esa-sceva/satcom-chunk-collection huggingface dataset:
# Throughput/Area Optimized Architecture for Elliptic-Curve Diffie-Hellman Protocol
## 1 Introduction
### Contributions  
The contributions to this work are summarized as follows:  
1. An ECDH architecture is presented, with a focus on high-speed with low-area utilization, over \(GF(2^{233})\) on FPGA.
2. The high-speed is achieved with the use of: (a) a two-stage pipelining and (b) a bit parallel Karatsuba multiplier. To deal with the pipelining, an efficient rescheduling of PAdd and PDbl operations for PM computation is proposed. The use of pipeliningincreases the clock frequency and reduces the critical path. Moreover, the proposed rescheduling and the employed Karatsuba multiplier reduce \(5\times m\) clock cycles, where \(m\) is the key length. Further details are illustrated in Section 3.4.
3. Apart from the high-speed, the low-area is achieved with the use of one adder and a Karatsuba multiplier. It implies that the squaring and inversion computations are operated with the same hardware resources (Karatsuba multiplier) which eventually reduces the overall implementation resources.
4. Finally, the two dedicated finite state machine (FSM) based controllers are used for controlling PM and ECDH operations respectively.
## 3 Multimodal HAR Model: Marfusion
### Attention Mechanism  
For better performance, different weights have been evaluated on different sensors and we use Dot-product scaled Self-Attention to process the outputs of different sensors. By evaluating different sensors, Marfusion dynamically selects the sensors that impact the result and focuses on them. The Attention Mechanism improves the anti-interference capability of Marfusion, which makes higher accuracy in the test set, and enhances the generalization ability of different inputs. The equation of Self-Attention mechanism used in the paper is shown in (7).  
\[Attention(Q,K,V)=softmax\bigg{(}\frac{QK^{T}}{\sqrt{d_{k}}}\bigg{)}V \tag{7}\]  
\(Q\), \(K\), \(V\) are all obtained from the input data \(X\) after a linear transformation, as Equations (8)-(10) show.  
\[Q =XW_{Q} \tag{8}\] \[K =XW_{K}\] (9) \[V =XW_{V} \tag{10}\]  
\(W_{Q}\), \(W_{K}\), \(W_{V}\) are the weight matrix of linear transformation corresponding to \(Q\), \(K\), \(V\).
## 3 Architecture
### Task Execution Time and Energy Model
#### 3.5.2 Execution Time on the Edge Cloud  
If subtask \(m\) of the task \(k\) is executed on the edge cloud, that means \(D_{bm}=1\), and the execution time \(T_{bm}^{e}\) of the subtask \(m\) is determined by the task size \(w_{bm}\) and the CPU capacity \(f_{e}\) of the edge cloud. The execution time \(T_{bm}^{e}\) of the subtask \(m\) can be expressed as follows:  
\[T_{bm}^{e}=\frac{w_{bm}}{f_{e}} \tag{2}\]
# Bandwidth Density Analysis of Coded Free-Space Optical Interconnects
## 3 System Model
It should be mentioned that in (9), we exploited the photodetectors' distribution symmetry and the rotational symmetry of the laser beam. Therefore, the noise amount received by photodetectors 1, 3, 5, and 7 is the same, and the amount received by photodetectors 2, 4, 6, and 8 is the same as well. For the other considered modes of operation--TEM\({}_{01}\), TEM\({}_{10}\), and TEM\({}_{20}\), respectively--the noises of each are defined as follows  
\[ \begin{split} N_{01}&=\frac{8}{\pi\times W_{D}^{4}} \sum\limits_{j=1}^{8}\iint\limits_{PD_{j}}r^{2}\times\exp\left(\frac{-2r^{2}}{W _{D}^{2}}\right)\times\cos(\theta)rdrd\theta\\ &=\frac{4\times 8}{\pi\times W_{D}^{4}}\int_{0}^{2\pi}\int_{0}^{ \Delta/2}r_{1}^{2}\times\exp\left(\frac{-2r_{1}^{2}}{W_{D}^{2}}\right)\times \cos(\theta)rdrd\theta\\ &+\frac{4\times 8}{\pi\times W_{D}^{4}}\int_{0}^{2\pi}\int_{0}^{ \Delta/2}r_{2}^{2}\times\exp\left(\frac{-2r_{2}^{2}}{W_{D}^{2}}\right)\times \cos(\theta)rdrd\theta,\end{split}  \tag{10}\]  
\[ \begin{split} N_{10}&=\frac{2}{\pi\times W_{D}^{2}} \sum\limits_{j=1}^{8}\iint\limits_{PD_{j}}\exp\left(\frac{-2r^{2}}{W_{D}^{2}} \right)\times\left(1-\frac{2r^{2}}{W_{D}^{2}}\right)^{2}rdrd\theta\\ &=\frac{4\times 2}{\pi\times W_{D}^{2}}\int_{0}^{2\pi}\int_{0}^{ \Delta/2}\exp\left(\frac{-2r_{1}^{2}}{W_{D}^{2}}\right)\times\left(1-\frac{2r_ {1}^{2}}{W_{D}^{2}}\right)^{2}rdrd\theta\\ &+\frac{4\times 2}{\pi\times W_{D}^{2}}\int_{0}^{2\pi}\int_{0}^{ \Delta/2}\exp\left(\frac{-2r_{2}^{2}}{W_{D}^{2}}\right)\times\left(1-\frac{2r_ {2}^{2}}{W_{D}^{2}}\right)^{2}rdrd\theta,\end{split}  \tag{11}\]  
and  
\[ \begin{split} N_{20}&=\frac{2}{\pi\times W_{D}^{2}} \sum\limits_{j=1}^{8}\iint\limits_{PD_{j}}\exp\left(\frac{-2r^{2}}{W_{D}^{2}} \right)\times\left(1-4\frac{2r^{2}}{W_{D}^{2}}+0.5\left(\frac{2r^{2}}{W_{D}^{ 2}}\right)^{2}\right)^{2}rdrd\theta\\ &=\frac{4\times 2}{\pi\times W_{D}^{2}}\int_{0}^{2\pi}\int_{0}^{ \Delta/2}\exp\left(\frac{-2r_{1}^{2}}{W_{D}^{2}}\right)\times\left(1-4\frac{2r _{1}^{2}}{W_{D}^{2}}+0.5\left(\frac{2r_{1}^{2}}{W_{D}^{2}}\right)^{2}\right)^{ 2}rdrd\theta\\ &+\frac{4\times 2}{\pi\times W_{D}^{2}}\int_{0}^{2\pi}\int_{0}^{ \Delta/2}\exp\left(\frac{-2r_{2}^{2}}{W_{D}^{2}}\right)\times\left(1-4\frac{2r _{2}^{2}}{W_{D}^{2}}+0.5\left(\frac{2r_{2}^{2}}{W_{D}^{2}}\right)^{2}\right)^ {2}rdrd\theta,\end{split}  \tag{12}\]  
Upon calculating the values of \(P_{s}\) and \(N\) for each of the aforementioned modes, the BD can be determined for any given SNR, \(P_{t}\), and NEP, as demonstrated numerically in the following section.
## 6 Case Study
### Dataset  
The dataset used in this study encompasses nearly two years of high-resolution spectral data, capturing essential electrical and mechanical dynamics of the system. Key spectral components, such as the supply frequency, the motor rotation frequency, the load-related harmonics, and the belt fault frequency, were recorded with high fidelity, at a resolution of 0.1 Hz and spanning a spectral range of up to 1250 Hz. From this rich dataset, a series of features was extracted to characterize both the signal's magnitude and its evolution over time.  
For the scope of this study, the analysis was intentionally focused on the belt fault frequency, as it represents the most direct and diagnostically relevant indicator of progressive belt degradation. This targeted approach enabled the precise assessment of the spectral behavior associated with the fault condition, improving interpretability and ensuring that the model addresses a specific, high-impact failure mode.  
To evaluate model performance, the dataset was split into training (80%) and testing (20%) sets using a strict chronological division. All training data preceded the test set in time, ensuring full preservation of temporal order and preventing any future information from leaking into the training process. This time-aware split was carefully chosen to reflect real-world predictive maintenance use cases, where historical data were used to forecast future asset behavior.  
Although more rigorous validation strategies, such as walk-forward or roll-window validation, could further enhance robustness, the chosen approach strikes a practical balance between methodological rigor and computational feasibility. Given the long time span, the quality of the data, and the naturally occurring degradation patterns captured, the adopted split provides a representative and reliable basis for evaluating model generalization.
## 2 Distortion and Perceptual Measures
### Perceptual Measures in Information Theory
#### 2.2.2 KL Divergence  
Another widely used divergence measure in information theory is the KL divergence. Given two distributions \(P_{X}\) and \(P_{Y}\) defined on the same \(\sigma\)-algebra \((\mathcal{X},\mathcal{F})\), when \(\mathcal{X}\) is finite,  
\[\mathcal{D}_{\text{KL}}(P_{X}\|P_{Y})=\sum_{x\in\mathcal{X}}P_{X}(x)\log\frac{P _{X}(x)}{P_{Y}(x)}.\]  
When \(P_{X}\) and \(P_{Y}\) are probability density functions, the KL divergence is defined as  
\[\mathcal{D}_{\text{KL}}(P_{X}\|P_{Y})=\int_{x\in\mathcal{X}}P_{X}(x)\log\frac{P _{X}(x)}{P_{Y}(x)}\;\mathrm{d}x,\]  
and, by convention, \(\mathcal{D}_{\text{KL}}(P_{X}\|P_{Y})=\infty\) when \(P_{X}\) is not absolutely continuous with regard to \(P_{Y}\). The KL divergence is not a distance metric because it is asymmetric between the two arguments and does not satisfy the triangle inequality.
## 2 Related Work
### Character-Based Methods  
Character sequence labeling has always been the primary method for NER. In the early stages, [PERSON] et al. [19] proposed the CNN-CRF model and obtained competitive performance for various best NER models. [PERSON] et al. [20] built the BiLSTM-CRF model, which achieved state-of-the-art results in NER tasks at that time. With the development of pre-trained language models (PLM), such as BERT [21], many researchers used PLMs to obtain character-level representations. [PERSON] et al. [22] used BERT to address the missing contextual information problem and added an extra mechanism to capture relations between words. [PERSON] et al. [23] proposed a model based on RoBERTa and character root features. They utilized RoBERTa to acquire medical features and applied BiLSTM to extract partial radical features. With the emergence of a lighter PLM, ALBERT, [PERSON] et al. [24] proposed a model based on ALBERT-AttBiLSTM-CRF and transfer learning for fine-grained entity recognition of manufactured text. Aiming at the problem of word ambiguity in Chinese clinical NER tasks, [PERSON] et al. [25] proposed an ALBERT-based method and introduced a multi-head attention mechanism to capture inter-character dependencies. Due to their simplicity and effectiveness, character-based methods serve as the foundation for most existing NER approaches.
## 3 Results
### Biostimulation of Indigenous Urea-Hydrozing Bacteria  
Biostimulation experiments were conducted on soils sampled from the Rotem Plateau to test whether indigenous urea-hydrolyzing bacteria are naturally present in this region and can be effectively stimulated. Soils were incubated in different media for ten days, and urea hydrolysis was tested by pH and urea concentration measurements (Figure 3). Based on our previous experience [31], it was expected that if indigenous urea-hydrolyzing bacteria would be present in the soil, then their stimulation in the presence of urea would result in a pH rise due to the production and accumulation of ammonium ions. Stimulation of urea-hydrolyzing bacteria was evident in soils incubated in the presence of urea and a carbon source (yeast extract), as shown by pH elevation and urea degradation (low yeast extract concentrations (YE_L) and high yeast extract concentrations (YE_H)). The addition of a carbon source was required to achieve the stimulation, as no significant ureolysis was observed in the absence of yeast extract (Urea). Low yeast extract concentrations (YE_L) resulted in inefficient stimulation when compared to high yeast extract concentrations (YE_H) in the two soils studied. As expected, the pH of the negative control samples from the two sites remained unchanged, and urea concentrations were negligible throughout the biostimulation experiment (Figure 3, AGW).  
Figure 3: Biostimulation of soils from the stimulated soils from the Rotem Plateau. (**a**) pH of soil 1; (**b**) pH of soil 2; (**c**) urea concentration of soil 1 and (**d**) urea concentration of soil 2. AGW is artificial ground water (o); urea is urea control (\(\
abla\)); YE_L is low (0.1 g/L) yeast extract enriched medium (\(\Delta\)); YE_H is high (1.0 g/L) yeast extract enriched medium (\(\phi\)).  
To provide direct evidence that MICP can be induced in these soils, precipitation experiments were conducted using bacteria stimulated with urea and 1.0 g/L yeast extract concentrations. Aliquots of bacteria stimulated from soils for seven days were spiked into fresh urea, and yeast extract-containing media were supplemented with CaCl\({}_{2}\). Within two days, the media became turbid, and white precipitates appeared at the bottom of the Erlenmeyer flasks (data not shown). Urea hydrolysis was confirmed in samples provided with stimulated bacteria from either soil, albeit higher rates were observed in soil 2 than in soil 1 (Figure 4). This effect probably resulted from differences in biostimulation rates between the two soils (Figure 3). After two weeks of incubation, precipitates were collected, dried, and identified by XRD as calcite (Figure 5). Neither precipitation nor urea hydrolysis took place in negative control samples (AGW) incubated in the same conditions.
## 6 Experimental Evaluation
### Numerical Results
#### 6.2.3 Quality of Service Results
Results reported in the histograms in Figure 8 demonstrate that the exact solution effectively reduces the waiting time for a significant percentage of lines in the studied instance. The exact solution has more lines with an average waiting time lower or equal to 1 (i.e., 23 in the exact solution vs. only 6 in the real solution). Furthermore, five lines in the exact solution have an average waiting time less than 0.5 of \(W_{b}\), whereas there is none in the real solution. Regarding larger waiting times, only 3 lines in the exact solution have more than 1.5 of the maximum value for a successful synchronization, whereas in 13 lines of the real solution, passengers must wait 1.5 more than expected to perform a multi-leg trip. The comparative results demonstrate that the solution found by the exact method synchronizes a larger number of lines, thus improving the QoS. Solutions computed by the proposed EA have a similar behavior regarding waiting times, as reported in Table 10. Similar results were obtained for other studied BSP instances, and even better results were achieved in the most restrictive instances (\(\lambda=30\)). Figure 8: Comparative results of waiting times for the real solution, EA for BSP variant #2 (\(\alpha=0.0\)), and exact solution for BSP variant #2 (\(\alpha=0.3\)) in scenario 70.63.100.2. Finally, regarding execution time, both the exact and evolutionary approaches were able to compute accurate solutions in very short execution times. For problem variant #1, the execution times were less than 10 seconds. For problem variant #2, the time limit of the exact method was two hours, and the EA found the best solutions in less than five minutes.
# Automated Measurement of Grid Cell Firing Characteristics
## 4 Step-by-Step Usage Protocol
### Configuration  
The software uses the configuration file named config.txt that is found in the config_convert folder. In this file, Boolean variables generally mean 1 = true and 0 = false. Example configuration settings are found in \"example_config.txt\".  
1. Set the non-field filtering parameter if a different value than the default (0.31) is desired. This parameter can affect the area of fields detected (Figure 5).  
2. Set the rate map file to be read into the program with the plot_filepath variable. For instance, plot_filepath can be set to use the earlier-mentioned \"heat_maps_real_custom/ example_rate_map.txt\" value or \"smooth_rate_map/saved_results/rate_map.txt\" to use the newly generated rate map based on the example data. The load_custom_rm should be  
Figure 5: Effect of the non-field filtering parameter. (**A**) Autocorrelogram from a grid cell. (**B**) Central seven grid fields detected with a parameter value = 31%. (**C**) Central seven fields detected with a parameter value = 60%.  
set to 1 in config.m if the plot is read from the plot_filepath variable. Choose to use a rate map or autocorrelogram with the use_ac variable. The convert_to_ac variable can be set to 1 to convert a rate map into an autocorrelogram (Figure 6).  
3. Set px2 cm to specify the length of the rate map plot in centimeters. We strongly recommend double-checking the correct px2 cm value prior to reporting results, as plots with similar-looking fields can yield many different statistics when converting to physical units (Figure 7).  
4. Choose plots to create with plot_fields_detected and plot_orig_firing variables.  
5. The ratio of firing rate (or correlation values in the case of autocorrelograms) within fields to outside of fields can be reported by setting the variable in_out_fields_ratio to 1. The fields reported on depend on whether a user chooses to only include the central seven fields. In that case, the remaining fields are excluded from the ratio calculation when the variable inout_excl is set to 1.  
6. If the variable cov_between_fields_reporting is set to 1, the software additionally reports the mean of the coefficient of variation in the firing rates (or correlation values in the case of autocorrelograms) across fields.  
Figure 6: Autocorrelogram created from a rate map.  
Figure 7: Rate maps with different environment area scales. (**A**) The px2 cm parameter should be set to 45/32 for this plot because the environment measures 45 \(\times\) 45 cm and the plot has 32 \(\times\) 32 pixels. (**B**) The px2 cm parameter should be set to 100/32 for this plot.  
7. The software also reports a grid score if the variable report_gridscore is set to 1. This option is only recommended for usage with rate maps as opposed to autocorrelograms.  
8. Other available options are described in the notes section of config.txt. The Github page contains further documentation.
## 4 Results from the Literature Review
### The Use of Blockchain
#### 4.2.4 Integration and Management of Transport Systems
Another benefit of real-time public transport management lies in its adaptability to passenger demand, allowing for dynamic schedule adjustments that minimize overcrowding and support social distancing--a feature particularly valuable during health crises, such as the COVID-19 pandemic [201; 202]. Traditional parking practices often lead to inefficiencies, including congestion caused by drivers searching for available spaces and challenges in revenue management for operators. Smart parking systems (SPS) address these issues by providing real-time information on parking spaces' availability, optimizing space utilization, and offering streamlined payment options. SPS comprises multiple interconnected technological components that collectively enhance car park management and user satisfaction. Key elements include occupancy sensors in parking spaces, a data communication network, a data management system for processing and analysis, and a user application [40; 203]. Smart parking systems reduce parking-related costs for cities and drivers alike by automating and optimizing parking processes, thus promoting better space utilization, decreased vehicle traffic, and lower management expenses. The ability for users to reserve spaces, navigate to locations, and pay via an app saves time and fuel [204; 205]. These systems also hold environmental relevance by mitigating congestion [206] and reducing the time required to find a parking space, thereby lowering greenhouse gas emissions from vehicle exhaust [207]. Studies also indicate that integrating smart parking systems with the electric vehicle (EV) charging infrastructure increases the availability of charging stations, improves user convenience, and supports sustainable transport development [208; 209]. SPS can also play a critical role in health crises by minimizing human interaction through cashless payments and remote reservations [210]. Furthermore, smart parking areas can be repurposed as distribution centers for vaccines, facilitating efficient vaccination processes [211].
## 2 Geological Environment and Engineering Properties  
As China's biggest nickel production base, Jinchuan Mine is situated in Jinchang City, Gansu Province, as shown in Figure 1. It consists of four mining zones, where the No. 2 mining area has the largest ore body, with an overall length of approximately 3000 m, a surface outcrop length of about 2500 m, a mean breadth of 380 m, and a downward extension of over 1000 m. The No. 2 mining area is situated in the desert at the southern edge of the Chaoshui basin, surrounded by the Longshou mountains in the southwest, as shown in Figure 2. The terrain is rugged, with an average altitude of 1750 m, so the influence of the arid climate and simple hydrogeological conditions is negligible.  
This mining area has a rather complicated history with regard to the geological background. It has experienced many geological tectonic activities, such as Indosinian movement, Yanshan movement and Lvliang movement. Many kinds of magma activities are also frequent in this area. The mining area's main structure consists of monocines with a southwest tendency, and between the layers folds propagate, frequently forming small anticlines and synclines. These difficult geological conditions, crisscrossed structural surface, weak rock strength and high ground stress all hinder the stability of the rock surrounding the roadway.  
In the No. 2 mining area, the current state of the on-site stress is compressive stress, with horizontal stress being the maximum principal stress. In the shallow part, within a depth of about 30 m from the surface, the maximum principal compressive stress is approximately 3 MPa. When the depth increases, the stress value increases. The maximum principal stress reaches 30 MPa and the highest value is 52.2 MPa between a depth of 400 and 800 m. Its direction is mostly NE-NNE, practically vertical to the ore body's orientation, and there is a large difference between the minimum and maximum principal stress [28; 29; 30].  
A downward filling mining method with a subsection height of 20 m is adopted in the Jinchuan Mine. Each subsection is further divided into five layers with a height of 4 m and the ore body buried at 900 m is the main mining area at present. Trackless transport is used in the stope, so it is necessary to construct transport roadways with large sections. The main object of this study is the transport roadway, which is often a permanent project and needs to provide long-term stable support.