esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The sensing region must not detach from the structure/reinforcement as a result of mechanical stress or be decomposed due to the high alkaline surrounding. These considerations emphasize the level of attention needed to evaluate the mechanical properties of the optical fiber embedded into concrete. Over the past few decades, much work has been focused on the survival of glass in high alkaline concrete environments [20], leading to embedding FOSs in concrete to evaluate its durability [21; 22]. Research has shown that polymeric coatings increase the durability of optical fibers in concrete [21]. In particular, optical silica glass fibers with high alkaline resistance were obtained by coating them with polyetherimide [23], fluorine-polymer [24], and carbon coating [25]. Previous work by the authors presented a Functionalized Carbon Structure (FCS), where fiber optic sensors were \"woven\" into carbon fiber reinforcement polymer (CFRP) strands in order to evaluate the bonding strength between the sensor element and concrete [26]. This was focused on FOSs embedded in FCSs and textile net structures (TNSs) based on alkaline resistant glass. This work was further extended to embed FCSs in concrete blocks to evaluate the performance of the FOSs and the detailed analysis on the performance and the results can be found elsewhere [27]. However, when FCSs are embedded into concrete structures, the highly alkaline environment would degrade the FCS, thus limiting its life-span and operation. The strength of resistance to these highly alkaline environments would depend on the type of fiber used and the material of the protective-coating surrounding the fiber, i.e., acrylate, polyimide, or carbon. To this end, the paper presented here consists of a detailed analysis on the durability of FCS embedded with optical glass fiber sensors in a highly alkaline concrete environment. In addition, the spatial variation of the optical fiber inside the FCS was also analyzed to evaluate its effects on the bonding between the optical glass fiber and FCS.
# Acoustic Scattering Models from Rough Surfaces: A Brief Review and Recent Advances
## 3 Historical Approximations
### The Perturbation Theory
The used parameters are respectively frequency = 2.25 MHz, angular beam width = 3\({}^{\circ}\), observation distance = 300 mm, RMS surface height \(\sigma\) = 10 \(\upmu\)m and the two correlation lengths of the rough surface both equal to 100 \(\upmu\)m. Reproduced with permission from [36], Elsevier, 1993. Only a few authors have been interested in the scattering from canonical non-planar rough targets. Notably [PERSON] [44; 45] recently investigated the scattering problem for an elastic rough sphere. He developed in that case a second-order perturbation model. He compared the scattering patterns obtained versus the adimensional wave-number ka and for different values of the ratio of the rms height to the sphere radius with its perturbation solution to the classical acoustic scattering solution for a smooth sphere [46; 47]. Measurements of the backscattering response of a cast iron sphere versus ka led to a good experimental validation of the proposed model: the roughness yields a decrease in the backscattered amplitude notably for large ka values. The accuracy is limited to small values of the ratio rms height to wavelength. Note also, for instance, a recent development in electromagnetism of the perturbation theory for a slightly deformed cylinder [48]. We also have to cite the phase perturbation method [49; 50] in which the phase (rather than the field) is expanded in a series of different orders in kh. This theory exhibits links with both PT and Kirchhoff [51]. A validation [52] of the phase perturbation method has been proposed and has consisted in comparing its results to numerical ones in a region where PT and Kirchhoff are found ineffective. The phase perturbation method is found to work well in such a region except for low grazing observation angles. However, applications of this new theory seem not numerous and are more focused on radar cross sections calculations [53]. Although the perturbation approach can take into account of the scattering and re-radiation of Rayleigh waves, it appears possibly more restrictive than the Kirchhoff method in its range of applicability. The perturbation method is notably an approach valid for low roughness (when compared to acoustic wavelength). Several validations of the PT are provided in the next sections; they also show that PT is rather accurate far from the specular direction.
# Improved Chaff-Based CMIX for Solving Location Privacy Issues in VANETs
## 2 Background  
### VPKI Architecture  
The heterogeneous VANET architecture primarily consists of three bodies, i.e., vehicles, _RSU_s and CAs. The vehicle is also called an OBU, a transceiver board placed on the vehicle to exchange information with CAs, _RSU_s and other vehicles. Each _RSU_ and vehicle has credentials and private keys for safe and secure communication. CAs register and certify the public keys to vehicles and _RSU_s. _RSU_s are used to monitor road traffic and minimise accidents. They also provide access points for vehicles and other _RSU_s to disseminate information securely and effectively. Although _RSU_s use wire-based communication, vehicles use wireless communication between each other and with _RSU_s. Note that this protocol can be executed in tunnels and other confined areas due to these communications features; see Figure 1 for a standard VANET architecture. In general, the VPKI should have the following list of CAs [8; 14]:  
1. The root CA (RCA) 1 is at the top of the hierarchy, serving as a governance body that certifies other intermediate authorities.
2. The long-term CA (\(LTCA\)) 1 is an intermediary authority that is responsible for vehicle registration and long-term certificate issuance for vehicles and _RSU_s.
3. The resolution authority (RA) 1 is a central authority that can address a pseudo-identity and thereby validate the long-term identity of the vehicle in the event of a fraudulent act by communicating with the \(LTCA\) and the \(PCA\).
4. The pseudo-identity CA (\(PCA\)) 1 is an intermediary authority responsible for issuing pseudo-identities for registered vehicles.  
In addition, the RCA manages various domains through cross-certification. There is only one \(LTCA\) in each domain, and vehicles must register in the home domain. However, they may cross domains and communicate with foreign \(LTCA\)s to gain pseudo-identities. As far as the \(PCA\) is concerned, it may be involved in one or even several domains. Therefore, if the vehicle requires a pseudo-identity, the \(LTCA\) can only offer one authentication ticket per vehicle, and the authorisation will be with the certificate gained from the \(LTCA\). In PKI-based systems, even though the use of pseudo-identities as anonymised certificates guarantees the anonymity of the identity [16], it cannot guarantee the privacy of the position. For example, a vehicle has to change its pseudo-identity during its trip. However, the eavesdropping monitor with two observation points in the same road will link the changing of a moving vehicle's pseudo-identity.
###### Abstract  
The development and calibration of a measurement system designed for assessing the performance of the avalanche photodiodes (APDs) used in the Compton scattering polarimeter of the CISP project is discussed in this work. The designed system is able to characterize the APD gain \(G_{APD}\) and energy resolution across a wide range of temperatures \(T\) (from \(-20\)\({}^{\circ}\)C to \(+60\)\({}^{\circ}\)C) and bias voltages \(V_{bias}\) (from \(260\) V to \(410\) V). The primary goal was to experimentally determine the \(G_{APD}\) dependence on the \(T\) and \(V_{bias}\) in order to establish a strategy for stabilizing \(G_{APD}\) by compensating for \(T\) fluctuations, acting on \(V_{bias}\). The results demonstrate the system capability to accurately characterize APD behavior and develop feedback mechanisms to ensure its stable operation. This work provides a robust framework for calibrating APDs for space environments. It is essential for the successful implementation of spaceborne polarimeters such as the Compton scattering polarimeter foreseen aboard the CubeSat Solar Polarimeter (CUSP) mission under development to perform solar flare X-ray polarimetry.  
X-ray polarimeter; avalanche photodiode; measurement system for APD calibration 2024248016 248016
## 3 Phenolic Compounds of Sweet Cherry By-Products
### Flavonoids
#### 3.2.3 Flavanones  
Flavanones, also called dihydrofulnesses, are usually found in citrus fruits, but are also present in cherries [1]. These compounds are characterized by the presence of a chiral center at position 2, and although they are similar to flavones, they do not possess the 2,3-double-bound at C\({}_{3}\)[59]. Generally, flavanones are glycosylated at position 7. The most common forms present in nature are naringenin, hesperidin, naringenin 7-O-rutinoside, and hesperetin [49].  
_P. avium_ stems were found to contain Sakuraneetin 5-O-glucose, followed by naringenin 7-O-glucoside, and aromandendrin 7-O-hexoside as the major flavanones [12]. Posteriorly, other works confirm these results, showing Sakuraneetin and naringenin 7-O-glucose as some of the main flavanones found in sweet cherry stem extracts (Table 3), comprising about 12% of the total phenolics [7; 10]. Sakuraneetin \(O\)-pentosyl-hexoside and pinocembrin were also reported in _P. avium_ stems [10; 12].  
Regarding leaves of sweet cherry Sakuraneetin and its derivative, Sakuraneetin 5-O-hexoside and naringenin hexoside were the flavanones detected [7; 10]. Sakuraneetin was found in considerable amounts in the aqueous infusion of _P. avium_ flowers [7].  
In the literature, there is little information about the presence of flavanones in cherry fruits. Recently, two naringenin hexosides were found in sweet cherries, but in small amounts [18]. This finding was similar to previous work, demonstrating that these compounds represent less than 1% of the total phenolic compounds [18; 44].
# A Packet Content-Oriented Remote Code Execution Attack Payload Detection Model
## 5 Experimental Results and Analysis
### Evaluation of the ELi and IDSER Detector  
In this section, we select six typical machine learning algorithms to train models for detecting ELi and IDSER on the dataset and evaluate these models. The machine learning  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Metric** & **Description** \\ \hline TP (True Positive) & The number of payloads correctly classified as malicious. \\ TN (True Negative) & The number of payloads correctly classified as benign. \\ FP (False Positive) & The number of payloads mistakenly classified as malicious. \\ FN (False Negative) & The number of payloads mistakenly classified as benign. \\ Precision & \(\mathit{TP}/(\mathit{TP}+\mathit{FP})\) \\ Recall & \(\mathit{TP}/(\mathit{TP}+\mathit{FN})\) \\ F1 Score & \(\left(2*Precision*Recall\right)/\left(Precision+Recall\right)\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Evaluation metrics.  
algorithms we select are naive Bayes, decision tree, logistic regression, random forest, SVM, and XGBoost. All of these algorithms are trained using their default parameters. It should be noted that due to the small size of the dataset, we do not choose to use deep learning algorithms.
# Portable Optical Fiber Bragg Grating Sensor for Monitoring Traffic Density
###### Abstract  
The paper examines the development of a portable sensor strip with fiber optic Bragg grating for monitoring urban traffic density up to 80 kph. It contains a 2.5-m-long and a 2-cm-high sensor created from a combination of silicone addition rubber (bicomponent addition silicone rubber) and Bragg grating placed inside a carbon tube. The design of the portable sensor permits traffic density and cars crossings to be monitored and detected in a single lane. The functionality of the sensor was verified in real traffic; the results of this study are based on the detection of 1518 vehicles of different types and sizes. According to the measurements, the sensor is characterized by a high detection rate of 98.946%.  
Fiber Bragg Grating; optical fiber; portable sensor; car detection; density traffic monitoring
# Revolution or Evolution? Technical Requirements and Considerations towards 6G Mobile Communications
## 6 Enablers for 6G and Relevant Technical Preparation and Implementation
### Quantum Communication  
As with the previously described technologies, QC [90], is another critical 6G technology that provides unconstrained communication security. The critical distinction between conventional binary communication and QC is whether on-site eavesdropping is detectable [91]. Due to the use of quantum principles, data encoded in a quantum state (using photons or quantum particles) cannot be accessed or duplicated without altering the data (e.g., correlation of entangled particles and inalienable law). Additionally, the superposition nature of the qubits in QC results in a faster data transmission rate. There are many types of QC at the moment, including quantum key distribution, quantum secret sharing, quantum teleportation, and quantum secure direct communication [92]. A significant advantage of this technology is that it can considerably improve data security and reliability. An adversary's interference in QC can change one's quantum state. Because of this, the recipient cannot remain oblivious of the interference [28]. Furthermore, QC considerably improves the network efficiency by providing information in a real-time and ultra-fast response to the user's demand [93]. Also, QC can improve to provide ultra-low latency in 6G networks. More features that QC provides are [94]:  
* relies on qubit;
* accelerates processing data for ML and deep learning models;
* allows for the simultaneous processing of millions of devices;
* allows for more efficient handling of vast amounts of data.
# Robust Fingerprint Minutiae Extraction and Matching Based on Improved SIFT Features
## 3 Proposed Methodology
### Minutiae Feature Matching
Additionally, the ratio of descriptor distances is employed for _outlier rejection_ to reduce the number of false positive matches (see Figure 9 below). Figure 9: Fingerprint minutiae matching results using (**a**) original SIFT algorithm and (**b**) improved SIFT algorithm. As can be seen in Figure 9, the improved SIFT algorithm gives prominently better results in terms of matching quality and robustness compared to the original SIFT matching algorithm, particularly when the fingerprint image contains much local similar characteristics. However, despite discarding a few correct matches, nearly all mismatches are removed.
# Multi-Exposure Image Fusion Based on Weighted Average Adaptive Factor and Local Detail Enhancement
## 4 Results and Analysis
To sum up, the algorithm in this paper has outstanding performance in detail and color information retention and avoids excessive detail enhancement to show a comfortable visual effect. In order to verify the robustness of the algorithm in this paper, we will use a set of 30 multi-exposure image sequences as a validation dataset and compare it with the other six algorithms. The fusion results are shown in Figure 13. The B-LP method produces a poor visual effect and the image's color is seriously distorted. The other six algorithms have achieved sound sensory effects. In Figure 13a, there is a gray shadow around the lamp. In Figure 13b, the color transition in some areas is unnatural, such as the dark arch in the red area. The overall contrast of the image in Figure 13c is low. The overall image in Figure 13d is brighter, resulting in the lack of details of the arched edge in the red frame. Figure 13f  
Figure 12: Comparison of fusion results for Figure 9c. (**a**) SPD-MEF method fusion result. (**b**) MRF method fusion result. (**c**) DSIFT method fusion result. (**d**) FEAF method fusion result. (**e**) LE method fusion result. (**f**) B-LP method fusion result. (**g**) LLF method fusion result. (**h**) The proposed method fusion result. contains much noise. From the fusion results, Figure 13g has good color saturation, such as the arched buildings are brightly colored, the texture details are clear, and there is no halo around the lights in the yellow areas. In general, our algorithm can consistently achieve satisfactory results in static scenes, even in large datasets of multi-exposure images. To better analyze the effectiveness of the MEF algorithm in this paper, we analyze the pixel intensity in the horizontal direction of the enlarged area in Figure 10, as shown in Figure 14. In Figure 14b, the pixel intensities of DSIFT, SPD-MEF, LLF, and MRF are almost a straight line, indicating that they contain almost no texture information. The LE and B-LP methods contain some detailed information but the whole image is distorted. The FEAF and the algorithm proposed in this paper have achieved consistent results.
# Attentional Keypoint Detection on Point Clouds for 3D Object Part Segmentation
## 3 Our Approach  
### Background  
PointNet++ [7] addresses one of the main limitations of the original PointNet [6], which is its inability to capture local geometric structures effectively. The key innovation of PointNet++ is the introduction of a \"set abstraction\" module, which groups points into overlapping local regions and applies PointNet to each group. The set abstraction module consists of three main operations: sampling, grouping, and PointNet. The sampling operation selects a subset of points as centroids, the grouping operation forms local regions around these centroids, and the PointNet operation extracts features from the points in each local region. Multiple layers of set abstraction modules are employed to progressively downsample the point cloud and extract hierarchical features. These features are then concatenated and processed by a series of fully connected layers to generate the final feature representation for the entire point cloud.  
DGCNN [9] is a neural network architecture specifically designed for processing point cloud data. The key innovation of DGCNN is the EdgeConv operation, which dynamically constructs a k-nearest neighbor graph for each point in the input point cloud. The EdgeConv operation operates on the edges of this graph and aggregates information from neighboring points to generate features that capture both local geometric structures and global context. It employs a series of EdgeConv layers, thus progressively refining each point's features. These refined features are then aggregated using a global max pooling operation to generate a global feature vector representing the entire point cloud. The main strengths of DGCNN are its ability to capture complex geometric patterns, its robustness for input permutations, and its efficiency in processing large-scale point clouds. It has achieved state-of-the-art performance on several benchmarks for point cloud processing tasks.
# Quantum Perspective on Digital Money: Towards a Quantum-Powered Financial System
## 4 Integration of Quantum Money with Quantum Networks
### Infrastructure for Quantum Money Transfers
#### 4.3.2 Fiber-Based Networks  
For shorter-range or regional transactions, fiber-optic networks could provide a secure means of distributing entangled states in urban or local areas. Fiber-based systems would enable fast and reliable entanglement distribution, making quantum money transfer feasible within regions served by such infrastructure.  
Recent advancements have significantly progressed towards the realization of complex QINs capable of handling multiple use cases, including quantum money operations. For instance, in 2022, entanglement of single atoms was demonstrated over a 33-km telecom fiber link [35], while in 2024, entanglement between two nuclear spin memories was achieved through a 40-km low-loss fiber link [36].
# Using Feature Selection with Machine Learning for Generation of Insurance Insights
## 5 Discussion
### Generating Insurance Insights  
We mentioned previously that a potential benefit of feature selection for insurance datasets is the revelation of the most important and least important features, as a pre-step to generating business insights. Our experimental results from use of our framework revealed the extent to which redundant and irrelevant features exist in the datasets. By identifying which features are influencing (or not) the downstream accuracy gains, we can use these feature level insights to assist business decision making.  
From our experimental results, two examples stand out in particular. Insurance companies tend to have more than one line of business to sell across these lines. In the car Insurance dataset, D1, the least important feature is HHinsurance, which indicates whether the target of the call has house insurance with this insurance provider. This immediately indicates that targeting existing customers with house insurance will be of little reward. Balance (yearly average bank balance) and PrevAttempts (no. of previous contacts prior to this campaign) are the second and third least important features respectively. These findings indicate that targeting customers with a high bank balance will be of little reward and that customers do not necessarily tire of being contacted multiple times. The most important features in the D1 dataset are CallStart (start time of the last call), CallEnd (end time of the last call), and Communication (contact communication type). This knowledge enables the company to target particular times of the day to call, choose the particular communication type that works best, and tailor call scripts to favour long or short calls as appropriate.  
In the Medical Insurance Claim Fraud dataset, D2, the most important features are gender, location, and number of claims. Knowledge of these factors will allow investigators to target groups of claims for further examination. Fee charge and cause are the least important features and this indicates that the value of the claim and the cause giving rise to medical treatment are not relevant and that focusing on a particular type of claim or value of claim is not likely to uncover fraud.
## 3 Receiver Design for NOMA
### Cooperative NOMA
We now focus on the combination of the different received replicas, weighted by the corresponding reliabilities, which is associated with the Mean Squared Error (MSE). Using the estimates of \(\mathbf{c}_{m}\) (that is \(\overset{\wedge}{\mathbf{c}_{m}}\)), which stands for the output training symbol vector from the \(m\)th receive branch, the data symbol \(\hat{b}\), resulting from the \(M\) (receive) branches becomes [19]:  
\[ \begin{array}{l}\hat{b}=\sum\limits_{m=1}^{M}\hat{b}_{m}^{weighted}\\ =\sum\limits_{m=1}^{M}\frac{\hat{b}_{m}^{detected}}{MSE(\mathbf{c}_{m})} \end{array}  \tag{9}\]  
where \(\hat{b}_{m}^{detected}\) is the signal obtained using \(y^{\prime}\) (as defined by (8), after soft or hard decision, from the \(m\)th transmission (the signal received directly from the base station or those that have been relayed by the other users).
## 3 Results and Discussions
### Corrosion Resistance
To sum up, the proposed sol-gel self-assembling approach of the silane layer coupled with the different roughening treatments significantly improved the water repellency and, thus, the corrosion resistance of the aluminum alloy AA6082-T6. In addition, this approach opens the door to various industrial applications thanks to the controllable wettability behavior. These results are promising for further investigation to understand the effect of hierarchical morphologies on the interactions of the surface with liquids, at different polarities, not only in static but also in dynamic conditions.