esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
At the same time, the bidirectional integration of the two technologies within an interoperable network is tested: this may allow for direct packet exchange among nodes regardless of the transmission technology, with an increase in the flexibility of the overall infrastructure with respect to the usage of the single communication technologies on their own. The rest of the paper is arranged as follows. Section 2 proposes a literature review composed of some related work, while Section 3 describes the network architecture as well as each of the network components. Section 4 reports some tests aimed at assessing network functioning, feasibility and reliability, while the relative results are reported and discussed in Section 5. Finally, Section 6 highlights the conclusions and final remarks.
# Study of the Process of Packet Arrival at a Multiservice Node
## 2 Mathematical Model of a Multiservice Node
### Handling of the Function \(a(t)\)
The complexity of assessing all numerical characteristics of random variables at a minimum value of \(\tau\) does not increase substantially due to the possibility of automatic input of statistical data and calculations on a personal computer or another computing device. It should also be noted that a queuing system with a stepwise function \(A(t)\) and a constant packet processing time knows a method for calculating the moments of waiting and delay duration [11]. These moments make it possible to assess the characteristics of the quality of service for packet multiservice networks and compare them with the standardized indicators [12].
## 3 Controller Design
### Attitude Controller Design Based on Quaternion
#### 3.2.2. Design of Fast and Robust Feedforward PD Controller Based on the Learning Rate
\begin{array}{rl}\dot{V}_{\Theta}&=\dot{V}_{\Theta 1}+\dot{V}_{\Theta 2}\\ &=\widetilde{\omega}^{\mathsf{T}}(f_{\Theta}+\tau_{v\Theta}+d_{\Theta})+k_{P} \widetilde{q}_{v}^{\mathsf{T}}\widetilde{\omega}\\ &=\widetilde{\omega}^{\mathsf{T}}\Big{(}f_{\Theta}+d_{\Theta}-k_{P}\widetilde{ q}_{v}-k_{d}\widetilde{\omega}-f_{\Theta}-d_{\Theta}\Big{)}+k_{P}\widetilde{q}_{v}^{ \mathsf{T}}\widetilde{\omega}\\ &=\widetilde{\omega}^{\mathsf{T}}(-k_{d}\widetilde{\omega})\\ &=-k_{d}\widetilde{\omega}^{\mathsf{T}}\widetilde{\omega}\leq 0\end{array}  \tag{65}\]  
Because of \(\omega_{d}=0\), \(\widetilde{\omega}=\omega-R(\widetilde{Q})\omega_{d}=\omega\), \(\dot{\widetilde{\omega}}=\dot{\omega}\). According to [PERSON]'s theorem, if we verify \(\dot{V}_{\Theta}\equiv 0\), then \(\widetilde{\omega}\equiv 0\); thus, \(\omega\equiv 0,\dot{\omega}\equiv 0\). We can further obtain \(\widetilde{q}_{v}=0\). When\(t\rightarrow\infty\), \(\widetilde{q}_{v}=0\), \(\widetilde{q}_{0}=1\), \(\widetilde{\omega}=0\); this also ensures that the Euler angle tracking error \(\eta_{2e}\) gradually converges to 0 [35,51,52].
# Micro-Satellite Systems Design, Integration, and Flight
## 3 ChipSat RTOS and RF Development
### Ground Station
#### 3.5.3 Receive FEC Signal on Raspberry Pi  
The final step to finish the SDR ground station was to receive a signal sent from the TI launchpad using the RTL-SDR and conduct both the matched filtering and the forward error correction. A script that could demodulate an unencoded raw signal had already been written by Dr. [PERSON] [15]. The script was made to demodulate a signal with no encoding. The script by Dr. [PERSON] was meant to run on a Raspberry Pi connected to the RTL-SDR. The script would look for the four 8-bit Barker codes before demodulating. The script was run on the Raspberry Pi 4 Model B. Unencoded GFSK data were successfully received by the Pi, demodulated, and saved in a.txt file. This satisfies requirement 7.1 (see Table 4). Additionally, FEC was added to the demodulating code. Every two 8-bit transmissions were repeated again to recreate the 16-bit FEC vector. The 16-bit vector was then multiplied by the parity check matrix, \(H\), to obtain the original data back. The parity check matrix can be made by adding the identity matrix, \(I\), to the negative transposed cross-correlation matrix, \(P\). See Equation (4) below:  
\[H=\left[I_{K}\left|-P^{T}\right]\right. \tag{4}\]  
The parity check matrix will mathematically fix errors within the allowable hamming distance of the received transmission. The code for demodulating included the parity check demodulator. A transmission with FEC encoding was sent from the TI launchpads to test the code. The signal was decoded correctly and successfully. The following step was to figure out how the ground station script could achieve matched filtering. Reversing matched filtering was hard because the search algorithm in the demodulating script could not recognize the preamble anymore since each Barker code was filtered. The answer to the problem is not covered in this manuscript. However, a different method for demodulation was discovered, and complete demodulation of a FEC and match-filtered signal can be performed.
## 3 Materials and Methods
### Electromagnetic and Magnetic Field Response Modeling
#### 3.1.1 Electromagnetic Forward Modeling
This response formula serves as a critical component for forward modeling, forming the basis of synthetic data generation used for training and validating inversion models in electromagnetic and magnetic field analysis.
## 2 Effect of Spoofing Signals on Acquisition
### Relationship between Satellite Elevation Angle and Received Signal Power  
The satellite signal strength received by receiver has a significant relationship with satellite elevation angle [27]. Therefore, for the spoofer that transmits the multi-channel spoofing signal, it is necessary to consider the influence of the satellite elevation angle on the power distribution of the spoofing signal. For some intermediate spoofing devices that receive authentic satellite signals to reconstruct spoofing signal, the elevation angle information of the simulated authentic satellite signals must be obtained according to the ephemeris received in real time, and the generated spoofing signals must consider the satellite elevation angle inside. The relationship between the satellite elevation angle and the power of spoofing signal applicable to this research is discussed below.  
As illustrated in Figure 4, the ground receiver is located at R, the satellite is located at S, the spatial distance between the satellite and receiver is \(d\), the Earth's center is O, the Earth's radius is \(R_{e}\), \(\alpha\) is the angle between SR and SO, and \(\theta\) is the angle between SR and RO. By applying the sine theorem to the triangle ORS in above figure, we obtain:  
\[\frac{R_{e}}{\sin\alpha}=\frac{d}{\sin(180^{{}^{\circ}}-\alpha-\theta-90^{{} ^{\circ}})} \tag{15}\]  
Then, the signal propagation distance \(d\) can be expressed as:  
\[d=\frac{R_{e}\cos(\alpha+\theta)}{\sin\alpha} \tag{16}\]  
As the free space propagation loss obtained by Equation (14) is \(20\lg\left(\frac{\lambda}{4\pi d}\right)\), the relationship between the satellite elevation angle and received signal power of the receiver is:  
\[P_{R}=P_{T}+G_{T}+G_{R}+20\lg\left(\frac{\lambda\sin\alpha}{4\pi R_{e}\cos( \alpha+\theta)}\right) \tag{17}\]  
For the subsequent analysis, the above formula can be simplified as:  
\[P_{R}\approx k_{1}+\frac{k_{2}}{\cos(\alpha+\theta)} \tag{18}\]  
Figure 4: Free space propagation of satellite signals.  
where \(k_{1}\) and \(k_{2}\) represent coefficients. Therefore, it can be considered that the lower the satellite elevation angle, the smaller the corresponding satellite signal power received by receiver, and the specific quantization expression is defined in Equation (17).  
It is shown in Sections 2.1 and 2.2 that for the multi-channel spoofing signal model, if the power of spoofing signal is increased without restriction, the noise floor of the receiver will increase, which may be alerted by the power or noise floor monitoring function of some commercial receivers and may also reduce the probability of spoofing signal acquisition and interference efficiency. Therefore, the following gives the optimization algorithm of power allocation of each spoofing signal.
# Critical Review of Basic Methods on DoA Estimation of EM Waves Impinging a Spherical Antenna Array
## 3 Mathematical Formulations  
### EM Waves in Free Space  
The behavior of EM waves in free space can be described as the measured response from a particular signal source to antenna \(i\) within a particular space. The plane wave propagates through free space and is trapped at the receiver by multipath. While propagating in free space, the plane wave is affected by various environmental phenomena, such as absorption, diffraction, and reflection [20]. These environmental phenomena alter the phase and amplitude of the propagating plane wave, possibly differently per incidence angle and per frequency.  
In addition, the plane wave is altered by the travel distance and attenuated by the channel. Specifically, the absorption rate depends on the composition of the air, distance traveled by the plane wave, and operating frequency [19; 20]. Therefore, the absorption by air alone suggests that the signal response varies with time and with frequency and is hence the basis for analysis in the frequency domain. Furthermore, if the signal source is considered a point source, the amplitude of the signal experiences attenuation because the plane wave is spherically spread out. The total number of signals or snapshots U due to multipath per time interval \(\Delta t\) can be expressed asymptotically as [20]  
\[\frac{U}{\Delta t}=4\pi\frac{c^{3}t^{2}}{V} \tag{1}\]  
where \(c\) is the speed of light, and \(V\) is the enclosure volume. This is applicable and correct for all geometries with a homogeneous medium. To reduce the total number of signals within a particular analysis window, a shorter time interval \(\Delta t\) is usually used. Furthermore, when investigating the response of a signal with a single frequency, the time window length is given as \(\Delta t>2\pi/\omega\) to ensure that one period of the wavelength can be obtained within the analysis window. Hence, the time instant \(t_{U}\) where \(U\) or a smaller number exists in an analysis window of a single frequency can be expressed as  
\[t_{U}=t(\omega)\leq\sqrt{\frac{UV\omega}{8\pi^{2}c^{3}}} \tag{2}\]  
where \(\omega\) denotes the angular frequency.
## 4 Results and Discussion  
### Slump Flow  
Figure 7 shows the variation of the slump flow of fresh concrete incorporating various rubber sizes (R1, R5 and R10) and dosages (10%, 20% and 30%). It was observed that the workability reduced with increasing dosages of rubber. For instance, the mixture incorporating R1 showed approximately 36%, 40% and 44% reductions in the slump for 10%, 20% and 30% dosages, respectively, compared to that of the similar mixture without the addition of rubber. This decrease in the slump with the higher dosage of rubber was mainly attributed to the rougher surface of the rubber particles, which might increase the friction between the fresh concrete ingredients [40].  
Figure 6: Impact test performance: (**a**) impact test setup, (**b**) placement of the specimen in the impact test apparatus.  
On the other hand, the slump loss becomes significant by increasing the fineness of the rubber aggregates. It was observed that the larger rubber particles (R5 and R10) did not significantly influence the workability compared to that of the very fine rubber particles (R1). The results showed that, for the mixture incorporating 10% of rubber, an approximately 53% increase in the slump was observed for the mixture incorporating R5 compared to that of the identical mixture with the R1 rubber size. However, the slump was reduced by approximately 5% for the mixture incorporating R10 as compared to the mixture incorporating R5. The higher slump reduction for the mixture incorporating the R1 rubber size might be due the higher water absorption of the finer rubber particles compared to that of the larger rubber particles [41]. Table 5 shows the results of various properties for the concrete mixture incorporating R1 rubber at various dosages at 28 days.
[24] concluded that Kinovea was a valid instrument compared to AutoCAD, and that its reliability was excellent. Kinovea has been used to measure the lower limb kinematics in frontal [25] and sagittal planes [26], and in several populations such as cyclists, soccer players [27], and paralympic athletes [28], among others. In addition, reliable measurements can be expected with no previous experience [29]. According to this, Kinovea seems to be a free, portable, and easy-to-use software that may be used by clinicians and researchers  
Clinicians and researchers have incorporated new objective assessments and feedback-driven interventions to evaluate movement quality. It is important to note that these new technologies try to improve primary and secondary injury risk. However, further economic and accessible technology development is required to address the clinical limitations in the kinematics assessment. In this sense, developing an economical and easy-to-use protocol and measurement tools could be essential in diagnosing, preventing, and controlling the risk factors of lower limb dysfunctions in HB athletes. In this sense, frontal plane kinematics may be related to the increasing injury incidence and prevalence in the lower limbs of HB athletes. Lower limb kinematics seem to be associated to the hip abductor muscles strength. However, no clinical study has been found assessing the relationship between lower limb frontal plane kinematics and hip abductor muscles strength using 2D biomechanical analysis in elite HB athletes. Thus, this study aims to evaluate the relationship between hip abductor muscles strength and lower limb frontal plane kinematics (contralateral pelvic drop, femoral adduction, and knee valgus) in elite HB athletes using 2D analysis. Our hypothesis was that hip abductor musclesstrength present a strong correlation with lower limb frontal plane kinematics measured with Kinovea software.
## 3 Results and Discussion
### _Clamping Test_
The path of the MHL2 gripper is simpler than that of the RG2 gripper, reducing the potential for gripping failure caused by the position of the seedling. * The RG2 gripper is driven electrically and has a force sensor. When the force reaches the upper limit, it stops clamping, and the set distance cannot be reached. Consequently, the seedlings are pulled up from the plug before the clamping is complete, causing clamping failure. The MHL2 gripper is powered pneumatically and does not have a force sensor, allowing it to reach the set distance when gripping. The design of the clamping structure in this study has soft pads to avoid clamping damage. Figure 9: Schematic diagram of the clamping structure’s clamping process and clamping range. (**a**) The seedlings grow in the red area of the hole grid there is a high probability that they cannot enter the clamping range (green area); (**b**) clamping range(green area); (**c**) The seedlings entering the clamping range are clamped toward the center point due to the action of the clamping structure. * Overall, the proposed clamping structure could effectively clamp seedlings regardless of whether the electric RG2 or pneumatic MHL2 structure was used, the unclamped seedlings only accounting for 1.2% of the clamping total. The RG2 electric gripper had the advantages of stroke flexibility, force sensing, and could be combined with the UR5 robotic arm, but the gripping success rate in the 8-jaw mode was not good. The design goal of the clamping structure in this study is to be able to clamp multiple seedlings. Consequently, the MHL2 pneumatic clamp with its high clamping success rate was selected for subsequent oblique cutting tests. It could be expected to consistently clamp multiple seedlings, and exhibit high clamping and oblique cutting success rates.
## 2 Materials and Methods
### Serum and Tissue MTHF Quantification by Sequential Binding Radio-Assay  
The procedure described above was only used to determine folate forms in serum during absorption of orally administered folates where it was critical to identify the unconverted and reduced forms of folate. Because of the methodologic limitations and complexities of assaying folate forms in tissues by the above procedure, we measured serum and tissue MTHF levels in non-pregnant and GD14 pregnant rats 24 h post-oral administration of various folate forms by a sequential binding radio-assay [33]. This time frame was chosen to study tissue distribution of orally administered folate forms because most of the folate forms are converted to MTHF during this period. Therefore, MTHF concentration provides a reasonable estimate of folate concentration in the tissue. For measuring MTHF concentration, an aliquot of tissue homogenate was diluted in 3 volumes of 2% Na ascorbate containing buffer in a glass tube, sealed with parafilm and foil, and placed in a boiling water bath for 10 min. The sample was cooled and centrifuged at 3000 rpm (4200\(\times\)\(g\)) for 5 min. The clear supernatant was transferred to another tube and assayed for MTHF concentration by a sequential binding assay using bovine milk folate receptor alpha (FR\(\alpha\)) as the binding protein, \({}^{3}\)H-folic acid as the tracer, and MTHF as the standard [34; 35] (see Supplemental Data, Figure S1).  
Serum and tissue extracts were prepared fresh for the assay and any extract remaining was discarded. The assay as modified can provide a quantitative estimate of MTHF in tissue extracts. Other reduced forms of folate such as THF, methylene-THF, methenyl-THF, and formyl-THF cannot be measured in this assay because they have low affinity for the FR\(\alpha\) used as the binding protein. However, any presence of folic acid in the tissue is likely to provide an overestimate of folate in the sample since it has a higher affinity for the FR\(\alpha\) protein used in the assay.
To accomplish this goal, we introduce Att-ResBiGRU, a novel deep residual neural network designed to efficiently categorize human activities irrespective of device placement on the body. The model is trained end-to-end within a deep learning framework, eliminating the need for manual feature engineering. This research introduces several advancements contributing to position-independent HAR:  
* Introduction of Att-ResBiGRU: We propose a novel deep learning architecture called Att-ResBiGRU. This innovative model combines convolutional layers for feature extraction, residual bidirectional gated recurrent unit (GRU) modules for capturingtemporal dependencies, and attention mechanisms for focusing on crucial information. This combination enables highly accurate recognition of activities, regardless of the device's position. * State-of-the-art performance: Extensive testing on benchmark datasets (PAMAP2, Opportunity, and REALWORLD16) demonstrates that our proposed model achieves the best results reported for position-independent HAR. It achieves F1-scores of 86.69%, 96.44%, and 96.23% on these datasets. * Robustness against position variations: We compare our model against existing position-dependent and -independent methods using various real-world scenarios to validate its robustness to sensor position changes. Att-ResBiGRU effectively addresses the challenges associated with position independence. * Impact of model components: Through ablation studies, we analyze the influence of incorporating residual learning and attention blocks on the model's performance compared to baseline recurrent models. This quantifies the specific contributions of each component to the overall accuracy. * Simplified deployment: This work establishes a highly reliable single model for classifying complex activities, regardless of a device's position. This simplifies deployment by eliminating the need for complex position-dependent models. The remainder of this paper is structured as follows. Section 2 provides a review of recent related works in HAR research. Section 3 presents an in-depth description of the proposed Att-ResBiGRU model. Section 4 details the experimental outcomes and results. Section 5 discusses the conclusions drawn from the research findings. Finally, Section 6 concludes this paper by considering challenging avenues for future work.
## 2 Data and Methods
### Methods
#### 2.2.3 The Akaike Information Criterion  
The Akaike information criterion (AIC) was used to evaluate the model quality. The AIC is a measure of the quality of each model, relative to each of the other models for a given set of data [77; 78; 79], and the AIC value of the model can be expressed as:  
\[AIC=-\ln L+2c \tag{1}\]  
where \(L\) is the maximum value of the likelihood function for the model, and \(c\) is the number of free parameters in the model. The model with the smallest \(AIC\) is the best performance. Thus, the good performance of different models in this study was normally based on the low \(AIC\) values.
## 1 Introduction
In other words, the complexity of the procedure is shifted from the acquisition phase to the data elaboration. This complexity increases when the target tasks are particularly dissimilar, like SiSt, SuSi, and Roll. Moreover, to the best of our knowledge, no IMU-based evaluation method has ever been proposed to analyze all five PTs mentioned above without modifications to the sensor location or adjustments in the data processing among the PTs. In our study, we chose a single body-attached IMU set-up applied to the trunk during the execution of PTs by a population of healthy subjects and a mixed sample of patients with rehabilitative needs. For the signal processing and data analysis, we modified and adapted the methodology used by [PERSON] et al. [25]. In their work, the authors presented a set of functional parameters for the assessment of a postural transition. However, the task considered was a complex transition, i.e., lie to sit to stand to walk. For this reason, only some of those parameters may be reasonably applied to the assessment of other PTs, and adaptations to their definition may be expected. A set of modified parameters, less prone to time dependency, was proposed in a previous work by [PERSON] et al. [26] that only focused on the analysis of sit to stand: the current work inherits that knowledge, improving the study and extending it to the different PTs. This study, therefore, has multiple aims: (i) to preliminarily verify the ability of our single-IMU set-up to differentiate a population of healthy subjects from one of pathological subjects, (ii) to define meaningful, rigorous, and task-independent parameters for movement assessment in a clinical context during PT execution, and thus (iii) to design and propose an exploratory evaluation paradigm that lays the foundation for future clinical applications.