esa-sceva/satcom-chunk-collection huggingface dataset:
# Real-Time Human Activity Recognition on Embedded Equipment: A Comparative Study
## 5 Results of Implementation
### Limits of Microcontrollers Regarding Handling Sampling Data and Activity Recognition  
When assessing the performances of the two microcontrollers, the question that arises is the following: what are the on-board limitations concerning data recording and activity classification? To address this, we test the capacity of each microcontroller to handle continuous data recording, determining the maximum duration achievable.  
The objective is to explore the onboard capacity of each microcontroller in relation to the sampled data processing rate, thus delimiting the number of activities to be recognized. Since the activity recognition algorithm is based on data sampled every second, by running the algorithm onboard, we noticed that microcontrollers have a limit when processing a large amount of sampled data and this limit differs from one MCU to another. The alternative of increasing the sampling period, thus aiming to reduce the quantity of data to manage influences the quality of classification.  
Experience shows that there is a performance difference between the MicroPython-based implementation and the C language implementation when considering the number of records processed per second. For a MicroPython implementation, the algorithm has the capacity to handle up to 750 records, each consisting of dates and times, sensor readings, and corresponding values. MicroPython uses dynamic typing and has automatic memory management (garbage collection). This can simplify code development and reduce the likelihood of memory-related errors. For high-frequency data sampling, the convenience of dynamic typing can be beneficial, allowing for more agile development and easier adaptation to changing data quantity. Also, it is known for its suitability in rapid prototyping and development. The quick iteration provided by MicroPython can be advantageous in scenarios where constant adjustments and improvements are needed, common in applications with high data sampling frequency. This robust data processing per second performance highlights the efficiency and versatility of MicroPython for applications requiring high data sampling frequency.  
Conversely, the C language implementation, while powerful, operates on a different scale. Faced with data sampled every second, the microcontroller running the C algorithm demonstrates an ability to manage a more modest number of 17 records. This significant contrast in throughput suggests that, despite the implicit efficiency of the C language, there may be inherent challenges or optimizations to consider when running the algorithm on this particular microcontroller. These results highlight the importance of programming language choice to study the practicality of data-intensive applications on limited hardware platforms.
# Novel Designated Ownership Transfer with Grouping Proof
## 4 Performance Analysis
The number of tags starts from 100 and is doubled until it reaches 12,800. According to Figure 7, OP involves fewer than 800 tags, and a longer computation time is required because the group key must be decrypted. As the number of tags increases, the computation time increases because computing time and tag number are linearly related. When the number of tags exceeds 800, OP is more efficient compared to the other methods. The computation times of ZY and ZS differ by only 1%. In addition, Figure 8 shows that, when processing fewer than 3200 tags, OP needs more computing time because the group message must be encrypted and transmitted between the readers. Later, the proof requires signatures from both the transporter and the recipient. However, when the number of the tags exceeds 3200, OP is more efficient compared to the other methods. This advantage shows that OP is a favorable choice in an environment (e.g., SCM) where large numbers of tags must be scanned concurrently to generate proofs and transfer cargo ownership. According to EPC Class-1 Generation-2 standard, the highest transmission rate from a reader to a tag is 160 kbps and that from a tag to a reader is 640 kbps. We used the transmission rate and the message lengths stated in Table 5 to compute the required transmission time from a reader to their tags and from the tags to their reader. Table 6 presents a comparison of the transmission time of OP with those of the other methods. In OP, we adopted a multilayered grouping proof structure, and a maximum of \(r\) tags were distributed to each reader. Thus, compared to other methods, an increase in the number of tags did not lead to an increase in transmission from the tags to the reader. Furthermore, the transmission time from the  
Figure 8: Comparisons of computation loads of readers. Figure 7: Comparisons of computation loads of tags.
# GNSS Spoofing Identification and Smoothing Localization Method for GNSS/Visual SLAM System
## 6 Experimental Analysis under Inducing Spoofing Attack  
In this section, only the experimental analysis under the GNSS inducing spoofing attack is analyzed in rural and urban scenes. We try to compare it with the GNSS method, VINS-Fusion method, and PTC-SLM-Forward method. The PTC-SLM-Forward method in this section is in the case where the PTC-SLM algorithm only considers forwarding spoofing identification (set \(E_{d}=8.2\) m, \(E_{s}=2000\) m to invalidate the induced identification) and can only identify forwarding spoofing but not induced spoofing. The PTC-SLM method in this section can identify both forwarding spoofing and induced spoofing.
# Transition Metal Coordination Compounds as Novel Materials for Dye-Sensitized Solar Cells
## 1 Introduction  
Dye-sensitized solar cells (DSSC) are one of the most recently developed types of solar cells, alleviating the harsh material purity and processing requirements associated with traditional inorganic semiconductor-based solar cells, as well as avoiding the use of highly toxic substances, such as compounds of cadmium [1]. Initially, DSSCs relied on iodine-based electrolytes and charge transfer dyes based on the coordination compounds of ruthenium, reaching power conversion efficiency values of up to 10% [2]. Currently, the development of DSSCs is attracting great research interest, focused on improving their design and constituents. Significant attention has been given to the development of electrolytes [3] and dyes [4] for DSSCs, as well as to optimizing their performance by extending the range of harvested electromagnetic radiation [5] and demonstrating new avenues of their application [6]. Such advances have allowed DSSCs to operate in different external conditions (e.g., temperature) with unchanging efficiency and to operate consistently under different brightness levels or with color selectivity [7].  
A particularly important issue in the design of DSSCs is the matter of selecting the material to be used as the charge transfer dye, as it determines both the ability of the DSSC to harvest electromagnetic radiation and the efficiency of charge separation. Although ruthenium-based dyes afford high power conversion efficiencies in DSSCs, such dyes typically have relatively low molar absorption coefficients. The drawbacks of such dyes are not limited to the above, as both ruthenium and its compounds are expensive, have limited availability and are often toxic or environmentally harmful [8].  
Due to those issues, significant attention has been given to alternative, Ru-free dyes. Among such alternatives, however, the majority of proposed dyes also suffer from the key drawbacks of ruthenium-based dyes, that is, the use of expensive, limited-availability and environmentally problematic materials, including heavy metals such as osmium, platinum and rhenium [9]. Cu-based dyes may be one of the few exceptions to the above, which explains the research attention given to those materials as the search for high-performance, low-risk and cost-efficient dyes for DSSCs continues [10; 11; 12].  
The aim of this work is to summarize the most recent (approx. 1-2 year old) and relevant developments concerning Cu-based charge transfer dyes for DSSCs. The main focus of our review is to showcase ligands that yield coordination compounds, which may become material alternatives to heavy metal-based dyes, both affording a high performance and limiting the environmental impact of producing DSSCs on the environment.
## 2 Influencing Factors of Permeability Coefficient
### Gradation Behaviors  
The permeability coefficient of continuously graded coarse-grained soils is closely linked to the shape of their gradation curve, primarily through the spatial arrangement of the particle skeleton, which regulates the connectivity of seepage pathways. The interplay between the uniformity coefficient (\(C_{\text{u}}\), i.e., \(d_{60}/d_{10}\)) and the curvature coefficient [\(C_{\text{c}}\), i.e., \(d_{30}{}^{2}/(d_{60}{}\cdot d_{10})\)] governs the nesting efficiency of multiscale particles. When the gradation curve exhibits a \"smoothly continuous\" distribution (\(5\leq C_{\text{u}}\leq 15\) and \(C_{\text{c}}\approx 1\)), particles of different sizes form a stable, progressively packed structure, significantly enhancing the continuity and flux of seepage paths. In contrast, a \"steeply continuous\" gradation (\(C_{\text{u}}>20\) or \(C_{\text{c}}\) deviating from 1) leads to excessive fine particles filling the voids between coarse grains, resulting in local pore blockage or rupture of flow channels, causing an exponential decay in permeability. Experimental studies indicate that when the fine particle content (<2 mm) in continuously graded soils exceeds 30%, the distribution pattern of interparticle contact force chains undergoes reconstruction. The originally interconnected preferential seepage pathways become fragmented into discrete networks by fine particle clusters. This topological heterogeneity at the microscale amplifies the sensitivity of macroscopic permeability behavior to gradation variations by approximately threefold, revealing a nonlinear coupling mechanism between gradation continuity and seepage efficiency. The continuous gradation of coarse-grained soils is characterized using the negative exponential gradation equation (NEGE) proposed by [39] (see Equation (2)). The proposed NEGE model enables accurate characterization of a wide range of continuous gradations--including concave, convex, linear, and S-shaped types--using only two parameters, which effectively reflect both the composition and distribution of soil particles.  
\[P=e^{\alpha(1-(\frac{d_{\text{max}}}{d})^{\beta})} \tag{2}\]  
where \(P\) is the percentage of soil mass smaller than a given particle size; \(d\) is the particle size; \(d_{\text{max}}\) is the maximum particle size; and \(\alpha\) and \(\beta\) are fitting parameters controlling the shape of the curve and the degree of inclination, respectively.
###### Abstract  
Background: The aim of this study was to develop nomograms predicting 5- and 7-year biochemical-recurrence (BCR)-free survival in high-risk prostate cancer (PCa) patients treated with carbon-ion radiotherapy (CIRT) and androgen deprivation therapy (ADT). Methods: We retrospectively evaluated 785 high-risk PCa patients treated with CIRT and ADT. Based on the least absolute shrinkage and selection operator model, two nomograms predicting 5- and 7-year BCR-free survival were developed and internally validated. The ability of each nomogram to predict BCR-free survival was determined by calculating the area under the survival curve (AUC). Results: The 5- and 7-year BCR-free survival rates were 92.1% and 89.3%, respectively. Age, prostate-specific antigen level, clinical T stage, and Gleason score were incorporated into the nomogram predicting 5-year BCR-free survival. In addition to these variables, the percentage of positive biopsy cores was also added to the nomogram predicting 7-year BCR-free survival. The AUC value of each nomogram showed suboptimal-to-good discrimination. Conclusions: We developed the first nomograms accurately predicting BCR-free survival in high-risk PCa patients treated with CIRT and ADT. These nomograms will enable adequate understanding and explanation of BCR-free survival to patients when clinicians use them.  
prostate cancer; high-risk; nomogram; carbon-ion radiotherapy; androgen deprivation therapy
## 5 Discovery of functional relevant TFBSs  
All the above approaches focus on identifying either experimentally or computationally putative binding sites. Regardless of the approach used, however, physical binding does not necessarily imply functional activity. As such, a major question concerns the functional characterization of the putative binding sites. Although, one can never be certain of the true activity of a TF, unless an appropriate experiment is conducted, computational approaches aim at reducing the number of alternatives on which further experimentation is conducted. Therefore, the next critical question to explore is whether we can identify those TFBS that are more likely to be functional among a set of candidates. Probably as expected, this has turned to be a very challenging question, particularly in higher eukaryotes. However, it has also served as an endless source inspiration for developing numerous computational strategies.  
Before delving into the specific details of the computational approaches, it is instructive to classify the putative functional binding sites into two major categories: (a) general vs. context specific functional binding sites. The former aims at identifying _cis_-regulatory elements of a single gene that are conserved across multiple species based on the evolutionary hypothesis (so-called phylogenetic footprinting) whereas the latter searches for overrepresented TFBSs across multiple genes of a single species that share common characteristics in a specific context e.g. co-expression and/or co-function.
###### Abstract
By changing the material structures or varying the external parameters, two types of modulations can be achieved, i.e., passive modulation and active modulation. Firstly, passive modulation is generally achieved by changing the geometric parameters of the structure [22; 23], but in each modulation the structure needs to be remanufactured, which becomes too complicated and confines its applications. Active modulation manifests as real-time adjustment of the transparent window. It can be operated by external fields such as temperature modulation, light modulation, or DC electric field modulation [10; 17; 24]. It is necessary to optimize the design of the metamaterial structure to achieve a better transparent window in the terahertz band and expand more novel application functions of terahertz metamaterials such as high sensitivity, high Q-factor, and tunable EIT-like effect. So far, many applications of the EIT-like phenomenon are investigated based on the bright-dark modes coupling theory [25]. In this work, two symmetrical/asymmetric metamaterial structures based on cut-wire resonators and U-shaped split ring resonators have been proposed to realize the EIT-like window. The EIT-like effect of several metamaterials with different structures as well as the properties of modulation and sensing are studied. By analyzing the modulation characteristics of passive and active modulation, and simulating the refractive index sensing of both structures, the potential value of metamaterial structure in a terahertz switch and terahertz sensor have been investigated.
# Design and Diagnostics of High-Precision Accelerator Neutrino Beams
## 5 Hadron Yields
Even if the Neutrino Factory is still beyond the reach of current technologies, the HARP data were included to constrain the simulation of practically all low energy neutrino experiments: K2K, MiniBoone and, more recently, in the SBN Fermilab programme to search for sterile neutrinos [103]. For higher energies as the ones exploited by the NuMI beam, Fermilab devised a dedicated hadron production experiment called MIPP [104] that reproduced the running conditions of NOvA, MINOS and MINERvA. MIPP ran with the NuMI replica target (primary protons: 120 GeV/c) accessing secondaries between 300 MeV/c to 80 GeV/c with a transverse momentum up to 2 GeV/c. The broader purpose fixed-target experiment in use today is the NA61/SHINE [105] experiment at CERN. NA61 has a richer programme than its predecessors (NA52-NA56), but also an unprecedented angular acceptance. It is made up of 5 TPCs -including a couple of TPCs located in two special superconducting \"VERTEX\" magnets- and a time-of-flight detector. NA61/SHINE measured the yields and the production cross-sections for T2K experiment (31 GeV/c proton on graphite targets) using both thin and replica targets. SHINE reported uncertainties in the production and inelastic cross-sections of the order of O(2-8%) for p+C, p+Be and p+Al at 60 and 120 GeV/c (see e.g., [106]). Before the CERN LS2, it also performed additional measurements for NuMI and recently extended beyond LS2 to serve DUNE, Hyper-Kamiokande and, possibly, NP06/ENUBET. A special, low  
Figure 5: Pion and kaon yields from NA56/SPY. Reprinted with permission from Ref. [101]. Copyright 1999 Springer. energy beam for measurements in the range of 1-10 GeV/c is being currently considered by the collaboration for construction until 2027. The MIPP and NA61/SHINE data contributed to the world best measurement of the flux published by the MINERvA Collaboration and discussed in Section 12.
# GNSS Spoofing Detection and Mitigation Based on Maximum Likelihood Estimation
## 4 MLE-Based GNSS Anti-Spoofing Method
### Implementation Architecture  
The implementation architecture of the proposed method is shown in Figure 4. An anti-spoofing module can be embedded into a conventional receiver. It consists of two MLE position modules, one signal reconstruction module and one spoofing validation module. The first MLE position module is used to obtain the refined PVT result \(\hat{\mathbf{\rho}}_{1}\) and the corresponding amplitude estimation \(\hat{\mathbf{a}}_{1}\). After that, the reconstructed signal \(\hat{\mathbf{x}}\) is subtracted from the original baseband signal \(\mathbf{x}\), and the residual signal \(\mathbf{y}\) is obtained. Then, \(\mathbf{y}\) is input to the second MLE position module. When the test statistic is over the threshold, evil signals are detected. Then, \(\mathbf{x}\), \(\mathbf{y}\), \(\hat{\mathbf{\rho}}_{1}\) and \(\hat{\mathbf{\rho}}_{2}\) are input to the spoofing validation module. If both \(r_{1}\) and \(r_{2}\) are larger than four, two groups of consistent signals are detected. Thus, the evil signals in \(\mathbf{x}\) are validated to be spoofing ones, and one of \(\hat{\mathbf{\rho}}_{1}\) and \(\hat{\mathbf{\rho}}_{2}\) corresponds to the authentic navigation solution.  
Figure 4: Implementation architecture.
# Evaluation of Parallel Computing on MPI Version PHITS Code
## 2 Materials and Methods
### MPI Activation  
The PHITS code's MPI function is a structure that brings and uses the CPU source of each computer connected via the local network. When setting up total number of simulation on PHITS, terminology such as \"bch\" and \"cas\" is used, meaning unit calculation count per operation and calculation number per bch, also known as \"history\", respectively. As can be inferred from their definition, the product of bch and cas means the total number of the iterative work. If participant processors and product of bch and cas are set, the quota is distributed to each processor. At every end of bch, calculation results from individual processors are automatically summed and moved to the next bch. PHITS code with MPI function proceeds with the calculation in the following manner:  
1. Node 1 reads input file, including bch, cas, and number of processor.
2. Equally calculated workload is distributed to participant CPU.
3. Each CPU sends its result to Node 1 through switching hub via MPI protocol.  
Procedures b to d are repeated until total work is completed. The computing structure scheme used in the study was configured as shown in Figure 2.  
In this study, three comparable computers were designated as calculation node, and the term of \"node\" was used in consideration of the entry computer's role. Each node consists of two CPU cores, and this core is able to divide into two processors. So, our cluster is equivalent to twelve processors, connected through a switching hub. This node's performance is tabulated in Table 1.  
Figure 1: Water phantom and radiation source setup: water with a volume of 1 m\({}^{3}\) was implemented based on Pacific Northwest National Lab material information at an SSD of 100 cm.  
Figure 2: Local network configuration: Three physically separated computers were used. Individual nodes have the same performance in terms of RAM and CPU, and data communication is possible through a switching hub.
## 4 Performance Evaluation  
### Simulation Environment  
To evaluate the performance of the NOMA system model, the MATLAB 2024a [34] platform is used along the 5G communication toolbox and required packages to carry out the simulations. This platform models the transmitter (BS), receiver (users) and channel condition of the NOMA system. Particularly, it can also model fading channels, such as Rayleigh fading, and simulate the path loss to mimic real-world propagation conditions.  
Moreover, Monte Carlo simulation is applied to generate Rayleigh fading channels using random complex Gaussian variables, add AWGN to received signals at the receiver end and run multiple simulation runs to achieve statistically meaningful numerical results.
## 2 Background
### Communication
Table 7 is a comparison table of some common techniques for outlier detection in FLS, including statistical methods, clustering algorithms, and robust machine learning models. The selection of an outlier handling method frequently relies on the data characteristics and the specific requirements of the FLS. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Technique** & **Approach to** & **Advantages** & **Disadvantages** & **Typical Applications** \\ \hline Statistical methods & Use statistical metrics & & & \\  & (like Z-score, IQR) to identify data points that deviate significantly from the norm. & Simple to implement; effective for univariate data. & Can be less effective with complex, high-dimensional data. & Data with a well-defined statistical distribution. \\ \hline Clustering algorithms & Group similar data points together; outliers are points that fall outside clusters. & Effective in identifying groups and anomalies in multi-dimensional space. & May misclassify outliers as a separate cluster, requires determination of the number of clusters. & Multi-dimensional data with distinguishable clusters. \\ \hline Isolation forest & Isolates anomalies by randomly selecting features and splitting values; outliers are easier to isolate. & Efficient for high-dimensional datasets; low linear time complexity. & Random forest mechanism may lead to inconsistent results. & Large datasets with many features. \\ \hline Autoencoders (NN) & Neural networks trained to reconstruct input data; outliers are data with high reconstruction error. & Effective in capturing complex, nonlinear relationships in data. & Requires substantial data for training; computationally intensive. & Complex datasets with intricate patterns. \\ \hline \hline \end{tabular}
\end{table} 
Table 7: A comparison table of some common techniques for outlier detection in FLS. This taxonomy offers a structured framework for comprehending and classifying the primary distinctions and factors that must be considered when dealing with FLSs. Depending on the specific use case and context, FLS implementations may vary significantly along these dimensions. Understanding these variations is essential for the proper development and deployment of FL solutions.