esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Brillouin Optical Correlation Domain Analysis: BOCDA
### Basic BOCDA System
In theoretical investigations, the basic formula describing the distributed measurement mechanism of the BOCDA is derived [8; 50], which is expressed as:  
\[g(t_{m},\omega_{d})=\frac{v_{g}\overline{P_{1}}}{A_{eff}}\int_{-\infty}^{ \infty}d\zeta\int_{-\infty}^{\infty}\frac{d\omega}{2\pi}g_{B}(\zeta,\omega)S_ {bp0}(\epsilon\tau-\zeta,\omega_{d}-\omega) \tag{1}\]  
where \(g_{B}\) is the intrinsic Brillouin gain spectrum as a function of frequency \(\omega\) and the position along the fiber \(\zeta\), and \(S_{bp0}\) is the beat spectrum that has the delta function-like shape, which is generated by the FM on the lsaering frequency. In Equation (1), \(\omega_{d}\) represents the pump-probe frequency difference, \(v_{g}\) represents the group velocity of the lightwave, \(P_{1}\) the time-averaged power, and \(A_{eff}\) is the effective fiber core area, respectively. We denote the modulation period as \(t_{m}\), which corresponds to the position along the fiber in this approximation [8; 50], and its relative variation from a certain standard \(t_{m0}\) as \(\epsilon\)= \((t_{m0}-t_{m})/t_{m0}\). We assume that the delay \(\tau\) at the correlation peak that is used for the measurement is N times as large as the modulation period; that is, \(\tau\) = \(Nt_{m0}\)/2. The explanation of Equation (1) is seen in Ref. [50] in detail, but the equation means that the BOCDA output spectrum is the two-dimensional convolution integral between the intrinsic BGS and the beat spectrum. Figure 19: First setup for Brillouin optical correlation domain analysis (BOCDA) (**a**). Mechanism of the position-selective measurement of the BGS by SOCF technology (**b**) [8]. The spatial resolution is also derived in the theory as [8; 50]:  
\[\Delta z=\frac{c\,\Delta\
u_{B}}{2\pi\,n\,f_{m}\cdot\Delta f}\text{ for }\Delta\
u_{B}>f_{m} \tag{2}\]  
\[\Delta z=\frac{1.52\,c}{2\pi\,n\,\Delta f}\text{ for }\Delta\
u_{B}<f_{m} \tag{3}\]  
where c is the light speed in vacuum, \(n\) is the refractive index of the fiber,\(f_{m}\) is the laser modulation frequency, \(\Delta\
u_{B}\) is the Brillouin spectrum width, and \(\Delta f\) is the FM amplitude. Due to the periodical sinusoidal modulation for the FM, the correlation peaks are generated periodically, whose interval \(d\) is also derived as [8; 49]:  
\[d=\frac{c}{2\,n\,f_{m}} \tag{4}\]  
The peak interval decides the basic measurement range in the basic BOCDA system. Schemes to elongate the measurement range have already been developed, which are shown in this paper later. Figure 20: Experimental results and simulation results of the BOCDA outputs [8]. **(a,c,e)** are for the experiments, and **(b,d,f)** are for the simulation; **(a,b)** show the BGS distribution; **(c,d)** show the Brillouin frequency shift (BFS) distribution; **(e,f)** show the intensity distributions for the BFS of 10.83 GHz and 10.56 GHz, respectively.
# Optimized Architecture for Efficient OFDMA Network Design
## 2 Related Works
A notable contribution is the simulator implemented in [24, 25], which provides insights into network performance under certain assumptions, such as a fixed TDD frame duration of 5 ms and simultaneous ON/OFF periods for all mobile users. However, these assumptions limit model flexibility, particularly in real-world scenarios where user activity may not follow strict timing rules. The current work extends previous models by implementing a modified simulator that alternates the assignment of ON and OFF periods to each mobile user based on a probabilistic distribution. This adaptation allows for more realistic modeling of traffic patterns, with users assigned download times (\(x_{on}\)) and idle times (\(t_{off}\)) using exponential or Pareto distributions. The model dynamically adjusts user states during simulation, offering a more precise representation of network behavior and enabling accurate estimation of performance metrics such as frame utilization and system throughput. By capturing user activity variability more effectively than traditional analytical models, the current work serves as a robust method for validating theoretical assumptions in dynamic environments. Table 1 provides a comparison of existing works. In conclusion, while existing studies contribute valuable insights into the optimization of IoT systems and wireless networks, the current work advances the field by addressing the dynamic nature of resource allocation in OFDMA networks. By integrating advanced scheduling algorithms and enhancing simulation techniques, this research significantly improves throughput, reduces latency, and ensures optimal resource utilization in real-time scenarios. The proposed study addresses gaps in the literature by integrating a Markovian analytical model with four advanced scheduling algorithms: throughput fairness, resource fairness, opportunistic scheduling, and throttling. Each is tailored for WiMAX networks. This approach dynamically manages resource allocation and considers the interactions between user traffic, channel conditions, and QoS requirements. By leveraging a more flexible and scalable Markov model, the current work achieves better performance in handling Best Effort services, improving both resource utilization and user fairness. By combining the strengths of Markov modeling with dynamic scheduling algorithms, the proposed model offers an adaptive solution that balances resource utilization, fairness, and scalability. It also addresses key challenges identified in prior work, such as resource allocation for Best Effort traffic under varying network conditions. This comparative analysis highlights how the proposed model outperforms previous models in managing dynamic traffic, especially in WiMAX networks, while offering a simpler, more scalable solution for Best Effort services.
## 2 Materials and Methods
### Data Integration and Analysis  
Considering the diverse origins of the data, a significant effort was dedicated to merging the varied time series data into a unified dataset suitable for analysis by the ANN models. This unification process included standardizing data formats, synchronizing time series from various trials according to phases of the gait cycle, and normalizing the data to minimize differences between subjects. When synchronizing the time series from various trials, we aligned the initial contact of the dominant limb across trials rather than adjusting the temporal length of individual gait cycles. This approach ensured that all gait cycles began consistently without causing the elongation or shortening of data segments [14].  
Normalization was performed to minimize differences between subjects and ensure the comparability of input variables for the ANN model. Table 1 shows all measured signals and their corresponding normalization processes. To account for differences in body anthropometrics across participants, the joint moments and GRF were normalized to each participant's body mass (N-m/kg for moments and N/kg for the GRF), while the metabolic cost was expressed in watts per kilogram (W/kg) [14]. The resulting dataset, encompassing 120 trials, formed an extensive collection of biomechanical variables crucial to the aims of our research.
## 6 Halophile PGPB for Sustainable Agriculture  
In the rhizosphere, plant-microorganism relationships are indicators of plant health, production, and soil fertility. Particularly, plant growth-promoting bacteria (PGPB) are microorganisms that could also promote plant increase and prevent plants from infection and abiotic factors via a wide range of processes. Those PGDBs that develop more intimate interactions with plants, like the endophytes, may be more effective in plant growth improvement [16]. Usage of PGPB as an important part of farming activity is an innovation whose time has arrived [1]. More particularly, some halophilic bacteria were proved able to alleviate plant salt stress, improving the development and productivity of crop production under salinity [142].  
PGPB represent a possible solution to mitigate salinity-induced plant stress [143]. The impact of five plant growth stimulating halotolerant bacteria on wheat growth [142] revealed that bio-formulation of these halotolerant bacterial strains can improve salinity stress (80, 160, and 320 mM) in wheat seedlings, resulting in a 70% improvement of root length comparatively to negative controls. In addition, variations in the bacterial community formulation with the soil salinity were observed, and attention is growing in experiments regarding functional interactions between plants and microbes that lead to salt stress tolerance [25]. The use of microorganisms for saline soil restoration may be an environmentally sustainable, safer, and more efficient method, as the halophilic microorganisms have the potential to eliminate salt from saline soils [144]. Such microbes also provide exceptional prototypes for studying the stress resistance, adaptation, and response processes which might consequently be integrated into agricultural crops to cope with the pressures caused by climate modification [145].
# Phase Calibrated Ring Oscillator PUF Design and Application
## 6 Experimental Results
### Stability  
As shown in Section 4, the average number of errors was 0.3688 in 128-bit responses, which means the theoretical bit error rate was 0.29%. To verify that, we generated challenges with a pseudorandom number generator. Each challenge was used twice in order to provide two responses for comparison. The test was repeated 100,000 times. Figure 20 shows the measured intra-PUF Hamming distance of our PCPUFs. Compared to the corresponding results with no ECC and phase calibration in Figure 8, the fitting curves for PCPUFs have an obvious shift to the left and approach the ideal bound in Figure 14.  
Table 3 shows the average bit error rate of three shift register-based RO PUFs and six PCPUFs. Compared to RO PUFs, PCPUFs have an obvious improvement on the response stability. Though the bit error rates were higher than the ideal bound, all the results were smaller than 1.00%. This is still acceptable if we take the measurement errors into consideration. Since we did not apply ECC in our PUFs, the bit error rate was low enough for authentication purposes. Within the temperature range of a normal testing environment, we observed no significant changes of the bit error rate.  
Figure 19: Inter-PUF Hamming distance.  
Figure 20: Intra-PUF Hamming distance with phase calibration.  
We compare the stability of PCPUF with four novel FPGA-based PUF implementations in Table 4. The improved configurable RO PUF is known for ID generation with a strict Hamming distance [24]. The composite PUF was developed for a larger challenge space and superior quality metrics with acceptable resource requirements [25]. Another improved RO PUF was proposed in [26], which was capable of generating multiple output bits from each RO pair and allowed one to create a higher number of pairs of ROs. The last one applies the Full Scan Technique (FST) on different numbers of RO stages to determine the one with the highest diverseness of RO frequencies [27]. Compared to these RO PUF designs with their own advantages, our work focused on the improvement of stability. Therefore, it provided a lower bit error rate in the responses.
## 3 Experimental Results and Analysis
### Experiment Design
#### 3.3.3 Multi-Scene Semantic Image Retrieval Experiments Design  
In order to examine whether CABIR fixed the problem of mutual interference between semantic information in multi-scene semantic image retrieval to some extent, this paper selected 40 RS images covering eight categories of scenes, \"Airport\", \"Farmland\", \"Dense residential\", \"Playfields\", \"Port\", \"Parking\", \"Center\", and \"River\", from RSICD as the multi-scene RS image retrieval test set (hereinafter referred to as MSIRTS), which included five multi-scene semantic images, as shown in Figure 6. In MSIRTS, all semantic information contained in the five multi-scene semantic images can be found in other images to ensure the difficulty for the model to distinguish these images. During the experiments, we retrieved these five images in separate semantic types before evaluating the model's performance by ranking the results of retrieval.
# A Versatile Shared-Aperture Antenna for Vehicle Communications
## 2 Antenna Configuration
### Pattern Reconfigurable Antenna  
The specific dimensions of the pattern-reconfigurable antenna are provided in Figure 9 and Table 3. The primary radiating structure of the pattern-reconfigurable antenna consists of slot antennas distributed around the antenna. These slots are paired in twos, corresponding to different radiation directions. The feeding structure of the pattern reconfigurable antenna is composed of microstrip lines at the bottom of the substrate. To ensure that the two slot antennas on the same side radiate in phase, bent microstrip lines are incorporated into the feeding network, introducing a 180-degree phase difference.  
Figure 8: Current distribution of antenna at 3 GHz.  
Figure 9: Geometry of proposed pattern reconfigurable antenna. (**a**) Top view; (**b**) bottom view.  
In the middle section of the feedline, four PIN diodes are used as switches to control the antenna's radiation direction. The model of the PIN diodes is BAP70-03 (NXP, Eindhoven, Netherlands). Their equivalent circuit model is measured using the TRL calibration method, with the specific equivalent circuit shown in Figure 10. When the PIN diodes are in the open state, the on-state resistance is approximately 4.7 ohms. When they are in the off state, the off-state resistance is about 10,000 ohms. Additionally, the PIN diodes also introduce a series inductance of approximately 1.5 nH due to packaging. The gain and reflection coefficient of the reconfigurable antenna with a directional pattern are shown in Figure 11, which shows that the antenna has a certain filtering effect and can resist interference from other frequency antennas. In addition, when the PIN diode is turned off, the port of the antenna exhibits total reflection, indicating that the reconfigurable antenna has good selectivity.
# A Secure Key Aggregate Searchable Encryption with Multi Delegation in Cloud Data Sharing Service
## 5 Security Analysis
### AVISPA Simulation Analysis  
We adopt the \"Automated Validation of Internet Security Protocols and Applications (AVISPA) Simulation Tools\" [12] to perform validation of security protocols against replay and man-in-the-middle attacks. AVISPA includes four backends: \"Tree Automata based on Automatic Approximations for the Analysis of Security Protocols (TA4 SP)\", \"SAT-based Model Checker (SATMC)\", \"Constraint-logic-based Attack Searcher (CL-AtSe)\", and \"On-the-fly mode-checker (OFMC)\". Neither the SATMC nor T theA4 SP backends currently support \"bitwise exclusive OR (XOR)\" operations. Therefore, official security validation-based simulations rely on two backends: CL-AtSe and OFMC.  
We use \"High-Level Protocol Specification Language (HLPSL)\" to implement the proposed scheme for the primary roles of data owner DO, data user DU, and cloud server CS, and also mandatory \"Sessions and Goals and Environments\". It is worth noting that AVISPA uses the DY threat model for validation. Figure 7 provides simulation results of OFMC and CL-ATse backends in the data retrieve phase and authentication for delegation phase, and clearly shows that the proposed protocol is safe from \"replay and man-in-the-middle attacks\" [34; 35].
# Improving Medical X-ray Report Generation by Using Knowledge Graph
## 4 Experiments
### Experimental Results
#### 4.3.2 Clinical Accuracy Performance  
We use the CheXpert [10] label as a measure to evaluate the clinical accuracy of generated reports. We compare 14 common diseases proposed in CheXpert and MIMIC-CXR based on precision, precision, recall, and F-1 metrics. We show the macro and micro scores, respectively. A high macro score indicates an improvement in the detection of all 14 diseases, while a higher micro score indicates an improvement in the impact caused by the imbalance of the dataset, such as the higher frequency of some diseases than others. The results of our comparison are shown in Table 2. Compared with other experiments, our clinical performance has improved in most of the indicators in the macro and micro scores.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Datasets** & **Methods** & **BLEU-1** & **BLEU-2** & **BLEU-3** & **BLEU-4** & **METEOR** & **ROUGE-L** \\ \hline \multirow{8}{*}{Open-I} & TieNet [21] & 0.330 & 0.194 & 0.124 & 0.081 & N/A & 0.311 \\  & [PERSON] et al. [24] & 0.359 & 0.237 & 0.164 & 0.113 & N/A & 0.354 \\  & KERP [6] & 0.482 & 0.325 & 0.226 & 0.162 & N/A & 0.339 \\  & HRGR-Agent [44] & 0.438 & 0.298 & 0.208 & 0.151 & N/A & 0.322 \\  & SD\&C [23] & 0.464 & 0.301 & 0.210 & 0.154 & N/A & 0.362 \\  & CoAtt [2] & 0.455 & 0.288 & 0.205 & 0.154 & N/A & 0.369 \\  & R2 Gen [22] & 0.470 & 0.304 & 0.219 & 0.165 & 0.187 & 0.371 \\  & PPKED [45] & 0.483 & 0.315 & 0.224 & 0.168 & 0.190 & 0.376 \\  & SGF [46] & 0.467 & 0.334 & 0.261 & 0.215 & 0.201 & 0.415 \\  & [PERSON] et al. * [26] & 0.490 & 0.362 & 0.286 & 0.233 & 0.213 & 0.440 \\  & Ours & **0.505** & **0.379** & **0.303** & **0.251** & **0.218** & **0.446** \\ \hline \multirow{8}{*}{MIMIC-CXR} & [PERSON] et al. [24] & 0.313 & 0.206 & 0.146 & 0.103 & N/A & 0.306 \\  & R2 Gen [22] & 0.353 & 0.218 & 0.145 & 0.103 & 0.142 & 0.277 \\ \cline{1-1}  & GumbelTransformer [39] & 0.415 & 0.272 & 0.193 & 0.146 & 0.159 & 0.318 \\ \cline{1-1}  & PPKED [45] & 0.360 & 0.224 & 0.149 & 0.106 & 0.149 & 0.284 \\ \cline{1-1}  & [PERSON] et al. * [26] & 0.489 & 0.351 & 0.267 & 0.211 & 0.209 & 0.381 \\ \cline{1-1}  & Ours & **0.491** & **0.358** & **0.278** & **0.225** & **0.215** & **0.389** \\ \hline \hline \end{tabular}  
* indicates that the experimental results are reproduced in our experimental environment.  
\end{table}
Table 1: A comparison of our method and many existing methods, using different linguistic metrics: BLEU-1 to BLEU-4, METEOR, and ROUGE-L, with the best results highlighted in bold.
# Adaptive Tolerance Dehazing Algorithm Based on Dark Channel Prior
## 4 Comparison and Analysis of Experiential Results
### Qualitative Comparison of Synthetic Images  
In Figure 8, the five algorithms, including the proposed algorithm, are tested on the stereo images where the ground truth images are known. Figure 8a shows the hazy images, which are synthesized from the haze-free images with known depth maps. The results of the five algorithms are shown in Figure 8b-f. Figure 8g shows the ground truth images for comparison. These haze-free images and their corresponding ground truth depth maps are taken from the Middlebury stereo datasets [31; 32]. It is obvious that [PERSON]'s results are quite different from the ground truth images, as the results are much darker (as shown in Figure 8b). By observing the images in Figure 8c, we find that [PERSON]'s results have a similar problem (for example, in the third image, the skin color of the child is obviously deepened, and the color of the background behind the hat in the second image is darkened). Figure 8d is the recovery result of [PERSON] et al. [13], and Figure 8e,f is the recovery result of the fixed tolerance value algorithm and the adaptive tolerance algorithm in this paper. They maintain the original color of the object, and the result is that there is no oversaturation such that the corrected image is more similar to the ground truth images.
# BFE-Net: Bidirectional Multi-Scale Feature Enhancement for Small Object Detection
## 3 Method
### Overall Architecture  
In the process of feature extraction, the shallow feature map contains location information with high resolution, which can be used to improve the accuracy of bounding box regression; however, due to less convolution, it has lower semantics and more noise. Although a deep feature map contains strong semantic information, its resolution is low and its detail expression ability is poor.  
Multi-scale fusion is an important method to improve the accuracy of small target detection. The feature pyramid network (FPN) [16] uses the feature expression structure of each network layer for the same scale image in different dimensions from the bottom to the top and refers to the feature information of the multi-scale feature map while taking into account the strong semantic features and location features, which is conducive to the detection of small objects. The standard RetinaNet [11] uses the feature pyramid network (FPN) in [16] as the backbone network. At the same time, in order to obtain richer semantic features and better detection results for large targets, the standard RetinaNet innovates the generation layers C6 and C7 in the bottom-up backbone network (Figure 2b). To further shorten the information transmission path and make full use of high-resolution information, PANet creates a bottom-up path enhancement based on the standard FPN (Figure 2a). Inspired by PANet, this work designs a bidirectional feature pyramid structure (Figure 2c) with levels N2 through N6, all pyramid levels in this structure have C = 256 channels.  
Figure 1: The overall architecture of our proposed model (BFE-Net).  
This structure uses feature pyramid levels P2 to P6, where P3 to P5 are obtained from the corresponding ResNet residual stages (C3 through C5) using top-down and lateral connections as in RetinaNet [11]. P6 is obtained through HGM, P2 is obtained by using DFE to enhance the C2 feature and then connecting it laterally with P3, which is different from [11]. N2 to N5 are obtained from the corresponding P2 to P5 via lateral connections and bottom-up paths, as in PANet [20]. N6 is obtained by lateral connections with C6 by stride convolution instead of down sampling (Figure 3a), which is different from [20].  
HGM obtains C6 by using C5 as a guide to extract lower-level feature information, which supplements the high-resolution information lost during bottom-up long-path propagation by standard backbone networks. DFE is used to increase the receptive field size of the convolution kernel by expanding the convolution, extract richer contextual information in the lower layers, and obtain richer low-level localization features. We introduce them in detail below.
## 3 Problem Description  
We focus on efficient ship detection in SAR images. The open datasets used are HRSID [9] and SSDD [10]. Both include SAR images with different resolutions, polarizations, sea conditions, sea areas, and coastal ports. Figure 1 provides SAR images obtained in different scenarios. Our target is to demarcate each ship target accurately and efficiently (e.g., the green rectangular boxes in Figure 1 represent ships detected). By comparing the area with the ground truth (identified via the red rectangular box in Figure 1), we can calculate _Precision_ (\(P\)), _Recall_ (\(R\)), \(F1\) score and _Average Precision_ (\(AP\)) to evaluate detection accuracy [34]. On the other hand, to show that the neural network we generated is efficient in computation, we carry out comparisons by the number of computations (represented by floating-point operations--FLOPs) and frames per second (FPS). Detailed definitions of evaluation metrics are given below:  
\[P=\frac{TP}{TP+FP}; \tag{1}\]  
\[R=\frac{TP}{TP+FN}; \tag{2}\]  
\[F1=2\times\frac{P\times R}{P+R}; \tag{3}\]\[AP=\int_{0}^{1}P(R)dR; \tag{4}\]  
\(TP\) (true positives), \(FP\) (false positives) and \(FN\) (false negatives) in Equations (1) and (2) are, respectively, the number of ship detected correctly, false alarms, and missing ships. To consider precision and recall together, we also adopt the F1 score in Equation (3) and \(AP\) in Equation (4). \(AP\) is defined by an integral equation, where \(P\) represents precision and \(R\) represents recall. \(P(R)\) refers to a set of \(\langle P,R\rangle\) pairs that are obtained by setting different confidence thresholds.