esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### State of the Art  
The current predominant approach for communication between CubeSats and commercial intersatellite link networks in Low Earth Orbit is based on systems such as Iridium, GlobalStar, and Orbcomm [19].  
In a 2008 investigation analyzing Inmarsat, Thuraya, Iridium, and GlobalStar, it was concluded that the GlobalStar network is best suited for nanosatellite payloads with an inclination of 52 degrees at altitudes ranging from 300 to 600 km [20].  
TechEdSat 4, launched in 2014, also utilized a communication module for GlobalStar. Although it operated nominally, information regarding its results is unavailable. On August 2016, the secondary payload \"exo-brake\" was deployed resulting in early orbital reentry [21]. The PicoPanther project has been studying the GlobalStar and Iridium networks since 2008, with its approach remaining controversial until the launch of PhoneSats, particularly PhoneSat 1 Bell [22].  
TechEdSat 8, deployed from the International Space Station on 31 January 2019, featured an integrated Near-Earth Network (NEN) S-band radio compatible with Iridium and GlobalStar transmitters. However, it was unable to undergo testing after deployment due to an onboard power system abnormality [23].  
In the nanosatellite database (www.nanosats.eu (accessed on 11 September 2023)), there are at least 40 registered CubeSat-type nanosatellites that utilize a GlobalStar-compatible communication module as their primary or secondary downlink band. However, some of these projects were either not released or were subsequently cancelled. Among notable success stories are TSAT, GEARRSSAT, CubeRRT, MakerSat1, and AztechSat-1 [24].  
The results of TSAT, a 2U CubeSat, demonstrated the effectiveness of the GlobalStar commercial network without the need for a ground station. The satellite's objective was to validate and characterize the link capacity of the GlobalStar constellation and conduct measurements in an Extremely Low Earth Orbit (ELEO). The team recorded successful simplex link transmissions within the field of view of the patch antenna. TSAT data was received by one or more GlobalStar satellites and transmitted to a gateway. The team received a total of 21,154 packets and noted that the system was capable of transmitting thousands of packets per day on a CubeSat. Although the quantitative capacity was not explored. At the beginning of the mission, TSAT transmitted its maximum number of messages, recording 26 dual-packet messages in 18 min. After ten days, this number was reduced to 7 messages per hour. The system implemented an STX-2 module and was deployed into a circular orbit at 325 km with an inclination of 51.6 degrees [25].
_Applied_  
_sciences_  
Acctic Acid as an Indirect Sink of CO2 for the Synthesis of Polyhydroxyalkanoates (PHA): Comparison with PHA Production Processes Directly Using CO2 as Feedback  
**[PERSON] *** and [PERSON](r)**  
Flemish Institute for Technological Research (VITO), Boeretang 200, 2400 Mol, Belgium; [EMAIL_ADDRESS]  
* Correspondence: [EMAIL_ADDRESS]; Tel.: +32-14-336-981 check for updates  
Received: 1 August 2018; Accepted: 19 August 2018; Published: 21 August 2018  
**Featured Application: production of bioplastics with tailored composition and properties from the feedstock CO2, in view of maximal CO2 fixation and minimal H2 consumption.**  
**Abstract:** White biotechnology is promising to transform CO2 emissions into a valuable commodity chemical such as the biopolymer polyhydroxyalkanaotes (PHA). Our calculations indicated that the indirect conversion of acetic acid from CO2 into PHA is an interesting alternative for the direct production of PHA from CO2 in terms of CO2 fixation, H2 consumption, substrate cost, safety and process performance. An alternative cultivation method using acetic acid as an indirect sink of CO2 was therefore developed and a proof-of-concept provided for the synthesis of both the homopolymer poly(3-hydroxybutyrate) (PHB) and the copolymer poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV). The aim was to compare key performance parameters with those of existing cultivation methods for direct conversion of CO2 to PHA. Fed-batch cultivations for PHA production were performed using a pH-stat fed-batch feeding strategy in combination with an additional Dissolved Oxygen (DO)-dependent feed. After 118 h of fermentation, 60 g/L cell dry matter (CDM) containing 72% of PHB was obtained, which are the highest result values reported so far. Fed-batch cultivations for PHBV production resulted in 65 g/L CDM and 48 g/L PHBV concentration with a 3 HV fraction of 27 mol %. Further research should be oriented towards process optimisation, whole process integration and design, and techno-economic assessment.  
**Keywords:** polyhydroxyalkanoate; carbon capture and utilization; autotrophic fermentation; _Cupriavidus reactor_; acetic acid
## 3 Frequency Domain Spectral Finite Element Method (FDSFEM)
### Wave Propagation in 3D Elements  
Modelling of wave propagation in structures with FDSFEM is inseparably connected with the application of FFT and IFFT. In the case of a 1D structure, the algorithm is typically built on a set of \(n\) points of the FFT. Including more dimensions into analysis leads to the increase of FFT points, \(n^{2}\) in the case of 2D elements, not to mention 3D examples. In cases when more advanced theories are applied, usually more than one wavenumber is taken into account. For example, for simple Timoshenko beam theory, two wavenumbers are already analysed, instead of one for Bernoulli-Euler case. Another aspect of modelling of wave propagation is the number of modes that propagate within the analysed range. For a typical case of wave propagation, usually three modes propagating in one direction are analysed. 2D analysis in its simplest version with three modes in one direction requires six modes to be analysed, in the case of 3D structures--9 modes. Another particular feature of FDSFEM is the throw-off element, which function is connected with drawing the energy out of the system. All of the above features cause this method to be practically inapplicable in 3D cases. Even though the author tried to identify any scientific report regarding 3D models with FDSFEM, it was hardly to find.
## 2 Material and Methods
### Filling of Missing Data
#### 2.3.2 Simple Linear Regression (SLR) with Variable \"Donor\" Sites  
Here, we resort to the framework described in the \"hyfo\" R package for estimating missing rainfall amounts from a single (yet variable) \"donor\" site. In short, the procedure for filling missing data, at daily, monthly, and annual time scales, is as follows. First, the Pearson correlation coefficient between the rainfall time series at the target site and each of the \"donor\" stations is computed (excluding the sample points with missing data). Next,  
Figure 3: Locations of the target sites and their respective “donor” stations for filling monthly rainfall values with the multiple linear regression model. Test stations: (**a**) Cachoeira de Goiás (**b**) Campo Alegre (**c**) Corrego do Ouro (**d**) Crominia (**e**) Montividiu, and (**f**) Quirinópolis.  
Figure 2: Locations of the target sites and their respective” donor” stations for filling daily rainfall values with the multiple linear regression models: (**a**) Cachoeira de Goiás 100, (**b**) Campo Alegre 150, (**c**) Corrego do Ouro, (**d**) Crominia, (**e**) Montividiu, and (**f**) Quirinópolis.  
the \"donor\" stations are ranked on the basis of the levels of linear correlation. Then, simple regression models, without the bias terms for allowing the simulation of null values, are identified for each pair of target and \"donor\" stations, according to Equation (2)  
\[Y=\beta x \tag{2}\]  
in which \(\beta\) is the regression coefficient and \(x\) is the rainfall amount at a given \"donor\" site. Finally, the missing values at the target site are estimated from the linear model derived for the gauging station with the highest value of the Pearson correlation coefficient and available data. This is simpler yet more flexible than the MRL approach; since the requirements of data availability in all gauges during the period of missing data at the target site are relaxed whenever the best \"donor\" station has a missing value, the next best gauging station may provide the predictor for the linear model. However, as per the original implementation of the R package, intercepts, which may remove systematic bias from the model estimates (i.e., offsets from the 1:1 line), are not included in the regression equation. Hence, model performance is inherently dependent on the level of linear correlation between the target and the selected \"donor\" station. Yet, the SLR models might provide a useful benchmark for comparing the performances of the MLR models and the imputation of satellite retrievals.
# A Novel Non-Parametric Spatiotemporal Scan Statistic: An Application to Detect Disease Outbreaks
###### Abstract  
The majority of the widely used scan statistics are based on distributional assumptions. Contrary to the existing methods, with a new perspective in clustering, the Mann-Whitney Scan Statistic was introduced to detect clusters in continuous data indexed by time or space, without any distributional assumptions or parameters to set up. We propose an extension of the Mann-Whitney Scan Statistic that can be applied to spatiotemporal data based on spatiotemporal distance measure. This novel scan statistic is distribution-free and seems to be powerful against parametric spatiotemporal scan statistics. The results are applicable in a wide variety of spatiotemporal domains, including epidemiology, socioeconomic analysis and climate sciences, irrespective of continuous or discrete data.  
scan statistics; non-parametric; spatiotemporal scan statistics; cluster detection; disease outbreaks; COVID-19 +
Footnote †: 2022: 12, 10513. [[https://doi.org/10.3390/appl122010513](https://doi.org/10.3390/appl122010513)]([https://doi.org/10.3390/appl122010513](https://doi.org/10.3390/appl122010513))
## 4 Results and Discussions
### Parametric Effects Discussions  
According to the previous sections of this paper, the following aspects affect the safety of 3D rock slopes: nonlinear H-B criterion parameters, seismic pseudo-dynamic parameters, anchorage parameters, and slope geometry parameters, whose effects on rock slopes are  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**B/H**} & \multirow{2}{*}{**Solutions**} & \multicolumn{4}{c}{\(\boldsymbol{\beta_{1}=\beta_{2}}\)} \\ \cline{3-6}  & & \(\boldsymbol{45^{\circ}}\) & \(\boldsymbol{60^{\circ}}\) & \(\boldsymbol{75^{\circ}}\) & \(\boldsymbol{90^{\circ}}\) \\ \hline
1.0 & \(N_{s}\) form [PERSON] and [PERSON] & 54.850 & 23.835 & 14.701 & 11.028 \\  & \(FS\) using the current approach & 0.9894 & 0.9824 & 0.9992 & 0.9848 \\
1.5 & \(N_{s}\) form [PERSON] and [PERSON] & 46.845 & 20.773 & 12.976 & 8.935 \\  & \(FS\) using the current approach & 0.9921 & 0.9742 & 0.9755 & 0.9927 \\
2.0 & \(N_{s}\) form [PERSON] and [PERSON] & 42.732 & 19.103 & 12.109 & 8.604 \\  & \(FS\) using the current approach & 0.9995 & 0.9918 & 0.9795 & 0.9599 \\
3.0 & \(N_{s}\) form [PERSON] and [PERSON] & 39.956 & 17.873 & 11.184 & 7.974 \\  & \(FS\) using the current approach & 1.0010 & 1.0011 & 0.9956 & 0.9924 \\
5.0 & \(N_{s}\) form [PERSON] and [PERSON] & 37.994 & 17.063 & 10.628 & 7.266 \\  & \(FS\) using the current approach & 1.0062 & 1.0032 & 0.9985 & 0.9938 \\
10.0 & \(N_{s}\) form [PERSON] and [PERSON] & 36.703 & 16.527 & 10.265 & 6.944 \\  & \(FS\) using the current approach & 1.0086 & 1.0025 & 1.0013 & 1.0003 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparisons between this paper’s findings and [PERSON] and [PERSON]’s solutions.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**B/H**} & \multicolumn{2}{c}{**Amended Pseudo-Dynamic Results**} & \multicolumn{2}{c}{**Original Pseudo-Static Results**} \\ \cline{2-7}  & \(k_{h}=0.2\) & \(k_{h}=0.5\) & \(k_{h}=0.8\) & \(k_{h}=0.2\) & \(k_{h}=0.5\) & \(k_{h}=0.8\) \\ \hline
1.5 & 1.981 & 1.215 & 0.926 & 1.932 & 1.135 & 0.920 \\
3.0 & 1.276 & 0.879 & 0.676 & 1.185 & 0.844 & 0.634 \\
5.0 & 1.136 & 0.835 & 0.660 & 1.091 & 0.795 & 0.577 \\
10.0 & 1.031 & 0.771 & 0.598 & 0.992 & 0.755 & 0.545 \\
2D & 1.016 & 0.735 & 0.579 & 0.962 & 0.718 & 0.503 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparisons of the original pseudo-static results with the amended pseudo-dynamic results.  
plotted in Figures 7-11, respectively. The rows of upper and lower step anchor cables are: \(\mathbf{n}=1\), \(\mathbf{m}=1\). And the corresponding fundamental parameters are: \(h_{ui}=9\), \(h_{lj}=3\), \(\delta_{ui}=\delta_{lj}=15^{\circ}\), \(T_{ui}=T_{lj}=40\) kN, \(H=12\) m, \(\sigma_{c}=10\) Mpa, and \(\gamma=25\) kPa.
# Measuring and Modeling the Polarized Upwelling Radiance Distribution in Clear and Coastal Waters
## 2 Methods
### Model-Data Comparison  
Visual comparisons provided a qualitative check that the Stokes vector components from our RT model were in agreement with the camera data. Quantitative comparisons between the model and data were performed by computing the model-data difference every \(10^{\circ}\) in nadir from \(0^{\circ}\) to \(80^{\circ}\) and every \(30^{\circ}\) in azimuth from \(0^{\circ}\) to \(180^{\circ}\). Thus, the difference in \(DOLP\), \(DOLP_{diff}\), between each model output, \(DOLP_{model}\), and the corresponding average PolRADS or DPOL image, \(DOLP_{data}\), was computed at 63 angles using Equation (15).  
\[DOLP_{diff}(\theta,\phi)=DOLP_{model}(\theta,\phi)-DOLP_{data}(\theta,\phi) \tag{15}\]  
where \(DOLP_{model}(\theta,\phi)\) and \(DOLP_{data}(\theta,\phi)\) are the \(DOLP\) predicted by the RT model and measured by the corresponding average PolRADS or DPOL image, respectively, at the same 63 nadir and azimuth angles.  
Figure 1: Non-zero, normalized Muller matrix, \(S\), elements for Rayleigh scattering (blue), V-F (red), and Mod-V-F (black).
# Augmented Reality and Machine Learning Incorporation Using YOLOv3 and ARKit
## 6 Results  
**Synthetic dataset performance on YOLOv3**. We conducted different experiments to evaluate our proposed method. All of the experiments used Python 3.8 to train the algorithm on the Linux system with Intel i7-9700F 3.0 GHZ CPU and Nvidia RTX 2080 Super (8 GB memory). We trained our synthetic data with two different YOLO models: YOLOv3 416 for 20 h and YOLOv3 tiny for 13 h. The training loss results are shown in Figure 15, and the mean average precision values (mAP and mAP50) are presented in Figure 16. The results indicate that the YOLOv3 model yields more than 80% in mAP and more than 55% in mAP50 when utilising our synthetic dataset.  
**Comparison with other dataset**. We also compare our synthetic dataset with MS-COCO [35], using the same YOLO family model, presented in Table 4. MS-COCO is a famous dataset for testing the performance of many object detection models. However, the data are collected and labelled manually. We would like to find out the performance of  
Figure 16: YOLOv3 training mean average precision (mAP) and mean average precision (mAP50) curves.  
Figure 15: The loss curve during training of YOLOv3.  
the COCO dataset and our synthetic dataset when they are feeding into the same YOLO models. This experiment is believed to present a general picture of the difference in performance between two different types of dataset generation techniques. Our dataset produces higher performance values while maintaining the same detection time at 0.01 s, which is equivalent to a real-time detection standard. Significantly, the mAP and mAP50 values almost doubled when we used our dataset for deep neural network training. These results indicate that our proposed method is effective and can minimise outliers and noise that could cause a reduction in prediction performance.  
**Images prediction under different lighting conditions**. The first experiment shows that the YOLOv3 416 and tiny are the most suitable models used for mobile devices. We compared their performance under the following natural lighting conditions:  
* Normal indoor light;
* Low light or dark background;
* Direct artificial light or high-contrast lighting.  
The experiment outputs are shown in Figure 17. The models can detect objects very well under good lighting conditions with an average precision of 80%. YOLOv3 416 could classify objects under poor lighting conditions; however, YOLOv3 tiny could not (Figure 17b).  
Figure 17: The input real-time images (left column) and detection results under different lighting conditions for the YOLOv3 model (middle column) and YOLOv3 tiny model (right column). (**a**) Indoor lighting; (**b**) Low lighting; (**c**) High contrast lighting.
# An Ordered Universal Accumulator Based on a Hash Chain
## 3 Hash Chain Accumulator Scheme
### Scheme Properties
#### 3.2.2 Batch Verification  
Batch verification is a feature unique to hash chain structures. Based on the characteristics of the hash chain structure, the accumulator value of the accumulator set \(X=\{x_{1},x_{2},\ldots,x_{n}\}\), \(n\in\mathbb{N}\) can be denoted by \(\mathfrak{Acc}_{n}=H\)(\(H\)(\(\ldots\)\(H\)(\(H\)(\(\mathfrak{Acc}_{0}\),\(x_{1}\))\(\|x_{2}\))\(\|\ldots\))\(\|x_{n}\)). According to Proposition 1, for the hash chain accumulator model at any point in time, the members and order of a given accumulator value are uniquely determined. If one hash chain node in the model is confirmed (by participants other than the administrator), the correctness and consistency of all nodes before that node can be confirmed. This property can be applied in the field of communication transmission, as described below.  
**Message Hash Chain (MHC)**: The two communicating parties negotiate a message signature interval of \(d\) before transmitting the message. The message sent by the sender is denoted by \(p_{i}=\{msg_{i},HC_{i},sig_{i}\}_{1\leq i\leq n}\), where \(msg\) is the message payload, and \(sig\) is the signature of the current hash chain node value and is signed only when the signing interval or receiver requirements are reached. The message transmitted by the sender is synchronized with the computation of its built hash chain node; that is, the hash chain \(HC\) is calculated based on the message \(msg\) to be sent. And, the sender encapsulates the pair \((msg,HC)\) into the packet \(p\) and then sends it to the receiver. When the negotiated signing interval is reached or the receiver requires authentication, the sender signs the packet. If the receiver can verify the correctness of the hash chain node value and signature, it can ensure the correctness and non-repudiation of the previously transmitted messages (including this time).
# Investigation of Microseismic Characteristics of Rock Burst Based on Fractal Theory
## 2 Mining Conditions and Methods
### IMS System  
The typical network composition and structure of the IMS microseismic monitoring system are shown in Figure 1. Microseismic sensors first receive various microseismic signals from the ground, transmit the analog signals to the collector for preprocessing, such as digital-to-analog conversion and data over-waves, and then send the digital microseismic signals to the modem. Finally, the modem transmits the microseismic signals of the underground sensors to the surface monitoring center computer through a fiber-optic cable for automatic recording and processing.
## 5 Discussion
### Comparison between X-Band and C-Band  
Only one year is available to investigate the comparability of X- and C-band. Similar rates are, however, observed but only after offset correction for the early thaw period. Results agree for different landcover types which are characterized by differences in evolution of subsidence throughout the thaw period (Figure 9). Previous studies which compare different wavelengths over permafrost have been limited to discussion of spatial patterns due to the observation timing differences [4; 19]. We demonstrate that the correction with degree-days of thawing allows for a comparison, but at least one year with early season acquisitions is required to determine the correction factor (early season subsidence rate). The applicability is also limited to records with small temporal offsets (relative to the beginning of thaw) as the rate can change throughout the season. This is for example the case when DDT exceed average values for central Yamal.
# Adaptive Hybrid Storage Format for Sparse Matrix-Vector Multiplication on Multi-Core SIMD CPUs
## 4 Our Approach
### Format Conversion  
We split the sparse matrix, A, into two sub-matrices, which are initially stored in the COO format. We then convert the left sub-matrix to the SELL-4-8 format, as shown in Figure 4. Here arrays val and col_idx are transposed from the row-major order to the column-major order. After sorting the rows of the matrix by the number of non-zero elements, we use array trpos in the SELL-C-\(\sigma\) format to the original row indices. We set \(C=8\) for Intel Knights Landing with 512-bit SIMD units, \(C=4\) for both AMD EPYC and Phytium 2000+. To reduce zeros entries, we set the sorting scope \(\sigma=8\). As indicated by the arrow in Figure 4, the matrix values and column indices are accessed in the column-major order. The SELL-C-\(\sigma\) format divides value and column index into \(N/C\) blocks, with each having \(C\times K\) elements.  
The right compressed sub-matrix in CSR can be directly converted into the CSR5 format. As shown in Figure 4, we partition the non-zero entries to two \(4\times 4\) tiles. When performing SpMV operations, a compute core can deal with multiple tiles, and each SIMD lane of the core can work on a column of a tile [8]. For each tile, CSR5 introduces a tile pointer tile_ptr, a tile descriptor tile_desc and reuses the three CSR data structures (value, row_ptr, and col_idx). Similar to the SELL-C-\(\sigma\) format, the val data and the col_idx data are transposed from the row-major order to the column-major order.
# Implementation of MS Circle Map in Digital Image Encryption
###### Abstract  
Digital data protection is crucial to prevent unauthorized modifications and tampering. A secure, reliable, and efficient encryption technique is needed to safeguard digital images. This paper proposes a novel MS Circle Map-based image encryption algorithm, integrating chaotic dynamics for enhanced security. The encryption process begins by transforming the plain image matrix into a row vector. A secret key is then used as the initial condition for the MS Circle Map to generate a chaotic keystream. The encryption is performed through pixel diffusion using an XOR operation between the pixel intensity vector and the keystream, ensuring high randomness. The proposed method features a large key space, high key sensitivity, and strong resistance to brute force, statistical, and differential attacks. Performance evaluation through key space analysis, initial value sensitivity, entropy, correlation coefficient, NPCR, and UACI shows that the encrypted image using MS Circle Map has strong security properties. Meanwhile, the quality test results based on MSE and PSNR values confirm that the decrypted image is exactly the same as the original image.  
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