esa-sceva/satcom-chunk-collection huggingface dataset:
# Drag and Attitude Control for the Next Generation Gravity Mission
## 3 Drag and Attitude Control Role in the Measurement of the Non-Gravitational Accelerations
### Drag and Attitude Control Requirements  
Besides the altitude maintenance, necessary to ensure a homogeneous, constant ground track repetition pattern along the mission, a tight control of the satellite's non-gravitational accelerations and attitude dynamics at all frequencies inside the instrument  
Figure 11: Worst-case analysis (WCA) of the acceleration measurement noise for linear and angular accelerations for the science output (SCI) or for DFAOCS outputs. Performance is shown for the X axis (less sensitive) and for Y/Z axes (ultra-sensitive) of the accelerometer reference frame.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Y or Z axes** & **X axis** \\ \hline \multirow{3}{*}{Bias/Noise} & By construction (DC value) & \(1.5\times 10^{-7}\) m/s\({}^{2}\) & \(2\times 10^{-6}\) m/s\({}^{2}\) \\ \cline{2-3}  & After calibration (DC value) & \(<\)1.5 \(\times 10^{-7}\) m/s\({}^{2}\) \\ \cline{2-3}  & In MBW & \(3.1\times 10^{-12}\) m/s\({}^{2}/\surd\)Hz & \(6.2\times 10^{-12}\) m/s\({}^{2}/\surd\)Hz \\ \hline \multirow{3}{*}{Scale Factor} & By construction (DC value) & \(1.2\times 10^{-2}\) \\ \cline{2-3}  & After calibration (DC value) & \(3\times 10^{-4}\) & \(<\)1.2 \(\times 10^{-2}\) \\ \cline{2-3}  & In MBW & \(10^{-7}\) 1/\(\surd\)Hz \\ \hline \multirow{3}{*}{Quadratic Factor} & By construction (DC value) & \(78\) s\({}^{2}\)/m \\ \cline{2-3}  & After calibration (DC value) & \(10\) s\({}^{2}\)/m (for all axes) \\ \cline{2-3}  & In MBW & \(1.1\times 10^{-2}\) s\({}^{2}\)/m/Hz \\ \hline \multirow{3}{*}{Internal Misalignment} & By construction (DC value) & \(148\)\(\surd\)rad & \(172\)\(\surd\)rad \\ \cline{2-3}  & In MBW & \(0.1\)\(\surd\)rad/\(\surd\)Hz \\ \hline \hline \end{tabular}
\end{table}
Table 3: Bias, scale factor, quadratic factor, and misalignments of the MicroSTAR accelerometer (in the accelerometer reference frame).  
measurement bandwidth (MBW) of NGGM (threshold: from 1 to 100 mHz; goal: from 0.1 mHz to 1 Hz) is necessary to fulfil the measurement requirements of \(\Delta\alpha_{\mathrm{D}}\).  
Control requirements are derived from the couplings between accelerometer and satellite dynamics feeding into the relative non-gravitational linear acceleration measurement performance requirements through the corresponding linear and angular accelerations and angular rates. Their required values are reported in Table 4.  
These control requirements allow the fulfillment of the mission's required performance for the measurement of the two mission observables when considered in conjunction with the calibration-dependent parameters in Table 3. Relaxation of these requirements (or of the calibration stringency) would lead to worsening of the performances, locally for some frequency ranges, or generally across the whole spectrum, as explored in more detail in Section 4.1.
## 2 Related Work
### Channel Interference  
Previous research works considered channel interference as detrimental and sought reasonable channel assignment methods, which is a common choice in resource management ([PERSON] et al., 2000; [PERSON] et al., 2001; [PERSON] et al., 2002; [PERSON] et al., 2003; [PERSON] et al., 2004; [PERSON] et al., 2005), and they mainly focused on two kinds of channel interference: one is interference between different communication systems ([PERSON] et al., 2005), and the other is channel interference with respect to the 802.11 communication system ([PERSON] et al., 2005).
# Review of Advanced Implementation of Doppler Backscattering Method in Globus-M
## 3 Application Results
### Alfven Mode
However, the location of TAE with higher toriodal number values (\(n=2\), 3) was determined to be closer to the periphery with \(R\sim\) 0.51-0.59 m. The examples of profiles obtained for a variety of Alfven eigenmodes is presented in Figure 6. It clearly highlights that there is a possibility of the existence of the toroidal Alfven eigenmodes in the core plasma regions. The shift of the main TAE harmonic (i.e., with \(n=1\)) to the inner plasma regions appears to be related to the increasing plasma current [73]. Figure 6: Distribution of amplitudes of magnetic field oscillations for various toroidal mode numbers n in discharges with different plasma currents: (**a**) # 37001, t = 141.2 ms; (**b**) # 36988, t = 147.5 ms; (**c**) # 36944, t = 146.2 ms; \(q\)—stability factor (\(\Circle\)), \(n=1\) (squares), 2 (circles), 3 (triangles) [73].
## 4 Discussion
### Correlation between the Reinforcement Index and the Mechanical Properties of Concrete  
#### 4.2.1 Correlation between the Reinforcement Index and the Ultrasonic Pulse Velocity of Concrete  
Figure 10 denotes the correlation between the Reinforcement Index and the ultrasonic pulse velocity of PPFRC. As mentioned in Section 2.4, three methods were used to evaluate ultrasonic pulse velocity: direct, semi-direct, and indirect. The value of UPV_ind = 4821.67 m/s was removed from the B-1.2%-50 mixture because it showed a DFFITS parameter of 0.97 (greater than 0.89). The regressions resulted in correlation coefficients of +0.66, +0.69, and +0.79, respectively, showing significant relations in all cases. The \(p\) values were 0.037, 0.026, and 0.011, respectively; all less than 0.100. Therefore, the null hypothesis of no correlation between variables in the three regressions is rejected. As seen in Figure 10, the indirect method shows a higher value of the Pearson correlation coefficient, and its slope is more marked. For each 10-unit increase in the Reinforcement Index, there is an increase of 26.05, 78.17, and 131.04 m/s in UPV related to the direct, semi-direct, and indirect methods, respectively. Moreover, the same image shows that the UPV results calculated via the indirect method are greater than the values calculated via the direct and semi-direct methods. These results contrast with the results of other studies. For example, [PERSON] et al. (2013) [43] worked with PP fiber volume fractions of 0.10, 0.15, and 0.20%, concluding that increasing fiber volume causes a decrease in UPV. Results with the same trend are reported by [PERSON] et al. (2019) [24], who worked with PP fibers (\(V_{f}\) = 0.42, 0.56, and 0.98%), and steel (\(V_{f}\) = 0.17, 0.40, 0.83%), using the method of direct and semi-direct prospecting, although hybrid fibers did not show this trend. However, the results of the current research study are consistent with those of [PERSON] et al. (2015) [19], who worked with steel fibers (\(V_{f}\) = 0.5, 1.3, 1.5, 2.0, 2.5%) using the three prospecting methods and concluded that an increase in fiber volumes causes an increase in UPV. Note that all these studies (except this one) work only with fiber volume and not directly with the Reinforcement Index. Figure 11 denotes the influence of fiber length on wave velocity calculated by the three prospecting methods. The figure shows that the longer the fiber length, the higher the resulting wave velocity. Similarly, the influence of fiber volume fraction on UPV can be seen in Figure 12. As shown, the more fibers, the higher the resulting wave velocity.
## 5 Validation and Analysis
### Disturbance Analysis  
To compare the performance under external disturbance of the improved ADRC and the traditional ADRC controller, a random disturbance with the amplitude of 10 N or 1 N\(\cdot\)m is put into the control system. The simulation time is set to be 0.1 s; the six reference signals are all kept constant at 0. The simulation result is shown in Figure 7.  
Figure 5: The force and torque errors between the deduced model and the harmonic model.  
Figure 6: The displacement/angle curve with step response.  
It can be seen from Figure 7 that under the random external disturbance, the position fluctuation for the developed ADRC is much smaller than that of the traditional ADRC. The root mean square deviation method is applied to make the quantitative analysis for the data in Figure 7a. The value of the \(x\)-axis is 0.0386 \(\upmu\)m, \(y\)-axis is 0.0351 \(\upmu\)m, \(z\)-axis is 0.0336 \(\upmu\)m, \(\phi\)-axis is 0.0588 \(\upmu\)rad, \(\theta\)-axis is 0.0612 \(\upmu\)rad, and the \(\phi\)-axis is 0.0768 \(\upmu\)rad. The same method is applied for the data in Figure 7b, the value of the \(x\)-axis is 0.0149 \(\upmu\)m, \(y\)-axis is 0.0132 \(\upmu\)m, \(z\)-axis is 0.0121 \(\upmu\)m, \(\phi\)-axis is 0.0382 \(\upmu\)rad, \(\theta\)-axis is 0.0335 \(\upmu\)rad, and the \(\phi\)-axis is 0.0481 \(\upmu\)rad. It can be seen that, through the deviation analysis, the influence on the planar motor position from force and torque disturbances is weakened by applying the improved ADRC. Thus, it can be concluded that the proposed developed ADRC has better anti-disturbance performance compared with the traditional ADRC.
# Energy Efficiency Perspectives of PMR Networks +
## 4 Energy Efficiency Strategies for PMR Systems  
### Carrier Sleep Mode  
Because of the impact of fixed power consumption, which is the power spent by the base station even for zero transmission power, the most common energy saving strategy proposed for commercial cellular system is the carrier sleep mode. As a matter of fact, in order to improve the capacity, operators could deploy more than one carrier per cell. As explained in Section 2, the cell signaling is transmitted just over one carrier, while the other ones only manage the traffic and the dedicated signaling. Therefore, carrier sleep mode works automatically by deactivating the unused carriers; the only carrier that cannot be ever deactivated is the one carrying the cell signaling in order to maintain the coverage of the area. Deactivating a carrier, or putting it on sleep mode, means putting the carrier in a low power consumption state, such that the base station controller can make it operating when needed in a very small time (a few seconds).  
By adopting the carrier sleep mode, the power consumption model presented in Equation (2) can be modified as follows:  
\[P_{i}=\begin{cases}\Delta_{p}P_{out}+P_{0},&\text{if the $i$-th carrier is ON},\\ P_{sleep},&\text{if the $i$-th carrier is sleeping},\end{cases} \tag{3}\]  
where \(P_{sleep}\) is the power consumption of the sleep mode state. In this study, \(P_{sleep}=140\) W has been assumed.  
\begin{table}
\begin{tabular}{c c c} \hline \hline Transmission power & \(P_{out}\) & \([0,20]\) W \\ Power Amplifier (PA) efficiency & \(\mu_{PA}\) & 0.25 \\ Radio Frequency (RF) power consumption & \(P_{RF}\) & 114 W \\ Baseband (BB) power consumption & \(P_{BB}\) & 75 W \\ Site Control Unit (SCU) power consumption & \(P_{SCU}\) & 23 W \\ DC loss & \(\sigma_{DC}\) & 7.5\% \\ Feeder loss & \(\sigma_{feed}\) & 50\% \\ Main supply loss & \(\sigma_{MS}\) & 9\% \\ Cooling loss & \(\sigma_{coal}\) & 10\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: TETRA base station power consumption parameters.  
Figure 7: Energy consumption versus traffic for TETRA.  
The performance of TETRA carrier sleep mode is depicted in Figure 7, which shows the energy consumption of a TETRA base station that is equipped with four carriers as a function of traffic load: the carrier sleep mode, as expected, provides significant energy savings, especially in the case of low traffic load, converging instead to the baseline consumption with peak traffic load.
# Parameters of Optokinetic Nystagmus Are Influenced by the Nature of a Visual Stimulus
###### Abstract
The rationale of this idea is an early finding of OKN onset time (starting time of the 1 st OKN-QP) to be dependent on the contrast level in a low-speed drifting grating, showing a faster onset (shorter latency) with increasing contrast [24]. In the current study, we re-tested this effect and as the stimulus duration limit based on its parameters was sought, we also examined the offset time of the robust OKN response (two OKN movements occurring in the respective direction to the direction of the moving stimulus [14; 25]) for visual stimuli of various spatial frequencies, contrast levels, and types. Here the OKN offset time was defined as the ending time of the third OKN-QP. Moreover, some of the already established procedures for OKN detection use the least OKN gain to successfully detect the eye movement event [13; 14]. This approach might, however, lead to events of false-negative detection (no OKN detected, although there was a visible OKN pattern) when using just one fixed parameter. As suggested in the previous work [14], using detection limits for the OKN gain based on the stimulus parameters might be a possible solution. The motivation behind this idea is the finding of [PERSON] et al., who found a linear correlation between the OKN gain and the contrast on a \(\log C\) scale [26] in a zebrafish model. In the current study, we aimed to replicate this effect in emmetropic participants tested with visual stimuli of various \(C\), \(SF\), and types. The last parameter of the robust OKN eye movement we evaluated with respect to the visual stimulus parameters was the OKN-SP amplitude. Here the analysis of the OKN amplitude was performed for all parameters of the visual stimulus used in the current study, first for its potential influence on the robust OKN offset time and second to complete the range of the eye movement parameters presented in the current study. As the study aimed to test the effects of chromatic gratings and also to propose the limits for the OKN gain and the presentation duration for such kinds of visual stimuli, two chromatic gratings were used in addition to the luminance grating. The two chromatic gratings were red-green (R-G) and blue-yellow (B-Y) presented in the same range of spatial frequencies and contrast levels as the luminance stimulus. These color combinations were used first because they have been already used in several works in the past [5; 27; 28; 29] and to accommodate clinical measurements. Moreover, as it was already performed in previous works, the data were collected under monocular stimulation conditions [10; 14], performing by patching one eye with an infra-red filter. This approach was used to follow clinical conditions for contrast sensitivity testing, without having a significant impact on the eye-tracking quality, while gaining the information from both eyes [10].
# Data Collection Based on Opportunistic Node Connections in Wireless Sensor Networks
## 5 Performance Evaluation
### Evaluation Results
#### 5.2.2 Energy Consumption
By employing the EOH metric, the  
Figure 16: The total EC varies by time in network, in addition, \(R_{l}=110\) m, the number of nodes (N) is 1100. Figure 15: The Hops varies by the long radio range \(R_{l}\) of the mobile sink, in addition, the number of nodes (N) is 1100.  
proposed energy efficient probe message forwarding mechanism can decrease the energy consumption for forwarding the probe message. By analyzing these messages, the mobile sink could construct the spanning tree and broadcast it, so the source node could obtain its optimal path with the max PC to the sink before sending its sensing data. Therefore, during data delivering, a node can save the energy consumed by selecting the forwarders.
# Secure Delivery Method for Preserving Data Integrity of a Video Frame with Sensitive Objects
## 2 Related Works
### Hash Function  
A hash function is a function that takes an arbitrary string of length as an argument and calculates a fixed length value n. The resulting value is called a hash value of the given string [16]. A cryptographic hash function is a type of hash function and has the following properties, making it difficult to find the relationship between the hash value and the original input value [16; 17].  
For large amounts of data, such as sets of images, ordered sequences of data blocks with variable sizes can be processed by generating hash values. Conventional techniques for computing a hash value over a sequence of data blocks involve concatenating the blocks in their specified order, treating the resulting concatenated data as a single input to a cryptographic hash function. As a result, the precise computation of the overall hash value requires the data blocks to be available in their original, sequential order.  
SHA-256 (Secure Hash Algorithm 256-bit) is a hash function with very strong properties in terms of collision resistance. Collision resistance means that the probability of two different inputs producing the same hash value is extremely low. From the perspective of preimage resistance, SHA-256 is designed to be a one-way function, which means that it is computationally infeasible to reverse the hashing process. It is a member of the SHA-2 family and produces a 256-bit (32-byte) hash value. The length of a hash value depends on the difficulty of finding an input that produces a given hash output.Given an input and its corresponding hash, this refers to the difficulty of finding a different input that produces the same hash output, in terms of second-preimage resistance.  
SHA-3 (Secure Hash Algorithm 3) is the latest member of the Secure Hash Algorithm family, developed by the National Institute of Standards and Technology (NIST). It is a cryptographic hash function designed to generate a fixed-size output (a hash) from an input of arbitrary size, ensuring data integrity, security, and authentication. SHA-3 supports multiple output lengths, whereas SHA-256 produces a fixed-length output of 256 bits (32 bytes). SHA-3 tends to be slower than SHA-256 in terms of pure hashing speed due to the complexity of the Keccak construction.  
SHA-256 is generally faster than SHA-3 in terms of raw performance and computation. It is highly optimized and widely used across many systems and applications. An important technique for confirming data integrity is to construct hash values for the data by a cryptographic hash function such as SHA-256 that maps an arbitrary binary string to a binary string with a fixed size. The cryptographic hash function is a hash function that tries to make it computationally infeasible to invert them. Table 1 summarizes the key parameters and characteristics of SHA-256 and SHA-3 hash functions.
# Lightweight Crack Automatic Detection Algorithm Based on TF-MobileNet
## 3 Crack Detection Method Based on TF-MobileNet
### Feature Extraction Network of TF-MobileNet  
Since we aim to build a lightweight algorithm for automatic crack detection, we adopt MobileNetV3 integrated with Transformer as the backbone of our network for extracting crack features. MobileNetV3 is a well-performing lightweight network that combines the advantages of the v1 and v2 versions and uses the nonlinear h-swish [27] function instead of swish [34]. The h-swish function brings faster computational speed and is more quantization-friendly. Equations (1) and (2) show the computations of _h-swish_ and swish.  
\[h-swish[x]=x\frac{ReLU6(x+3)}{6} \tag{1}\]  
\[f(x)=x\cdot sigmoid(\beta x) \tag{2}\]  
To reduce the latency while maintaining high-dimensional features, the previous layer of the average pooling layer in the inverted bottleneck structure in MobileNetV2  
Figure 1: The architecture of TF-MobileNet.  
was removed and the feature map was computed using convolution. Figure 2 shows the changed inverted bottleneck structure.  
Although MobileNetV3 has the advantage of being lightweight, it does not perform particularly well in feature extraction, especially for fine and long features such as cracks. With the increase in the number of convolutional layers, the local perceptual field of the feature map expands, which can be detrimental to the detection of fine features of cracks. The introduction of BoTNet does not add much computational effort, but its internal global attention mechanism allows the model to better combine contextual information and solve the problem of small target density more effectively.  
To enhance the model's robustness, we used the Mosaic [35] data enhancement technique. Using four random images for random scaling and then random stitching, the random scaling operation added many small targets, which not only improved the robustness of crack detection but also enriched the dataset.
# Manufacture of a Potential Antifungal Ingredient Using Lactic Acid Bacteria from Dry-Cured Sausages
## Materials and Methods
### Screening of Antifungal Properties by Overlay Method  
The first screening of antifungal activity was performed using bacterial isolates grown overnight in 10 mL of MRSb at 37 \({}^{\circ}\)C. Then, the overlay assay was used to determine the antifungal activity of LAB isolates [24]. Bacterial suspensions (10 \(\upmu\)L) were inoculated on MRSa plates and incubated at 37 \({}^{\circ}\)C for 48 h. Afterward, the agar plates were filled with 10 mL of soft (7% agar) PDA containing 10\({}^{6}\) conidia/mL and were incubated at 30 \({}^{\circ}\)C. The inhibition growth zone was determined 48 h later and measured on a cm scale. The experiments were conducted in triplicate.  
Identification of Isolate by Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry (MALDI-TOF MS) Fingerprinting  
LAB was identified by extracting isolated cultures according to [PERSON] et al. [25]. The MALDI-TOF MS was a Microflex L20 (Bruker Daltonics, Billerica, MA, USA) mass spectrophotometer equipped with an N\({}_{2}\) laser. The sample spectra were acquired in positive linear ion mode, with a voltage acceleration of 20 kV, and the mass ranged from 2000 to 20,000 Da. Moreover, the spectra were obtained in triplicate as suggested by MALDI Biotypear Realtime Classification (RTC). The identification of the samples was the spectra of the largest log score organism. Finally, the results were compared with the MBT 7854 y MBT 7311_RUO database (Bruker Daltonics).  
Preparation of the Bacteria-Free Supernatant (BFS) and Development of a Postbiotic Antifungal Ingredient  
Meat broth (MB) was formulated by modifying the composition of the MRSb, as plotted in Supplementary Material Table S1. Meat extract and yeast extract were substituted with freeze-dried pork loin at different concentrations: 2.00 (MB2), 4.00 (MB4), 8.00 (MB8), and 10.00 (MB10) g/L. The ingredients were mixed and dissolved in distilled water for 10 min, and the pH was adjusted to 6. Then, the media were autoclaved at 121 \({}^{\circ}\)C for 21 min. Afterward, the isolated LAB were defrosted and incubated in MRSb at 37 \({}^{\circ}\)C for 12 h to reach the exponential growth phase. Subsequently, the LAB were placed at 10\({}^{7}\) CFU/mL in 50 mL of the MB and incubated at 37 \({}^{\circ}\)C for 48 h. After fermentation, the LAB were mechanically separated by centrifugation at 4000\(\times\)_g_ for 10 min to separate the BFS. Finally, BFS were kept at \(-\)80 \({}^{\circ}\)C for 24 h and freeze-dried to obtain the postbiotic antifungal ingredient [26]. Freeze-dried BFS of fermented MRSb was carried out as a control group.