esa-sceva/satcom-chunk-collection huggingface dataset:
# Sitting Posture Recognition Systems: Comprehensive Literature Review and Analysis
## 3 Sensing Technologies
### Image Sensors
#### 3.6.1 Video Camera-Based Technologies
Parameters such as wavelength, attenuation in the atmosphere, the influence of optical reflection, and optical transmission to glass have increased the accuracy of the identification systems when using an infrared camera. Pose identification of drivers has several limitations. The proposed algorithm uses the following steps to find out the driver's pose via infrared camera data:  
* Extract the driver's skin area using image-processing techniques. * Extract the face skin area using the labeling process. * Find out possible feature areas such as nose, mouth, and ears, because this place has a lower temperature than other places. * Classification of the driver's face based on the direction. Extract other features of the driver rather than the face. Studies such as [110; 113; 114; 115; 116; 117; 118; 119; 120; 121; 122] used image and video frames as input to recognize sitting posture. In this work, object detection, tracking, and feature extraction are used to create an input matrix for the classification. Depth images are employed alongside RGB images to enhance understanding and analysis of visual data. There are several works based on depth images in sitting posture recognition, such as [68; 110; 116; 118; 119; 121; 123; 124; 125; 126; 127; 128], which collected data using smartphone cameras and static cameras. Experiments show that camera-based applications have a greater number of features that increase the accuracy of the recognition. Infrared cameras have low resolution and reduced color information if compared to regular videos. It affects object detection and feature quality in the processing phase. In addition to that, infrared cameras are sensitive to environmental changes, which affect their performance. Infrared cameras are expensive, and high-resolution ones have limited availability. Sitting posture estimation can be complex with infrared cameras when there is an overlap between moving objects. High-resolution and depth-sensing cameras are very suitable for automated pose detection. Consistent and well-distributed lighting increases the robustness of the estimation.
# A Novel DFA on AES: Based on Two-Byte Fault Model with Discontiguous Rows
###### Abstract  
Differential fault attack (DFA) is a distinctive methodology for acquiring the key to block ciphers, which comprises two distinct strategies: DFA on the state and DFA on the key schedule. Given the widespread adoption of the Advanced Encryption Standard (AES), it has emerged as a prominent target for DFA. This paper presents an efficient DFA on the AES, utilizing a two\(-\)byte fault model that induces faults at the state with discontiguous rows. The experiment demonstrates that, based on the proposed fault model, the key for AES-128, AES-192, and AES-256 can be successfully recovered by exploiting two, two, and four faults, respectively, without the need for exhaustive research. Notably, in the case of AES-256, when considering exhaustive research, two (or three) faults are needed with [23] (or \(2^{16}\)) exhaustive searches. In comparison to the currently available DFA on the AES state, the proposed attack method shows a higher efficiency due to the reduced induced faults.  
differential fault attack; advanced encryption standard; two\(-\)byte fault model; information security +
Footnote †: journal: Applied  
2023
# Corn Plant In-Row Distance Analysis Based on Unmanned Aerial Vehicle Imagery and Row-Unit Dynamics
###### Abstract
The creation of the optimization model for efficient irrigation is related to local evapotranspiration potential, which is strongly related to plant population. [PERSON] et al. [18] tested the plant counting accuracy under different spatial resolution setups. The results of the study showed that the methodology used allowed counting plants with an accuracy of 95% and a spatial resolution of 8 mm/pixel. [PERSON] et al. [19] attempted to develop computer vision techniques that enable automatic com plant counting based on a deep learning approach, using images of several corn crops obtained with an unmanned aerial vehicle (UAV) equipped with a low-cost RGB camera. The authors concluded that the best results achieved for corn plant counting were obtained with plant densities of around 70,000 plants/hectare at the earliest growth stage (V4) and a flight height of 20 m. The accuracy of automatic plant detection from RGB images is unclear in the presence of weeds. They compromise the clarity of the plant's distinctive features. A few studies have been carried out [20; 21] with the aim of determining plant in-row distance variability by combining traditional methods for seeding quality evaluation. [PERSON] et al. [22] presented a UAV-based image-processing algorithm that was developed to calculate maize plant distances. They underlined that the combination of area and shape parameters is useful for distinguishing maize from other vegetation (weeds). Nonetheless, the processing of UAV imagery for early crop growth stages remains challenging due to the crop size and complex backgrounds [23]. Traditional computer vision techniques for plant detection in UAV images rely on methods such as thresholding-based image segmentation applied to vegetation index masks, followed by template matching (TM) approaches [14; 24]. Recent developments in deep learning have shown significant promise in accurately separating plants from the soil background in low-altitude UAV images [18; 24; 25]. Segmentation of different types of crops is successfully addressed using various deep-learning models: Segnet and VGG-UNet for corn and tobacco [25], U-Net for corn and purple rapeseed leaves [26], and LeNet for corn [27]. All cited studies were exclusively centered on the subject of plant identification and quantification, neglecting to delve into the underlying factors that may contribute to irregularities in plant spacing. As a result, we expanded our research from [28] by examining the dynamics of the seeding unit and its impact on the identified distances between plants. The established research objectives are as follows:  
1. Develop a deep learning (ML) algorithm for accurate plant detection;
2. Analyze the seeder performance based on images captured by the unmanned aerial vehicle (UAV);
3. Examine the vibrational dynamics of the seeding unit and its impact on seeding accuracy through machine learning-estimated corn plant spacings.
# Smooth Optimised A*-Guided DWA for Mobile Robot Path Planning
## 2 Global Path Planning
### Comparative Analysis of Improved A* Algorithms  
To further verify the path planning performance of the improved A* algorithm in different environments, and at the same time to enhance the statistical validity of the experimental results, this paper conducts additional simulation experiments under random obstacle scenarios in addition to the original specific simple and complex environments. Specifically, two types of environments, simple (approximately 20% obstacle coverage) and complex (approximately 25% obstacle coverage), are constructed in a 30 \(\times\) 30 grid map, with 30 randomly generated obstacle layouts for each type. In each group of scenarios, the starting point is set at (0, 0) and the end point at (29, 29). Both the traditional A* algorithm and the improved A* algorithm are employed for path planning, with the key metrics including the number of path points, turns, non-smooth turns, and the path lengths being recorded. The mean and standard deviation are then calculated. Figures 3 and 4 present comparisons between the traditional A* algorithm and the improved A* algorithm* in randomly generated simple and complex environments, respectively, while the quantitative results are shown in Table 1.  
The experimental results are shown in Table 1, demonstrating that the improved A* algorithm exhibits significant advantages in both types of environments. Compared with the traditional A*, in the simple environment, the number of path points is reduced by approximately 86.2% on average, the number of turn points is reduced by approximately 43%, the non-smooth turn points are all eliminated, and the path length is reduced by approximately 2.1% on average. In the complex environment, the number of path points is reduced by approximately 88% on average, the number of turn points is reduced by approximately 61.4%, the number of non-smooth turn points is 0, and the path length is reduced by approximately 15.6% on average. Furthermore, the reduction in standard
# Technique Evolutions for Microorganism Detection in Complex Samples: A Review
## 2 Current Detection Methods
### Cellular Components Detection and Analytical Methods
#### 2.3.3 Mass Spectrometry  
By exposing microbial cells to a laser beam in a vacuum, its molecules are ionized and vaporized. Recording the time of flight of the different molecules provides a mass spectrum, distinctive of the species. As for infrared spectroscopy, the mass spectrum canbe compared with known spectra for identification [31]. Matrix-assisted laser desorption-ionization time of flight (MALDI-TOF) mass spectroscopy allows for minimal fragmentation during the ionization. A growth phase on agar may be required before the mass spectrum acquisition and the culture conditions must be standardized. The method is destructive but quantitative.
# A Survey on Monitoring Quality Assessment for Wireless Visual Sensor Networks
## 4 Reviewing Content Quality
### Miscellaneous  
Several other aspects can be considered when assessing content quality. In this sense, we organized metrics that were created based on different parameters other than sharpness and perspective, defining a particular category in our proposed classification.  
Image content can be measured in terms of entropy, a metric related to the image's exposure to the correct amount of light, avoiding over and under-exposure [48; 49]. It is also possible to quantify the quality of the data gathered from a visual node through peak signal-to-noise ratio (PSNR) based metric, evaluating the ratio between the maximum possible value of a signal, which is the information of each pixel, and the power of noise that affects the accuracy of the signal representation [77; 78]. These are some interesting ways to assess visual monitoring quality in WVSN.  
Quality can be also associated with a priority metric, assigning more importance to some region of interest of an image and scoring the content based on the location of a target at the image [72]. The region of interest can also be determined based on Quality of Experience, which assigns a percent score for some region of the monitoring area according to the user experience [79].  
Applications with different requirements demand different quality metrics. In order to quantify the visual quality of mobile targets coverage applications, target speed, velocity and moving direction have been considered [73]. In an application for calibration and selection of cameras, a well-defined structure with a large central sphere surrounded by eight differently colored smaller spheres is used as reference element to determine the application quality. For this, the shape of the spheres in the gathered images are used into a fitting algorithm to infer several features such as axes length and center coordinates a general measure of ellipse fit root mean square error [80; 81].  
Another interesting approach is to use occlusion as a quality metric. In this case, it is defined the energy of monitoring, which is a composition of the amount of occluded pixels of a target and the amount of pixels of a target within the camera's field of view, expressed by the ratio with relation to the total number of target's pixels [82].  
Quality of service is other way to determine content quality. An example is requiring that the targets must be covered by a determined frame rate and quantity of pixels on targets, under the constraint of not exceeding the available resources (processing, memory and energy) [83; 84].  
Actually, the number and types of visual monitoring quality metrics vary considerably when processing the gathered sensed data. Nevertheless, since some rules are still followed when defining the perceptions of quality, we have united all these metrics into the same group.
## 5 Experiments  
In this section, we compare LPLC-HDL with the representative dictionary learning methods including D-KSVD [5], LC-KSVD [6], LCLE-DL [7], FDDL [13], DL-COPAR [16] and CLSDDL [19] on the Yale face dataset [29], the Extended YaleB face dataset [30], the Labeled Faces in the Wild (LFW) dataset [31] for face recognition, the Caltech-101 object dataset [23] and the Oxford 102 Flowers dataset [32] for object classification and flower classification, respectively. Moreover, we further compare it with the Sparse Representation based Classification (SRC) [33], the Collaborative Representation based Classifier (CRC) [34], the Probabilistic CRC (ProCRC) [35], the Sparsity Augmented Collaborative Representation (SACR) [36] and some other state-of-the-art methods on the particular datasets.  
In the proposed LPLC-HDL method, the three parameters \(\lambda\), \(\gamma\) and \(\eta\) are selected by five-fold cross validation on the training set, and their optimal values on each dataset are shown in Table 1. In addition, the atom numbers of the shared dictionary and the class-specific dictionary for each dataset are elaborated as detailed in the following experiments.
# Identification and Optimization Study of Cavitation in High Power Torque Converter
## 4 Optimized Design of Cavitation in High Power Torque Converter
### Simulation Analysis of Torque Converter Stator Optimization Scheme
Figure 12c-f display the pressure cloud and localized liquid flow vectors in the nose region of the stator in both the original scheme and Scheme 2. The pressure cloud and liquid flow vector cloud indicate that a smaller liquid inflow deviation angle, \(\Delta\beta_{Dif}\), leads to a better fit between the shape of the stator's nose in the stalled condition and the liquid inflow angle, \(\beta_{incl}\). This, in turn, significantly reduces vortices above the forward suction surface at the peak location of the stator bending. Figure 12e,f show a significant reduction in the optimized low-pressure distribution region, which is below the cavitation pressure of the hydrodynamic oil. Scheme 2 optimizes the \(P_{lowest\_D}\) while maintaining basic performance. Figure 11: Simulation results of zero-speed conditions for different schemes: (**a**) relationship between \(TR\) and \(\Delta\beta_{Dif}\); (**b**) relationship between \(P_{lowest\_D}\) and \(\Delta\beta_{Dif}\); (**c**) relationship between \(M_{bg0}\) and \(\Delta\beta_{Dif}\); (**d**) relationship between \(M_{T}\) and \(\Delta\beta_{Dif}\). The external characteristic diagram of the torque converter resulting from further simulation analysis of Scheme 2 in the full-speed domain is shown in Figure 13. It can be observed that the maximum rate of difference between the \(TR\) of the optimized Scheme 2 and that of the original scheme is 2.55% at 0.1 \(SR\) in the speed domain the range of 0-0.8. Additionally, the maximum rate of difference of the \(M_{bg}\) is 7.08% at 0.8 \(SR\). In the low  
Figure 12: Comparison of simulation results of optimization schemes: (**a**) velocity cloud of the stator for the original scheme; (**b**) velocity cloud of the stator for the optimized scheme; (**c**) local pressure map of the original scheme; (**d**) local pressure map of the optimized scheme; (**e**) original scheme streamline map; (**f**) optimized scheme flow streamline map; (**g**) original scheme the low-pressure distribution cloud; (**h**) optimized scheme the low-pressure distribution cloud. speed ratio region, the maximum difference rate of \(M_{bg}\) is only 3.53%. At a speed ratio of 0.8, the maximum difference rate of \(\eta\) is 1.89%. In the high speed ratio region of \(\geq\)0.5, the \(\eta\) of Scheme 2 is slightly lower than the original scheme. Overall, the new scheme maintains the same external characteristics as the original scheme. However, the stator's cavitation characteristics have been greatly improved.
## 4 Simulation Setup and Parameters
### Predictive Beamforming, DE-ACSW-R, and System Integration  
At every slot, the system generates the full signal environment using MATLAB 9.13, where all model equations have been developed for simulation, producing realistic base-band data for all users, including interference and channel impairments. The O-RU performs MUSIC-based AoA estimation and stores per-user AoA histories. DE-ACSW-R is applied to these histories, detecting in-window angular jumps and segmenting stable clusters for improved prediction. Real-Time Weighted Linear Regression is run at the O-RU for fast slot-level AoA prediction. At the same time, curvature-adaptive SVR is executed at the O-DU to model non-linear trajectories. Fused predictions are computed using confidence- and error-weighted logic, and a fallback to full MUSIC is triggered whenever model residuals, abrupt jumps, or low-confidence scores are detected. Beamforming weights for each user are generated based on the most recent fused AoA estimate, and multi-user spatial orthogonality is enforced through orthogonal or MMSE precoding. All key signals, predictions, and performance data are logged for post-processing and visualisation.
## Abbreviations  
The following symbols and abbreviations are used in this manuscript:  
\begin{tabular}{l l} \(A\) & Set of PV power plants \\ \(B\) & Set of BESSs \\ \(\Gamma\) & Set of EVs \\ \(\alpha\) & PV index \\ \(\beta\) & BESS index \\ \(\gamma\) & EV index \\ \(P_{\rm g,s}\) & Grid supply power \\ \(P_{\rm g,f}\) & Grid feed-in power \\ \(P_{\alpha}\) & Supply power of PV power plant \(\alpha\) \\ \(P_{\rm A}\) & Summarised supply power of all PV plants \\ \(P_{\beta,{\rm ch}}\) & Charge power of BESS \(\beta\) \\ \(P_{\beta,{\rm dc}}\) & Discharge power of BESS \(\beta\) \\ \(E_{\beta}\) & Energy content of BESS \(\beta\) \\ \(E_{\rm B}\) & Summarised energy content of all BESSs \\ \(P_{\gamma,{\rm ch}}\) & Charge power of EV \(\gamma\) \\ \(P_{\gamma,{\rm dc}}\) & Discharge power of EV \(\gamma\) \\ \(P_{\gamma,{\rm a}}\) & Discharge power of EV \(\gamma\) by driving \\ \(P_{\rm T,{\rm ch}}\) & Summarised charge power of all EVs \\ \(P_{\rm T,{\rm dc}}\) & Summarised discharge power of all EVs \\ \(E_{\gamma}\) & Energy content of EV \(\gamma\) \\ \(E_{\rm F}\) & Summarised energy content of all EVs \\ \(k\) & Discretisation timestep \\ \(t_{\rm k}\) & Time at step \(k\) \\ \(i\) & Generalised index of the components \\ \(\Delta t\) & Sampling interval \\ \(N\) & Number of timesteps \\ \(\eta_{\beta,{\rm dh}}\) & Charging efficiency of BESS \(\beta\) \\ \(\eta_{\gamma,{\rm ch}}\) & Charging efficiency of EV \(\gamma\) \\ \(\eta_{\beta,{\rm dc}}\) & Discharging efficiency of BESS \(\beta\) \\ \(\eta_{\gamma,{\rm dc}}\) & Discharging efficiency of EV \(\gamma\) \\ \(P_{\rm a,f}\) & Power forecast of PV plant \(\alpha\) \\ \(P_{\rm b,f}\) & Power forecast of load \\ \(P_{\rm j,max}\) & Maximum power of general component \(i\) \\ \(P_{\rm j}^{k}=P_{\rm j}(t=t_{\rm k})\) & Power of general component \(i\) at timestep \(k\) \\ \(E_{i}^{k}=E_{i}(t=t_{\rm k})\) & Energy content of general component \(i\) at timestep \(k\) \\ \(SOC_{\beta}\) & State of charge of BESS \(\beta\) \\ \(SOC_{\gamma}\) & State of charge of EV \(\gamma\) \\ \(C_{\beta}\) & Capacity of BESS \(\beta\) \\ \(C_{\gamma}\) & Capacity of EV \(\gamma\) \\ \(c_{\rm f}\) & Feed-in tariff \\ \(c_{\rm s}^{k}\) & Dynamic electricity tariff at timestep \(k\) \\ \(c_{i,\beta}\) & Feed-in tariff of PV plant \(\beta\) \\ \(P_{\rm g,inst}\) & Installed power of PV plant \(\beta\) \\ \(P_{\rm B,inst}\) & Installed power of all PV plants \\ \(\bf P_{\rm i}\) & Power vector of general component \(i\) \\ \(\bf E_{\rm i}\) & Energy vector of general component \(i\) \\ \(\bf x\) & State vector \\ \(\bf c\) & Cost vector \\ \(A_{\rm eq}\) & Equality constraint matrix \\ \(A_{\rm ub}\) & Upper bound inequality constraint matrix \\ \(\bf b_{\rm eq}\) & Equality constraint vector \\ \(\bf b_{\rm ub}\) & Upper bound inequality constraint vector \\ \(\bf 1\) & Lower bounds \\ \(\bf u\) & Upper bounds \\ \end{tabular}
## 2 Experimental Setup and Theoretical Analysis  
### Optical Trap Setup  
The optical trap system used (see scheme in Figure 1) consists of a THORLABSTM (Newton, NJ, USA) butterfly laser diode type infrared (IR) laser source (model BL976-SAG300, with a wavelength (\(\lambda_{IR}\)) of 976 nm, and a maximum power of 350 mW) and an oil immersion microscope objective (Nikon brand, model MRP71900, with 100X magnification, NA = 1.25, and a working distance (WD) of 0.23 mm). The immersion oil (Non-Drying Immersion Oil for Microscopy, Cargille Type LDF [27]) has a refractive index (\(n\)) of 1.5181. The substrate shown in Figure 1 is a PEARL coverslip (used as a slide) of 24 \(\times\) 50 \(\times\) 0.13 mm in dimensions and with a refractive index of 1.52. Therefore, the maximum distance available for particle trapping is approximately 100 \(\upmu\)m, which means that to keep the optical trap above the slide, the immersion oil must not exceed approximately 100 \(\upmu\)m in thickness; this is controlled by moving the slide along the trap optical axis. The sample under study is illuminated using a white LED light source through a condenser lens in the opposite direction to the IR laser.  
Figure 1: Representation of the usual operation of an optical trap. (MO1—Condenser Microscope Objective, MO2—Oil Immersion Microscope Objective).
## 4 Discussion
### Non-Targeted LC-QTOF-MS Based Metabolomics Data Analysis  
In the present study, _C. sativa_ chemotype III (with a phytocannabinoid content consisting of 12.78% CBD, 3.21% CBDA, and 0.54% THC as determined by LC-PDA) was chosen for the integration of metabolomics data into the reconstruction. The polar extract used in LC-QTOF-MS facilitates the identification of polar and low volatility compounds, mainly cannabinoids, some terpen, and flavonoids [43]. In addition, the LC-QTOF-MS data show a complementarity between the positive and negative ionization modes. The positive ionization mode possibly reveals a higher quantity of CBD when compared to the quantity of THC. However, some THC isomers may play a role in peak discrimination. The metabolic profile obtained tentatively agrees with the initial chemotype III of the plant and also corresponds to the chemotype exposed by [PERSON] [44; 67] in obtaining the reference genome of _C. sativa_.  
LC-QTOF-MS data described high levels of secondary metabolites modules such as cannabinoids, terpenoids, coumarins, phenylpropanoids, and steroids and notable single metabolites such as delta-9-THC, cannabidionic acid, cannabichromene, geranylhydroquinone, cannflavin A, pregna-4,9(11)-diene-3,20-dione, and neriantogenin.  
The metabolites obtained from the analysis were found to exhibit a diverse range of chemical structures and functionalities. For the organization and classification of these metabolites, they were grouped into four distinct clusters. These clusters represent the main categories of plant secondary metabolites, highlighting the chemical diversity present in the sample [64] (Figure 7). This clustering approach provides valuable insights into the composition and distribution of secondary metabolites in the studied plant system [64].