esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient LDPC Encoder Design for IoT-Type Devices
### This Research Contributions  
In the research presented in this paper, we investigate how one of the modern and power-efficient coding schemes--namely, a quasi-cyclic (QC) subclass of LDPC codes--can be applied in a device equipped with a microcontroller. The great error-correction performance of LDPC codes can then guarantee a power-efficient usage of the communications channel, regardless of the transmission medium and protocols used.  
While the number of research works, including the mentioned above, involve high-throughput hardware implementations, there is a lack of LDPC codec development for the case of computationally constrained, low-power CPU devices. In this context, the research presented in this paper was initiated.  
In this work, an IoT system is considered, in which the energy efficiency, due to the utilization of an advanced ECC coding, is mostly desired in an uplink direction, from the microcontroller-based IoT node, which is usually battery powered, to the IoT gateway. Therefore, the focus is on the LDPC encoding procedure, which is formulated and implemented in a low-cost CPU device. We present an efficient encoding scheme developed for a microcontroller implementation, with elementary operations corresponding directly to a CPU instructions, as well as the experimental results showing the computing time and energy reduction that can be then achieved. The contributions can be listed as follows:  
1. We show how the Richardson-Urbanke encoding algorithm [43] utilized for QC-LDPC codes can be decomposed into simple, repeated operations of cyclic shifts and GF(2) additions.  
2. We present a CPU implementation of this encoding with a QC-LDPC matrix representation that can be efficiently stored in the system's RAM.
3. We provide the experimental results of the implementation in comparison with other classic block ECC schemes: the RS (Reed-Solomon) and BCH (Bose-Chaudhuri-Hocquenghem) codes. Comparisons are made in terms of memory usage, block encoding time, energy savings and error-correction performance.  
For an IoT system, short-to-moderate block length codes are likely to be utilized, due to the short message lengths; therefore, we do not consider the application of state-of-the-art 5G NR codes, but instead we base the experimental part on the proprietary codes designed with a graph optimization algorithm [24].  
The paper is organized as follows. In Sections 2 and 3, we provide preliminary information about our system model of ECC in the context of IoT systems and basic definitions of LDPC coding. In Section 4, the Richardson-Urbanke encoding algorithm with a formulation specifically for QC-LDPC codes is presented. Then, in Section 5, we provide the details of the proposed microcontroller application. Finally, in Section 6, we provide the numerical results, and the paper is concluded in Section 7.
## 3 Results and Discussion
### Catalytic Application in the Solvent-Free Friedel-Crafts Alkylation Reaction
#### 3.2.2 Effects of Reaction Time on the Friedel-Crafts Alkylation Reaction of p-Xylene with BnCl  
The reaction time is one key factor that can be used to determine the BnCl conversion and DPM selectivity of this reaction. Therefore, the influence of reaction time is investigated under 20 mole % 5c at a reaction temperature of 80 \({}^{\circ}\)C. The reaction times are 10, 20, 30, 60, and 120 min, and the results are presented in Figure 1. It can be clearly observed that the Friedel-Crafts alkylation of p-xylene with BnCl begins to react at 10 min, and that the BnCl conversion increases rapidly with increases in the reaction time, where for 10 to 30 min, the BnCl conversion in 10 min is 22.1%, and at 20 min, it is 41.6% until the equilibrium point (BnCl conversion: 98.6%) is reached at 30 min. The BnCl conversion raises slightly to 98.9% at 60 to 120 min. The DPM selectivity reaches 91.7% at 30 min, and then slightly decreases to 82.9% at 60 min and to 83.6% at 120 min. These results may be due to a uniform molecular collision with the increase in the reaction time [56]. Hence, the optimal reaction time in this reaction is 30 min.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Entry a** & **B-L MIL** & \multicolumn{2}{c}{**Molar Ratio of**} & **Molar Ratio of** & **BnCl Conversion** & **DPM Selectivity** \\  & **II/BnCl (mole \%)** & **p-xylene: BnCl** & **(\%) b** & **(\%) b** \\ \hline
1 & **5a** & 20.0 & 2:1 & 51.4 & 95.6 \\
2 & **5b** & 20.0 & 2:1 & 20.0 & 41.1 \\
3 & **5c** & 20.0 & 2:1 & 98.6 & 91.7 \\
4 & **5d** & 20.0 & 2:1 & 5.0 & 99.5 \\
5 & **5c** & 0.1 & 2:1 & 14.6 & 99.8 \\
6 & **5c** & 0.3 & 2:1 & 96.0 & 93.0 \\
7 & **5c** & 0.5 & 2:1 & 98.9 & 88.4 \\
8 & **5c** & 0.7 & 2:1 & 99.6 & 81.1 \\
9 & **5c** & 1.0 & 2:1 & 99.2 & 91.0 \\
10 & **5c** & 1.0 & 4:1 & 95.0 & 93.0 \\
11 & **5c** & 1.0 & 6:1 & 99.1 & 96.0 \\
12 & **5c** & 1.0 & 8:1 & 98.6 & 98.3 \\
13 & **5c** & 1.0 & 10:1 & 99.3 & 99.3 \\
14 & **5c** & 1.0 & 1:1 & 99.9 & 75.0 \\ \hline \hline \end{tabular}  
* All entry conditions are at a reaction temperature = 80 \({}^{\circ}\)C and a reaction time = 0.5 h. b BnCl conversion and DPM selectivity values are based on GC.  
\end{table}
Table 1: The results of the B-L MILs 5a–d application for catalysis in the Friedel–Crafts alkylation of p-xylene with BnCl. DPM: diphenylmethane.  
Figure 1: The effects of reaction time on the Friedel–Crafts alkylation reaction of p-xylene with BnCl (under 20 mole % 5c, a reaction temperature of 80 \({}^{\circ}\)C, and a molar ratio of p-xylene:BnCl = 2:1).
# In-Depth Analysis of GAF-Net: Comparative Fusion Approaches in Video-Based Person Re-Identification
## 5 Experimental Results
### Strengths and Limitations  
GAF-Net demonstrates superior performance in synthesizing appearance and motion cues, outperforming existing methods across various accuracy metrics. Its integration of skeletal gait information enhances lightweight architectures, indicating potential for efficient implementation. However, the method's effectiveness is heavily dependent on reliable gait feature extraction, leading to performance discrepancies between controlled and real-world environments. GAF-Net shows sensitivity to video sequence length, performing optimally with longer, uniform sequences but struggling with shorter, variable ones. This characteristic potentially limits its applicability in scenarios requiring rapid identification from brief video segments (up to 10 frames). These limitations highlight areas for future improvement, particularly in developing more robust gait feature extraction techniques capable of handling the variability inherent in real-world surveillance footage and in optimizing the method for shorter video sequences and reduced computational load.
## 4 Data-Driven Prediction Model
### Deep Learning
#### 4.3.2 Time-Series  
A Recurrent Neural Network (RNN), or LSTM, is a feed-forward network with a backpropagation loop. LSTM provides additional benefits by holding onto the value of the previous output for short periods, serving as a small part of a network's memory that supports feedback analysis. Dynamic time-series models, such as the Autoregressive Integrated Moving Average (ARIMA), are the most widely used time-series approach. Other models, such as the Autoregressive (AR), Moving Average (MA), and Autoregressive Moving Average (ARMA), are subclasses of the ARIMA model. [PERSON] and [PERSON] suggested a popular version of the ARIMA paradigm, the Seasonal ARIMA (SARIMA), for seasonal time-series forecasting. The ARIMA model's popularity is primarily due to the associated Box-Jenkins methodology for constructing the best model, in addition to its ability to represent a wide range of time-series effortlessly. The complex variables and combinational inputs are the most common utilisation examples. Vegetative index, weather, and climatic data are used to track the progress of palm oils. Accordingly, LSTM is used to analyse the data for the attribute variables to better understand the time-series data. It yields an estimated 83 per cent of the crop [77]. It produces an estimated accuracy of 83 per cent of palm oil age detection using the Landsat time-series [77]. The LSTM model possesses two layers, one for processing genotypes and another for environmental factors. The two-layered approach provided a more effective process and straightforward suggestions with a value of 8.52 of RMSE [138]. The palm oil's satellite-based SIF was analysed to determine the vegetative index, exhibiting 0.69 of \(r^{2}\)[139]. The LSTM method combined price and time-series data to better estimate yield. The one feedback connection of the RNN can be directly processed by the one feedback connection data [140]. Furthermore, a low training time indicates that the processing time and storage space efficiency were due to this data type. The predicted palm oil yield suggested 2.7098% of MAPE. Table 8 describes the time-series forecasting based on previous literature.
# A Fluid-Structure Coupling Analysis of a Far-Field Flat Mirror for AliCPT-1 Telescope Calibration
## 2 Structure of the Far-Field Flat Mirror  
The FFF mirror, composed of a highly reflective mirror, reinforcing frame, and supporting bracket, is designed to ensure efficient high reflection and radiation transmission.  
Various solutions were researched and tested. Experimental results revealed that achieving a planarity of 0.1 mm for a 1 m \(\times\) 1 m honeycomb panel was extremely complex and quite difficult for the FFF mirror with 2 m width by 3 m height. Glass mirrors with metallic coating are not suitable due to both the complex manufacturing process and high costs. Considering process, performance, and cost, we chose ultra-flat aluminum panels with the reinforcing frame glued on the back as the solution. The panels are supported by 12 rods connected to six fixing points. The FFF reflector's final dimensions are 3108.2 mm in height, 3000 mm in length, and 2791 mm in width. The back of the reflector is constructed with a frame consisting of 14 aluminum square tubes measuring 150 \(\times\) 100 \(\times\) 3 mm, as depicted in Figure 3.  
The flatness requirement of the FFF mirror within the context of optical components should be better than 0.1 mm (\(\lambda\)/20). This stringent requirement is necessary because a rough reflective mirror may result in diffuse reflection instead of proper reflection, thereby  
Figure 3: Far-field flat mirror structure.  
Figure 2: The FFF is installed on the mount structure.  
rendering it ineffective for beam calibration. The signal being calibrated is at a frequency of 150 GHz, with an approximate wavelength of 2 mm. Therefore, in order to ensure its functionality, the flatness requirement for the FFF mirror should be less than 0.1 mm. There are several factors that can affect the flatness of the FFF mirror, including machining precision, weight, installation errors, and wind loading.  
Achieving high flatness of the FFF mirror was challenging due to its large size. The ultra-flat aluminum panel of the FFF mirror is 0.08 mm. The panel (mirror) is bonded to a welded and reinforced frame, the surface flatness of which is within 0.05 mm. Besides 12 support rods that are adjustable, we designed adjustment points on the back crossbeam to control the central depression. A laser tracker was employed to measure the mirror and accordingly adjust the mirror flatness, mainly by loosening or tightening the stud of the back crossbeam. A flatness RMS value of 0.05 mm was achieved in the factory by comprehensive design, process, and assembly process measures. To validate the response of the mirror structure under extreme conditions, the finite element method will be used to analyze the far-field calibrated mirror structure in this paper.
## 4 Proposed Scheme
### Phase 2: Sensing and Reporting
#### 4.2.1 Encryption Algorithm  
As mentioned in the introduction, the scheme uses a homomorphic encryption algorithm to encrypt data so that it can be aggregated. Moreover, since encryption algorithms are usually expensive and complex to compute, we have used a very lightweight encryption algorithm that was proposed by [PERSON] et al. [42]. The encryption function is as follows \(c\ =\ Enc(d\), \(k\), \(M)\ =\ d\ +\ k(modM)\), where \(d\) is the data to encrypt, \(k\) is the secret key for the node, and \(M\) is the modulus.
# Emerging Roles of Toxin-Antitoxin Modules in Bacterial Pathogenesis
###### Abstract  
Toxin-antitoxin (TA) cassettes are encoded widely by bacteria. The modules typically comprise a protein toxin and protein or RNA antitoxin that sequesters the toxin factor. Toxin activation in response to environmental cues or other stresses promotes a dampening of metabolism, most notably protein translation, which permits survival until conditions improve. Emerging evidence also implicates TAs in bacterial pathogenicity. Bacterial persistence involves entry into a transient semi-dormant state in which cells survive unfavorable conditions including killing by antibiotics, which is a significant clinical problem. TA complexes play a fundamental role in inducing persistence by downregulating cellular metabolism. Bacterial biofilms are important in numerous chronic inflammatory and infectious diseases and cause serious therapeutic problems due to their multidrug tolerance and resistance to host immune system actions. Multiple TAs influence biofilm formation through a network of interactions with other factors that mediate biofilm production and maintenance. Moreover, in view of their emerging contributions to bacterial virulence, TAs are potential targets for novel prophylactic and therapeutic approaches that are required urgently in an era of expanding antibiotic resistance. This review summarizes the emerging evidence that implicates TAs in the virulence profiles of a diverse range of key bacterial pathogens that trigger serious human disease.  
toxin-antitoxin complexes; biofilm formation; persistence; pathogenesis; virulence; antibiotic resistance
## 2 SiF Retrieval Method and Setup
### Monthly SiF Maps  
SiF is retrieved for GOME-2A pixels that have solar zenith angles below 70\({}^{\circ}\) and for which the FRESCO effective cloud fraction is below 0.4. This means that if FRESCO is in snow/ice mode, pixels are excluded from the retrieval. Fluorescence retrievals are rejected whenever their root-mean-square residual is larger than 1%. Finally, retrieved SiF values are averaged into 0.5\({}^{\circ}\) by 0.5\({}^{\circ}\) latitude-longitude grid boxes for every month. An overview of the algorithm flow is given in Figure 1. In SiF NASA (v26), a first bias correction to SiF maps is made based on retrieved SiF values where zero SiF is expected ([59]; see also [37]).  
Figure 1: Flow diagram of the GOME-2 SiF retrieval algorithm. One year of fluorescence-free reference pixels is selected from the GOME-2 Level-1b data as input for the Principal Component (PC) algorithm. The 35 most significant PCs are used to build a cost function and minimization of the cost function for each pixel yields 41 fit parameters (one fluorescence parameter, five surface albedo polynomial coefficients and 35 PC coefficients). The peak fluorescence at 737 nm is then averaged monthly in 0.5\({}^{\circ}\) by 0.5\({}^{\circ}\) latitude–longitude grid cells resulting in the final Level-3 product.
## 6 Experimental Verification and Analysis  
To verify the feasibility of the CCFD scheme in the BDS-3 ISL scenario and analyze the ranging performance in CCFD, this section will conduct experimental simulations and analysis, mainly including the following four parts: (1) building the BDS-3 ISL simulation scenario; (2) analyzing the topological characteristics of the BDS-3 ISL, including inter-satellite visibility, inter-satellite distance, and its rate of change; (3) analyzing link budget to verify the feasibility of the CCFD scheme; (4) building a digital simulation system for two-way inter-satellite ranging, and analyzing the inter-satellite ranging performance in CCFD.
# A Multidimensional Data Collection and Edge Computing Analysis Method
## 2 System Hardware Design
### Edge Computing Module  
We desire to conduct data analysis and research on the data retrieved. If the data is transmitted to the cloud for analysis, the results of the data analysis will not be fed back to the front end of the Internet of Things and respond in time. To resolve this problem, edge computing is proposed. The hardware structure of the data processing end is illustrated in Figure 1c. The low power consumption, edge and low latency of edge computing make it a good platform for real-time data processing. As an intermediate link between the sensor end and the cloud, the results of the data processing are transmitted to the cloud as a type of storage, and the processed results are fed back to the sensor end in time.  
There are many embedded platforms for edge computing, such as Raspberry Pi [33], Intel Movidius Neural Compute Stick (NCS) [34], and Nvidia's Jetson Nano and Jetson AGX Xavier [35]. We evaluate each edge computing platform to finally select one that meets the algorithm requirements and data processing volume. The Jetson Nano was finally selected as the edge computing platform on which the algorithm would run. NVIDIA announced the Jetson Nano hardware architecture at the 2019 NVIDIA GPU Technology Conference, and it provides the power of modern AI with full software programmability on a hardware platform. With a quad-core 64-bit ARM CPU and 128-core integrated NVIDIA GPU, the Jetson Nano can provide 472 G FLOPS of computing performance with 5 W/10 W low power mode and 5 V DC input. The designed algorithm model is ported to the edge computing platform to complete effective monitoring.
## 5 Analysis of Results and Field Testing
### Results of The Experiment
#### 5.3.2 Subjective Comparison of Segmentation Performance  
The SegFormer-UNet proposed in this paper demonstrates the best performance in terms of objective evaluation metrics. To establish a perceptual understanding of the segmentation effect of the network model, this paper selects five representative images from the test set for demonstration, as shown in Figure 10. From top to bottom, the images are the original image, the mask image, our model segmentation effect, the FCN segmentation effect, the UNet segmentation effect, the DeepLab V3+ segmentation effect, the PSPNet segmentation effect, the HRNet segmentation effect, and the SegFormer segmentation effect. From the segmentation effect, it can be observed that our model has the best segmentation effect on the edge position.
###### Abstract  
To obtain low-phase-noise microwave signals with a widely tunable frequency range, an optoelectronic oscillator (OEO) was constructed on the basis of an optically injected semiconductor laser (OISL) for the generation of high-quality microwave signals. Our OEO relied on the effect of wavelength-selective amplification and the period-one (P1) oscillation under optical injection. The signal's frequency stability, side-mode-suppression ratio (SMSR) and linewidth were optimized by the subharmonic microwave modulation technique in the OEO loop. The experimental results showed that the frequency of the signal obtained by the proposed OEO could be tuned up to 18 GHz. Using the dual-loop OEO structure, the SMSR was increased to 55 dB. Moreover, the phase noise of the obtained microwave signal was lower than \(-81\) dBc/Hz at 1 kHz frequency offset and \(-119\) dBc/Hz at 10 kHz frequency offset. This was achieved by introducing subharmonic microwave modulation in the OEO loop, respectively. Furthermore, via the utilization of a Fabry-Perot laser diode (FP-LD) in the proposed structure, a dual-loop OEO with different dual-frequency configurations (which could be tuned up to 12 and 18 GHz) was obtained.  
optoelectronic oscillator; dual-loop; dual-frequency; optically injected semiconductor laser; phase noise +
Footnote †: journal: Applied Sciences
## 4 Flared SBF Antenna with a Superstrate Lid
The antennas with the 1 mm lid thickness at 5.6 GHz had peak gains of 17.01 dBi and 16.8 dBi for the PLA and ABS lids, respectively. The worst case cross-polarization ratio results for the flared antenna with the lids are shown in Figure 10. For the PLA lid, there is no observable difference between the results of the no lid case and the varying lid thickness cases between 4.4 to 5.4 GHz. Between 5.4 to 5.8 GHz, the minimum cross-polarization increased from \(-\)21 dB to \(-\)17 dB as the lid thickness increased from 1-4 mm. This is the same case for the ABS lid between 4.4 to 5.4 GHz, but the increase in the cross-polarization ratio is higher as it increased to \(-\)13 dB for a 4 mm lid thickness. Figure 9: Simulated peak gain result for the flared SBF antenna with: (**a**) PLA lid; (**b**) ABS lid.
## 3 Results and Discussion
### Surface Structuring of MgO-MgSZ Eutectic by Laser Remelting and Phase Dissolution in SBF
However, the poor HAp crystallization on the surface of zirconia does not exclude the possibility of protein and cell adhesion promoting stable bonding between implant and the living bone. In the interaction with cells and tissues, the topography of the bioceramic surface can influence the absorption of proteins from body fluids influencing cell attachment [24]. By avoiding the collapse of the zirconia sheets and with the formation of small pores on the surface, in the range of 0.2 \(\mu\)m, the wettability of the biomaterial's surface could be modulated and with it its biological response as reported by [PERSON] and [PERSON] [25]. Both the surface topography and the surface free energy are the key factors which mostly influence on the wettability of a surface. The surface of the zirconia, which presents hydrophilic features, will present higher hydrophilic characteristics after surface roughening. Biological fluids prefer to spread on a hydrophilic surface, providing more area for  
Figure 10: FESEM images of eutectic samples of MgO–MgSZ superficially remelted with different laser power (increasing power from left to right: 15, 25 and 30 W, (**a–c**), respectively) and kept in SBF for 4 weeks at 37 \({}^{\circ}\)C.  
Figure 9: FESEM image of a cross section of MgO–MgSZ eutectic after soaking in SBF for 4 weeks. Right: interface detail. Below: Chemical composition obtained by EDS of a region in the porous layer (**1**) and in the eutectic zone (**2**). protein adsorption and interaction with cell receptors. However, it is also possible to make the surface hydrophobic by laser treatment as described by [PERSON], who found that laser-treated surfaces composed of micro/nano grooves improved the surface hydrophobicity of yttria-stabilized zirconia [26]. However, in this type of surface, the contact area between cell and surface is reduced as air bubbles may be confined in between, resulting in a lower adsorption of proteins. To provide more information about this behavior, a comprehensive study on the effect of the topography on the wettability of the surface is being carried out in our samples. This method could be considered a soft way to obtain a surface with microporosity avoiding any mechanical or thermal cracking like those caused by other machining techniques such as sandblasting or laser ablation. It is known that thermal damage generated by laser texturing or laser ablation reduces the mechanical strength of the material [27]. Etching techniques do not apply stresses but could make some adverse chemical changes on the surface of the material and therefore should be taken into consideration when designing surface textures for certain biological functions. Microporosity is an important parameter, as it determines the dissolution properties, protein adsorption and surface area of bioceramics. On the other hand, free Mg\({}^{2+}\) ions released from the material are considered as positive influence on various cell reactions in the human body such as proliferation, osteogenic differentiation, and matrix formation [28].