esa-sceva/satcom-chunk-collection huggingface dataset:
# Design and Control of an Ultra-Low-Cost Logistic Delivery Fixed-Wing UAV
## 4 Flight Control System
### Implementation and Verification of the Control Algorithm  
The control algorithm is divided into three primary layers: control, stabilization, and maneuvering. The control layer plans the UAV's trajectory and desired posture based on mission requirements and GPS data [73]. The stabilization layer calculates the UAV's attitude error and desired angular velocity, leveraging the expected posture and sensor data. The maneuvering layer computes the UAV's control inputs from the expected angular velocity and sensor data, outputting them to the actuators.  
The control algorithm is predominantly written in C and operates on the STM32F103 microcontroller. This research employs Keil \(\upmu\)Vision5 as the development environment, FreeRTOS as the real-time operating system, and the HAL library for hardware abstraction.  
Hardware-in-the-loop simulations (HILSs) and field flight testing are employed for the evaluations. HILS is a methodology that connects actual hardware with computer  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Command** & **Value** & **New Value** \\ \hline INS\_GYR1\_CAL & 39.49276 & 17.75362 \\ INS\_GYR2\_CAL & 22.48535 & 12.24728 \\ INS\_GYR20 FFS\_X & 0.02180705 & \(-\)0.002505591 \\ INS\_GYR20 FFS\_Y & \(-\)0.007266462 & \(-\)0.008429104 \\ INS\_GYR20 FFS\_Z & 0.007931517 & 0.006754024 \\ INS\_GYR3\_CAL & 37.83039 & 25.74678 \\ INS\_GYR30 FFS\_X & 0.007584155 & \(-\)0.003129626 \\ INS\_GYR30 FFS\_Y & \(-\)0.03437863 & \(-\)0.02893007 \\ INS\_GYR30 FFS\_Z & 0.01442171 & 0.01368616 \\ INS\_GYR0 FFS\_X & 0.002235635 & 0.006046154 \\ INS\_GYR0 FFS\_Y & 0.005703443 & 0.01224557 \\ INS\_GYR0 FFS\_Z & \(-\)0.01396238 & \(-\)0.01341222 \\ STAT\_BOOTCNT & 163 & 161 \\ STAT\_RUNTIME & 67,684 & 62,540 \\ TECS\_PITCH\_MAX & 20 & 15 \\ TKOFF\_THR\_DELAY & 2 & 0 \\ TKOFF\_THR\_MT & 15 & 0 \\ TKOFF\_THR\_MT & 1 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Comparison between the optimized control rate and the traditional control rate.  
simulation models, allowing for effective control-algorithm testing and debugging without the necessity of flights [74]. This research utilizes MATLAB/Simulink as the simulation platform and X-Plane for the flight dynamics model, with serial and UDP communications facilitating data transmission, thus implementing the UAV's HILS system. Field flight tests serve to ultimately validate the control algorithm, assessing its performance and robustness in real-world settings. Multiple flight trials have confirmed the UAV's capabilities for autonomous flight, stability, and maneuvering, as well as the completion of reconnaissance, identification, and strike missions.
# Evaluation of 5G Positioning Based on Uplink SRS and Downlink PRS Under LOS and NLOS Environments
## 2 Methodology
### The 5G Signal Design of PRS
For each configured downlink PRS resource, the user will assume that the sequence \(r(m)\) is multiplied by an amplitude scaling factor \(\beta_{PRS}\) and mapped to the resource elements as given by  
\[a_{k,l}^{(p,\mu)}=\beta_{PRS}\cdot\ r(m) \tag{9}\]  
The maximum value of index m is constrained by the requirement that all resource elements \(\left(k,l\right)_{p,\mu}\) associated with the PRS must be located within the resource blocks assigned for the downlink PRS resource. \[k=mk_{comb}^{PRS}+\left(\left(k_{offset}^{PRS}+\hat{k}\right)\ mod\ k_{comb}^{ PRS}\right) \tag{10}\]  
Furthermore, an additional frequency offset, denoted by \(\hat{k}\), can be introduced to diversify subcarrier allocations across symbols in the time domain. In order to mitigate the issue of mutual interference between PRS transmissions from neighboring base stations or sectors, a frequency offset will be applied. The resource-element offset parameter, denoted by \(k_{offset}^{PRS}\), which is defined within the range of \(\left\{0,1,\ldots,k_{comb}^{PRS}-1\right\}\) enables up to \(k_{comb}^{PRS}\) distinct PRS configurations within a single PRS resource. This configurable offset facilitates flexible frequency planning and orthogonal PRS allocation, thereby improving positioning accuracy and minimizing cross-correlation among PRS signals.
## 3 Results and Discussion
### Adsorption Studies
#### 3.2.8 Experiments with Real Contaminated Water  
Industrial effluents typically contain a variety of metal ions and other pollutants. To implement the biosorption process in practical applications and better understand ion competition, increasing investigations are conducted on multicomponent systems. However, most studies utilize synthetic solutions, which do not fully represent real environmental conditions where effluents contain diverse pollutants, including organic compounds that interact with each other [29; 45; 46].  
In this study, the sorbent MO, which demonstrated the highest adsorption capacity, was employed to explore the potential for purifying actual polluted water from the Gelev Chuchur area near the Asarel-Medet mining enterprise in Bulgaria. This area serves as the collection point for drainage water from the Asarel mine. Quantitative multielement TXRF analysis revealed that the water sample contains the following elements: Ca (219 mg L\({}^{-1}\)), Cu (184 mg L\({}^{-1}\)), Mn (22.3 mg L\({}^{-1}\)), K (21.6 mg L\({}^{-1}\)), Zn (5.1 mg L\({}^{-1}\)), Fe (3.8 mg L\({}^{-1}\)), and Ni (1 mg L\({}^{-1}\)). Notably, the copper concentration exceeds the maximum permissible limits by several times. Additionally, the measured pH was 3, indicating high acidity.  
The results showing the degree of purification after the first, second, and third adsorption cycles are presented in Figure 14. Three cycles of adsorption were performed, revealing a purification efficiency of 40.3% after the first cycle, 74.7% after the second, and 90.3% after the third cycle (cumulative adsorption percentage). This indicates that using the plant material for three consecutive cycles can achieve near-complete purification of Cu(II)-contaminated water from the studied source.  
For comparison, the adsorption efficiency of Cu(II) ions in a model solution containing only Cu(II) ions at a concentration of 200 mg L\({}^{-1}\) and the same pH was 84% after the first  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Sorbent** & **C, at.\%** & **O, at.\%** & **N, at.\%** & **Cu, at.\%** \\ \hline MO & 79.21 & 17.93 & 2.86 & - \\ MOS & 83.24 & 14.59 & 1.66 & 0.51 \\ M & 79.46 & 18.35 & 2.20 & - \\ MS & 78.40 & 19.35 & 1.76 & 0.49 \\ LA & 84.71 & 13.14 & 2.15 & - \\ LAS & 83.80 & 14.47 & 1.42 & 0.31 \\ L & 86.72 & 11.73 & 1.54 & - \\ LS & 81.35 & 15.70 & 2.46 & 0.49 \\ \hline \end{tabular}
\end{table}
Table 6: Percent content of C, O, N \(u\), and Cu (in atom %) for investigated plant materials.  
Figure 14: Degree of purification of contaminated water (in %) with the aid of plant material MO after I, II, and III cycles.  
cycle--approximately double the efficiency observed with the real sample. This difference highlights the impact of other ions present in the polluted water. These ions also compete for adsorption sites on the sorbent surface, reducing the overall adsorption capacity for Cu(II) ions.
# Experimental and Modeling Study for the Solar-Driven CO2 Electrochemical Reduction to CO
## 1 Introduction
Despite several studies reporting the connection between the PV and EC and the modeling of the two components, to the best of our knowledge, there is a lack of a complete simulation of the whole system. In this framework, the present study introduces, for the first time, a comprehensive model for a solar-driven CO2 electroreduction to CO, developed using the COMSOL(r) Multiphysics software 6.3. The model combines experimental data with advanced simulations, offering an in-depth analysis of the system's performance under various operating conditions. Key parameters such as the applied voltage, electrolyte flow rate, and cell dimensions have already been examined in previous work [21]: in particular, the model was able to reproduce the experimental results, along with inspecting the chemical conditions inside the electrochemical cell (such as the pH, ionic species diffusion on the boundary layer, etc.). In the present work, two further steps are implemented: (1) A time-dependent analysis, and (2) the integration of a dye-sensitized solar cell (DSSC) module as the energy source, which in this setup, allows for an evaluation of the device's performance as a function of solar irradiance, supporting the goal of maximizing solar-to-fuel efficiency. For this purpose, the equivalent circuit model for the photovoltaics module is exploited, where the key electrical parameters have been fit to experimental I-V curves. This research not only validates the model against the experimental results but also opens possibilities for future optimization and scale-up of the technology, making it a valuable tool in the pursuit of sustainable, solar-driven chemical production.
# Review and Evaluation of MAC Protocols for Satellite IoT Systems Using Nanoatellites
## 5 MAC Protocols for Satellite IoT and M2M
### Aloha-Based Protocols
#### 5.1.3 Spread Spectrum Aloha (SS-Aloha)  
The protocol is proposed to provide random multiple access over an unsynchronized channel. SS-Aloha uses spread spectrum techniques to send messages; it is similar to a CDMA protocol where each terminal uses the same code to spread the signal and accesses the channel without coordination (like in Aloha) [53]. The multiple access capability is given by the large bandwidth employed instead of the assignment of different codes. In Figure 5, we illustrate the responses of a correlator detector (at the satellite's receiver) applied to signals from one terminal (see Figure 5a) and four terminals (see Figure 5b). In this example, a spreading factor \(SF=60\) is used, in consequence, 60 chips are placed between two consecutive bits from the same terminal. In order to achieve multiple access, spread spectrum Aloha (SSA) makes use of the offset in chips between two signals, so in Figure 5b, the messages from the four terminals are still decodable. Previous evaluations of the SS-Aloha protocol in a satellite environment are reported in [54].  
Regarding the performance of SS-Aloha, thanks to the use of proper error correction codes, the protocol achieves a maximum normalized throughput close to \(S\simeq 0.6\) for a given load of \(C\simeq 0.7\). In the case of \(PLR=10^{-3}\), the system supports a load of \(C\simeq 0.5\)[5]. This protocol has a reduced level of complexity because it does not require synchronization. However, the spread spectrum technique has a strong dependency on the signal to noise plus interference ratio (SNIR) threshold in the demodulator to operate correctly. SS-Aloha shows to be efficient in terms of energy consumption; similar to Aloha, SS-Aloha only requires the sending of data packets with no need to send extra control signaling or synchronization information.  
The simplicity required in transmitters in addition to the good performance of SS-Aloha, compared to previous protocols, may make this protocol suitable for IoT scenarios. However, when employed in LEO satellite links, where the expected power imbalance is high, the performance of this protocol drops drastically, behaving similarly to S-Aloha.  
Figure 5: In (**a**), the signal received by the satellite from one transmitter. In (**b**), the signal received by the satellite from four transmitters; signals are still decodable because of the offset of chips between the different terminals. Figure adapted from [55].
## 3 Results
### Study Characteristics  
#### 3.2.1 Design  
Four retrospective studies [24; 25; 26; 27] two cross-sectional studies [28; 29] and one pilot randomized controlled trial [12] were included in the present systematic review. The studies were conducted in Chile [24], China [25], Morocco [26], Pakistan [27], Serbia [28], Spain [29] and Syria [12]. The number of participants ranged from 30 to 98; a total of 486 subjects participated in these studies. Five studies [24; 25; 27; 28; 29] indicated craniofacial or craniocortical anomalies as exclusion criteria for involved patients. Moreover, six papers specified eliminating participants with a history of orthodontic treatment [24; 25; 26; 28; 29]. Considering respiratory anomalies, obstructive sleep apnea was mentioned as an exclusion criterion in three studies [26; 28; 29]. Lastly, four studies [24; 26; 27; 29] stated including patients with no history of surgery in the head area. In terms of classification of the participants, three studies [24; 25; 28] grouped them by the class of malocclusion, while a retrospective cohort study [27] and a pilot randomized controlled trial [12] analyzed orthodontic functional appliances and divided subjects into ortho-treatment groups and controls. One retrospective study [26] and cross-sectional study [29] focused on mouth and nose breathers.
## 3 Results  
### Aspangus Antioxidants Variation: Effect of UV-C Treatment, Storage Time, and Their Interaction  
All phenolic acid, flavonoid, and antioxidant activity levels were affected by the UV-C treatment at nearly every evaluated intensity (Table 1 and Supplementary Table S1). TPC content significantly increased after UV-C2 by about 10.6% and the content did not vary up to UV-C4. TFC at UV-C1 increased significantly by about 36% and reached the highest value; the further application of UV-C2 and UV-C3 did not show a significant variation, but a significant drop was observed at UV-C4 with respect to UV-C1. ABTS at UV-C1 was significantly affected with a decrease of 8.85%; at the subsequent UV-C levels, significant increases were observed, and the highest value was measured at UV-C4 with an increase of about 14.5%. When compared to the control, DPPH showed a significant increase up to UV-C2 of about 7.5%, but the UV-C3 and UV-C4 applications had no effect, and the antioxidant activity (AA) measured at these intensities was the same as the control. Chlorogenic acid also showed a similar increasing trend up to UV-C2, but a significant drop in UV-C3 and UV-C4 of about 28.6% was observed. In respect of the control, ferulic acid increased significantly at UV-C3 and UV-C4 (7.7% and 23.2%, respectively) while caffe acid increased significantly only in UV-C2 samples; rutin, quercetin, and kaempfer-3-O-rutinoside significantly increased up to UV-C2 of 14.3%, 27.03%, and 21.25%, respectively. Storage time (Table 2 and Supplementary Table S1) affected the content of all phenolic acids, flavonoids, and antioxidant activities. The TPC, after a slight decline at T1, showed a significant increase at T3 (7.19% compared to T1). The TFC showed a constant decrease throughout the storage times, with a loss of 15.66% going from T0 to T3. The antioxidant activity measured by ABTS showed no significant difference during the storage time, except for T2, where it reached the highest and significant value. On the contrary, no variation over time was observed for the AA measured by DPPH, which remained constant until T2. Only at T3 was a significant decrease of 5.88% measured. These behaviors were associated with variations in phenolic acids and flavonoids. Chlorogenic acid and trans-cinnamic acid showed a notable variation over time, with a minimum increase going from T0 to T1 (9.64% and 5.14%, respectively) but almost three-fold and four-fold at T2 and T3, respectively. Quercetin also showed an increasing trend over time, reaching 55% more at time T2 and
# Enhancing Flow Field Performance of a Small Circulating Water Channel Based on Porous Grid Plate
## 4 Discussions of the Fluid Field Characteristics
### Influences of the Screens
#### 4.2.3 Influences of Screen Spacing \(d\)  
Three-layer screens with the porosity of 0.6361 are used for rectification. The turbulence intensity contours of the cross-sections at different locations are shown in Figure 16, and the maximum and minimum turbulence intensity values corresponding to each section are given. As can be seen, the turbulence intensity of the fluid field gradually decreases after the rectification of the honeycomb and screen, and with the increase of the spacing between the screens, the turbulence intensity of the test section decreases first and, then, increases.  
Figures 17 and 18 show the flow characteristics of the test section when the screens are arranged at different spacings. It can be seen that screens with too-large or small spacings will cause an increase of the velocity fluctuation intensity in the test section. The turbulence intensity gradually decreases along the axial direction and gradually increases from both sides to the middle along the lateral direction. Accordingly, the results of the velocity inhomogeneity and average turbulence intensity in the test section are given in Table 7. With the increase of the spacing between the screens, the velocity inhomogeneity and average turbulence intensity of the fluid field in the test section all first decrease and, then, increase. Therefore, the spacing between the screens should be moderate. In this work, 50 mm is selected as the screen spacing to obtain the required flow characteristics.  
Figure 15: The pressure distribution contours when the screens with different porosities are selected.  
Figure 16: The turbulence intensity contours of the cross-sections at different locations.  
Figure 17: The velocity distribution characteristics of the fluid field when the screens are arranged at different spacings.
# Vehicle Detection with Self-Training for Adaptative Video Processing Embedded Platform
###### Abstract  
Traffic monitoring from closed-circuit television (CCTV) cameras on embedded systems is the subject of the performed experiments. Solving this problem encounters difficulties related to the hardware limitations, and possible camera placement in various positions which affects the system performance. To satisfy the hardware requirements, vehicle detection is performed using a lightweight Convolutional Neural Network (CNN), named SqueezeDet, while, for tracking, the Simple Online and Realtime Tracking (SORT) algorithm is applied, allowing for real-time processing on an NVIDIA Jetson Tx2. To allow for adaptation of the system to the deployment environment, a procedure was implemented leading to generating labels in an unsupervised manner with the help of background modelling and the tracking algorithm. The acquired labels are further used for fine-tuning the model, resulting in a meaningful increase in the traffic estimation accuracy, and moreover, adding only minimal human effort to the process allows for further accuracy improvement. The proposed methods, and the results of experiments organised under real-world test conditions are presented in the paper.  
vehicle tracking; vehicle detection; self-training; unsupervised domain adaptation Article
## 2 Motivation and Contribution  
Our literature review underscores the potential of cooperative relay assistance in enhancing overall communication system performance through interoperability among different communication technologies. While considerable research has been conducted on cooperative relay-assisted RF-VLC, RF-THz, VLC-FSO, and FSO-THz configurations, to the best of the authors' knowledge at the time of writing, no studies have been published on the performance analysis of cooperative relay-assisted THz-VLC systems in terms of BER and outage probability.  
Given the notable improvements in system performance achieved through the integration of THz and VLC technologies, this study endeavors to explore the potential benefits of a THz-VLC communication link facilitated by a DF relay architecture. The analysis focuses on BER, with the derivation of closed-form expressions for E2E metrics, marking the first attempt in the literature to do so.  
The main contributions of this paper can be summarized as follows:  
* The proposal of a dual-hop UAV-assisted communication system employing a DF relay architecture, utilizing THz communication in the primary hop and VLC in the secondary hop.
* The derivation of closed-form expressions for E2E BER with and without the presence of THz pointing errors.
* The presentation of simulation results to investigate the performance of the proposed system. A comparative analysis of BER and outage probability performances between the proposed system and FSO-VLC and RF-VLC configurations is conducted to highlight the advantages of integrating THz with VLC.
# Current Filaments in Asymmetric Surface Dielectric Barrier Discharge
## 5 Experimental Results
### Rogowski Coil
#### 5.1.2 Charge Analysis  
Next, we consider the charge \(Q\) (peak area) associated to bursts. The distributions of charge, \(P(Q)\), are shown in Figure 9a for FS, and in Figure 9b for BS, respectively. The distributions are plotted in semi-logarithmic scale for clarity of presentation.  
For a more quantitative analysis, we consider the charge distribution \(P(Q)\) for a single HV, in the illustrative case HV = 5.48 kV, shown in Figure 10. The two half-cycle responses, the FS and BS, are plotted for comparison. Both distributions display a maximum near \(Q=0\), while for BS, the decay is approximately exponential at large \(Q\).  
Thus, for BS we use the exponential form to fit the charge distributions,  
\[P(Q)=\frac{1}{Q_{0}}\exp\left(-\frac{Q}{Q_{0}}\right), \tag{3}\]  
where \(Q_{0}\) is a characteristic charge. The values of \(Q_{0}\) are shown in Figure 11a, as a function of the applied HV. As one can see, the characteristic charge \(Q_{0}\) first increases with HV, reaching a plateau for HV larger than \(\simeq 5.4\) kV. The latter can be seen as the separation voltage between two different regimes, similar to that found for DBD [26]. The plateau value \(Q_{0}\simeq 0.7\) nC suggests that BS bursts reach a maximum saturation charge at high voltages.  
Figure 8: Distribution of bursts counts vs. HV phase. The FS occurs for phases \(0\leq\phi\leq\pi\), and the BS for \(\pi\leq\phi\leq 2\pi\). Note the conspicuous difference in the counts between the FS and BS phases.  
Figure 9: Burst charge distribution \(P(Q)\) vs. charge \(Q\) for different HV. (**a**) Forward stroke (negative charge). (**b**) Backward stroke (positive charge).  
The corresponding mean charges, \(\left\langle|Q|\right\rangle\), are shown in Figure 11b. The mean charge (absolute) values are very different, \(\sim\)0.35 nC for BS and \(\sim\)(\(-\))0.15 nC for FS. This asymmetry, both in the number and charge of bursts, is consistent with the existence of different discharge mechanisms due to the setup asymmetry.