way there will be no bursts in generated traffic, but the actual average packet transmission rate will be lower than nominal.  
The second option has been selected for the experiments described in this paper, to avoid stressing the DUT with bursts of packets whose transmission rate would necessarily be higher than nominal. No measurable impact has been observed on the actual versus nominal packet transmission rate due to the very low probability of overload during the experiments. Upon transmission, the user-space thread provides only the UDP payload while the in-kernel protocol stack and Ethernet device driver are responsible for overall packet assembly and its on-wire transmission, respectively. The whole process is started by means of a send() system call with the MSG_EOR flag set.  
High-resolution timestamps are taken at various critical points within the main loop. They come from the clock_gettime() system call, querying either the CLOCK_MONOTONIC or the CLOCK_MONOTONIC_RAW clock. Besides being used to detect overloads, as described previously, they also provide a way to calculate the actual packet generation rate and ensure it is correct. Based on the same information it could also be possible to fine-tune the reference timing points passed to clock_nanosleep(), but this has been left to future work.