esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### System Design
#### 2.2.1 2D MEMS Mirror  
Most murine retinal images reported in the scientific literature include post-objective scanning systems. In these systems, galvanometers are placed between the final lens (objective) and the retina. Collimated light is directed by mirrors at different angles to reach different parts of the retina. This design limits the field of view of the OCT systems because the ocular pupil clips OCT source light at the system's most extreme angle.  
This problem can be mitigated by placing the mirrors at conjugates to the ocular pupil plane and thus steering the beam beyond the aperture of the pupil. However, engineers using this approach have typically used two-mirror galvanometer systems. In this case, the designer must choose either the slow axis, the fast axis, or somewhere in-between as a conjugate plane to the ocular pupil. The slow-axis has historically been chosen to maximize collection efficiency and minimize vignetting [20]. New approaches to limit vignetting and maximize collection efficiency have included pupil tracking to control where the beam enters the pupil [21].  
Dual-axis MEMS mirrors have been used in OCT catheter designs and free-space optical setups [22; 23], but adoption of dual axis-mirrors for retinal imaging has been more slow. Recent examples include hand-held OCT retina and anterior segment imaging systems [24; 25]. The system reported here is the first OCT system to use dual-axis MEMS mirror scanning for mouse retinal imaging, and first reported in less detail in 2017 [19].
## 5 Results and Discussion
The parameters for rolling resistance, denoted as \(C_{r}\), \(C_{c}\), and \(C_{t}\), are assigned values of 1.75, 0.0328, and 4.575, respectively, andare subject to variation based on factors such as road surface type, road conditions, and vehicle tire type. The air mass density, represented as \(\rho_{Air}\), is 1.2256 kg/m\({}^{3}\), while the frontal projection area of the Prius, identified as \(A_{f}\), measures 2.3316 m\({}^{2}\). Additionally, the aerodynamic drag coefficient \(C_{D}\) is determined to be 0.28 [26]. As a result of the resequencing strategy modifying the sequence of CAVs entering the MZ, the trip duration of CAVs is prolonged. Recognizing the potential for this alteration to raise fairness concerns among drivers, we will also assess the fairness of each strategy. The fairness metric, symbolized as \(\rho\), is delineated as follows:  
\[\rho=\sqrt{\frac{1}{N}\sum_{i=1}^{N}\left(t_{i}^{m}-\overline{t^{m}}\right)} \tag{27}\]  
\[\overline{t^{m}}=\frac{1}{N}\sum_{i=1}^{N}t_{i}^{m} \tag{28}\]  
\(N\) symbolizes the aggregate count of CAVs during 15 min, while \(\overline{t^{m}}\) signifies the mean trip duration of all CAVs. A heightened \(\rho\) value suggests diminished fairness.
## 1 Introduction
Its high hardware complexity is unacceptable for a single node the in the application of CR. Besides, the synchronization of each parallel channel is difficult to guarantee. However, considering our ultimate goal is spectrum sensing and detection, there is no need to reconstruct the original signal. Consistent with the idea presented in [5; 14], we plan to sense the power spectrum of the interested bandwidth from sub-Nyquist samples. Meanwhile, in order to simplify the hardware complexity, we intend to explore a more succinct architecture. In our previous work [15], we first proposed a single channel modulated wideband converter for power spectrum sensing and reconstruction with some expressions and simulations. However, in order to apply in CR system, parameter selections, prototype design as well as algorithm modification need further research. In this paper, a new spectrum sensing hardware prototype based on single channel modulated wideband converter (SCMWC) was designed and tested by actual circuits. It has the ability to treat analog multiband models as well as MWC. More importantly, practical implementation, low hardware complexity and low computational load make it possible to be used for CR system. Our main contribution of this paper is in proving feasibility of the SCMWC strategy using commercial devices. In detail, it can be divided into two parts. The first one is that we design and implement a hardware prototype based on SCMWC scheme. The proposed SCMWC hardware prototype only consists of one modulator, low-pass filter (LPF) and low rate ADC. Particularly, we introduce Rocket I/O on FPGA to generate high speed periodic sequences. By using this, the implementing process is simplified. The second one is that a series of simulations and experiments are conducted to acquire the proper parameters as well as evaluate the performance of the hardware prototype. Results show that our design can treat 2 GHz Nyquist-rate input signals with spectrum occupancy up to 15 MHz. And the total equivalent sampling rate is only 70 MHz, which is only 3.5% of the Nyquist rate. Besides, the high dynamic range and low complexity make it more suitable for being applied to CR system. The rest of this paper is organized as follows. Section 2 describes the spectrum sensing model. Then the architecture of SCMWC is presented and analyzed in Section 3. This leads to a parameter selection which guarantees successful sensing from the digital samples. Section 4 detailed describes the design methods and exact specs of the proposed circuit board. In Section 5, we present the some simulations and experiments based on our prototype. Besides, comparisons with related work are discussed. Finally, Section 6 concludes the paper.
# Effective Multi-Frame Optical Detection Algorithm for GEO Space Objects
## 4 Experiment
### Model Deployment  
The developed algorithm is oriented towards practical application of space-based or ground-based space object surveillance system. Hence, compared with GPUs and AI chips designed for high-performance parallel computing processing, resources of the onboard processor platform are limited and there are strict constraints on computational complexity and power consumption for executing deep neural network. In this subsection, we investigate the proposed GEO object detection pipeline and analyze the performance on NVIDIA Jetson Nano. Table 5 shows the specifications of Jetson Nano boards.  
The operating system is Ubuntu 18.04. The versions of CUDA and cuDNN are 10.2 and 8.0, respectively. Based on the higher-level Python API of Paddle Inference that has built-in support for deploying most common computer vision models, we run the PP-YOLOv2 model on Jetson Nano to test the inference speed. The TensorRT library is also used to boost the inference efficiency of the deep model, which provides a tuning function of deep learning networks. The average execution time are enlisted in Table 6.  
It can be observed that the average inference time is 0.71 s per image with 640 \(\times\) 480-sized input images. Considering the metric of FPS, the proposed method achieves an inference speed at 1.42 FPS, with a ResNet50 backbone other than common lightweight backbone networks. Since the typical onboard remote sensing systems have limited storage and compute capacity, this research work will pave the way for further optimization and improvement to meet the demand of future real-time image processing.
# Improved Visual SLAM Algorithm Based on Dynamic Scenes
## 5 Discussion  
In line with existing literature [12; 13], our experimental results also support the effectiveness of dynamic object removal in improving SLAM localization and mapping performance. However, although the above two methods also contribute to eliminating dynamic targets (by using image segmentation methods [15] or optical flow methods [25] to eliminate dynamic targets), our research considers the important role of the visual odometry in the SLAM front end, selecting the method from the literature [10] that improves the feature extraction step, thereby achieving better localization and mapping results. This difference may be attributed to the fact that, in our approach, we recognize that simply removing dynamic objects is not enough to achieve the best localization results; instead, it is crucial to consider whether the algorithm can be optimized for specific environments (e.g., indoor environments). For instance, in environments with complex and diverse indoor objects, the feature extraction process becomes particularly important for the overall algorithm.  
Although the improvement in SLAM's ATE was slightly smaller than that of DynaSLAM, when we consider the errors during the experimental process, this difference can be attributed to experimental bias. Nevertheless, there is no doubt that the improved SLAM demonstrated a clear advantage in terms of average computation time, and this difference may stem from the architectural differences in the dynamic object removal networks between the two methods. DynaSLAM uses an image segmentation network (Mask R-CNN) for dynamic object removal, which relies on a two-stage processing pipeline, thus reducing its real-time performance. On the other hand, when discussing object detection algorithms for detecting and segmenting dynamic objects, we found that SSD [17] and YOLO [11], both single-stage object detection methods, performed excellently in terms of real-time capability, but showed certain differences in other aspects. SSD is known for its high detection speed, but it is less accurate than YOLOv5 in detecting small objects. Although SSD is also suitable for real-time applications, it may not be as accurate as YOLOv5 in complex scenes. This discrepancy may be due to YOLOv5 adopting a feature pyramid network (FPN) [27] and a path aggregation network (PANet) [28], which enable the aggregation of features from different levels.
# Effects of Ethanol Concentration on Oral Aroma Release After Wine Consumption
## Material and Methods
### Intra-Oral SPME Sampling
#### 3.2.2 Intra-Oral SPME Procedure for Aroma Monitoring  
To study how ethanol affects the oral processing of typical wine esters, subjects rinsed their mouths with three aromatized wines containing different ethanol content (0.5% _v/v_, 5% _v/v_ and 10% _v/v_), and then, the aroma released into the oral cavity after wine expectoration was monitored following the intra-oral SPME procedure previously developed and validated by [PERSON] and collaborators [24]. The representation of the analytical procedure is schematized in Figure 1. Briefly, subjects were instructed to introduce the aromatized wine (15 mL) into their oral cavity, kept it for 30 s and then spit it off. During rinsing, special care was taken to keep the lips closed, not to swallow and not to open the velum-tongue border prior to expectoration. At 30 s after expectoration, a DVB/CAR/PDMS (Divinylbenzene/Carboxen/Polydimethylsiloxane 50/30 \(\upmu\)m thickness, 2 cm length) coated SPME fiber (Supelco, Bellefonte, PA, USA) with a home-made adaptor, was introduced into the oral cavity of the volunteer. After 2 min of oral aroma extraction, in which swallowing was not allowed, the fiber was removed from the oral cavity and immediately placed into the split/splitless injector (splitless mode) of the Gas Chromatograph (GC) Agilent 6890N coupled to a quadrupole Mass Detector (MS) Agilent 5973 (Santa Clara, CA, USA). This measurement is referred to as an immediate oral release. Four minutes after wine expectoration, a second fiber was introduced in the oral cavity following the exact protocol described previously (Figure 1). This second measurement is referred to as a prolonged oral aroma release.  
After each injection, the fiber was cleaned for 10 min to avoid any memory effect. Analyses were performed three times with each wine type (0.5% _v/v_, 5% _v/v_ and 10% _v/v_) by the ten volunteers (3 wine \(\times\) 10 volunteers \(\times\) 3 repetitions = 90 injections). An inter-fiber repeatability study was previously carried out. This allowed the selection of the most similar fibers (RSD values <10% for the extraction of the same aroma compound) to complete the study. Moreover, the use of a short sampling time also known as \"true headspace\" [25], avoids any problems derived from the use of SPME, such as the changes in the distribution of the molecules between the liquid and gas phases at equilibrium, due to adsorption phenomena onto the fiber itself, or to problems of saturation, due to an excess of ethanol absorbed onto the fiber compared to the compounds of interest [25]. In our case, the equilibrium is not reached, the fiber is not saturated, and therefore, the detected composition can be considered as representative of the real intraoral-headspace composition.
# An Improved Reversible Image Transformation Using K-Means Clustering and Block Patching
## 4 Experimental Results
### Comparison the Proposed Method with Previous Methods  
#### 4.3.1 Performance Comparison  
In order to represent the results of each transformed phase and prove the algorithm's feasibility, the transformed results of secret image are shown in Figure 8. There is the secret image, composite image, transformed image and stego-image of four example. Take Figure 8(a1-d1) as an example, we firstly transform the secret image (a1) blocks in the last (K-T)-classes to generate the composite image (b1). Note that the pixel values of unmatched blocks in the first T-classes is set to 0. By the composite image, we only obtain the texture details of target image because these matched blocks have slightly big SDs. Then, we patch the secret blocks according to the AI from the last (K-T)-classes transformation to obtain the transformed image (c1). Finally, we embed the AI by the RDH method to achieve the stego-image (d1).  
Figure 8: _Cont._  
To make comparison with [PERSON] et al.'s method, the proposed method uses the Huffman coding method to compress AI, and encrypts the compressed information by using secret key sk, then adopts RDH scheme in [8] to embed the encrypted information into the transformed image, which are same with [PERSON] et al.'s method.  
In Figure 9, we select another target image (Figure 9b) which is irrelevant with the secret image (Figure 9a). Although the transformed image (Figure 9c) in [PERSON] et al.'s method has less visual distortion than the proposed method (Figure 9e), the ultimate stego-image created by the proposed method (Figure 9f) has better visual quality than [PERSON] et al.'s method (Figure 9d). Note that stego-image quality is the key because it is the final image sent to the receiver. Therefore, we can develop the RIT by improving the stego-image quality.  
Figure 8: The transformed results of secret images.  
Figure 9: (**a**) Secret image. (**b**) Target image. (**c**) Transformed image created by [PERSON] et al.’s method. (**d**) Stego-image created by [PERSON] et al.’s method. (**e**) Transformed image created by the proposed method. (**f**) Stego-image created by the proposed method.  
To shows the performance intuitively, we test the secret and target images in Figure 7, and the average results are listed in Table 3. From the table we can see, the proposed method outperforms [PERSON] et al.'s method on both lower AI and higher stego-image quality. There are two reasons for this: firstly, we can patch the secret blocks in the first T-classes to hide the partial AI produced by transforming the last (K-T)-classes, which leads to the quite large reduction of AI. Secondly, we need to embed the AI into the transformed image to generate the stego-image. Thus, the visual quality of stego-image is determined by the amount of AI and the visual quality of the transformed image. In this experiment, the visual quality of transformed image is not changed too much, while the AI is decreased significantly. Therefore, the visual quality of stego-image can be improved.
## Appendix E Performance Metrics of the CHT Behavior
### Descriptive Statistics for Study Case 1: Four-Bar Linkage Mechanism
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**CHT** & **Algorithm** & **Mean** & **std** & **Median** & **Minimum** & **Maximum** & **IS** \\ \hline FR & DER1B & 1836 & 1915 & 960 & 240 & 6120 & 0 \\ FR & DER1E & 1796 & 1088 & 1440 & 360 & 3600 & 0 \\ FR & DEB1B & 1844 & 1711 & 1320 & 240 & 6720 & 0 \\ FR & DEB1E & 1232 & 641.2 & 1140 & 240 & 2640 & 0 \\ FR & DECR & 1432 & 1160 & 1140 & 240 & 5040 & 0 \\ FR & DECB & 1144 & 798.3 & 840 & 240 & 3240 & 0 \\ FR & DECR1B & 1676 & 1139 & 1440 & 240 & 4200 & 0 \\ FR & DECR1E & 1752 & 1525 & 1140 & 240 & 6120 & 0 \\ FR & GA & 1392 & 804.4 & 1320 & 240 & 3000 & 0 \\ FR & PSO & 1604 & 1382 & 1200 & 240 & 5880 & 0 \\ SR & DER1B & 4716 & 4616 & 2820 & 240 & 1.584 \(\times\) 10\({}^{4}\) & 0 \\ SR & DER1E & 2740 & 2040 & 2280 & 480 & 9000 & 0 \\ SR & DEB1B & 1952 & 2291 & 1200 & 240 & 1.032 \(\times\) 10\({}^{4}\) & 0 \\ SR & DEB1E & 2016 & 1660 & 1740 & 240 & 6120 & 0 \\ SR & DECR & 3036 & 2027 & 3240 & 240 & 7800 & 0 \\ SR & DECB & 2448 & 2228 & 1800 & 360 & 1.092 \(\times\) 10\({}^{4}\) & 0 \\ SR & DECR1B & 3240 & 2621 & 2520 & 240 & 9960 & 0 \\ SR & DECR1E & 2260 & 1643 & 1680 & 240 & 6000 & 0 \\ SR & GA & 2096 & 1663 & 1680 & 240 & 6600 & 0 \\ SR & PSO & 2660 & 3709 & 1800 & 240 & 1.944 \(\times\) 10\({}^{4}\) & 0 \\ \(\epsilon\)C & DER1B & 6415 & 5308 & 5596 & 245 & 1.759 \(\times\) 10\({}^{4}\) & 0 \\ \(\epsilon\)C & DER1E & 7675 & 5490 & 7724 & 422 & 2.033 \(\times\) 10\({}^{4}\) & 0 \\ \(\epsilon\)C & DEB1B & 2185 & 1848 & 1701 & 245 & 8232 & 0 \\ \(\epsilon\)C & DEB1E & 2894 & 2330 & 2381 & 248 & 8761 & 0 \\ \(\epsilon\)C & DECR & 6501 & 5848 & 3458 & 248 & 2.123 \(\times\) 10\({}^{4}\) & 0 \\ \(\epsilon\)C & DECB & 4521 & 4713 & 3065 & 253 & 1.42 \(\times\) 10\({}^{4}\) & 0 \\ \(\epsilon\)C & DECR1B & 9087 & 7118 & 6334 & 261 & 2.154 \(\times\) 10\({}^{4}\) & 0 \\ \(\epsilon\)C & DECR1E & 9799 & \(1.014\times 10^{4}\) & 6297 & 271 & 3.267 \(\times\) 10\({}^{4}\) & 0 \\ \(\epsilon\)C & GA & \(1.267\times 10^{4}\) & \(1.53\times 10^{4}\) & 6227 & 260 & 6.385 \(\times\) 10\({}^{4}\) & 0 \\ \(\epsilon\)C & PSO & 3250 & 3580 & 1729 & 248 & 1.363 \(\times\) 10\({}^{4}\) & 0 \\ PF & DER1B & 2128 & 1259 & 1920 & 240 & 6000 & 0 \\ PF & DER1E & 1324 & 952.5 & 1020 & 240 & 4080 & 0 \\ PF & DEB1B & 1560 & 978.4 & 1620 & 240 & 3600 & 0 \\ PF & DEB1E & 1380 & 924 & 1200 & 240 & 4080 & 0 \\ PF & DECR & 1680 & 1106 & 1500 & 240 & 3960 & 0 \\ PF & DECB & 1344 & 952.9 & 1200 & 240 & 3720 & 0 \\ PF & DECR1B & 2228 & 1821 & 1860 & 240 & 8640 & 0 \\ PF & DECR1E & 1572 & 1481 & 1020 & 240 & 5040 & 0 \\ PF & GA & 2496 & 2216 & 1560 & 360 & 7200 & 0 \\ PF & PSO & 1724 & 1075 & 1680 & 240 & 4080 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table A17. EVALS descriptive statistics for study case 2: Cam-linkage mechanism. IS means the number of infeasible solutions through the executions. Appendix E.5.3. Descriptive Statistics for Study Case 3: Eight-Bar Linkage Mechanism  
The measures of the descriptive statistics for the eight-bar mechanism are presented in Table A18.
# Post-Quantum Key Exchange and Subscriber Identity Encryption in 5G Using ML-KEM (Kyber) 1
###### Abstract  
5G technology addresses user privacy concerns in cellular networking by encrypting a subscriber identifier with elliptic-curve-based encryption and then transmitting it as ciphertext known as a Subscriber Concealed Identifier (SUCI). However, an adversary equipped with a quantum computer can break a discrete-logarithm-based elliptic curve algorithm. Consequently, the user privacy in 5G is at stake against quantum attacks. In this paper, we study the incorporation of the post-quantum ciphers in the SUCI calculation both at the user equipment and at the core network, which involves the shared-key exchange and then using the resulting key for the ID encryption. We experiment on different hardware platforms to analyze the PQC key exchange and encryption using NIST-standardized CRYSTALS-Kyber (which is now called an ML-KEM after the standardization selection by NIST). Our analyses focus on the performances and compare the Kyber-based key exchange and encryption with the current (pre-quantum) elliptic curve Diffie-Hellman (ECDH). The performance analyses are critical because mobile networking involves resource-limited and battery-operating mobile devices. We measure and analyze not only the time and CPU-processing performances but also the energy and power performances. Our analyses show that Kyber-512 is the most efficient and even has better performance (i.e., faster computations and lower energy consumption) than ECDH.  
post-quantum cryptography; 5G; SUCI; elliptic curve integrated encrypted scheme; CRYSTALS-Kyber; ML-KEM 2025
# Intelligent Transmit Antenna Selection Schemes for High-Rate Fully Generalized Spatial Modulation
## 3 Antenna Selection Schemes for FGSM
### FD-TAS Based FGSM System  
The purpose of FD-TAS is to find antenna pairs that optimize the least FD between the received constellations [3; 13; 14; 15; 16; 17; 21]. FD-TAS results in \(\mathbf{H}\) of size \(N_{rx}\times N_{tx}\) from \(\mathbf{H}_{T}\) of size \(N_{rx}\times N_{T}\). FD-TAS involves the following major steps:  
Step 1: Compute the number of possible subsets \(N_{s}=\left(\begin{array}{c}N_{T}\\ N_{tx}\end{array}\right)\).  
Step 2: Determine all feasible transmit vectors \((2^{\mathbf{s}_{FGSM}})\) for every antenna subset.  
Step 3: Determine the minimum FD for every antenna subset using  
\[d_{min}(\mathbf{H}_{T})=\min_{\mathbf{\chi}_{i}\
eq\mathbf{\chi}_{j}}\big{\|}\mathbf{H}_{T} \big{(}\mathbf{\chi}_{i}-\mathbf{\chi}_{j}\big{)}\big{\|}^{2}, \tag{14}\]  
here, \(\mathbf{\chi}_{i},\mathbf{\chi}_{j}\in\mathbb{X}\) is the set of all feasible transmit vectors.  
Figure 1: Fictorial representation of TAS for FGSM.  
Step 4: Find an antenna subset that has the maximum FD.  
\[\hat{l}=\operatorname*{arg\,max}_{l\in\{1,2,\ldots N_{x}\}}d_{min}(\mathbf{H}_{T}^{l}), \tag{15}\]  
here, \(\mathbf{H}_{T}^{l}\!\!=\!\left[\mathbf{H}_{T}^{1},\mathbf{H}_{T}^{2},\ldots H_{T}^{N_{x}}\right]\).  
Based on the antenna subset index obtained in step 4, the antenna indices and the associated channel matrix of size \(N_{rx}\times N_{tx}\) are computed.
# FairRAG: A Privacy-Preserving Framework for Fair Financial Decision-Making
## 7 Conclusions  
In this paper, we introduce FairRAG, a robust architecture that makes a primary contribution by providing a new blueprint for applying large language models to structured tabular data in a privacy-preserving and interpretable manner. The novelty of our approach lies in creating a system with capabilities that no existing method offers in combination. Specifically, against the backdrop of prior work, FairRAG makes three distinct contributions. First, while conventional retrieval-augmented generation systems are designed for unstructured text documents, we successfully repurpose the paradigm for structured tabular data by serializing customer profiles into a retrievable knowledge base. Second, in contrast to common privacy-preserving machine learning techniques that directly modify model training with methods like DP-SGD, FairRAG decouples the privacy mechanism from the model training process. By applying differential privacy to the data generation stage, we create a safe static knowledge base that can be queried by a standard pre-trained LLM, avoiding the high computational cost and complexity of the private fine-tuning process. Our experiments on the Bank Churn and Telco Churn datasets validate the effectiveness of this architecture. FairRAG outperformed the traditional machine learning baselines, demonstrating strong balance between privacy and utility. It achieved high predictive accuracy while operating under formal differential privacy guarantees. In summary, FairRAG presents a significant step forward by offering a computationally efficient and privacy-preserving solution. However, we acknowledge key limitations, particularly the need for a rigorous fairness audit that provides a robust and generalizable framework to effectively balance the often competing demands of accuracy, privacy, and explainability in modern machine learning systems.