esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 A More Flexible MHz, Serial Crystallography Experiment
### Mixing Experiments at MHz Pulse Rates  
Mixing SFX experiments, for a variety of time points, is made viable by the increased data collection rate and advancements in reliable sample jetting. Past crystallographic experiments could easily determine structural changes between pre- and post-binding states. However, only recently, with the use of XFELs, have researchers been able to measure intermediate conformations during ligand binding using mixing SFX experiments [65]. Mixing experiments involve the introduction of one solution to (typically) a crystal suspension to invoke a reaction just prior to X-ray interaction. At that point, the time of interest can be investigated by increasing the travel distance between mixing and probing, much like for pump-probe experiments. Any reagent mixed with a crystal suspension ideally requires immediate diffusion into the crystal core for a reaction to occur simultaneously, so as to achieve true homogeneity of the protein's conformational state. This is, of course, impossible in practice. A detailed explanation of the diffusion of reagents into crystals goes beyond the scope of this review; however, it is worth mentioning that a decrease in crystal size results in a decrease in the time until homogeneity [66; 67]. For example, [PERSON] et. al. show that a cube-shaped crystal with dimensions of 400 \(\times\) 400 \(\times\) 1600 um\({}^{3}\) will have a diffusion time of tens of seconds, while crystals more appropriately sized for SFX injection, that is, 0.5 \(\times\) 0.5 \(\times\) 0.5 um\({}^{3}\), will have a diffusion time of 17 us [67]. Therefore, the coincidental benefit of using nanocrystals over larger crystals is a decrease in the time to reach homogeneity, allowing the recording of faster time points in dynamical systems. Functional mixing jets have already proven effective at other XFEL facilities [68; 69], and similar experiments will be possible at the EuXFEL SPB/SFX instrument--just at much higher data rates. Measuring transient intermediate states has the potential to experimentally confirm and support movie-like protein dynamics from simulated models, including reactions such as inhibitor binding and much more [70]. Mixing experiments become more viable with the high repetition rates possible at the EuXFEL. Used in collaboration with simulations, such time-resolved mixing experiments will provide valuable and novel insights into binding dynamics.
## 3 Bioactive Compounds in Fresh Fruits and Vegetables
### Protection and Disease Prevention
#### 3.1.6. Glucosinolates  
Glucosinolates are a large group of sulfur-containing compounds present in Brassica vegetables (broccoli and cauliflower). These compounds are degraded into isothicyanates by the enzymatic action of plant-specific myrosinase or intestinal flora in the body. Isothiocyanates have gained a lot of importance due to their anticarcinogenic properties, especially against colon, rectum, and thyroid cancers [41]. They also act as antioxidants, due to the sulfur atom in their molecule, and have been found to suppress the activity of acetylcholinesterasekinase which is very important in neurotransmission in neurodegenerative diseases [17; 42].
# Using Zigzag Persistent Homology to Detect Hopf Bifurcations in Dynamical Systems
## 1 Introduction  
Topological data analysis (TDA) is a field consisting of tools aimed at extracting shape in data. Persistent homology, one of the most commonly used tools from TDA, has proven useful in the field of time-series analysis. Specifically, persistent homology has been shown to quantify features of a time series such as periodic and quasiperiodic behavior [1; 2; 3; 4; 5] or chaotic and periodic behavior [6; 7]. Existing applications in time-series analysis include studying machining dynamics [7; 8; 9; 10; 11; 12; 13], gene expression [1; 14], financial data [15], video data [16; 17], and sleep-wake states [18; 19]. These applications typically involve summarizing the underlying topological shape of each time series in a persistence diagram and then using additional methods to analyze the resulting collection of persistence diagrams. While these applications have been successful, the task of analyzing a collection of persistence diagrams can still be difficult. One way to approach this problem is to use machine learning on persistence diagrams; however, the space of persistence diagrams is not amenable to machine-learning tasks. Many methods have been created to convert persistence diagrams into a form amenable for machine learning [20; 21; 22; 23]. However, since so many methods have been developed, it can be difficult to choose one appropriate for the task. Additionally, the task of computing numerous persistence diagrams is computationally expensive.  
Our method aims to circumvent these issues using zigzag persistence, a generalization of persistent homology that is capable of summarizing information from a sequence of point clouds in a single persistence diagram. While less popular than standard persistent homology, zigzag persistence has been used in applications, including studying optical flow in computer generated videos [24; 25],analyzing stacks of neuronal images [26], and comparing different subsamples of a dataset [27]. However, to the best of our knowledge, it has not been used in the context of dynamical systems or time-series analysis. In this paper, we present Bifurcations using ZigZag (BuZZ), a one-step method to analyze bifurcations in dynamical systems using zigzag persistence.
## 4 Experiments Analysis and Discussion
### Statistical Significance Testing  
To verify the statistical significance of the model, we ran the model 50 times with different random seeds and then performed the paired t-test of the result. Table 5 shows the results of model testing, including mean, standard deviation, t-value, significance \(p\)-value, Cohen's d, etc. It is indicated that the average prediction error of TSNN-ESTD is significantly lower than that of vanilla iTransformer, and the \(p\)-value is less than 0.001. The Cohen's d values of the differences in ECL, Exchange, and Weather datasets are 9.29, 63.512, and 2.975, respectively, which shows that the improvement of the model is very significant.
# 5G-IPAKA: An Improved Primary Authentication and Key Agreement Protocol for 5G Networks
## 1 Introduction
To cover this issue, they proposed a secure authentication scheme by utilizing the existing public key infrastructure (\(PKI\)) mechanism. Further, they found a location sniffing attack, which can be implemented by an attacker through inexpensive devices [24]. Similarly, they proposed a fix scheme based on the existing PKI mechanism. In [25], the authors modeled the 5G AKA protocol with symbolic modeling using ProVerif based on three and four entities models, and then proposed their security consideration. Further, [PERSON] et al. [26] proposed an enhanced version of the authentication and key agreement protocol for 5G system that surmounts the limitations existing in the 5G AKA protocol. [PERSON] et al. [27] introduced a protocol that preserves the privacy of the user identity and overcomes the identified problems of the 5G AKA protocol. Similarly, 3 GPP has also been used to enhance the security of the 5G AKA protocol [2; 3; 4]. However, there may still be some shortcomings in the latest version of the 5G AKA protocol. To solve this problem, we first point out these possible shortcomings. Then, we propose an improved primary authentication and key agreement protocol for 5G networks, named 5G-IPAKA. Finally, we prove that the 5G-IPAKA protocol is secure and that it is efficient and backward-compatible. The main contributions of this paper are as follows:  
* By analyzing the latest version of the 5G AKA protocol, we point out that the protocol still has seven shortcomings;
* We propose a new 5G-IPAKA protocol by improving the latest version of the 5G AKA protocol from four aspects;
* We formally analyze the security of the 5G-IPAKA protocol in the mixed strand space model for mixed protocols [28]. As a result, the 5G-IPAKA protocol is secure in the mixed strand space model;
* Through discussion and analysis, we are able to overcome the above shortcomings of the latest version of the 5G AKA protocol;* Through discussion and a comparative analysis, we show that the new 5G-IPAKA protocol is better than the recently improved 5G AKA protocols in overcoming the various shortcomings, and is efficient and backward-compatible. The rest of this paper is organized as follows. Section 2 provides an overview of the latest version of the 5G AKA protocol. In Section 3, we point out seven shortcomings of the latest version of the 5G AKA protocol. Section 4 describes our proposed 5G-IPAKA protocol. Section 5 provides a formal verification of the 5G-IPAKA protocol in the mixed strand space model. In Section 6, we present the discussion and analysis, and conclude the paper in Section 7.
# Autonomous Vehicles in Rural Areas: A Review of Challenges, Opportunities, and Solutions
## 2 Materials and Methodology  
A systematic review (Figure 1) was conducted using Google Scholar and Google Search for broad and comprehensive coverage. The review included peer-reviewed papers, technical reports, theses, and governmental sources. No timeframe restrictions were applied, but only English-language documents were considered to ensure thorough coverage of AV research in rural areas. The methodology comprised three main phases: the literature retrieval, extended web-based search, and thematic analysis using a large language model. The scholarly Python library version 1.7.11 was used to query Google Scholar for academic publications on AVs in rural areas. Keywords aligned with research objectives were combined using Boolean operators (e.g., autonomous vehicles OR Self-Driving Vehicles AND rural areas). Metadata, including title, abstract, author, year, and publication venue, were extracted and structured for analysis. The data were exported as a CSV file to facilitate future updates and reproducibility. This systematic process not only ensured the inclusion of a wide range of academic perspectives but also established a dynamic framework that can evolve with new findings, providing a continually expanding foundation for further investigation. To supplement academic findings, the search was extended to governmental, industrial, and institutional sources, focusing on state DOTs, industry leaders, and authoritative institutions providing data on AV policies and deployments.  
Documents such as reports, policy briefs, and technical papers were collected and categorized based on their methodology, including simulation, field test-, survey-, interview-, and conceptual framework-based studies. Extracted metadata (titles, URLs, publication dates, and source types) was structured for integration with academic sources. A thematic analysis was performed using OpenAI's API to identify key themes across collected references. Abstracts from academic publications and summaries from web-based documents were analyzed programmatically. To ensure accuracy, 90% of references were manually reviewed for relevance. Documents mentioning rural contexts only briefly or focusing on urban settings were excluded. Metadata, including keywords, titles, and abstracts, were verified.
## 3 Echo Enabled Harmonic Generation Seeding
Control of the FEL process is important for moving the saturation point downstream in the radiators as is required to keep the FEL source point unchanged while the FEL wavelength is changed. For the longer wavelengths spectral range (30-60 nm), the simpler High Gain Harmonic Generation scheme (HGHG) [19] will be used while the intermediate range could be reached with both the HGHG and the EEHG schemes. The possibility of operating HGHG with both the first and second seed lasers would give two independent lasers in the two modulators in the near future. Such a system with a split undulator configuration of the radiator allows for two completely wavelength-independent FEL pulses produced out of the same electron bunch. This advanced scheme will open essential opportunities for FEL pump FEL probe experiments. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Units** \\ \hline Wavelength & 4–60 & nm \\ Peak power * & 0.5–2 & GW \\ Polarization & Variable & \\ Pulse length (FWHM) * & 10–30 & fs \\ Energy per pulse * & 5–20 & \(\upmu\)J \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Expected parameters of the external seeded FEL (* these values might be wavelength dependent).
# The Integration of Azure Sphere and Azure Cloud Services for Internet of Things
## 4 System Implementation
### Hardware  
The designed hardware prototype is given in Figure 4. The MT3620 development kits are designed to perform as direct connected device and gateway device. For the former scenario, sensors and actuators are directly connected to the MT3620 development kit via on-board interfaces. For the latter scenario, ZigBee network device can be connected to the MT3620 field gateway with UART interface. On the system implementation, ZigBee end device is equipped with sensors and actuator as shown in Table 3, including temperature and humidity sensor, light sensor, gas sensor, passive infrared (PIR) sensor and a relay.  
Figure 4: Hardware prototypes: (**a**) MT3620 as gateway device; (**b**) MT3620 as direct connect device.  
Figure 3: Proof of concept for the proposed solution.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sensor** & **Model** & **Type** \\ \hline Temperature and Humidity & Grove \({}^{1}\)-DHT11 & Digital \\ Light & Grove-Light Sensor & Analogue \\ Sound & Grove-Sound Sensor & Analogue \\ Gas & Grove-Gas Sensor (MQ2) & Analogue \\ PIR & Grove-PIR Motion Sensor & Digital \\ Relay & Grove-Relay & Digital \\ \hline \hline \end{tabular}  
* All sensors are manufactured by Seed Studio.  
\end{table}
Table 3: Sensors and actuator used in this project.  
For MT3620 as direct connected device, since Azure Sphere SDK has no ADC and does not support I2C communications, the MT3620 Shield is used as an interface between MT3620 and external sensors. By importing MT3620 Grove Shield Library to the project solution, Grove sensors listed in Table 3 are enabled through the functions that are provided in the library.  
In addition, there are a few settings for ZigBee device that are related to the radio core, UART and wireless network topology. They are presented in Table 4.  
IEEE 802.15.4, the basis of ZigBee protocol [45], specifies 16 channels within 2.4 HGz band in which the carrier frequency ranges from 2405 MHz to 2480 MHz with 5 MHz apart, and it is controlled by the 7-bit SFR FREQCTRL FREQ. The radio channel used in this project is configured as channel 11, while the carrier frequency is 2405 MHz. In addition, the receiver sensitivity is set to \(-85\) dBm by the SFR CCACTRL0, while the transmit power is initialized as 4.5 dBm in SFR TXPOWER. For network topology in this project, star network is chosen for simplicity. The coordinator is responsible for establishing the ZigBee wireless network, processing the incoming join requests and managing the joined end devices in the network. The ZigBee end devices are equipped with multiple sensors for data collecting. Specifically, the light and gas sensor are connected with the Port 0_6 and Port 0_7 of CC2530, which means that the AD channel should be configured as AIN6 and AIN7 respectively in SFR ADCCON3 during the AD process. The UART communication between MT3620 and CC2530 ZigBee Coordinator is configured as 9600 baud, 8 data bits, 1 stop bit, no parity bit and no flow control. The UART configuration can be achieved by setting SFR POSEL, U0 CSR, U0 GCR and U0 BAUD.
## 4 Experimental Results
### Experimental Setup  
The experiments were conducted on a robust computing setup equipped with Ryzen 7 7700 with MSI PRO B650M-A DDR5 motherboard with a core i7, 12 th generation processor having 12 cores and 20 threads, 32 GB RAM, M.2 1 TB NVME SSD, and an MSI GeForce RTX 4060 8 GB Ventus 2X Black 8 GB graphics card. The experimental framework was established within a Jupyter Notebook environment, which offers a flexible and interactive platform for conducting analyses. The proposed methodology uses Python with libraries like Scikit-learn, Keras, TensorFlow, NumPy, Pandas Seaborn, and Matplotlib. The proposed model implemented leave-one-out cross-validation for SMOTE and k-fold cross-validation for model training to ensure the robustness and generalizability of the model. Three sub-datasets were used, including the original dataset, the basic augmented dataset, and the SMOTE augmented dataset. All datasets were split using the 70-15-15 approach for training, testing, and validation. The Adam optimizer with a learning rate of 0.0001 was employed using a batch size of 32 and early stopping. The model was trained on 99 epochs before it was stopped by meeting the early stopping criteria.
# Inter-Data Center RDMA: Challenges, Status, and Future Directions
## 5 Future Directions
### Multipath and Load Balance  
Existing optimizations on load balancing for intra-DC RDMA schemes are generally designed based on network links. In the inter-DC RDMA application scenarios, there is typically one dedicated optical link between two DCs. Consequently, traditional multipath concepts do not apply to inter-DC RDMA. However, current optical transmission systems leverage spatial division multiplexing and wavelength division multiplexing to enhance transmission capacity. Therefore, when designing multipath schemes for inter-DC RDMA in the future, wavelengths and fiber cores can be considered as the rerouting granularities for multipath transmission. Additionally, as the scale of multi-DC interconnection networks increases, such as in the case of the C\({}^{2}\)NET, rerouting and replanning across DCs will be necessary. For instance, when a certain DC's computing tasks are overloaded, redistribution will be required, and load balancing can be performed based on the current status of each DC during task planning. Therefore, inter-DC multipath RDMA and its load-balancing solutions must not only adapt existing intra-DC schemes to inter-DC environments but also drive innovation in new interdisciplinary research areas.
# Learn and Tell: Learning Priors for Image Caption Generation
## 1 Introduction
For example, the object words referring to the objects mentioned in the caption, such as \"cat\" or \"car\", could be obtained from the object visual feature set; the attribute words, such as \"red\" or \"colorful\", could be obtained from the attribute visual feature set; and the relation words, such as \"standing\" or \"walking\", could be obtained from the relation visual set. We take Figure 1 as an example to illustrate our intuition. There are several bounding boxes on the image, which indicate the local region of corresponding local visual features and are presented in different colors. When the decoder tries to generate the caption word \"soccer\", our proposed POS module would first compute its part-of-speech distribution; this prior could pass the message that the next word is with high probability a noun to the decoder, and thus the attention module should put more attention on the object regions (features) rather than attribute and relation regions. In our work, we propose a novel priors-based attention neural network (PANN) to incorporate the PBM priors and POS priors in the decoder. The whole work could be divided into two stages, i.e., the encoding stage and the decoding stage. In the encoding stage, the PBM priors for each region proposal are obtained according to the similarities between the local feature vectors and caption vector, in which the latter is extracted through the auxiliary multi-class multi-label task, while in the decoding stage, the POS priors of caption words are first predicated by taking the hidden state of the decoder as an input, and after that the PBM and POS priors are used in the weight calculation process in PANN. Finally, the assembled feature vector is output for the next work generation. The study's contributions lie in the following three aspects:  
* We propose a novel prior-based attention neural network, in which two kinds of priors, i.e., the probability of being mentioned for region proposals (PBM priors) and part-of-speech of caption words (POS priors), are incorporated to help the decoder extract more accurate visual information at each step of word generation;* We propose new methods to obtain the PBM and POS priors, in which we obtain PBM priors by computing the similarities between local feature vectors and the caption vector, while the POS priors are obtained by predicting the reduced POS tags so as to connect to the categories of visual features. * We performed comprehensive evaluations on the image captioning benchmarks dataset MS-COCO, demonstrating that the proposed method outperforms several current state-of-the-art approaches in most metrics, and that the proposed priors-based attention neural network (PANN) could improve previous approaches. The paper is structured as follows: we introduce related works in Section 2. After that, the extraction of the PBM priors, the POS priors, and the proposed PANN is discussed in Section 3. Then, implementation details and experiments are discussed in Section 4. Finally, conclusions are presented in Section 5.
# Performance Analysis of ICA in Sensor Array
## 3 Performance of ICA _vs._ Practical Factors
### Condition Number vs. Practical Factors
#### 3.2.1 Element Spacing of the Array
 \tag{26}\]  
Extending the relationship for all \(D\geq 1\) and \(\theta_{i}\in[-90^{\circ},90^{\circ}]\); \(\varphi_{i}\in[-90^{\circ},\;90^{\circ}]\) yields:  
\[\beta<1 \tag{27}\]  
Since \(\left|cos\theta_{2}cos\varphi_{2}-Dcos\theta_{1}cos\varphi_{1}\right|\in(0,D)\). This indicates that so long as the element spacing of the array is smaller than wavelengths of the sources, the cases of singular mixtures can be well prevented for sources with diverse carriersand from all possible directions. The only exception happens when cos\(\theta_{2}\)cos\(\phi_{2}-D\)cos\(\theta_{1}\)cos\(\phi_{1}\) = 0, then the mixture would be singular regardless of \(\beta\). The conclusion can act as a meaningful guidance while designing the sensor array for applications adopting blind source separation. More specifically, if the array is designed for the frequency band (\(f_{\mathrm{L}}\)\(\sim\)\(f_{\mathrm{H}}\)), this means that the element spacing should satisfy \(d\) < \(c\)/\(f_{\mathrm{H}}\). For instance, consider the satellite communication on the C band (4-8 GHz), then the element spacing should be designed below 37.5 mm for the sake of avoiding failures of separation. Moreover, if in the specific application, the DOAs of the sources can be further confined, say \(\theta_{1}\phi_{1}\in[-Ang^{\circ},Ang^{\circ}]\) (\(Ang\leq 90\)), then Equation (27) can be relaxed to:  
\[\beta<\frac{1}{1-\frac{f_{\mathrm{L}}}{f_{\mathrm{H}}}\,cos^{2}(Ang^{\circ})} \tag{28}\]  
Notice that in many of previous works concerning independent component analysis via multi-sensor arrays, the element spacing is usually presumed as half of the wavelength of the sources, in both theoretical analysis and simulations (for instance, in [16; 35]). However, the presumption may be invalid for actual scenarios. Because first, the accurate prior information of the wavelength cannot be obtained in most applications requiring ICA; second, since there may be radiators working on different carriers, the equality is impossible to be maintained. Hence, conclusions here provide a more practical instruction for the selection of the element spacing, and the traditional settlement can be regarded as one of its special case with \(\beta\) = 0.5.