esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### People Counting Using the LOI  
Unlike ROI counting, LOI counting involves drawing a virtual line in the image, and counting is only performed when individuals in the image pass through the line. The LOI counting method pays special attention to the movement directions of people. Therefore, in related research, different algorithms have been designed for counting people's flow in and out, left and right, and front and back with respect to the LOI. [PERSON] et al. (2018) proposed a methodology comprising two consecutive stages. Firstly, a detection task is executed to identify individuals within the current frame, with the MobileNetv2-SSD deep learning architecture employed for detection purposes. Subsequently, if any individuals are detected, the tracking phase, founded on visual tracking techniques, is initiated to monitor the positions of individuals. [PERSON] et al. (2018) placed lines in the areas of interest in the image. They converted the image into individual frames, and counting was triggered when people in the image crossed the line segment. In comparison with previous methods that treated target objects as \"points\", this approach avoids misjudgments caused by overlapping points when multiple people walk together, which would otherwise be considered as one large point, leading to errors in the system's determination. [PERSON] et al. (2018) proposed a people-counting system that is applicable to department stores. They utilized three methods: (1) foreground extraction for each frame image, (2) a dilated motion search using the maximum a posteriori probability (MAP), and (3) a flow analysis using multiple touching sections (MTSs). The system first applies the line-wise average path length (APL) for foreground extraction, employs the MAP for a dilated motion search to locate the target regions on the line, and then analyzes the foreground to generate a fluctuation graph based on the MTSs. With the estimation of the motion and flow analysis, the number of people entering and exiting the store can be determined. The hardware implementation of this system utilizes wireless transmission-capable microcomputers with easy-to-install software. The experiment results show that this system achieves a high accuracy while maintaining a low computational complexity, as the computational complexity is reduced using MTS and MAP. [PERSON] et al. (2018) used cameras placed at high angles to count the number of people entering and exiting the target area. The wireless surveillance cameras recorded with a focal length of 3.5 mm and a resolution of 352 \(\times\) 288. The study discusses the relationship between different LOI widths and lengths in the counting algorithm and the counting accuracy.
# Engine Speed Control System for Improving the Fuel Efficiency of Agricultural Tractors for
###### Abstract  
This study was conducted to develop a load-sensitive engine speed control system to maximize the fuel efficiency of an agricultural tractor. The engine speed controller was developed through a model-based design approach using a tractor simulation model. The simulated engine speed and torque values were measured with an average error range of 1.4-4.9% compared to results obtained from field experiments. Using the tractor model, the gain parameters of the proportional-integral (PI) controller were optimized under the step, ramp, and actual load conditions. The simulation results using the actual load showed that the engine speed could be adjusted to within 2-3% of the desired value using the proposed engine speed controller. The throttle control system was constructed using four parts of a tractor engine, a microprocessor with an engine speed control algorithm, a throttle actuator, and a data acquisition system. Using the developed system, the operating engine speed values showed an average 1.17 % error compared to the desired engine speed. Three fuel efficiency parameters were used for evaluating the fuel-saving performance of the control system: specific volumetric fuel consumption (SVFC), fuel consumption per tilled area (FCA), and fuel consumption per work hour (FC). The values for SVFC, FCA, and FC obtained from the engine speed control system during plowing operations were 23.03-57.87%, 4.11-42.06%, and \(-\)7.24-38.48%, respectively, showing an improvement over the same operations without the control system.  
agricultural tractor; fuel-saving; control system design; control algorithm; load sensing 2019 9 3898 3898 10.3390/app91839898
## 2 Model  
The Lang-Kobayashi rate equations [1] describing a single-mode semiconductor laser with optical feedback and sinusoidal pump current modulation, in simplified notation [22,23], are as follows:  
\[\dot{E} = k(1+i\alpha)(G-1)E+\eta E(t-\tau)e^{-i\omega_{0}\tau}+\sqrt{D}\xi, \tag{1}\] \[\dot{N} = \gamma_{N}(\mu_{dc}+a_{mod}\sin(2\pi f_{mod}t)-N-G|E|^{2}). \tag{2}\]  
In these equations, \(E\) represents the slowly varying complex optical field (\(|E|^{2}\) is proportional to the laser intensity) and \(N\) represents the carrier density. \(\eta\), \(\tau\), and \(\omega_{0}\tau\) are the feedback strength, the delay time and the feedback phase, respectively; \(k=1/(2\tau_{p})\) where \(\tau_{p}\) is the photon lifetime, \(\gamma_{N}=1/\tau_{N}\) where \(\tau_{N}\) is the carrier lifetime, \(G=N/(1+\epsilon|E|^{2})\) is the gain and \(\epsilon\) is the gain saturation coefficient, \(\alpha\) is the linewidth enhancement factor. \(\mu_{dc}\), \(a_{mod}\) and \(f_{mod}\) are the \(dc\) value, the amplitude and the frequency of the sinusoidal modulation of the pump current, respectively.  
Spontaneous emission noise is taken into account by a complex additive Gaussian white noise, \(\xi\), with strength \(D\). In the deterministic model (\(D=0\)), for typical parameters, the spikes are a transient dynamics, after which the laser output is stable (the laser emits the stable external cavity mode that has maximum gain). With large enough noise, the spiking dynamics can become stable, sustained by noise [24,25]. Therefore, in this model, the spikes are generated by the interplay of deterministic and stochastic mechanisms. The coexistence of stable emission and spiking behavior was reported in Ref. [26]. While this model explains many observations, it has several simplifications: it assumes single-mode emission, it considers a single reflection in the external cavity, and it neglects spatial inhomogeneities in the optical field and in the carrier population.
## 4 Experimental Results and Analysis
### Evaluation Indicators  
This study evaluates model performance using four metrics after logarithmic transformation: MSE, MAE, RMSE, MAPE, and IoA. The transformation reduces the impact of large magnitude differences, improving evaluation robustness.  
The formula for IoA is:  
\[IoA=1-\frac{\sum_{i=1}^{n}(\hat{y}_{i}-y_{i})^{2}}{\sum_{i=1}^{n}(|\hat{y}_{i}- \hat{y}|+|y_{i}-\hat{y}|)^{2}} \tag{29}\]  
The formula for MSE is:  
\[\text{MSE}=\frac{1}{n}\sum_{i=1}^{n}(\log(\hat{y}_{i}+1)-\log(y_{i}+1))^{2} \tag{30}\]  
The formula for MAE is:  
\[\text{MAE}=\frac{1}{n}\sum_{i=1}^{n}|\log(\hat{y}_{i}+1)-\log(y_{i}+1)| \tag{31}\]  
The formula for RMSE is:  
\[\text{RMSE}=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(\log(\hat{y}_{i}+1)-\log(y_{i}+1) )^{2}} \tag{32}\]  
Figure 7: Monthly sales trend in different regions.  
The formula for MAPE is:  
\[\text{MAPE}=\frac{1}{n}\sum_{i=1}^{n}\left|\frac{\log(y_{i}+1)-\log(y_{i}+1)}{ \log(y_{i}+1)}\right|\times 100\% \tag{33}\]  
Here, \(\hat{y}_{i}\) represents the predicted value of the model, \(y_{i}\) is the actual value, and \(n\) is the total number of samples. By applying a logarithmic transformation (\(\log(x+1)\)), the metrics adjust for differences in scale and magnitude, making the evaluation more robust to large deviations and extreme values. Logarithmic transformation offers several advantages for FMCG sales data. It helps reduce scale differences between data points, especially when the data spans a wide range of values. This makes the loss functions, such as MSE and MAE, more stable by minimizing the impact of extreme values. Sales data often have a skewed distribution due to promotional spikes, and the logarithmic transformation brings the data closer to a normal distribution, which benefits models assuming normality. The transformation also improves model stability, reducing the influence of outliers and ensuring robustness. Additionally, it is effective for handling seasonal variations, allowing the model to capture cyclical trends more accurately.
# Indoor WiFi-Beacon Dataset Construction Using Autonomous Low-Cost Robot for 3D Location Estimation
## 3 Overall System Architecture
### Robot Sub-System Description
#### 3.1.2 Schematic Description  
The schematic of the overall electronic system is depicted in Figure 3. The main control board also functions as a WiFi transceiver, for which a NodeMCU logic board is used. The number of logic inputs/outputs from the main controller to each client controller is reduced to two digital IOs: RX and TX. The TX connection is shared between the two client boards, which enables the main controller to transmit to both clients in a broadcast manner. The RX connection is not shared, which results in the client boards being able to relay feedback information individually to the main controller. The connections from the main control board towards the motor-control sub-circuit are achieved using two digital outputs: left-drive and right-drive control signals. The motor-drive signals are treated as analog signals using pulse-width modulation (PWD), which is sufficient to drive the robot to the designated signal-collection points. In addition, this setup also gives complete control of the speed and direction of movement of the robot to the main control board. Finally, each of the two infrared sensors is connected to the main controller board using two digital-input connections, which are used as an indication of the path markings and enable the robot to follow a predefined path throughout the building in addition to identifying specific checkpoints for WiFi-signal-strength collection.  
Finally, the power of the mechanical part of the system is provided by two Li-ion batteries connected in series to provide the motors with 8 V of power. The logic and WiFi transceivers are powered using a more powerful setup of four Li-ion batteries, each two  
Figure 2: (**a**) Robot base unit (top view); (**b**) robot overview (tower height not to scale).  
batteries are connected in series, and the two sets are connected in parallel to provide 8 V of power with double the wattage. The two sets of two series-connected batteries are connected in parallel to provide the additional power needed for all WiFi transceivers, which are usually power-hungry. In addition, the system spends a fraction of the time moving between checkpoints and most of the time in WiFi-signal-strength collection. About 3% of the time is spent moving, whereas 97% of the time is spent collecting the required data. This was determined based on many development cycles monitoring power and performance and applying multiple improvements to the system to reach the current minimalist yet very effective system.
## 2 Materials and Methods
### Assessment of Antioxidant Activity
#### 2.4.1 Ferric Reducing Antioxidant Power (FRAP) Assay  
The FRAP assay was accomplished following the methodology presented by [PERSON] et al. [42] with minor modifications. In this assay, the reduction of ferric 2,4,6-tris(2-pyridyl)-1,3,5-triazine [Fe(III)-TPTZ] to ferrous complex is carried out at low pH via a spectrophotometric analysis.  
Sample and Reagent Preparation  
A 20% (\(w/v\)) solutions of pure honeys and their formulations were prepared. The FRAP reagent was prepared through the mixing of 10 mM TPTZ (prepared in 40 mM HCL), 20 mM FeCl\({}_{3}\) (aqueous) and 300 mM acetate buffer (aqueous at pH3.6) in a ratio of 1:1:10 (\(v/v/v\)). The standard curve was constructed with Ferrous sulphate (FeSO\({}_{4}\) 7H\({}_{2}\)O); standards (200 uM to 1200 uM) were used to construct the standard curve for the quantification of antioxidant activity.  
Working Procedure  
An amount of 20 uL of the honey solutions, their corresponding formulations or the ferrous sulphate standards were mixed with 180 uL of FRAP reagent in a 96-well microplate (Greiner Bio-One 96-well Microplate Flat Bottom, Frickenhausen, Germany), and after 30 min incubation, the absorbance of the solution was measured at 620 nm (BMG Labtech POLARstar Optima Microplate Reader, Mannedorf, Switzerland) at room temperature. The recorded FRAP antioxidant activity was expressed as mmol Fe\({}^{2+}\) equivalent (FE)/kg of the honey or its formulations.  
4.2 High-Performance Thin-Layer Chromatography (HPTLC) Coupled with 2,2-Diphenyl-1-Picrylhydrazyl (DPPH)  
The HPTLC-DPPH antioxidant activity of the five pure honeys and their corresponding formulations were determined using the method narrated by [PERSON] et al. [43] with minor modifications. The employed HPTLC-DPPH analysis enabled the visualization of in  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Origin** & **Supplier, Year** \\ \hline WA Manuka Honey 1 (_Leptospermum scoparium_) & Hive and Wellness, 2019 \\ WA Manuka Honey 2 (_Leptospermum scoparium_) & Manuka Life, 2019 \\ WA Coastal Peppermint (_Agonis flexuosa_) & Margaret River Honey Company, 2019 \\ WA Jarrah Honey (_Eucalyptus marginata_) & Hive and Wellness, 2019 \\ New Zealand Manuka Honey (_Leptospermum scoparium_) & Hive and Wellness, 2018 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Honey samples including botanical origin.  
dividual components present in the honeys' non-sugar part and expressed their individual antioxidant activity as gallic acid equivalents.
# Channel Estimation Based on IOTA Filter in OFDM/OQPSK and OFDM/OQAM Systems
## 3 OFDM/OQPSK System Model
### Convolutional Encoder and Viterbi Decoder  
In OFDM systems, the most widely used channel coding types are block codes and convolutional codes. Convolutional codes have memory, unlike block codes, which provides output using past information bits. If the number of registers is 6, then the constraint length equals to 7 [17; 18]. The convolutional encoder consists of a code rate of 1/2, memory of 6, with code generator polynomials 133 and 171 in octal format [17; 18]. The convolutional encoder can improve the accuracy of channel estimation for the receivers in both OFDM-IOTA and CP-OFDM systems.  
The Viterbi algorithm operates by computing a metric for every possible path in the trellis [13]. The path with the lower metric is retained and the other path is discarded. This process is repeated until the algorithm completes its forward search through the trellis and reaches the termination node, and finally makes a decision on maximum likelihood path. The sequence symbols associate with the path are then released to the destination as the Viterbi decoder output. The Viterbi decoder algorithm can find the lowest Hamming distance to demodulate the input binary bits correctly.
# Numerical Investigation of Tonal Trailing-Edge Noise Radiated by Low Reynolds Number Airfoils
## 2 Experimental and Numerical Approaches  
### Experimental Approach  
The concurrent experimental measurements employed for validation in this work were carried out in an anechoic low-speed wind tunnel facility at Ecole Centrale de Lyon (ECL), with the details of the two conducted experimental campaigns described in [PERSON] et al. [8] and [PERSON] et al. [10]. The tonal signature of the low-speed NACA-0012 airfoil was examined for a range of transitional flow regimes characterized by variable flow velocity, AoA, and unsteady inflow conditions to allow for thorough mapping of the regions of tonal production, including the effects of upstream unsteadiness. Figure 1 illustrates the locations of the Remote Microphone Probes (RMPs) in the experimental measurements ([PERSON] et al. [8]), where the surface pressure data were collected and employed for validation in the current numerical study. For velocity measurements, a hot-wire anemometer methodology was utilized.
# Role of Protein Glycoslation in Host-Pathogen Interaction
## 6 Hosts Sense Glycoprotein of Pathogens to Induce Resistance  
Pathogen glycoprotein is used both by pathogens and hosts. It can facilitate infection by increasing pathogen pathogenicity but also triggering the host immune response. During the invasion of parasitic helmines, proteases are secreted [115] and glycosylated to activate their function in degrading multiple host defensive proteins, and subsequently suppress and modulate host immune response [28]. Cysteine protease (Hc-CPL-1) is essential for the parasitism of _Haemonchus contortus_. Interestingly, Hc-CPL-1 is recognized by the host protective antibody to induce immunity [116]. Recombinant cysteine protease (rHc-CPL-1) that expressed in _E. coli_ only induces weaker immunity responses in sheep than the original Hc-CPL-1 [117]. Previous studies have reported that carbohydrate chains are covalently linked in original Hc-CPL-1 but absent in rHc-CPL-1. The host protective antibody mainly recognized glycan epitopes for triggering defenses [117], showing that the glycosylation of Hc-CPL-1 plays a major role in parasite recognition. Similarly, glycoproteins of other pathogens, like hemagglutinin of influenza viruses [31], glycoprotein E1 of alphavirus [30; 118] and envelope protein (gp120) of HIV [29], are also recognized by hosts to inhibit pathogen invasion. They all can be bound by the host retrocylcin 2 (RC2) to inhibit virus infection, but the deglycosylation form can't be bound by RC2 [31].
## 3 Development History of Navigation Signal Authentication Technology
### Technical Trials and On-Orbit Tests  
* Galileo In 2017, the European Union officially announced that Galileo will provide navigation authentication services. The E1 frequency (E1B) provides the OSNMA, and the E6 frequency provides the commercial service authentication [60]. At the end of 2021, the ESA officially announced that Galileo's public signal message authentication service OSNMA provides testing services. Galileo adopts cross-authentication technology. In addition to broadcasting its own satellite authentication messages, it also broadcasts other satellite authentication messages, which will improve the authentication efficiency of the entire constellation. The service will be officially provided in 2023 [61; 62; 63].
* GPS In 2018, the United States officially announced that the CHIMERA signal would be broadcast on the NTS-3 satellite. The signal is based on the GPS-L1C signal and adopts a combined NMA and SCA authentication signal design. On the basis of NMA, an unpredictable chip is added to the spreading code of the civil signal, and the receiver checks the unpredictable code position and level of the spreading code to verify the authenticity of the spreading code. The security of the pseudorange measurement process is improved [64; 16].
* QZSS Since 2018, a team from the University of Tokyo in Japan has used the QZSS L1S signal to carry out satellite navigation signal authentication design and on-orbit testing. It adopted digital signature-based message authentication technology to carry out GPS L1C/A message and Galileo message authentication tests [14].
* NavIC In 2022, India announced the progress of NavIC signal authentication on-orbit testing at the International Committee on Global Navigation Satellite Systems (ICG) conference. It adopted message authentication technology based on the TESLA protocol and carried out message authentication tests based on L5 and S frequency [15].  
Table 5 shows the status of signal authentication for the GNSS system. At present, the main GNSS suppliers carry out satellite navigation signal authentication research and construction to augment the capability of the national satellite navigation system based on their respective satellite navigation systems (the European Union for the Galileo E1 and E6 signals, the United States for the GPS L1C BOC signals, Japan for the QZSS L1 signals, and India for the NavIC signals).  
\begin{table}
\begin{tabular}{c} Simulation \\ verification \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 5: Status of GNSS signal authentication.  
Key Technologies and Challenges for the Construction of the Authentication Service System for the BDS  
From the perspective of BDS signal authentication system construction, the authentication architecture for BDS is proposed and it is discussed from the aspects of security, key management, authentication system design, authentication performance evaluation technology, and terminal authentication processing technology.
## 2 Materials and Methods
### Solar Cell Fabrication and Characterization  
In this work, solar cells were fabricated with the following device architecture: ITO/PEDOT:PSS/ M1:PC7 IBM/Al. Prior to use, ITO substrates were cleaned by sonication separately in soapy water, acetone, and isopropyl alcohol; dried in an oven (Wisd, Dublin, Ireland); then subjected to ultraviolet (UV)/ozone treatment. A 40 nm thick poly(3,_A_-ethylenedioxythiophene): polystyrene sulfonate (PEDOT:PSS) layer was spun cast from aqueous solution (5000 rpm for 40 s). The substrates were then dried at 140 \({}^{\circ}\)C for 10 min in air and transferred to a N2-glovebox for spin-casting of the active layers. Solar cells were fabricated using **M1** as the donor and [6,6]-phenyl C71 butyric acid methyl ester (PC71 BM) as the acceptor. Solutions of **M1**:PC71 BM with different ratios (30:70, 40:60, 50:50, 60:40, and 70:30 \(w/v\)) in chloroform, and solutions with an optimized blend ratio (40:60 \(w/v\)) in chlorobenzene with or without additives were spun cast onto the substrates. The final step in device construction involved the thermal evaporation of the 100 nm thick Al electrodes. All data were obtained under AM 1.5 G illumination from a calibrated solar simulator with an irradiation intensity of 100 mW/cm\({}^{2}\) under an ambient atmosphere. For each case, five devices were fabricated and each device had two electrodes on top.