esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Proposed Method
### Spatial-Channel Mutual Attention-Guided Transformer Module (SCATM)
#### 2.3.2 Spatial-Channel Mutual Attention Guiding Block-Bottom (SCAGB-B)  
Unlike the above SCAGB-T, the SCAGB-B in the bottleneck stage aims to guide the enhancement of the low-level encoded features. The bottleneck stage contains a large number of encoded feature maps; however, each map is relatively small compared to the ones in the encoding stage. Therefore, it is essential to introduce a dynamic selection mechanism that allows each neuron to adjust its receptive field size adaptively. The dynamic selection mechanism introduced here, which is the major difference between SCAGB-T and SCAGB-B, is the selective kernel (SK) network [55] shown in Figure 3b. Given a sequence of feature maps, the SK network firstly conducts two convolution layers with different respective fields, followed by a batch normalization layer and a ReLU layer. The calculated upper and lower inner feature maps are denoted as \(\mathbf{U}\) and \(\mathbf{V}\), respectively. Then, the feature maps are integrated via an element-wise summation and sent through the global average pool (GAP) layer to generate channel-wise statistics with different respective fields. Afterwards, two full connection layers are applied to the inner feature maps to enable the guidance for the adaptive selections. Finally, a soft attention layer across different channels is applied to adaptively select the information from different respective fields. Assuming that the upper and lower input of the Select layer in Figure 3b is \(F(\mathbf{U})\) and that \(G(\mathbf{V})\in\mathbb{R}^{C\times 1}\), where \(F(\cdot)\) and \(G(\cdot)\) denote the previous inner feature map process and \(C\) denotes the number of channels of the inner feature map, the output weight is  
\[\mathbf{w}_{c}^{upper}=\frac{e^{F_{c}(\mathbf{U})}}{e^{F_{c}(\mathbf{U})}+e^{ G_{c}(\mathbf{V})}},\quad\mathbf{w}_{c}^{lower}=\frac{e^{G_{c}(\mathbf{V})}}{e^{F_ {c}(\mathbf{U})}+e^{G_{c}(\mathbf{V})}}, \tag{2}\]  
where \(c\) in \(\mathbf{w}_{c}^{upper}\) denotes the \(c\)-th element of the \(\mathbf{w}^{upper}\), likewise \(\mathbf{w}_{c}^{lower}\), \(F_{c}(\mathbf{U})\), and \(G_{c}(\mathbf{V})\).  
The final attention maps of the SK network are obtained through the attention weights on the inner feature maps from various respective fields:  
\[\mathbf{A}_{c}=\mathbf{w}_{c}^{upper}\times\mathbf{U}_{c}+\mathbf{w}_{c}^{ lower}\times\mathbf{V}_{c},\quad\mathbf{w}_{c}^{upper}+\mathbf{w}_{c}^{lower}=1, \tag{3}\]  
where \(\mathbf{A}=[\begin{array}{cccc}\mathbf{A}_{1},&\mathbf{A}_{2},&\dots,& \mathbf{A}_{C}\end{array}]\) denotes the output attention maps, \(\mathbf{A}_{c}\in\mathbb{R}^{H\times W}\), and \(H\) and \(W\) denote the height and width of the feature maps, respectively.
# Pilot-Aided Frame Synchronization in Optical OFDM Systems
## 4 Conclusions  
In this paper, a frame synchronization algorithm is formulated in the PA O-OFDM system. The PA system utilizes a pilot signal that can be used for PAPR reduction at the transmitter and channel estimation at the receiver of an O-OFDM system. The proposed timing metric uses the mirror symmetric property of the received pilot signal without requiring prior knowledge of other system parameters for frame synchronization at the receiver. This metric has low computational complexity when compared with the Park's timing metric modified for O-OFDM because it requires only simple addition operation. The pattern of the time domain pilot symbol makes the method robust to the effect of cyclic prefix length on the timing metric such that it gives a distinct principal peak at the start of pilot symbol. Results show that the proposed timing metric is capable of performing precise frame synchronization in an indoor VLC channel when SNR \(\geq\) 4 dB and SNR \(\geq\) 8 dB in DCO-OFDM and ACO-OFDM, respectively. However, the ability of the algorithm to give precise frame synchronization timing reduces when the SNR decreases.  
Conceptualization, F.B.O., S.S. and W.O.P.; Data curation, F.B.O., S.S. and W.O.P.; Formal analysis, F.B.O., S.S. and W.O.P.; Funding acquisition, S.S.; Investigation, F.B.O., S.S. and W.O.P.; Methodology, F.B.O., S.S. and W.O.P.; Project administration, S.S. and W.O.P.; Resources, F.B.O.; Software, F.B.O.; Supervision, S.S. and W.O.P.; Validation, F.B.O.; Visualization, F.B.O.; Writing original draft, F.B.O.; Writing review and editing, S.S. and W.O.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} ACO-OFDM & Asymmetrically Clipped Optical Orthogonal Frequency Division Multiplexing \\ ADC & Analog-to-Digital Converter \\ AWGN & Additive White Gaussian Noise \\ CCDF & Complementary Cumulative Distribution Function \\ DAC & Digital-to-Analog Converter \\ DCO-OFDM & Direct Current Optical Orthogonal Frequency Division Multiplexing \\ FFT & Fast Fourier Transform \\ IFFT & Inverse Fast Fourier Transform \\ IM/DD & Intensity Modulation and Direct Detection \\ LOS & Line-of-Sight \\ M-QAM & M-ary Quadrature Amplitude Modulation \\ N-LOS & Non-Line-of-Sight \\ OFDM & Orthogonal Frequency Division Multiplexing \\ O-OFDM & Optical Orthogonal Frequency Division Multiplexing \\ OWC & Optical Wireless Communications \\ PA & Pilot-Assisted \\ PAP & Peak-to-Average Power Ratio \\ PA O-OFDM & Pilot-Assisted Optical Orthogonal Frequency Division Multiplexing \\ RF-OFDM & Radio Frequency Orthogonal Frequency Division Multiplexing \\ SNR & Signal-to-Noise Ratio \\ \end{tabular}
## 4 Discussion
The strong effect of the laser fiber on the lung parenchyma is absolutely desirable when peripheral lung resections are to be performed. The amount of time needed to remove a lesion in the lung can be markedly reduced. However, if more central areas of the lungs are involved in which the density of larger vessels and bronchi increases, there is an increased risk of damaging these structures. This can lead to considerable local bleeding. Due to the scalpel-like character of the laser fiber, there is also the risk of small segmental bronchi being cut unnoticed. This would result in persistent air leakages from the lungs, which in the worst case would have to be closed in a separate surgical procedure. Our experience has shown that, whenever lasers are used in these parts of the lungs, the laser power should be markedly reduced to 20 W.  
The use of laser fibers in our tests was unproblematic. There is generally the risk of tissue adhering to the tip of the fiber, which could lead to self-ignition and destruction thereof. In this case the laser fiber would have to be prepared again, which is time-consuming in practice. However, the bare fibers can be rapidly and easily used. The slightly thicker 600 \(\upmu\)m fibers have become established in clinical practice, as they bend less during use. Our experimental data (albeit collected using pig lungs) demonstrate how effectively the laser fiber can be used in applications involving lung parenchyma. The latest-generationlaser fibers even allow for use of very high laser energies, which significantly reduces the application time. No fiber problems occurred in any of the applications despite unusually high laser energies of 120 W.  
As a trend towards open surgery to minimally invasive surgical procedures can be seen in thoracic surgery [6, 7], laser fibers are extremely interesting, especially in minimally invasive surgeries [8]. In these procedures, probe-like instruments are advanced into the thorax using trocars. The lung parenchyma can be cut (like with a scalpel) and coagulated using a thoracoscope for visual control [9, 10]. For superficial defects, closure of the lung parenchyma by sutures is not required [11]. In addition, adhesions of the lungs with the thoracic wall can be safely separated with the laser fiber. In contrast, the focusing handpiece is too short and too thick to be effectively used within the scope of minimally invasive surgical procedures. In the field of minimally invasive thoracic surgery, the future belongs to laser fibers thanks to their range of possible applications.
# Novel Technologies Used in the Assessment of Patellofemoral Pain: A Scoping Review
## 2 Materials and Methods
### Eligibility Criteria  
The two-step investigation was performed for eligibility of the articles. First, titles and abstracts were screened to detect the suitability of the article for the present scope. Articles using physical examination and non-technological assessments, study protocols, book chapters, congress proceedings, editorials, infographics, and reviews were excluded.  
After this first step, the remaining articles were determined to be related to assessments with technology and to include PFP populations, and thus selected for the following full-text analysis. Only those articles whose full text could be accessed in English were examined. Among the suitable articles, studies using novel technology and focusing on patella-related features were retained and extracted.  
From the starting point, we needed this two-step investigation and changed our inclusion strategy since kinematic, kinetic, and EMG analyses are well-known methods for PFP. We excluded kinematic and kinetic analyses unless they were not specifically  
\begin{table}
\begin{tabular}{p{113.8 pt} p{227.6 pt}} \hline \hline
**Medical imaging** & “patellofemoral pain” AND (“brain mapping” OR “computed tomography” OR CT OR “dynamic magnetic resonance” OR electroencephalography OR EEG OR fluoroscopy OR “functional magnetic resonance imaging” OR “functional MRI” OR FMRI OR “kinematic magnetic resonance” OR “kinematic MRI” OR “magnetic resonance spectroscopy” OR MRI OR sonography OR stererophotogramry OR tomography OR ultrasonography OR \"weight-bearing CT” OR “weight-bearing magnetic resonance” OR X-ray OR in-vivo OR “in vivo” OR in-vitro OR “in vitro” OR “conbeam CT” OR CBCT OR “medical imaging” \\ \hline
**Musuloskeletal computer modeling** & “patellofemoral pain” AND (modelling OR modeling OR in-silico OR “in silico” OR simulation OR “musculoskeletalelat computer modelling” OR “musculoskeletalelat computer modeling”) \\ \hline
**Statistical analyses** & “patellofemoral pain” AND (“artificial intelligence” OR “ayesian analysis” OR “ayesian model” OR ayesian OR “hierarchical cluster analysis” OR “hierarchical clustering”  
related to the patella and included if they explicitly had an arranged marker set for patella tracking as well. Also, since EMG assessment is not novel and is very well known, EMG studies were excluded unless they reported any specific novel device or electrode targeting patello-femoral-based evaluations.  
Inclusion and exclusion criteria were thus as follows:  
Inclusion criteria:  
* If novel technologies were used in the assessment;
* If the focus of the assessment and measurements was on patellar area.  
Exclusion criteria:
* If assessments were not related to patellar area;
* Full text not available;
* Articles that are not in English;
* Well-known assessment methods and devices;
* Assessment made during surgery;
* Computer calculations, model simulations, and similar approaches where these analyses were performed after instrumental assessments;
* Simple range of motion analyses.
## 3 Innovative SHM Techniques
### Image and Computer-Vision Based Approach
#### 3.2.2 Laser Scanning  
Laser scanning (LS), also referred to as LiDAR (light detection and ranging) or LaDAR (laser detection and ranging), can record millions of measured points in a short period by emitting laser pulses towards these points and measuring the distance between the target and the measuring device. The result is a 3D point cloud, which can be successfully used to reconstruct the 3D geometry of the analysed structures. The terrestrial laser scanning (TLS) monitoring technique has already been applied to control movements in large civil structures (bridges, dams, tunnels) and specific landscape situations. An extensive review of the TLS application for the deformation monitoring of structures is provided by [PERSON] et al. [167]. Building applications, especially regarding historical uses, for monitoring purposes are still rather limited and need further investigation [168]. However, using LS is particularly attractive when dealing with historical structures due to their intrinsic complexity, which is sometimes hard to record in every detail. The integration of an LS survey with other surveying techniques usually represents the best solution for assuring high quality and complete results, especially in the case of large and complex historical buildings where particular attention to preservation and restoration aspects is also required [166; 168; 169; 170].  
[PERSON] et al. [169] tested the efficiency of TLS in monitoring the horizontal displacements of the historical Palazzo del Capitano facade in Mantua, Italy. The monitoring was realized by performing seven measurement campaigns from October 2011 to October 2012. By comparing these results with total station measurements, significant divergences appeared. These are mainly ascribable to errors committed by the operator and to the stability of the TLS reference system. This outcome attested to the importance of operational rigour in the methodology to achieve reliable results in detecting or monitoring structural movements.  
[PERSON] et al. [171] used several monitoring systems (digital levelling, 3D total stations, TLS, and digital photogrammetry) to control the structural stability of the ancient walls of Ferrara, seriously compromised by the earthquakes of May 2012. The laser scanning survey was repeated yearly from December 2011 to December 2013 to check for possible variations in the wall configurations. The comparison of the vertical sections deriving from the 3D point clouds shows an ongoing tip rotation, with a variation of around 0.1 degrees per year.  
[PERSON] et al. [172] discussed the combined use of LS and panoramic photography to create geo-referenced enriched digital models and support the design of a proper preventive conservation plan. The authors showcased two case studies demonstrating the successful integration of surveying and virtualization techniques. These studies resulted in value-added reference information that can be utilized for condition mapping and for assisting building owners and facility managers in taking proactive preventive actions.
## 2 HSM for Base Station PA  
### HSM Output Power Requirement  
To design HSM, it is essential to understand the performance characteristics of the target PA. Figure 1 presents the performance of the TGA2307-SM PA for base station applications. In Figure 1a, the saturated output power (P\({}_{\mathrm{sat}}\)) reaches 47 dBm. In Figure 1b, the corresponding drain current at P\({}_{\mathrm{sat}}\) is approximately 4 A. Given a fixed supply voltage of 28 V, the SM must deliver up to 112 W.  
For high linearity operation, the PA typically operates under power back-off (PBO). At 9 dB PBO, which corresponds to the average output power condition, Figure 1b shows that the drain current (I\({}_{\mathrm{d}}\)) is 1.25 A. In (1), under a fixed supply operation at 28 V, the average supply power (P\({}_{\mathrm{out,avg}}\)) becomes 35 W. In (2), when operating with ET, the rms supply voltage (V\({}_{\mathrm{SM,rms}}\)) is reduced to 17 V. Assuming the rms SM current (I\({}_{\mathrm{SM,rms}}\)) is approximately equal to the I\({}_{\mathrm{d}}\), the average supply power (P\({}_{\mathrm{SM,avg}}\)) becomes 21.25 W. In (3), based on these values, the power saving compared to fixed supply operation is calculated as \(\frac{35\ W-21\ 25 W}{35W}\approx 39.3\%\). In (4), the SM must maintain the supply voltage above the knee voltage (V\({}_{\mathrm{knee}}\)) to ensure proper PA operation. Table 2 summarizes the target SM output power, voltage, and current based on the operating region of the TGA2307-SM PA.  
\[\mathrm{P_{out,avg}}=\mathrm{V_{fixed,VDD}}\cdot\mathrm{I_{d}} \tag{1}\]  
\[\mathrm{P_{SM,avg}}=\mathrm{V_{SM,rms}}\cdot\mathrm{I_{SM,rms}} \tag{2}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Spec** & **QPA4501** & **TGA2307-SM** & **BLM9D2327-26 BZ** & **BLM10D3438-35 AB** \\ \hline Supply Voltage (V) & 28 & 28 & 28 & 26 \\ Saturated Power (dBm) & 43.5 & 47 & 45 & 45.5 \\ Power Efficiency (\%) & 38 & 44.4 & 41.1 & 41 \\ PA Type & GaN & GaN & LDMOS & LDMOS \\ \hline \end{tabular}
\end{table}
Table 1: Summary of commercial base station power amplifier key performance.  
Figure 1: Performance of the base station PA (TGA2307-SM): (**a**) output power vs. input power; (**b**) drain current vs. input power [17].  
\[\mathrm{Power\,Saving}(\%)=\frac{\mathrm{P_{out,avg}-P_{SM,avg}}}{ \mathrm{P_{out,avg}}}\cdot 100 \tag{3}\] \[\mathrm{V_{SM,min}}\geq\mathrm{V_{knee}} \tag{4}\]
# Exploring Data Augmentation Algorithm to Improve Genomic Prediction of Top-Ranking Cultivars
## 3 Results
### [PERSON]_2  
Figure 2 presents the results obtained by evaluating the entire test set using two metrics (MAAPE and NRMSE). Method C (conventional) exhibited a greater prediction performance over method A (augmented), with improvements of 58.71% in MAAPE and 54.47% in NRMSE. In summary, method C outperformed method A in both metrics. However, when focusing exclusively on the top 20% of the complete test set, the analysis of the metrics revealed that method A showed an advantage in performance over method C, with a reduction of 36.52% in MAAPE_20 vs. MAAPE and 35.52% in NRMSE_20 vs. NRMSE. These results indicate a significant improvement in the performance of method A compared to method C in this specific context. For more details, refer to Table 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Method** & **Metric** & **Average** & **SE** \\ \hline Maize\_1 & A & MAAPE & 0.2865 & 0.0031 \\ Maize\_1 & A & MAAPE\_20 & 0.0781 & 0.0009 \\ Maize\_1 & A & NRMSE & 1.8675 & 0.0024 \\ Maize\_1 & A & NRMSE\_20 & 1.4063 & 0.0048 \\ Maize\_1 & C & MAAPE & 0.1286 & 0.0014 \\ Maize\_1 & C & MAAPE\_20 & 0.1423 & 0.0014 \\ Maize\_1 & C & NRMSE & 0.9129 & 0.0013 \\ Maize\_1 & C & NRMSE\_20 & 2.4764 & 0.0085 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Prediction accuracy results for dataset Maize_1 using conventional (C) and augmented (A) methods, with metrics MAAPE and NRMSE for the total testing and MAAPE_20 and NRMSE-20 for the best 20% testing.  
Figure 2: Prediction accuracy performance results for the Maize_2 dataset using conventional (C) and augmented (A) methods in terms of mean arctangent absolute percentage error (MAAPE), mean arctangent absolute percentage error for the best 20% cultivars (MAAPE_20), normalizing the mean square error (NRMSE), and normalizing the mean square error for the best 20% cultivars (NRMSE_20).
# Propolis as a Potential Novel Histological Tissue Fixative: A Preliminary Analysis
## 3 Results
### Changes in the Mean Score at Different Points Compared with 12 h  
#### 3.3.1 Changes from 12 to 24 h  
Between-Group Comparison  
When all the mean scores of various solutions were compared from 12 to 24 h, a statistically non-significant improvement in the fixative ability of 10% formalin (Group F), propolisis (Group P), and propolisis followed by 10% formalin (Group P + F) was observed at 24 h compared with 12 h. In contrast, Group S tissue samples showed a statistically non-significant decrease in the mean score (Table 4).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Solution**} & \multicolumn{6}{c}{**Changes from 12 h to**} \\ \cline{2-7}  & \multicolumn{2}{c}{**24 h**} & \multicolumn{2}{c}{**48 h**} & \multicolumn{2}{c}{**72 h**} \\ \cline{2-7}  & **Mean** & **SD §** & **Mean** & **SD** & **Mean** & **SD** \\ \hline
0.9\% Saline & 0.04 & 0.45 & 0.15 & 0.38 & 0.11 & 0.53 \\ \hline
10\% Formalin & \(-\)0.37 & 0.39 & \(-\)1.04 & 0.51 & \(-\)0.52 & 0.34 \\ \hline
6.6\% Propolis & \(-\)0.15 & 0.24 & \(-\)0.26 & 0.15 & \(-\)0.22 & 0.17 \\ \hline
6.6\% Propolis + 10\% Formalin & \(-\)0.22 & 0.24 & \(-\)0.26 & 0.28 & \(-\)0.26 & 0.22 \\ \hline \hline \end{tabular}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{4}{c}{Pairwise comparisons by [PERSON]’s multiple post hoc procedures} \\ \hline
0.9\% Saline vs. 10\% Formalin & \(p=0.1569\) & \(p=0.0001\)* & \(p=0.0152\)* \\ \hline
0.9\% Saline vs. 6.6\% Propolis & \(p=0.8262\) & \(p=0.2024\) & \(p=0.4077\) \\ \hline
0.9\% Saline vs. 6.6\%Propolis + & \(p=0.5797\) & \(p=0.2024\) & \(p=0.3033\) \\ \hline
10\% Formalin vs. 6.6\% Propolis & \(p=0.7104\) & \(p=0.0014\)* & \(p=0.5254\) \\ \hline
10\% Formalin vs. 6.6\% Propolis + & \(p=0.9142\) & \(p=0.0014\)* & \(p=0.6482\) \\ \hline
6.6\% Propolis vs. 6.6\% Propolis + & \(p=0.9931\) & \(p=1.0000\) & \(p=0.9997\) \\ \hline \hline \end{tabular}  
* Indicates statistical significance. § SD: Standard deviation.  
\end{table}
Table 4: Comparison of fixative ability of different solutions with change in scores from 12 h to 24, 48, and 72 h treatment times.  
Figure 6: A graph showing between-group comparisons at different time points.  
Within-Group Comparison  
Storing the tissue samples for more than 12 h in 10% formalin (Group F) and propolisis followed by 10% formalin (Group P + F) showed statistically significant improvements in tissue fixation compared with tissues fixed only for 12 h (0.0212 and 0.0222, respectively) (Table 5).
# Assessment of the Impact of Electric Vehicle Batteries in the Non-Linear Control of DC Microgrids
## 2 Background: Non-Linear Control of DC Microgrid
### Non-Linear Control  
DC microgrids could experience stability problems, since the PV generation and the electrical load presents significant uncertainties and fluctuations, as previously explained. Hence, to ensure stability, the voltage needs to remain at adequate levels. In this work, a non-linear control is considered for the considered DC microgrid. Figure 2 depicts the assumed configuration scheme for the non-linear control.  
Although the linear PI control technique is the most common strategy for power-converters and microgrids implementations, since a PI control is designed for working around a linearized operation point, this is generally not able to cope with the system's non-linearities and disturbances. Specifically, in microgrid applications, the PI strategy presents a scarce dynamic response (such as bigger overshoots in transients and a slower speed response) as compared to the better performance and greater energy efficiency given by the nonlinear choice, see [26; 27; 28]. In addition, implementing a non-linear control does not require high computing resources [29].
This strategy aims to consider the full spectrum of options for satellite-based QKD downlinks by assessing their potential and limitations across different operational contexts. After this initial consideration, the approach, based on specific criteria or considerations such as technical feasibility or the specific requirements of quantum communication technologies, involves narrowing down the choices by progressively focusing on certain orbits that favor the satellite-to-ground QKD in terms of secure key generation. Following this analytical framework, Section 5 shifts focus toward a more forward-looking perspective, where the future possibilities of satellite-based QCI are discussed by leveraging state-of-the-art specifications from both the space and ground segments. This section aims to give a rough idea of the full potential of each orbit, considering state-of-the-art parameters that could redefine the capabilities of quantum communications infrastructure. Finally, Section 6 concludes this work.