esa-sceva/satcom-chunk-collection huggingface dataset:
# Using Improved Brainstorm Optimization Algorithm for Hardware/Software Partitioning
## 2 The Mathematical Model of Hardware/Software Partitioning  
The problem of hardware/software partitioning can be expressed as \(G=<V,E>\). \(V=\{V_{1},V_{2},\ldots,V_{L}\}\) is a task which has many subtask nodes, where \(L\) is the number of subtask nodes and \(V_{i}\) is the \(i\)-th subtask node. \(E=\{(V_{i},V_{j})|V_{i},V_{j}\in V\}\) represents data dependencies or control process between subtask nodes. A subtask node can be expressed as  
\[V_{i}=<V_{type}(i),T_{s}(i),T_{h}(i),C(i,j),A_{h}(i),\cdots>, \tag{1}\]  
where \(V_{type}(i)=\{0,1\}\) represents the subtask assignment method, \(V_{type}(i)=0\) means the subtask is assigned to software processing unit while \(V_{type}(i)=1\) means the subtask is assigned to hardware processing unit. \(T_{s}\) represents the execution time when the subtask is assigned to software processing unit and \(T_{h}\) represents the execution time when the subtask is assigned to the hardware processing unit. \(C(i,j)\) represents the communication time between \(V_{i}\) and \(V_{j}\) when there is dependency between them. \(A_{h}(i)\) is the required hardware area when the subtask is assigned to hardware processing unit. Compared with hardware processing unit, the required area of software processing unit is much less. So the required area of software processing unit is ignored. In this paper, the total required area of processing units is taken as the constraint, and the time required for the system to complete the task (the completion time of the whole task) is taken as the optimization objective. The mathematical description is given in (2).  
\[\begin{split}\text{min:}\\ T(V)=max\{TE(V_{i})|\forall i\in\{1,2,\ldots,L\}\}\\ \text{subject to:}\\ A=\sum_{i=1}^{L}A_{h}\times V_{type}(i)\leq AreaLimit,\end{split} \tag{2}\]  
where \(T(V)\) denotes the completion time of the whole task. \(TE(V_{i})\) denotes the completion time of the \(i\)th path of the task. \(A\) denotes the total required area of processing units and \(AreaLimit\) is the constraint value.  
The solution space of hardware/software partitioning problem can be represented by a \(L\)-dimensional discrete space \(\{0,1\}^{L}\), where \(L\) is the number of subtask nodes. A solution can be represented by a \(L\)-dimensional vector \(x_{k}\) in this space, \(x_{k}=[x_{k}^{1},x_{k}^{2},\ldots,x_{k}^{i},\ldots,x_{k}^{L}]\), where \(x_{k}^{i}=0\) represents the \(i\)-th subtask is assigned to software processing unit, while \(x_{k}^{i}=1\) represents the \(i\)-th subtask is assigned to hardware processing unit. For example, a solution of hardware/software partitioning \([1,0,1,1,\ldots]\) means that the first, the third and the fourth subtasks are assigned to the hardware processing unit, and the second subtask is assigned to the software processing unit.
# Plant Height Estimation in Corn Fields Based on Column Space Segmentation Algorithm
## 3 Results and Discussion
### Plant Height Estimation in Maize Populations
#### 3.1.4 Distribution of Extracted Heights  
In this study, the population point cloud was segmented into AABB enclosing boxes containing single-plant point clouds, and their heights were extracted. For statistical analysis and comparative validation, the extracted height data were multiplied by 100 to convert the measurements to centimeters. The experiment consisted of five groups, namely a, b, c, d, and e, with corresponding numbers of the extracted height data 765, 775, 805, 875, and 889, respectively.  
A box plot was used to describe the distribution of the data for each test. The box plots show the overall data distribution through statistics, such as median, quartiles, and upper and lower bounds, and identify outliers (if present). As shown in Figure 12, the extracted height data for trials a, b, and c are distributed between 0 and 150 cm. Trials b and c have data concentrated between 0 and 125 cm, trial d between 0 and 150 cm, and trial e between 0 and 175 cm. The absence of outliers indicates the high quality of the extracted point cloud. Outliers, when present, may be caused by missing boundary point clouds (if smaller than the lower bound) or by a very small number of exceptionally tall plants (if larger than the upper bound). In the box plot, Triangles: Represent outliers, i.e., data points outside the range of the box, either above the upper bound or below the lower bound. These can be extreme values due to data errors or exceptional cases; **Lines**: The \"lines\" in the box  
Figure 11: Example diagram of the column space algorithm.  
plot represent the upper and lower quartiles (the boundaries of the box) and the median (usually a horizontal line).  
The statistics in Table 3 show that the average heights of trials a, b, and c were about 55 cm, trial d about 78 cm, and trial e about 105 cm. These results reflect the differences in the maize growth periods. Trials a, b, and c had similar growth stages. Trial d was later, and trial e was the latest, which aligns with the actual growth situation. A histogram (Figure 13) was plotted using the five sets of data, revealing distinct differences in maize height distributions across the groups. Trial a was concentrated at 30 cm and 75 cm, with trial b at 37 cm and 70 cm, trial c at 40 cm and 70 cm, trial d at 75 cm and 112 cm, and trial e at 75 to 150 cm.
# A Novel Cryptography-Based Architecture for Secure Data Asset Sharing and Circulation Systems
## 6 System Implementation
### System Function Comparison
#### 6.5.3 Trustworthy Operations  
In uncontrolled environments, there is a need to accurately identify, dynamically manage and evaluate the trustworthiness of data operations by different users, applications, and at different times. A new method for fine-grained trustworthy management of data operations is proposed. By conducting fine-grained identification of data assets, operation subjects, and operation environments, and performing comprehensive rule-based evaluations, a mapping relationship between multi-policy rules and smart contracts is established. A trustworthy supervision chain is constructed to ensure the reliable execution of rules and the traceability of operations. From the comparison of security features provided in Table 3, it can be seen that the proposed approach uses smart contracts and blockchain technology to enable trustworthy recording of data operation behaviors by different data providers. Trustworthiness metrics are derived from these records, and trust values are combined with access control methods to provide the basis for data access authorization and rights determination. This achieves fine-grained control over data operations, evidence preservation during operations, and traceability after operations.  
\begin{table}
\begin{tabular}{c l l l} \hline
**Paper** & **Sharing Encryption** & **Ciphertext Deduplication** & **Distributed Storage** \\ \hline
[7] & Does not support differential encryption for data with different sensitivity levels & Supports fine-grained data deduplication & Supports distributed storage based on PoW \\ \hline
[19] & Does not support differential encryption for data with different sensitivity levels & \(\mathsf{x}\) & \(\mathsf{x}\) \\ \hline
[20] & Does not support differential encryption for data with different sensitivity levels & \(\mathsf{x}\) & utilizes IPFS for decentralised data storage \\ \hline ours & Support dynamic adjustment of storage redundancy, distribution, and security based on data categories. & Support a combined coarse-grained and fine-grained deduplication method based on data categories. & Support an efficient retrieval and rapid recovery/reconstruction mechanism for fragmented data based on distributed multi-hash. \\ \hline \end{tabular} Note: \(\mathsf{x}\)’ represents that this security feature is not available.  
\end{table}
Table 2: Comparison of security features in outsourcing storage.
## 5 Simulation Experiments
### Acceleration KF-Based Optimization Model  
The acceleration value of IMU was integrated and converted to obtain the velocity and position, as shown in Figure 6. Then, the obtained data were filtered based on EKF technology.  
IMU measured the initial position of the carrier as the origin of the coordinates. Therefore, it can be seen from Figure 6 that as the distance of the X-axis changed from 0, the acceleration signal and speed signal appeared smoother and more stable after filtering, and the speed kept changing within 4 m/s.
# Is Satellite Ahead of Terrestrial in Deploying NOMA for Massive Machine-Type Communications?
## 4 Developed E-SSA-Based NOMA System Elements  
For mobile broadcasting IoT applications, on the industrial side in 2008, Eutelsat and SES satellite operators joined their forces in the Solaris joint venture, which was in charge of developing the hybrid system and procuring the S-band payload of the Eutelsat W2A satellite. The payload included, in particular, a multi-port amplifier (MPA) allowing a flexible allocation of power among the six linguistic-shaped beams, and a 12 m reflector antenna capable of delivering typically an EIRP of \(\sim\)60 dBW and a G/T of \(\sim\)10 dB/K on each beam. The W2A satellite depicted in Figure 6 was launched in 2009, but a problem related to incorrect antenna deployment rendered the commercial development of the foreseen S-band services impossible in practice. Despite the satellite non-nominal antenna performance, extensive field trials were performed in France, Spain and Italy.  
The S-MIM and F-SIM ground segment technologies have been developed by different companies, under the coordination of Eutelsat.  
In particular, in the following, we focus on the most recent F-SIM ground equipment:  
* MBI (Italy) has developed the gateway, implementing, in particular, the E-SSA packet demodulator, and integrated it into a commercial hub product dubbed \"HyperCube\".  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **F-SIM Cr38405f16 Ds38** & \multicolumn{2}{c}{**F-SIM Cr38405f64 Ds38**} \\ \hline  & **AWGN** & **AWGN + PN** & **AWGN** & **AWGN + PN** \\ \hline Throughput (bit/chip) & 0.67 & 0.67 & 1.46 & 1.44 \\ PLR & 0.001 & 0.001 & 0.001 & 0.001 \\ \hline Throughput (bit/chip) & 0.7 & 0.7 & 1.59 & 1.53 \\ PLR & 0.01 & 0.01 & 0.01 & 0.01 \\ \hline Throughput (bit/chip) & 0.95 & 0.86 & 1.71 & 1.62 \\ PLR & 0.1 & 0.1 & 0.1 & 0.1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: UC#1: F-SIM results.  
Figure 6: The Eutelsat W2A satellite pictorial image showing the 12 m deployable reflector for the S-band mission.  
* Enensys (France) has improved its \"SmartGate\" DVB-S2 modulator to fully support the F-SIM forward link.
* Egated (Spain) and Ayecka (Israel) have developed fixed terminals, named \"SmartLNB\", to be used with DTH-like parabolic dishes (75-80 cm diameter).
* The terminals have also been integrated into auto-pointing nomadic antennas, as well as maritime antennas from Intellian (US) and KNS (Korea).
* Work is in progress to integrate the F-SIM modem into a flat antenna with electronic steering from Satisfy (Israel).
## 4 Technique Validation and Discussion
### Influence of Intra-Day DCB Variations  
[PERSON] et al. [60] revealed that the intra-day DCB variations result in a mis-modeling error of several tenths of TECU. Grounding change could lead to such variations [13]. Figure 16a shows an example of pseudorange TEC that features by rapid step-like changes (jumps). Such huge errors destroy the solution of equation system based on (1). Black line in Figure 16b shows the obtained vertical TEC dynamics without typical daily TEC variation.  
Figure 14: DCBs estimate for GPS (**a**) and GLONASS (**b**) satellites from the IRKJ 1 March 2015 data. The red circles show the results of the TuRBOTEC algorithm operation; the black diamonds present the CODE data.  
Figure 15: GNSS absolute slant TEC after least squares without constraint (black dots) and TuRBOTEC techniques (blue dots).  
We can solve the emerged problem by excluding DCB from Equation (1):  
\[I_{M}=I_{S}+I_{DCB}=I_{V}\cdot S+I_{const}, \tag{10}\]  
where, \(I_{const}\) is a term corresponding to the phase ambiguity. The term is constant for a continuous TEC series. We change (3) in the same way. Thus, for Equation (7), we exchange \(N\) variables (where \(N\) is a number of satellites) by \(N^{\prime}\) those (where \(N^{\prime}\) is a number of continuous series). This improves the solution (see red line in Figure 16b), but does not provide DCB anymore. It is the users, who should choose, if they need DCB, and if the pseudorange data quality is sufficient. We would note additionally, that short series should be removed from the treated data.
## 3 Methodology
### Detailed Procedure
#### 3.3.1 Phase 1: Data Collection and Pre-Processing  
Importantly, MA-based TRM develops from data selection. The suggested methodology depends on quantitative analysis. To analyze documents to depict the important characteristics of each layer, text mining tools were utilized to deal with the large amount of data information and extract keywords. Appropriate data sources should be selected for each layer, including the technology and product layers in this study. Patents were considered as technology documents because patents are critical elements for technology analysis, and they also help promote several kinds of science and technological development. In this study, patent documents included essential items: titles of inventions, abstracts, specific descriptions and claims, and were gathered from the database of the US Patent and Trade Organization (USPTO). Regarding the product layer, product manuals extracted from professional websites are used to analyze product information, such as mechanisms, functions and energy sources. The technological content of patents can be linked with designing products. A product manual usually deals with content concerning the product itself, which could be effectively utilized to enrich the information of the technological shapes in MA in turn. Both the patent documents and product manuals are preprocessed by a Python program to remove stop and function words and split the sentences into single words, with the aim of simplifying the process and improving the analysis results.
## 3 Preliminaries  
### Energy Model  
In energy model, the difference of this study from most energy efficient WSN studies is to consider and analyze the energy consumption of the network in four categories.  
* Energy consumed during the transmission process.
* Energy consumed during the receiving process.
* Energy consumed for sensing.
* Energy consumed for listening during the TDMA (Time Division Multiple Access)-cycle.  
In previous studies, authors ignored the energy consumption analyzes in sensing and listening processes, because they claimed that the sensor nodes consumed less energy in these processes. However, as our contribution for the energy model, the energy consumption mentioned cases above, as well as the energy that is consumed for transmission and receiving has been taken into account. In this way, more realistic and accurate energy analysis has been done.
## 2 Materials and Methods
\begin{table}
\begin{tabular}{c c c c c} \hline
**Postman to IoT Hub** & \begin{tabular}{c} **Event Received \&** \\ **Enqueued From IoT Hub** \\ **to Stream Analytic** \\ \end{tabular} & \
\begin{tabular}{c} **Event Processed** \\ **in SQL Database** \\ \end{tabular} & **Total Delay (s)** &
\begin{tabular}{c} **Total Delay \\ **(ms)** \\ \end{tabular} \\ \hline
0.581 & 10:04:09.60/0000 & 10:04:09.8347807 & 0.8087807 & 808.7807 \\ \hline
0.510 & 10:04:11.2320000 & 10:04:12.4144696 & 1.6924696 & 1692.4696 \\ \hline
0.564 & 10:04:12.3110000 & 10:04:12.4160150 & 0.6690150 & 669.0150 \\ \hline
0.57 & 10:04:13.2480000 & 10:04:13.3988495 & 0.7208495 & 720.8495 \\ \hline
0.546 & 10:04:14.1540000 & 10:04:14.2737461 & 0.6657461 & 665.7461 \\ \hline
0.554 & 10:04:25.3420000 & 10:04:25.5392885 & 0.7512885 & 751.2885 \\ \hline
0.531 & 10:04:29.3580000 & 10:04:29.4769821 & 0.6499821 & 649.9821 \\ \hline
0.536 & 10:04:31.0300000 & 10:04:31.2268609 & 0.7328609 & 732.8609 \\ \hline
0.576 & 10:05:22.1090000 & 10:05:22.1033898 & 0.5703898 & 570.3898 \\ \hline
0.548 & 10:05:25.2500000 & 10:05:25.3832814 & 0.6812814 & 681.2814 \\ \hline
0.544 & 10:05:26.4380000 & 10:05:26.4774513 & 0.5834513 & 583.4513 \\ \hline
0.544 & 10:05:27.5940000 & 10:05:27.6801260 & 0.6301260 & 630.1260 \\ \hline
0.548 & 10:05:29.2970000 & 10:05:29.4301508 & 0.6811508 & 681.1508 \\ \hline
0.521 & 10:05:30.5940000 & 10:05:30.7422885 & 0.6692885 & 669.2885 \\ \hline
0.502 & 10:05:31.5630000 & 10:05:31.7270345 & 0.6660345 & 666.0345 \\ \hline
0.534 & 09:01:40.6520000 & 09:01:40.8793610 & 0.7613610 & 761.3610 \\ \hline \end{tabular}
\end{table} 
Table 2: After the deployment of security mechanisms. \begin{table}
\begin{tabular}{c c c c} \hline
**Component** & **Light Use** & **Medium Use** & **High Use** \\ \hline
**VPN Gateway (Basic Tier)** & \(\xi\)65 & \(\xi\)90 & \(\xi\)120 \\ \hline
**DDoS Protection Standard** & \(\xi\)250 & \(\xi\)250 & \(\xi\)250 \\ \hline
**Network Security Groups** & \(\xi\)0 & \(\xi\)0 & \(\xi\)0 \\ \hline
**Data Egress (10–50 GB)** & \(\xi\)5 & \(\xi\)15 & \(\xi\)30 \\ \hline
**Total Estimated Cost** & \(\xi\)320 & \(\xi\)355 & \(\xi\)400 \\ \hline \end{tabular}
\end{table} 
Table 3: Estimated Monthly Cost of Security Features. analyzed the impact of \(\lambda/\mu\) ratios ranging from 0.4 to 0.9 on queue length, waiting time, and utilization. The results show that as \(\lambda\) approaches \(\mu\), the average waiting time and queue length grow exponentially, demonstrating the system's vulnerability to congestion under high load. This analysis supports the importance of resource overprovisioning and dynamic scaling in production environments. The results of this analysis are summarized in a new plot, which visualizes how performance metrics behave under fluctuating load conditions.
# Peak Fitting Applied to Fourier Transform Infrared and Raman Spectroscopic Analysis of Proteins
## 1 Introduction
Furthermore, the area under the second derivative peaks is proportional to the area of the corresponding peak in the original spectrum [27]. Thus, derivative spectroscopy can theoretically also be used for quantitative analysis. However, one has to keep in mind that the derivatives are especially sensitive to noise in the original spectrum. It is, hence, of utmost importance to have a good signal-to-noise ratio in the original spectra [27]. Alternatively, one can use smoothing techniques to reduce the noise level. One of such smoothing techniques is the Savitsky-Golay algorithm [27; 31]. Several papers have used the second derivative analysis to estimate the contribution of each secondary structure in protein solutions [2; 5; 23]. However, the accurate measurements of the contribution of each secondary structure to the amide I band using this method have been elusive. Band curve-fitting was used in this paper to calculate quantitatively the area of each secondary structure [2; 5; 20; 21; 22], which is basically a three step process: (i) having a good initial idea about the physical state (i.e., hydration level) of your system to identify potential hidden overlapping peaks, (ii) using derivative spectroscopy in order to locate the overlapping hidden peaks [31; 34], and finally (iii) curve fitting of the experimental spectrum by a function which is the sum of the individual peaks [31]. The goal of this paper is to describe a solid method for measuring and analyzing the spectral data to study protein secondary structures in high moisture matrices using vibrational spectroscopy techniques. For this purpose, a range of standard proteins with well-known secondary structures were analyzed in order to develop and test the method. Hydrated zein and gluten samples were then measured with the described method of data analysis. FT-Raman is used as a complementary technique to FTIR to study the protein secondary structures. The interference of water with the amide I band signal of proteins is minimized in FT-Raman analysis and, in contrast to what is done on FTIR data, no water subtraction is needed prior to peak fitting of the resulting Raman spectra.
## 3 Results  
### NDR Length and Expression Levels of Native Gene of S. cerevisiae  
Studies have shown that NDR length in the transcriptional start region correlates closely with gene expression in _S. cerevisiae_[22]. To determine the validity of the theoretical conclusion, we examined the relationship between NDR length and gene expression levels. We obtained and analyzed 1258 genes from _S. cerevisiae_, and the NDR lengths ranged between 80 bp and 370 bp. Among them, about 80% of NDR lengths were less than 150 bp, and 20% were greater than 150 bp. The length distribution of the 1258 NDR sequences is shown in Figure 1A. We obtained the FPKM values from the RNA-seq data for _S. cerevisiae_ BY4741 and analyzed the relative mRNA expression levels of 1258 genes. The FPKM values were used as expression levels. Genes with NDRs longer than 150 bp showed approximately 30% higher relative mRNA expression levels compared to those with NDRs shorter than 150 bp (Figure 1B). After excluding genes with extremely low FPKM values (FPKM \(<\) 1), a total of 86 genes were randomly selected from the 1258 native genes of _S. cerevisiae_. In order to ensure the reliability and reproducibility of statistical analyses and the balance of the two datasets, we selected 37 genes with long NDR (L-NDR) and 49 genes with short NDR (S-NDR). The length of L-NDR is between 150 bp and 400 bp and of S-NDR is between 80 bp and 149 bp. We examined the relative mRNA expression levels of the two groups of genes using RT-qPCR and analyzed the potential correlation between NDR length and gene expression. The results showed that the relative mRNA expression levels of genes with long NDRs were 15 times higher than those of genes with short NDRs (\(p<\) 0.0001; Figure 1C). The results indicated the possibility of a positive correlation between the length of the NDR and the level of gene expression in a portion of the genes under investigation.