esa-sceva/satcom-chunk-collection huggingface dataset:
# A Power Control and Intervention Algorithm for Co-Existing IMT Base Stations and Satellite Services
###### Abstract  
IMT-2020 (International Mobile Telecommunications-2020) is the prevailing mobile communication technology at the moment, significantly affecting societal progress. Nevertheless, the roll-out of the IMT-2020 system has introduced numerous interferences to existing services. The coexistence with fixed satellite services has become a topical issue currently under consideration. This paper discusses the compatibility and interference issues between IMT-2020 and the 14 GHz FSS (fixed-satellite service) uplink, as well as the spectrum access issue solved by artificial intelligence methods. The study shows that the interference from IMT-2020 macro-base stations to FSS space stations exceeds the ITU standard by approximately 10 dB. To control the interference, a partition-based power control algorithm is proposed, which divides ground base stations into multiple areas and virtualizes each area's base stations into a single large base station then applies power control to maximize the total transmission power of the base stations within the area. Furthermore, three intra-partition power control algorithms are introduced: average power allocation, power allocation based on channel gain, andna power allocation method based on the maximum intra-partition sum rate. Additionally, under the assumption that dynamic satellite nodes are available in the system for ground user access, a spectrum access algorithm utilizing deep reinforcement learning is designed. Simulation results confirm the effectiveness of the proposed scheme, which can reduce the interference from the IMT-2020 system to the FSS service below the threshold, ensuring harmonious coexistence of the two services.  
IMT-2020; fixed-satellite service (FSS); interference management; power control
## 2 Design of the Space-Borne X-Band SAR System
Lastly, it is used to interpret and configure the work instructions and parameters of the satellite platform. The non-uniform hybrid sampling module is mainly composed of the programmable delay line and the high-speed ADC. The minimum time-delay step of the programmable delay line is 10 ps, and the maximum time delay is 5 ns. The time delay of the programmable delay line can be periodically configured through the service provider interface (SPI) interface of the field programmable gate array (FPGA). For the high-speed ADC, the maximum input bandwidth is 6 GHz, the sampling rate is 2.6 GSPS, and the quantization resolution is 14 bit. To receive wideband echoes from 0.5 GHz to 4.0 GHz, the sampling rate of the high-speed ADC is set to 2.5 GSPS, and the time-delay step of the programmable delay line is set to 100 ps. Therefore, the equivalent sampling interval of non-uniform hybrid sampling is 100 ps, and four consecutive pulse sampling periods can complete the sampling and quantization of one wideband echo signals. The main parameters of the space-borne high-resolution X-band SAR system are listed in Table 1. \begin{table}
\begin{tabular}{c c} \hline \hline
**Description** & **Value** \\ \hline Center frequency & 9.75 GHz \\ Bandwidth of the SAR signal & 3.5 GHz \\ Polarization of the antenna & VV \\ Size of the antenna & 0.28 m (A) \(\times\) 0.28 m (R) \\ Peak power of the transmitted signal & 40 dBm \\ Pulse width of the transmitted signal & 100 ns\({}^{-}\)10 us \\ Gain of the RF receiver & 50–70 dB \\ Real-time sampling rate of the high-speed ADC & 2.5 GSPS \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The main parameters of the proposed X-band SAR system. Figure 2: Diagram of the X-band SAR with the non-uniform hybrid.
## 3 Results and Discussion
Overall, the increasing presence of Cs in the RFX structure decreases its thermal stability, which could be attributed to the already observed increasing disorder or defects. Figure 1: (**a**) Raman and (**b**, **c**) FTIR spectra of Cs–RFX hybrid samples RFX–Cs-0 through RFX–Cs-5 (curves 1 through 6, respectively). FTIR spectra in figure (**b**) range from 4000–400 cm\({}^{-1}\) and in subfigure (**c**) range from 1700–500 cm\({}^{-1}\). Figure 3a,b illustrates the optical photographs and SEM photomicrographs of the six RFX-Cs samples. It was observed from Figure 3a that the color of samples changes gradually from an almost black color for RFX-Cs-0 to a pale yellow color for RFX-Cs-5. This can be attributed to the increasing distance between RF cross-linked segments upon the inclusion of Cs. Further, it was observed that the samples with higher Cs content exhibited less shrinkage than those of low Cs content upon drying. Figure 3b shows that RFX-Cs-3 (image 4) has a distinct topography that starts to form microspheres of RFX-Cs gel rather than interconnected lumps of samples RFX-Cs-0 through RFX-Cs-2 (image 1 through 3, respectively). On the other hand, noticeable changes appear between the morphologies of RFX-Cs-3 and RFX-Cs-4 samples (images 4 and 5, respectively). This could indicate a critical composition between these two samples whereas the behavior changes considerably, which is worthy of future investigations. Samples RFX-Cs-4 and RFX-Cs-5 (images 5 and 6) show more voids between in the matrix of the gel, which is consistent with the observation obtained from Figure 3a. Therefore, the presence of Cs into the matrix of RFX samples has a significant effect on both the visual and morphological properties of the resulting hybrid RFX-Cs gels. Figure 2: (**a**) XRD patterns and (**b**) TGA thermograms of the samples RFX–Cs-0 through RFX-Cs-5 (curves 1 through 6, respectively).
# KCFS-YOLOv5: A High-Precision Detection Method for Object Detection in Aerial Remote Sensing Images
## 1 Introduction
Although the above algorithm has excellent detection effect in a single category of object detection, the applicability is poor because remote sensing images often contain different categories of objects. Aiming at the problem of object detection in aerial remote sensing images, an improved YOLOv5 object detection method called KCFS-YOLOv5 was proposed. The improved algorithm realizes the high-precision detection of multi-scale objects in aerial remote sensing images such as aircraft, vehicles, baseball fields and other common remote sensing objects. We used KCFS-YOLOv5 detection method in three aerial remote sensing image datasets [22; 23; 24], and achieved better detection average accuracy than the existing algorithms. The innovation of our KCFS-YOLOv5 is reflected in the following aspects:  
* We adopted the K-means++ clustering method to optimize the initial clustering points of anchor box. * We embed the CA attention module into the backbone network of the YOLOv5 to improve the feature extraction ability of the algorithm for objects in aerial remote sensing images. * We embedded BiFPN in the neck network of YOLOv5 to optimize the network framework to strengthen the fusion ability of the neck network for different dimensional features. * Based on main characteristics of aerial remote sensing images which contain many tiny objects, we added a new tiny object detection head based on the conventional YOLOv5 to preserve the texture features of tiny objects and improve the detection accuracy for tiny objects. * We selected the SIoU loss function to replace the conventional GIoU loss function to reduce the deviation between the predicted box and the ground truth box which can improve the detection performance. The rest of our article is arranged as follows: Section 2 recommends the universal advanced object detection modules and their practical value of object detection in aerial remote sensing images, and the conventional YOLOv5 object detection algorithm model is also introduced in this section. The proposed KCFS-YOLOv5 object detection method is covered in Section 3. Section 4 presents the simulation environment and the preparation for experiments. The simulation results and the discussion are demonstrated in Section 5 and Section 6, respectively. The conclusion is presented in Section 7.
## 1 Introduction
Furthermore, the structural and optical properties of Cys-AuNCs and Cys-AgNCs were characterized using transmission electron microscopy (TEM), ultraviolet-visible (UV-Vis) spectroscopy, fluorescence spectroscopy, and Fourier transform infrared (FTIR) spectroscopy. Antibacterial activities of Cys-AuNCs, Cys-AgNCs, and AgNPs were respectively investigated against _E. coli_. Moreover, _E. coli_ incubated with Cys-AgNCs, Cys-AuNCs, and AgNPs were separately cultured on LB agar plates and their viabilities were also calculated. Electron paramagnetic resonance (EPR) spectroscopy was applied to detect the precise free radicals and species with unpaired electrons induced by Cys-AgNCs and Cys-AuNCs in _E. coli_. The detailed mechanism of the ligand of cysteine as an ROS scavenger was investigated for the elimination of ROS caused by Cys-AgNCs and Cys-AuNCs in _E. coli_. To demonstrate the bacterial bioimaging application, confocal microscopy was employed with the use of Cys-AuNCs as a fluorescence probe for _E. coli_.
## 6 Simulation Results and Discussions
### Different Hardware Impairments  
Figure 8 reveals that the impact of RIS HWI diminishes in high SNR regions. In particular, when the moving distance is from 400 m to 600 m, i.e., the system is in a high SNR region, then the HWI in transceivers mainly decreases the SE instead of the RIS HWI. For example, the case \(a=\pi/6\), \(\kappa_{t}=\kappa_{r}=0.07^{2}\) performs the worst with the high SNR, but after 700 m, its performance is the same as \(a=\pi/2\), \(\kappa_{t}=\kappa_{r}=0.03^{2}\). In other words, after 700 m, the main factor that decreases the SE is the RIS HWI. Besides, in high SNR regions, the SEs of the two cases with \(\kappa_{t}=\kappa_{r}=0.03^{2}\) are higher than the case with \(\kappa_{t}=\kappa_{r}=0.07^{2}\). However, if the SNR becomes lower (e.g., the moving distance larger than 600 m), the impact of the RIS HWI becomes severe.  
Figure 7: Delay spread for different spacings of RIS. \(K=4,8\).  
Figures 9 and 10 validate _Special Case 2_. The figures show the relationship between the highest SE for one vehicle pass and different \(K\) (\(P_{t}\)) when the optimal phase shift set (33) is used. It can be seen that when \(K\) (\(P_{t}\)) becomes large, the main factor that decreases the SE is transceiver HWI, i.e., \(\kappa_{t}\) and \(\kappa_{t}\). For example, the SE grows significantly from 3.5 bit/s/Hz to 6.5 bit/s/Hz when the transmit power \(P_{t}\) is from 0 dBm to 20 dBm. When \(P_{t}=30\) dBm, however, there is almost no SE increment. The same pattern can also be observed when \(K\) increases from 2 to 16. Besides, the RIS HWI has minimal impact on SE when \(P_{t}=30\) dBm or \(K=16\).  
Figure 8: Spectral efficiency of the multi-RIS system with different HWI for one vehicle pass. \(K=8\).  
Figure 9: The highest spectral efficiency for one vehicle pass with different transmit power \(P_{t}\). \(K=4\), \(M\times N=128^{2}\), and \(P_{t}=0,10,20,30\) dBm. Note that the upper bound for SE is based on (42).
## 4 Extraction Methods
The main mechanism behind EAE is the enzymatic degradation of the plant matrix, which improves mass transfer and reduces resistance to solvent penetration [66]. EAE offers several benefits. It significantly increases extraction yield: for example, [PERSON] et al. [62] showed that applying pectinase to grape skins improved polyphenol extraction by more than 60% compared to untreated samples. Moreover, the process is conducted under mild conditions--typically 30-55 \({}^{\circ}\)C and pH 4-6--which preserves thermolabile polyphenols such as gallic acid, catechins, or ellagic acid [67]. It also enables the use of green solvents such as water or ethanol, aligning with principles of sustainable processing [63]. Finally, the polyphenol-rich extracts obtained via EAE often demonstrate enhanced biological activity, including antioxidant and antimicrobial effects, due to increased release of free and bound phenolic forms [68]. However, EAE also faces limitations. The high cost of purified enzyme preparations can restrict its industrial scalability [66]. Moreover, the process must be carefully optimized in terms of enzyme concentration, incubation time, and matrix composition to prevent over-hydrolysis or the degradation of sensitive compounds [67]. EAE is most effective when tailored to a specific substrate and can be further enhanced in combination with physical techniques such as ultrasound or microwave-assisted extraction [69]. Extraction conducted with supercritical fluids provides additional functionality. Controlling the parameters of the process conditions enables tuning of solvent properties. This phenomenon may change the mechanism of tannin extraction involving secondary reactions such as hydrolysis or depolymerization. Therefore, solvent choice, temperature, and pressure may be crucial for the composition of tannin extract in terms of both polyphenolic compounds and impurities.
## 3 Algorithms  
The design characterization is performed based on the resonator geometry and dimensional scaling, as well as through the coupling matrix (CM). The input-output coupling factors and inter-resonator coupling coefficients are derived from the external quality factor \(Q_{e}\) expression and even-odd mode analysis, respectively.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Parameter** & **DRCF** & **Microstrip** \\ \hline Frequency stability & Excellent & Moderate \\ Quality factor (Q) & High & Low \\ Insertion loss & Low & High \\ Size/Envelope & Bulky & Compact \\ Mass & Heavy & Light \\ Manufacturing complexity & High & Low \\ Temperature stability & Lower & Higher \\ Production cost & High & Low \\ Tuning flexibility & Mechanical screws & None \\ Assembly complexity & High & N/A \\ \hline \hline \end{tabular}
\end{table}
Table 1: DRCF vs. microstrip filter comparison for space applications.  
Figure 6: Implementation of zeros in a third-order DRCF [16].
# Network Allocation Vector (NAV) Optimization for Underwater Handshaking-Based Protocols
## 3 Optimization of NAV
### Extra NAV  
In the case where a neighbor tries to communicate with the destination, and the source is located between the neighbor and the destination or the neighbor is located between the source and the destination, an additional silent period that we have named _extra NAV_ is required to avoid possible collisions at the source. Figure 7 shows the case where the source is located between a neighbor and the destination, and the neighbor tries to send an RTS to initiate new communication with the destination. The RTS departing from the neighbor (point P), after the \(NAV^{DATA}\) obtained by Equation (9) and a random back-off (denoted by the contention window in Figure 7), may collide at the source with the ACK that is supposed to be sent by the destination. In order to avoid such a collision, the neighbor should be silent during an additional period of \(2T_{ACK}\), which is the _extra NAV_. In Figure 7, the point D denotes the time when the ACK reception is expected to be completed at the source and the point C is \(2T_{ACK}\) ahead of D. Then, the period between points C and D is when the RTS arriving from the neighbor may collide with the ACK from the destination. Considering \(\tau(S,N)\), the period between points A and B corresponds to the period between C and D, where  
\[B=D-\tau(S,N) \tag{13}\]  
\[A=B-2T_{ACK} \tag{14}\]  
In order to avoid collision, point P should be controlled such that it is not located within the period between A and B. That is, the neighbor needs that extra NAV. On the time line of the neighbor, point B is easily obtained as  
\[B=D^{DATA}-2\tau(S,N) \tag{15}\]  
In a similar manner, the extra NAV for the case where a neighbor is located between the source and the destination is also obtained. Regarding the node arrangement, a neighbor is able to calculate the arrangement based on the propagation delays \(\tau(S,D)\), \(\tau(S,N)\), \(\tau(D,N)\) which are calculated or obtained by referring to the message fields--_TimeStamp_ and _Prop.Delay_.  
Figure 7: Extra NAV setting during destination–source–neighbor deployment.
## 3 Results and Discussion  
### Laser Alignment and Tip Engagement  
For an OBD based AFM system, laser alignment is a critical step prior to imaging. In our AFM setup, the laser spot can be adjusted precisely to the back of a cantilever using an \(XYZ\) translation stage which is attached with the objective lens. Next, a smaller \(XYZ\) translation stage (attached with the photodiode module) is fine-tuned so that the reflected laser beam is on the center of the photodiode module. Using NI ELVISmx (National Instruments Corp, version 4.4, Austin, TX, USA), this task can be achieved effectively. NI ELVISmx is the LabVIEW-based software that is used to support NI ELVIS hardware. It provides functionality of common laboratory instruments such as the oscilloscope, the  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Item** & **Cost** \\ \hline Personal computer & \$500 \\ NI ELVIS & \$4000 \\ Piezo positioning system & \$3800 \\ Optical components & \$900 \\ \(XYZ\) manual translation stages & \$2500 \\ Custom-built mechanical parts & \$500 \\ Signal conditioning electronics and photodiode & \$100 \\
**Total** & \$**12,300** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Approximate cost of major components.  
Figure 9: GUI with controls and indicators for AFM image acquisition.  
function generator, and the multimeter. As shown in Figure 10, the soft front panel oscilloscope of NI ELVISmx is configured to display the deflection signal from the signal conditioning module. When the reflected laser beam is exactly on the center of the photodiode, the measured voltage will be 0 V.  
After laser alignment, a tip engagement process is required to bring the sample into contact with the AFM cantilever. The software is first started with zero scan size (no motion in the \(X\) and \(Y\) directions). The subroutine of the feedback controller is activated at the same time. Since there is no contact between the tip and the sample initially, the \(Z\)-piezo is fully extended in anticipation of engagement due to the feedback mechanism. The micrometer screw of the \(XYZ\) translation stage is slowly adjusted to move the piezo positioning stage towards the tip. When the sample first comes into gentle contact with the cantilever, the value of the deflection signal changes quickly and is equal to the user defined set point. The \(Z\)-piezo is retracted slightly in order to maintain the set point value. At this point, the system is ready for the actual scanning process.
## 5 Technologies Stack
### Server-Side  
One advantage of the microservices architecture is the flexibility it offers. Each microservice can be developed using various technologies to accommodate different development teams or to reuse microservices from other applications. For consistency, all of the microservices of the system were developed with the Spring Boot framework [45] backed by the Java programming language [46].  
Java features automatic memory management, strong type-checking, and a vast ecosystem of libraries. The Java programming language is an industry standard when it comes to API and server-side application development [47]. A very popular library used for the implementation of the microservices described previously is Spring Boot.  
Spring Boot is an open-source framework designed to simplify the development of Java applications. It streamlines the setup and configuration, promoting convention over configuration. With embedded servers, it eliminates the need for external deployment.  
Figure 6: Application diagram with storage layer.  
Spring Boot provides a rich set of pre-built components for common tasks, enabling rapid development and reducing boilerplate code. Its modularity allows developers to choose only the required features, enhancing efficiency.
# Performance Validation of Control Algorithm Considering Independent Generator Torque Control in PCS
## 2 Wind Turbine and Control Algorithm
### Control Algorithm
#### 2.2.1 Power Regulation Algorithm  
The power control of the targeted wind turbine, which is managed by the PLC, was designed to apply only pitch control. Typically, the pitch control of a wind turbine operates to maintain rated power in regions exceeding the rated wind speed, such as Region III. Pitch control uses PI or Proportional-Integral-Derivative (PID) control to maintain the rated generator speed, and this study adopted PI control [12; 13]. Figure 3 shows the structure of the pitch PI control, where the measured generator speed and the error from the rated generator speed serve as inputs for the PI control. In this case, the control mode is input as a value of 0 or 1 based on the output of the mode switch logic for control mode transitions. When 0 is input, the control algorithm perceives the current operating condition as Region II and calculates the fine pitch angle to be transmitted as the pitch command. Conversely, when 1 is input, it recognizes the current operating condition as Region III and transmits the pitch command calculated through the PI control [13].  
Figure 1: Target wind turbine (100 kW model).  
In the actual targeted wind turbine, torque control was implemented through the PCS torque control, which cannot be managed by the PLC. In this study, to implement the PCS torque control in the simulation, the generator torque values corresponding to the generator speed applied in the PCS were formatted as a schedule, ensuring that there were no other parameters or signals influencing the calculation of torque commands aside from the generator speed. That is, the PCS torque control generates torque commands independently of the output values of the mode switch logic.
# Food Passports and Intelligent Food Recipes: The Data-Oriented Way of Producing Food
## 2 Methodological Approach and Reference Architecture
### Methods
#### 2.1.1 Digital Food Identity Card Requirements
_The DFIC should include information about the energy efficiency of processing the food it describes using different processes._  
As mentioned before, different foods will have different thermal properties [25], which directly influences the amount of energy that needs to be utilized to process them. Energy efficiency has been pin-pointed has a priority area for the industry [3] and, as such, the DFIC should, whenever possible, include such data. **Requirement 5**. _The DFIC should include information about the processes that were utilized to prepare the food it describes as well as the relation of those processes with the other foods mentioned in the DFICs from Requirement 4._  
This requirement is directly connected to the previous one, whereby transparency but also the correct calculation of aggregated sustainability metrics (e.g., energy efficiency) for the whole product requires that measurable process information is included. **Requirement 6**. _The DFIC should have the ability to track the state of the food it describes along several stages of that food's handling and usage: production, transportation, storage, sale and consumption._  
Unlike other products, a large number of food products are perishable. Variations in their handling may decisively affect their properties to different degrees. The information in the DFIC must therefore be able to track and monitor such deviations as well as adjusting the technical products of the described food accordingly. **Requirement 7**. _The DFIC will update its information autonomously by directly collecting data from different systems whereby the food it describes is being handled._  
To fully fulfill Requirement 6, the DFIC needs, ideally, to have access to relevant system and sensorial data; otherwise, its abilities to dynamically adjust food data are very limited.