esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
As our study did not find any of the calculated BMP metrics to be significant predictors of nutrient concentrations, it is possible that instream nutrient concentrations are being driven by processes other than surface runoff (e.g., groundwater flow) during the drier, summer season. Additional studies are needed to assess the seasonal effectiveness of BMPs in mitigating agricultural stressors known to impact ecosystem function. A third potential hypothesis for the lack of association between current BMP use and a response in ecosystem condition is the potential for agricultural stressors, in this case nutrients, to persist through legacy effects. Many studies have outlined the importance of legacy effects in ecological assessments of stream ecosystems [66, 67, 68, 69]. Alterations to the ecological condition of stream ecosystems as a result of agriculture have been observed to influence the current physical, chemical, and biological characteristics, including the whole-stream GPP, even after 55-years of agriculture abandonment and succession [68]. In regards to BMPs implemented in active agricultural catchments such as the ones in our study, [PERSON] _et al._[70] proposed that the lack of response in water quality and affiliated indicator parameters is likely a result of lag time. Lag time realizes the persistence and legacy of agricultural stressors and encompasses the time required for the BMP to produce a beneficial effect, for that effect to be delivered to the aquatic system, and for the aquatic system to respond to the effect [70]. Depending on the aim of a BMP, this lag time can vary from years to several decades differing with the BMP treatment and mitigation target, spatial scale, hydrologic flow pattern, flushing rate, and intensity and residence time of the stressor [70]. For example, long-term effects of nitrogen fertilizer use in small agricultural watersheds has been proposed to last greater than 30 years if leeched into soil and ground water pools, which can be a particularly important non-point source input into streams during dry seasons [71]. Likewise, the presence of phosphorus in agricultural soils can be evident for approximately 14 years [72, 73]. As the BMPs implemented through the Rural Water Quality Program were instituted from 3 to 15 years prior to sampling it is possible that not enough time has passed for the beneficial effects of the BMPs to impact stream ecosystem function. Although BMPs are advocated as effective conservation strategies [74], the abundance and persistence of agricultural stressors may be overwhelming and delaying the effectiveness of BMPs in improving the function of agricultural streams in the GRW. As BMPs in our study are still relatively young, it is recommended that continued BMP implementation and long term monitoring be employed as a strategy to achieve and realize ecosystem improvements. Data analyses and written manuscript were completed by [PERSON]. Study design and field sampling, as well as manuscript review were completed by [PERSON]. The authors declare no conflict of interest.
## 3 Bridge Vibration Response Based on Vehicle-Bridge Coupling
### Bridge Vibration Response Based on Vehicle-Bridge Coupling  
From the data, it can be seen that the bridge Z4# abutment single-pier jacking transformation had the most significant impact on the fourth span. Therefore, in this section, only the acceleration, velocity, and dynamic deflection of the fourth span mid-span node and 1/4 node are analyzed for the vehicle-bridge coupling. According to China's regulations on pavement unevenness [31], pavement unevenness is divided into eight grades from A to H. Normal pavement is usually regarded as unevenness grade A. In the following sections, if the level of unevenness, speed, or load is not specified, the default level of unevenness is A, the speed is 60 km/h, and the load is complete; as the vehicles driving on this bridge are mostly four-axle transport vehicles, this study takes four-axle transport vehicles as the research object.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline
**Pier Number** & & **Z0\#** & & & **Z1\#** & & & **Z2\#** & & & **Z3\#** & & & **Z4\#** \\ \hline Bridge pier location & 1 & 3 & 5 & 1 & 3 & 5 & 1 & 3 & 5 & 1 & 3 & 5 & 1 & 3 & 5 \\ Settlement/(mm) & 107 & 101 & 95 & 37 & 34 & 25 & 34 & 22 & 18 & 37 & 30 & 22 & 124 & 121 & 116 \\ Average settlement/(mm) & & 101 & & 32 & & 25 & & & 30 & & 120 & \\ Relative Settlement/(mm) & & 69 & & 7 & & \(-\) & & 5 & & 91 & \\ \hline \end{tabular}
\end{table}
Table 1: Settlement values for the bridge.  
Figure 5: Finite element model of box girder.  
The peak dynamic deflections of the bridge and parapet span nodes at vehicle speeds of 30 km/h, 40 km/h, 50 km/h, and 60 km/h are collated in Table 2.  
As can be seen from Table 2, when the vehicle speed reaches 60 km/h, the dynamic deflection of the span node reaches 0.62 mm. In the case of constant vehicle load, as the speed increases, the dynamic deflection of the span node and the span node of the bridge guardrail also gradually increase, and the dynamic deflection of the guardrail node is smaller than the dynamic deflection of the span node. From vehicle speed 30 km/h to 60 km/h, span node dynamic deflection increased by 44.2%, guardrail span dynamic deflection increased by 37.1%, and the vehicle driving has little influence on the dynamic deflection of the guardrail span node.  
The vertical velocities of the bridge span nodes when travelling at 30 km/h, 40 km/h, 50 km/h, and 60 km/h are shown in Figures 10-13:The peak vertical velocities at the mid-span nodes of the bridge at different vehicle speeds are shown in Figure 14.  
Figure 11: Vehicle speed 40 km/h.  
Figure 12: Vehicle speed 50 km/h.  
Figure 13: Vehicle speed 60 km/h.  
As can be seen from Figure 14, the greater the vehicle speed, the greater the vertical velocity at the node in the bridge span. However, as the vehicle speed increases from 40 km/h to 50 km/h, it is clear that the vertical speed at the node in the span of the bridge changes more rapidly. When the vehicle speed is reduced from 30 km/h to 40 km/h, the speed change at the bridge span node is less.
## 5 Results and Discussions
### Results of Compositional Data Analysis
#### 5.3.3 Results of MSC Method  
Using the 1:200,000 geochemical data as the calibration medium, the sparsified 1:50,000 data for both the northern and southern regions were leveled to their corresponding levels. Then, the corrected relationships obtained from the sparsified data were applied to all the data, offering another approach to processing. Essentially, this method assumes that the 1:200,000 data serves as the baseline at the same or nearby sampling locations, and thus, the 1:50,000 data (including both the northern and southern regions) is leveled accordingly.  
By calculating the Euclidean distance, the closest 1:50,000 sampling point to each 1:200,000 sampling point was selected. As shown in Figure 14, the distributions of the sparsified 1:50,000 sampling points and 1:200,000 sampling points are nearly identical. Comparing the distribution of the sparsified samples, it can be concluded that the sampling locations are essentially the same. This means that for single elements, such as the Ag element in the northern region, the data from 1:200,000 should closely align with the data from 1:50,000, and the fitting curve of the percentiles should show a slope of 1, corresponding to the X = Y distribution.  
Figure 14: Map showing the distribution of 1:200,000 data and sparsified 1:50,000 data.  
However, in practice, there is still some systematic error. Therefore, we can correct this systematic error using the slope and intercept. Furthermore, the parameters for the slope and intercept apply to the entire northern 1:50,000 data set, and not just the sparsified data.  
First, the fitting parameters between the sparsified 1:50,000 data and the 1:200,000 data were calculated. The fitting results are shown in the Table 9 (since the table and figure provide the same information, they are not displayed here). From the R\({}^{2}\) values, which are consistently above 0.85, it is evident that the linear relationship is strong, and leveling based on the 1:200,000 data is valid.  
Similar to the data after BL, the percentiles of all elements are also very close after the MSC method. The specific parameters are shown in Table 10.  
It should be emphasized that even though the 1:50,000 and 1:200,000 data were analyzed using different detection methods, it does not affect the leveling process. This is because, through linear regression, we focus on the trends or ratios between the data. As long as the data does not contain non-systematic errors, there will always be a clear linear relationship between the measurement values, and therefore, leveling can be performed using linear regression.  
The results of the MSC method, as shown in Figure 15, are essentially consistent with the BL method. Most of the elements are improved to some extent, and no issues similar to those in the CR method were observed. Since the geochemical map distribution is essentially consistent with that of the BL method, no further explanation is provided here.
## 3 Regulation of Intracellular Ap4A Levels
### Degradation of Ap4A  
Ap4A levels are also tightly regulated by specific degradation pathways. The degradation of Ap4A is essential for maintaining cellular nucleotide balance and preventing its accumulation. Thus, Ap4A hydrolase (Ap4H) is a principal enzyme responsible for Ap4A degradation, and it is also known as bis(5'-nucleosyl)-tetraphosphatase (asymmetrical), encoded in humans by the _NUDT2_ gene [9]. It was observed a 175-fold increase in the intracellular Ap4A by deleting _NUDT2_[3] confirming its significance as a major Ap4A hydrolase. This enzyme hydrolyzes Ap4A into AMP and ATP or into two molecules of ADP, depending on the organism and the specific hydrolase involved [9], which could otherwise lead to dysregulated signaling pathways and cellular dysfunction. Ap4A can also be targeted by other nucleotides, such as Nudix family enzymes, which have broad substrate specificity and can hydrolyze various nucleotide diphosphates. By generating mutant strains defective in Ap4A-degrading enzyme(s), the accumulation of Ap4A was observed at high intracellular levels in different bacteria, highlighting Ap4A pleiotropic effects, such as loss of motility, higher heat and oxidative stress sensitivity or lower antibiotic tolerance [22]. Recently, a recombinant protein YqeK (COG1713) from Staphylococcus cereus aureus copurified with ADP was identified as a class of symmetrically cleaving Ap4A hydrolases, showing a catalytic efficiency toward Ap4A similar to that of the symmetrically cleaving Ap4A hydrolases of the known ApaH family [23]. Overall, the degradation of Ap4A by specific hydrolases and nucleosidases is a critical aspect of cellular homeostasis, ensuring that Ap4A functions appropriately within its signaling and regulatory roles. In cancer cells, degradation is often reduced due to the downregulation or mutation of hydrolases, leading to the accumulation of Ap4A, which can interfere with tumor suppressors (e.g., HINT1) and modulate gene expression.
# EAIA: An Efficient and Anonymous Identity-Authentication Scheme in 5G-V2V
## 2 Related Work
Although bilinear pairing-based schemes offer robust security, they typically entail high computational costs, making them unsuitable for resource-constrained vehicles. The authors in [24] highlighted the main drawback of pseudonym-based CPPA schemes--frequent pseudonym updates due to link attacks--posing a significant burden on VANETs. To address this, the scheme proposed V2V communication without pseudonyms, avoiding pseudonym update and management issues while supporting secure V2V communication, ensuring only legitimate vehicles access transmitted messages. Some researchers proposed pseudonym authentication schemes for 5G vehicular networks, involving complex and time-consuming operations. The authors in [25] proposed a fog-computing-based pseudonym authentication (FC-PA) scheme to reduce performance overhead in 5G vehicular networks, using a single scalar multiplication operation of elliptic curve cryptography to prove information. The protocol in [26] addressed the many challenges of secure and efficient mobility management due to frequent handovers and large-scale vehicle communication, proposing a protocol to overcome existing authentication protocol shortcomings. Elliptic-Curve Diffie-Hellman (ECDH) is used for secure key transmission; the Elliptic Curve Digital Signature Algorithm (ECDSA) is used for signing message verifiers, timestamps, and random numbers to prevent Replay Attacks; and a combination of group keys, temporary group keys, tickets, and message authentication codes is also used. The solution in [27] proposed a lightweight authentication scheme for V2V communication, using cryptographic concepts for inter-vehicle message transmission, cross-checking vehicle identities with stored TPD values, and ensuring only vehicles with proven authenticity participate in the message transmission phase. The authors in [28] introduced a new certificate-less AKE protocol for V2V communication in vehicular networks, achieving partial non-repudiationby computing partial keys by users and resisting temporary key leakage attacks by merging temporary secrets and private keys into shared information. These studies highlight the importance of designing a lightweight, efficient, and secure direct inter-vehicle mutual authentication mechanism. An ideal solution should effectively handle authentication and data security issues without relying on external infrastructure while ensuring efficient communication remains unaffected. Our research aims to fill this gap by proposing a new authentication framework to address the limitations of existing methods in infrastructure-free environments.
## 5 Conclusions  
Every company, including telecommunications providers, aims to maximize profit. To accomplish this goal, telecommunications firms should understand what their customers need and think about the price, product, marketing, service quality, customer service, and other elements that might affect their decision about which telecommunication service providers to use. Additionally, if these businesses want to continue having a steady consumer base in the future, they should emphasize addressing the demands and attitudes of young people. Proper and adequate model predictions can help the company address these problems of preferences and be able to improve and attract consumers' interest. Therefore, the purpose of this study was to examine the predictive performance of two machine-learning models for discrete choice analysis and make recommendations for model selection. To build a locally optimal DCE for our option sets, we employed readily available principles from blocked fractional factorial designs as opposed to alternative approaches that may be more realistically complex, especially when the number of attributes is large. A two-stage sampling procedure was used to pick 180 University of KwaZulu-Natal, South Africa (Edgewood Campus) students for the study. The data were analyzed using XGBoost and the ANN models. The findings show that, in modeling the consumer's preference for mobile telecoms plan, the XGBoost model is preferred over ANN as it produced minimum MSE, MAE, and RMSE values and also higher accuracy, precision, recall and F1 score values.  
For future research, we intend to explore additional factors that may affect model performance, such as socioeconomic variables. Socioeconomic factors, such as income, education, and occupation, can have a direct impact on the preferences and decision making of consumers. For feature selecting and model building, it is important to understand which socioeconomic factors have the greatest influence on decisions.  
In conclusion, consumer choice theory has undergone a paradigm shift with the use of ML to model customer preferences. It offers nuanced analytical tools, scalability, and predictive capability, although it also necessitates careful consideration of interpretability, data ethics, and the integration of behavioral insights to improve understanding of consumer decision making.  
Conceptualization, C.O.; methodology, C.O.; software, C.O.; validation, K.C. and T.Z.; formal analysis, C.O.; investigation, C.O.; resources, K.C. and T.Z.; data curation, C.O.; writing--original draft preparation, C.O.; writing--review and editing, K.C. and T.Z.; supervision, K.C. and T.Z.; project administration, K.C. and T.Z.; funding acquisition, K.C. and T.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors are grateful for the support from the University of Kwa-Zulu Natal in undertaking the study.  
The authors declare no conflicts of interest.
## 3 Materials and Methods
### Data Visualization Using UMAP  
Before training the neural network classifier, we propose to visually inspect the data using unsupervised learning techniques. The objective is to explore and assess if the different sonic events, as characterized by their mbe features, form groups or clusters. As these data might be too complex for linear methods, we propose to perform dimensionality reduction using the Uniform Manifold Approximation and Projection (UMAP) [24], a state of the art algorithm that non-linearly projects the data to a low dimensional manifold, aiming to preserve the topology of the original space.  
The UMAP algorithm is applied on 1,950,438 frames from 720 recordings belonging to single-events. The input dimensionality is 40, which corresponds to the number of mel filter banks (two channels). The output dimensionality is set to 2. Due to the large volume of data, we consider the high-performance and GPU-ready implementation of the UMAP algorithm found in the cuML library [49]. The hyperparameters for obtaining the low-dimensional embeddings are as follows:  
* Size of the local neighborhood: 35;
* Effective minimum distance between embedded points: 0.1;
* Effective scale of embedded points: 1;
* Initialization: spectral embedding.  
These hyperparameters were obtained by qualitative assessment of the resulting visualizations.  
As an example, we present a visualization of the mbe features of seven audio recordings (33,431 frames) containing single events in Figure 5. From the embedding, it is clear that the five labeled classes, birds, amphibians, dogs, rain and motors, can be easily separated by their mbe features. We note, however, that most of the time, the audio data coming from the field recordings show the presence of multi-events, which makes class separation extremely difficult for an event detection model in a machine-learning perspective.
# Design of Time-Synchronized Switch Based on Zynq
## 3 System Hardware Design  
Xilin's Zynq products are mainly categorized into the following types based on different application scenarios and performance requirements:  
(1) Zynq-7000 SoC Series  
The Zynq-7000 series integrates ARM processors with programmable logic in FPGA, supporting the co-design of hardware and software. This series offers a variety of models with different logic resources, storage capacities, and interface counts to meet diverse application needs.  
(2) Zynq UltraScale+ MPSoC Series  
The Zynq UltraScale+ MPSoC series provides more powerful performance and richer functionality, equipped with more processor cores, larger storage capacity, and higher communication bandwidth. These devices support various security features, making them suitable for applications that require high levels of security.  
(3) Zynq UltraScale+ RFSoC Series  
The Zynq UltraScale+ RFSoC series has a similar architecture to the MPSoC but is optimized for radio frequency (RF) signal processing. It integrates a large number of RF-related modules and functionalities, such as ADCs and DACs, enabling direct acquisition and processing of RF signals, which significantly simplifies the design complexity of RF systems.  
The goal of this design is to achieve time synchronization while selecting faster transmission interfaces and rich external interfaces for future improvements. Therefore, we have decided to use a chip from the Zynq UltraScale+ MPSoC series. Additionally, the hardware logic required for time synchronization needs at least four MMCMs and four PLLs to support the overall performance of the system. Considering resource utilization and manufacturing costs, the Zynq UltraScale+ XCZU9 EG-1 FFVC1156 chip was ultimately chosen.  
The specific design block diagram is shown in Figure 5. The PS side mainly includes hardware such as DDR4, SD card, QSPI Flash, and EEPROM, while the PL side mainly includes hardware such as SFP, UART, and FMC. The PS side is responsible for running the Linux operating system and related software, while the PL side is responsible for implementing the time synchronization function.
# Facial Privacy Protection with Dynamic Multi-User Access Control for Online Photo Platforms
## 4 Proposed System
### System Workflow
#### 4.2.1 User Registration  
The user registration process begins when an individual (e.g., [PERSON], [PERSON], or [PERSON]) creates an account by submitting credentials through the web client application. For example, as illustrated in Figure 3, [PERSON] submits her username (_uname_) and password (_pwd_) to the server. Upon receiving these credentials, the server validates them and issues a JSON Web Token (JWT), which is then securely stored on [PERSON]'s web client. The JWT serves as a session token for subsequent requests, enabling secure communication between the client and the server.  
Subsequently, the server forwards [PERSON]'s registration request to the CSP, which generates a unique public-private key pair, denoted as \(pk_{alice}\) (public key) and \(sk_{alice}\) (private key). The key pair is generated using the \(\mathbf{RSA}(256,ID_{alice})\) function, where 256 specifies the key size and \(ID_{alice}\) is a unique identifier associated with [PERSON]. The \(ID_{alice}\)  
Figure 3: User registration process.  
serves as the key identifier that will be used to retrieve the stored key from the CSP's key storage management.  
After generating the key pair, the CSP retains the public key (\(pk_{alice}\)) in plaintext while encrypting the private key (\(sk_{alice}\)) as \(sk_{alice}^{enc}\). The encryption process employs the function \(\mathbf{E}\) and a secret key derived from [PERSON]'s password (\(pwd\)) using a key derivation function (\(\mathbf{KDF}\)). The \(\mathbf{KDF}\) uses \(pwd\) and a randomly generated salt (\(salt_{alice}\)) to derive the encryption key. The salt is generated using a cryptographically secure pseudorandom number generator, \(\mathbf{CSPRNG}(128,ID_{alice})\), where \(128\) denotes the salt's bit length. Finally, the CSP securely stores \(salt_{alice}\), \(sk_{alice}^{enc}\), and \(pk_{alice}\) in its secure key storage system.  
This password-based encryption mechanism ensures that only [PERSON] can access her private key. Even if the keys stored within the CSP are compromised, an attacker cannot retrieve sensitive facial data protected by the system.