esa-sceva/satcom-chunk-collection huggingface dataset:
# The Holistic Perspective of the INCISIVE Project--Artificial Intelligence in Screening Mammography
## 4 Results to the Existing Data Repository
### INCISIVE Retrospective Data  
_Breast Cancer Mammography Collection_  
The INCISIVE retrospective data repository, as a result of multisite efforts, had to be validated for image and clinical data quality and consistency by following the introduced conventions in terms of data structure, de-identification, and annotation. The process of quality evaluation refers to all segments of the data collection process, and has to ensure the smooth and privacy-preserved usage of medical images in AI model training, as well as the end efficient use of INCISIVE AI models on new/donated images. Mammography, as a main tool in the breast cancer screening and follow-up process, is a cheap and commonly used modality, and has been a relevant focus for AI development. In the retrospective INCISIVE breast cancer database, MG images are present for almost every breast cancer patient (Table 3) and, sometimes, where appropriate/available are followed by breast tomosynthesis (DBT), refined 3D mammography imaging. Table 3 indicates the number of MG/DBT studies (examinations), the number of 2D images, the number of images with an accompanying annotation file, and the number of images without expert annotations. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**DP**} & **MG** & **MG** & **Annotated** & **DBT** & **DBT** & **DBT Annotated** \\  & **Studies** & **Images** & **Images** & **Studies** & **Images** & **Images** \\ \hline AUTH & 41 & 160 & 109 & 0 & 0 & 0 \\ GOC & 49 & 190 & 0 & 4 & 599 & 0 \\ HCS & 1950 & 7282 & 1214 & 3 & 5 & 0 \\ UNS & 4119 & 15,278 & 655 & 1618 & 131,653 & 26 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The number of studies, images, and annotated images for MG and DBT in the INCISIVE retrospective data repository. Figure 8: INCISIVE UI search process (data search and search results pages). All MG images in the database are scanned for duplicates, as well as for the content (MG examination or accidental surgical guidance images), dynamic range, processing status (raw or preprocessed), image size, bit depth, actual dynamic range, manufacturer, and annotation labels. A variety of properties in the collected MG images can be best depicted if the manufacturers, bits available for pixel presentation, bits per pixel stored, image size, and service-object pair (SOP) class used are compared (Table 4). Hospitals can store both raw MG images and processed MG images (\"for presentation\"), but most often only one option is used for scarce memory resources. This information is relevant in AI pipelines and reflected through the SOP field in the DICOM header. \begin{table}
# Asphalt Pavement Transverse Cracking Detection Based on Vehicle Dynamic Response
## 6 Application Effect  
The classification thresholds were set for 100 dB/Hz (PSDmax) and 95 dB/Hz (\(\Delta\)PSD). If the PSDmax \(>\) 100 dB/Hz and \(\Delta\)PSD \(>\) 95 dB/Hz, it is judged to be a transverse crack, and otherwise is classed as normal pavement. We applied this criterion to a large verification set of 139 sections (crack: normal = 14:125). The confusion matrix is shown in Figure 16.  
\[A\mathrm{c}=\frac{TP+TN}{TP+FN+FP+TN} \tag{22}\]  
\[TPR=\frac{TP}{TP+FN} \tag{23}\]  
\[FPR=\frac{FP}{FP+TN} \tag{24}\]  
This study adopts accuracy (Ac), TPR, and FPR as evaluation indicators (Table 4). The overall accuracy is high, reaching 94.96%, which is satisfactory because there is no machine learning training. It was also noted that the likelihood of detection in all crack samples is 92.86% (TPR). The probability of being misjudged as transverse cracks in all normal pavement samples is 4.80% (FPR). As most pavement sections are normal, a lower FPR means that the classification model can help to reduce the maintenance personnel's on-site error correction workload. At the same time, transverse cracks require repair to be effectively detected. Therefore, using the PSDmax and \(\Delta\)PSD as classification indices is reasonable.
## 4 Experimental Results and Discussions
### Performance Comparisons  
We illustrate the predictions by auto_arima, Prophet, LSTM, and SLSTM models using the lettuce data in Figure 9. The data in Figure 9 is the same as the test part (last 20%) of the data at the bottom of Figure 5. The predictions of the auto_arima model in Figure 9 reveals a large mismatch to the ground truth data. The predictions of the Prophet model in Figure 9 follow the trend of the ground truth, with a noticeable gap. The gap of the Prophet model increases after time-step 150. The Prophet model predicted smaller values than the ground truth, leading to lower accuracies. Compared to Prophet, LSTM's predictions in Figure 9 and SLSTM's predictions in Figure 9 follow the trend of the ground truth more accurately. These observations explain the superior accuracies of LSTM and SLSTM to auto_arima and Prophet.  
Our main concern is for the comparison of LSTM and SLSTM. In the ground truth data in Figure 9, small and large peaks and drops in sales volume appear in many time steps. Looking carefully at the drops below 100, SLSTM predicts values that are much closer to the ground truth than LSTM. The same phenomena can be observed at the peaks reaching between 200 and 300. The same effect is noticed at almost every peak and drop. The effect explains the reason why SLSTM is superior to LSTM. For the large peaks appearing near time-steps 170, 210, 280, and 310, no model managed a close prediction.  
Figure 9: The result predicted by auto_arima (**a**), Prophet (**b**), LSTM (**c**), and SLSTM (**d**) models (only using \(s^{w}\)) with lettuce data.  
The performance of the proposed SLSTM model was quantitatively compared with those of auto_arima, Prophet, and LSTM in terms of three performance metrics (MAE, RMSE, and NMAE). In Table 2, \(s^{\text{up}}\), \(s^{\text{q}}\), \(s^{\text{sum}}\), \(s^{\text{mq}}\), \(s^{\text{mq}}\), and \(s^{\text{sum}}\) represent the seasonal components, namely, the weeks, months, and quarters, as well as their combinations. The sales volumes of five items (WelshOnion (WO), Lettuce, ChineseMallow (CM), Onion, and JujuubeMiniTomato (JMT)) were used in our experiment.  
Overall, SLSTM shows the least error. As an example, the NMAE of SLSTM is below 0.20, while LSTM and Prophet show 0.26 and 0.39, respectively. The Prophet model is inferior to auto_arima, except for JMT and Onion. The LSTM model is better than Prophet, except for Onion.  
The SLSTM model compared the different combinations of seasonality. We emphasize the best case by using bold typeface in Table 2. The seasonality factors \(s^{\text{up}}\), \(s^{\text{sum}}\), \(s^{\text{mq}}\) produced good accuracies. Among them, \(s^{\text{sum}}\) (combination of week and month) seasonality shows the best accuracy.
# Dynamic Routing in Flying Ad-Hoc Networks Using Topology-Based Routing Protocols
## 2 Dynamic Routing Protocols in FANETs  
In the literature, a wide range of routing protocols exist in traditional ad-hoc networks, which have been suggested for FANETs in order to establish an efficient and reliable communication among the UAVs. However, due to the UAVs explicit characteristics, such as link quality variations and fast movement in the 3D space, most of these routing protocols cannot directly address the requirements of FANETs [17; 18]. Routing protocol plays a vital role in consistent end-to-end data delivery and reducing signaling overhead. The challenge of designing an optimal routing protocol is difficult and still being researched, due to the complexity of FANET networks. For this reason, some previous dynamic routing protocols have been implemented and some new ones have been modified for FANETs. These protocols are broadly placed into three major categories: (i) topology-based routing protocols; (ii) swarm-based routing protocols; and (iii) position-based routing protocols. This article is dedicated to the most important topology-based routing protocols, which are assumed to be the most appropriate for this new ad-hoc network family.  
Recently, an important number of approaches have been proposed for topology-based routing protocols. This class of routing protocols exploits IP addresses to use the existing link information to forward data packets on the optimal path. These protocols also require topology information for all communicating nodes in order to establish and maintain the optimal route before data transmission. Topology-based routing protocols are furthermore divided into three classes: proactive, reactive and hybrid routing, as shown in Figure 1. All of these routing protocols are intended to improve FANET performance with regard to throughput, minimized delay and resource consumption. In the following subsections, different classes of topology-based routing protocols are presented in detail.
## 2 NB-IoT System Detailed Design and Implementation
### NPUSCH Transmitter Chain Implementation
#### 2.1.10 Inverse Fast Fourier Transform (IFFT)  
IFFT block is responsible for transforming the frequency subchannels into the time domain samples to be transmitted through the radio frequency (RF) blocks. It is challenging for the IFFT block to be at the end of the transmitting chain, as it must satisfy the required output rate (i.e., new SC-FDMA symbol in each SC-FDMA symbol duration) and not only generate this symbol within this duration, but also with a fixed rate [12].  
The IFFT subcarriers are packed into groups of 12 subcarriers with 15 kHz spacing between each adjacent subcarrier; each group is recognized as a resource block, and the NB-IoT has only one resource block per the UE transmitter. To implement the IFFT block, which supports 12 subcarriers (the maximum number of allocated subcarriers), a 16-point IFFT is used due to its minimum area utilization in order to decrease the power consumption and the latency of the block. In addition, the reduced output sample rate of this block is compensated using an upsample filter to improve the rate to a proper rate for the digital-to-analog converter (DAC) block [12].  
The design of the IFFT block consists of 4 stages, each consisting of butterflies. The first stage needs one butterfly, the second needs two butterflies, the third needs four butterflies, and the last one requires eight butterflies as shown in Figure 6.  
Figure 5: FFT resource sharing.
## Materials and Methods  
All subsequent analysis and results presented here were acquired from nanoparticles (NPs) stored after freeze-drying.  
Synthesis of poly(ethylene glycol)-block-poly(lactic-co-glycolic acid) (PEG-b-PLGA) Nanoparticles (NPs)  
Double emulsion, or the water/oil/water (W/O/W) technique (Figure 1), was used to synthesize the NPs. In brief, the PEG-b-PLGA polymer has hydrophilic and hydrophobic parts at the PEG and PLGA, respectively. For this reason, there is a need to create the emulsion of the PEG-b-PLGA, with the PEG and PLGA regions constituting the outer and inner shell of the NP, respectively. A more polar solvent (near water polarity) like acetonitrile acts as a continuous phase for PEG and a disperse phase for PLGA. Therefore, first, 30 mg of PEG (average M\({}_{n}\) 2000)-b-PLGA (average M\({}_{n}\) 11,500) polymer (Sigma Aldrich, St. Louis, MO, USA) were dissolved in 3 mL of acetonitrile (Fisher Chemical, Fair Lawn, NJ, USA), and 600 \(\mu\)L of a Pluronic F-127 solution (Molecular Probes, Eugene, OR, USA) was added and dissolved by sonicating (150 VT Ultrasonic Homogenizer, BioLogics Inc., Manassas, VA, USA) for 20 s in an ice bath to obtain the first emulsion (W\({}_{1}\)). Next, 800 \(\mu\)L of Pluronic solution was added and sonicated for 30 s at 60% amplitude. Here, Pluronic was used as a surfactant or stabilizing agent for the PEG-b-PLGA emulsion in acetonitrile. Meanwhile, non-polar solvent (near oil polarity) like hexane (Fisher Scientific, Fair Lawn, NJ, USA) acts as a disperse phase for PEG. Then, 90 mg of sodium dodecyl sulfate (Fisher Scientific, Fair Lawn, NJ, USA) were added to 60 mL of hexane,and the resultant double emulsion was poured into the 60 mL of hexane and evaporated by stirring for 1 h at 950 rpm (O). Sodium dodecyl sulfate was used as a surfactant or stabilizing agent for the PEG-b-PLGA emulsion in hexane. The nanoemulsion was then centrifuged at 6000 rpm to separate the NPs from the microparticles. After centrifugation, the pellet was washed and centrifuged with distilled water three times (W\({}_{2}\)). Finally, the pellet (PEG-b-PLGA NPs) was suspended in distilled water [18; 19], freeze-dried (Eyela FDU-1200 Freeze Dryer, Eyela, Bohemia, NY, USA) for 24 h, and stored at 4 \({}^{\circ}\)C before use in further experiments.
## 6 Conclusions  
A gradient-index solar sail is able to generate a nonzero thrust component along the nominal plane of the sail that is greater than that in the direction normal to the plane of the sail. This allows for heliocentric orbital transfers while keeping the plane of the sail constantly in a Sun-facing condition, that is, orthogonal to the direction of the Sun's rays. This feature is very interesting from an application point of view because the Sun-facing attitude can be maintained passively, greatly simplifying sail maneuvering. In practice, the direction of thrust depends solely on a single control variable (the clock angle), i.e., the angle of rotation of the sail with respect to the radial direction. The performance of a gradient-index solar sail in a Sun-facing condition was studied from an optimal point of view by preliminarily determining the control law that minimized the total transfer time between two heliocentric orbits. Several optimal trajectories were studied, including Earth-Venus, Earth-Mars, Earth-Mercury transfers, and the optimal trajectory from Earth to asteroid 433 Eros. To further simplify the sail control, the optimal transfer problem was also studied in the case where the clock angle was constrained to take only a finite number of values (three or five depending on the cases studied). In an Earth-Venus transfer scenario, simulations revealed that the constrained clock angle case provided similar performance to the unconstrained problem, with an increase in flight time of about 1% only. These results were obtained under the assumption of instantaneous maneuvers. However, in the last part of the paper, the problem of estimating the time required for the sail to perform such maneuvers was studied. Assuming that the maneuvers were performed with the aid of two control vanes generating a pure windmill torque, the results were expressed as a function of a single parameter that took into account the surface of the control vanes, the mass of the sail, and the distance from the Sun. The simulations obtained showed that a sail rotation maneuver of 1 rad took about 2 h at a distance of 1 AU from the Sun. Since the characteristic transfer times are several hundred days, the assumption of instantaneous maneuvering is amply justified.  
Figure 19: Optimal control parameters of the PDF controller as a function of \(k\).  
**Author Contributions:** Conceptualization, A.A.Q.; methodology, M.B., G.M. and A.A.Q.; software, A.A.Q. and M.B.; writing--original draft preparation, M.B., G.M. and A.A.Q.; writing--review and editing, M.B., G.M. and A.A.Q. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 4 Discussion
### Study Limitations and Future Research  
This systematic review presents some limitations that must be mentioned: (i) the small number of included studies, meaning that these results should not be generalized; (ii) most of the included studies were conducted with males, and thus, it would be interesting to have more studies with females to understand their differences and similarities; (iii) there is a lack of studies in volleyball using wearable devices to assess the performance in the upper limbs and on the specific movements of each player during training/competition; (iv) more studies are needed with other wearable technologies and new evaluation procedures to understand the sports performance of volleyball players better; (v) no study presented data related to the receiver operating characteristic (ROC) curve and the respective area under the curve. This analysis could give important insights regarding the discriminant ability of the devices to measure the different volleyball performance parameters, such as jump height. Therefore, based on these limitations, future research with wearable devices is needed to better understand jump characteristics during training and competition across different age groups, player levels, and sex. In addition, analyzing the players' movements during training and competition should be studied using wearable devices.
## 3 Ranked Set Sampling
### Median Ranked Set Sampling  
[PERSON] [24] suggested a modified version of the RSS scheme called Median RSS (MRSS), which efficiently estimates the population mean by minimizing error in ordering. The complete procedure of MRSS is as follows:  
Similar to RSS, we acquired \(m^{2}\) units from the underlying population randomly and distributed them to \(m\) sets of the same size \(m\) and all the \(m\) units within each set are ordered with the help of the variable under study.  
In the second step, if \(m\) is odd, then draw middle units, i.e., \(\big{(}(m+1)/2\big{)}th\) from all the sets selected as samples. In case when \(m\) is even, select the lowest ordered units from the two middle sampling units of the first \((m/2)th\) sets and draw the largest ordered units from the two middle sampling units remaining \((m/2)th\).  
1. The above two steps complete one cycle of MRSS of size \(m\); if needed, repeat the whole method \(r\) times to meet the requirement.  
The estimator for the population mean of MRSS of one cycle when the sample size is odd is defined as follows:  
\[\overline{Z}_{(MRSS)O}=\frac{1}{m}\Bigg{(}\sum_{i=1}^{m}Z_{i(\frac{m+1}{2})} \Bigg{)} \tag{12}\]  
with variance  
\[\text{var}\Big{(}\overline{Z}_{(MRSS)O}\Big{)}=\frac{1}{m}\Big{(}\delta_{( \frac{m+1}{2})}^{2}\Big{)}. \tag{13}\]In a situation when the sample size is even, the estimator of a population mean of MRSS with one cycle is  
\[\overline{Z}_{(MRSS)O}=\frac{1}{m}\biggl{(}\sum_{i=1}^{n/2}Z_{i(\frac{n}{2})}+ \sum_{i=1}^{n/2}Z_{\frac{n}{2}+i(\frac{n+1}{2})}\biggr{)} \tag{14}\]  
with variance  
\[\mathrm{var}\Bigl{(}\overline{Z}_{(MRSS)O}\Bigr{)}=\frac{1}{m}\Bigl{(}\delta_ {(\frac{n}{2})}^{2}+\delta_{(\frac{n+2}{2})}^{2}\Bigr{)}. \tag{15}\]
## 4 Simulation Results and Analysis
### Key Sensitivity  
It is crucial that encryption algorithms are sensitive to even small changes in the secret key. This is because even a slight change in the key can significantly affect the output. As shown in Figure 8, we tested the initial value \(x_{0}=0.0117\) of our proposed algorithm during the encryption process and slightly changed it to \(x_{0}=0.0117\pm 10^{\pm 14}\) during decryption. The results demonstrate that our algorithm is highly sensitive to changes in the key, making it an effective and secure method for protecting data. This is an important consideration when choosing an encryption algorithm, as it ensures that the output will be protected even if the key is slightly altered during the encryption or decryption process.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Proposed** & **Ref [29]** & **Ref [30]** & **Ref [31]** & **Ref [32]** \\ \hline Average & \(2^{149}\) & \(2^{555}\) & \(2^{196}\) & \(2^{187}\) & \(2^{199}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Key space comparison.  
Figure 7: Image used for testing.
## 3 Drone Swarms as Networked Control Systems
### Non-Interactive Deployment Strategy
#### 3.2.2 Major Technologies  
According to a non-interactive deployment strategy, drones gather relevant information from themselves and from the environment. The collected data needs to be processed, for which the drone needs to communicate with a ground control station. The result of the computational system in the ground station is communicated back to the drones, being used by the relevant local actuators.  
Figure 1: Non-interactive deployment.  
From one hand, cellular technologies, such as LTE, can be used to provide improved system capacity, coverage, high data rates and reduced latency deployment [29]. For instance, LTE provides data rates delivery range between 0.07 to 1 Gbps with coverage range of up to 350 km on flexible spectrum. On the other hand, Low Power Wide-Area Network (LPWAN), such as LoRaWAN, provides broad area connectivity operating on unlicensed frequency bands with low data rate, power consumption, and throughput [30]. For instance, LoRaWAN provides data rates that vary from 0.3 kbps to 50 kbps [31].  
In what concerns the computational system, this deployment strategy may rely simply on accurate mobility models or path planning functions based on intelligent algorithms, such as Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO) and Bee Colony Optimization (BCO).
# On Analyzing Beamforming Implementation in O-RAN 5G
## 4 O-RAN Beamforming  
Beamforming has been introduced with great benefits to overcome several communication challenges, such as enhancing the precision of radio connections, increasing throughput, increasing the number of parallel connections in a given cell area, and saving energy consumption during transmissions. In mmWave transmission, beamforming is particularly beneficial to improving the signal-to-noise ratios (SNR) through direct targeting of user groups, mainly indoor coverage; further, beamforming also may nullify the interference between UEs singles in high-density environments. All these benefits have put the developers, testers, and antenna designers in the face of other challenges, such as cost of implementation, design complexity, and the testing methods that should be done in an actual physical environment. However, all these challenges have been taken into consideration with 3 GPP and O-RAN specifications. 3 GPP has introduced the split options that helped the designers achieve fixed implantation of the RAN. O-RAN adopted the Split 7-2x option, which introduces simplified communication interference between O-RAN units, especially in front-haul, where the C-plane communication carries the beamforming data in several beamforming methods. O-RAN has supported all beamforming technologies (digital, analog, or hybrid) in the front-haul interface. Figure 5 describes the block diagram of the popular beamforming solution in O-RAN, which is hybrid beamforming.  
Figure 5: Hybrid beamforming block diagram in O-RAN.  
As we mentioned in Section 3, O-RAN has adopted Spit Option 7-2x in the front-haul interface that required a high bandwidth. To overcome this challenge, we suggest using Category B to perform the precoding in O-RU. This implementation will lead to inter-RU interferences in O-RU, because each O-RU will calculate beamforming matrices individually. Thus, it has been suggested to design the beamforming matrices in O-DU through the estimated channel information and generate a precoding beam in O-RU. We propose the use of the zero-forcing (ZF) algorithm as a possibility to reduce the interference in received signals while maintaining the beamforming in only one unit (thanks to the introduction of the precoding into the O-RU unit). Thus, the cost of implantation and hardware complexity will be reduced [29; 32; 33; 34; 35].