esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
### Overview  
The electromagnetic spectrum is a finite and non-renewable resource [1]. Consequently, optimizing its utilization becomes imperative in various real-world engineering applications, such as aeronautical navigation systems, satellite communications, TV broadcasting, wireless LANs, and mobile communications. In addition, the inherent characteristics of the electromagnetic spectrum impose constraints on telecommunications channels and their frequency bandwidths in mobile networks, highlighting the critical necessity for efficient utilization. To address the high demand from mobile users, it is crucial to efficiently allocate and reuse channels to optimize specific system objectives. This challenge is commonly referred to as the Channel Assignment Problem (CAP) or Frequency Assignment Problem (FAP) [2, 3]. The majority of CAP-related optimization problems seek to enhance network traffic, improve service quality, reduce the number of frequencies allocated to a given service, or minimize overall network interference [2; 3; 4; 5]. Two main reasons justify the modeling and resolution of this issue in wireless networks:  
1. Allocation problems concern not only physical channels, but also the allocation of network resources to be reused, such as time-slots, sub-carries, sub-channels, channel coding, or transmission rate for a given QoS, which are required in a variety of modern technologies based on Frequency Division Multiple Access (FDMA), Time Division Multiple Access (TDMA), Code Division Multiple Access (CDMA), Space Division Multiple Access (SDMA), Orthogonal Frequency Division Multiple Access (OFDMA), and Non-orthogonal Multiple Access (NOMA) [6; 7].
2. The physical channels are closely associated with the transmitted physical frequencies within the system. These frequencies undergo various propagation phenomena, including attenuation, selective fading, and the Doppler effect, which impact the system's coexistence due to their influence on electromagnetic interference conditions from neighboring users.
## 1 Introduction
Upon reaching a waypoint, the situational data are immediately updated for subsequent decision making. While this approach minimizes environmental information utilization, it increases communication and decision-making consumption. Alternative research [25; 26; 27] proposes dividing areas based on global situational awareness, assigning specific sub-regions to individual UAVs for search missions. However, sub-region sizes typically exceed the coverage of single-path planning, and constrained by computational capacity and system robustness requirements, partitioning updates lag behind situation information updates, resulting in inadequate adaptability in complex environments. To avoid the above problems, extending UAV path lengths while utilizing only situation information along planned trajectories presents a viable solution. The PSO algorithm has the characteristics of few parameters and fast computation. Although using this algorithm may lead to getting stuck in local optima, accurate decision making is unnecessary in the scenario of fast decision making in multi-UAV collaborative search tasks. When using PSO algorithm for route planning, when there are too many candidate waypoints, the computational load of the decision-making algorithm will become large. In this article, we will use path grid determination algorithm to replace the path with a small number of waypoints for planning, reducing the computational load. Furthermore, path planning must consider route curvature, which is typically reflected through yaw cost parameters in UAV path-planning algorithms. Current studies [28; 29; 30] primarily treat this parameter as a means to reduce energy consumption without investigating its impact on target search effectiveness. To address these two problems, this study will undertake the following work:  
1. Propose a waypoint update mechanism based on the path grid determination algorithm and multi-objective PSO algorithm: by using the path grid determination algorithm, the path-planning problem is transformed into a waypoint selection problem, allowing UAVs to make quick decisions using PSO;
2. Analyze and study the impact of planned path length on collaborative search effectiveness, such as the coverage rate, target capture rate, capture time, and communication and decision-making consumption;
3. Analyze and study the impact of the yaw cost on target search effectiveness and reveal its influence mechanism through simulation data analysis.
## 5 Results
### Smoothing Algorithm
#### 5.1.2 Smoothing Algorithm Applied with Broadband RFI
Table 2 summarizes the optimal \(P_{\rm fa}\) and threshold values and the maximum error in the retrieved \(T_{\rm A}\) for the OFDM RFI case; this table is similar to Table 1, but with lower thresholds values and higher error in the retrieved \(T_{\rm A}\) values. Detection and elimination of a PRN signal results in a high error on the retrieved \(T_{\rm A}\) as applying an FFT to a PRN RFI signal does not concentrate the RFI signal, as it happens with a sinusoidal signal. PRN signal behaves like noise. In contrast, error on the retrieved \(T_{\rm A}\) produced by the contamination of an OFDM interfering signal is lower than in the PRN signal's case, as this signal is based in a frequency modulation, and the FFT process can concentrate the energy of the interfering signal. The main problem of the presence of broadband RFI is that, even if it is correctly detected, radiometric resolution of the measurements will be degraded due to the minimum introduced \(T_{\rm A}\) error and the loss of radiometric resolution due to the high number of eliminated pixels, as it can be seen in Figures 14,15. Errors in the retrieved antenna temperatures obtained in the Figures 14,15 are summarized in Tables 3,4. \begin{table}
\begin{tabular}{|c|c|c|c|c|c|} \hline
**Filter Window** & **35 \(\times\)35 Hann** & **25 \(\times\)25 Hann** & **15 \(\times\)15 Hann** & **5 \(\times\)5 Hann** & **Without Smoothing** \\ \hline \(P_{\rm fa}\) & 2.16 \(\times\)10\({}^{-2}\) & 2.4 \(\times\)10\({}^{-2}\) & 2 \(\times\)10\({}^{-2}\) & 1.76 \(\times\)10\({}^{-2}\) & 3.02 \(\times\)10\({}^{-2}\) \\ \hline Threshold value & 1.18 \(\sigma^{2}_{{}_{n}}\) & 1.24 \(\sigma^{2}_{{}_{n}}\) & 1.42 \(\sigma^{2}_{{}_{n}}\) & 2.21 \(\sigma^{2}_{{}_{n}}\) & 3.5 \(\sigma^{2}_{{}_{n}}\) \\ \hline Max \(\varepsilon_{T_{\rm A}}\) & 3.8 K & 5.88 K & 9.07 K & 21.97 K & 44.5 K \\ \hline Max \(\varepsilon_{T_{\rm A}}\) & 3.51 K & 5.57 K & 9.01 K & 21.97 K & 44.5 K \\ for RFI = 0 dB & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} \\ \end{tabular}
\end{table} 
Table 2: Maximum retrieved \(T_{\rm A}\) error for the best threshold \(P_{\rm fa}\) for four different cases of Smoothing Algorithm filtering, including the case of not applying the algorithm; for the OFDM interfering signal case. Maximum retrieved \(T_{\rm A}\) error in absence of RFI for the most suitable threshold is also shown. Figure 14: Cont. Figure 15: Smoothing Algorithm applied to a simulated OFDM RFI contaminated radiometric signal. (**a**) RFI contaminated signal’s spectrogram, \(\sigma^{2}_{\text{RFI}}=\sigma^{2}_{n}-5\) dB; Convolution of the spectrogram with a: (**b**) 5 \(\times\)5 Hann window; (**d**) 15 \(\times\)15 Hann window; (**f**) 25 \(\times\)25 Hann window; (**h**) 35 \(\times\)35 Hann window; RFI-mitigated spectrogram using the most suitable threshold for a smoothing of a (Table 3); (**c**) 5 \(\times\)5 Hann window; (**e**) 15 \(\times\)15 Hann window; (**g**) 25 \(\times\)25 Hann window; (**i**) 35 \(\times\)35 Hann window.
# A Vector Sensor-Based Acoustic Characterization System for Marine Renewable Energy
## Materials and Methods
### Field Testing  
A series of field trials were conducted in and near Sequim Bay, Washington in July 2018 in collaboration with the Pacific Northwest National Laboratory (PNNL). The field sites (Figure 2) represented a quiescent shallow site (SB2, water depth 26 m) and an energetic tidal channel (Marine Sciences Laboratory (MSL), water depth 7-10 m). NoiseSpotter system performance was tested with the goals of evaluating data quality, ease of deployment and recovery, flow noise removal, and performance of the location estimation algorithm. At each location, the NoiseSpotter was deployed on the sea bottom, with an inverse catenary mooring to the sea surface such that surface-induced motions are decoupled from the sensor platform. A controlled acoustic source (icTalk(r), Ocean Sonics Ltd.) was deployed over the side of the vessel, the R/V Strait Science, at 1 m below the sea surface, and it was programmed to repeatedly transmit a series of low-frequency pulsed sinusoids, followed by chirps. The acoustic signals spanned the frequency range 100 Hz to 3 kHz, at a source level of 120 dB re 1 mPa at 1 m. Testing at the SB2 location typically involved the repeated transmission of the controlled acoustic signals at source-receiver separations of 50 m, 200 m, 500 m, and 1000 m. Due to the more involved effort required to moor the vessel in the energetic tidal channel at MSL, the vessel was allowed to drift past the sensor platform to/from a distance of 500 m, while continuously transmitting the controlled acoustic signals. Additionally, ambient noise measurements were made at all locations to characterize the noise floor, to evaluate flow noise contributions, and to assist in acoustic propagation modeling.
## 3 CAD Systems of Brain Disorders Based on Multimodal Fusion  
### Motivation for the Application of Multimodal Fusion  
Given its clinical accessibility, magnetic resonance imaging technology has been widely used as a non-invasive tool for diagnosing brain diseases because it does not use ionizing radiation, which makes it safe. However, MRI is sensitive to movement, which limits its effectiveness especially in the diagnosis of mobile organs. To overcome this problem and in order to obtain better performance from the CAD system, several researchers have attempted to combine MRI with other modalities using multimodal fusion. With this technology, one could predict and reconstruct missing information that is not available in the MRI. We could also extract additional characteristics, not visible in the MRI images.  
Citing, for example, the multimodal CT/MRI fusion, whereby, thanks to the CT image, dense structures (bones and implants) are visualized with less distortion; however, physiological changes are not localized. While the MRI image detects normal and pathological information on the soft tissue, while information relating to the bones is not considered [183]. In addition, the MRI-T1/MRI-T2 fusion, whereby, thanks to the T1-weighted MRI, details on the anatomical structures are provided, while the T2-weighted MRI detects a greater contrast between normal and abnormal tissues. Added to all of this is the MRI/PET merger, whose functional information is extracted using the PET image. This information locates the metabolic changes caused by the growth of abnormal cells before an anatomical abnormality. While the MRI image, thanks to its high-resolution, provides anatomical information about the regions (or tissues) affected by the disease.  
The MRI/PET fusion was widely used for the diagnosis of AD. Recent studies [184; 185; 186; 187; 188] have shown that this fusion effectively contributes to accurately interpreting the location and extent of AD with combined information. In fact, the MRI measures the early structural changes in the medial temporal lobe, in particular the entorhinal cortex and the hippocampus. Then, PET--FDG (FluoroDeoxyGlucose) [189] makes it possible to observe, in AD patients, the reduction of glucose metabolism in the parietal, posterior cingulate and temporal regions of the brain [190].  
Table 4 reports some work related to brain disease diagnostic systems [183; 191; 192; 193; 194; 195; 196; 197; 198; 199; 200; 201; 202], while Table 5 summarizes some CAD systems related to AD [184; 185; 186; 187; 188; 203; 204; 205; 206; 207; 208; 209; 210; 211], with a comparative study with systems using only MRI for the purpose of exploring the efficiency of multimodal fusion. In this context, the researchers proposed techniques for merging data from artificial intelligence, and applied in a multimodal imaging environment, in order to create an improved fusion image more suited to image processing tasks such as than segmentation and diagnosis. The most widely used fusion techniques in the literature are summarized below.
# A Distance-Vector-Based Multi-Path Routing Scheme for Static-Node-Assisted Vehicular Networks
## 4 MP-RDV: The Proposed Scheme  
### Overview  
In this paper, we extend RDV and design MP-RDV in terms of the two drawbacks of RDV. The two drawbacks are the load-balancing and the delivery delay performance described as follows.  
**On Load Balancing Performance.** It is observed in [13] that throughput performance is largely degraded when the volume of data traffic exceeds a certain level. This is due to concentration of data traffic on the shortest-path links. Different from SADV that distributes packet copies into the primary and the secondary next-hops, RDV transmits packet copies into the shortest paths only, which makes the load balancing performance degraded. In MP-RDV, we propose to use multiple paths at each node, i.e., we distribute data traffic into primary and the secondary next-relay nodes. With the multi-path extension to RDV, we show that the load-balancing performance as well as the network capacity significantly improve.  
**On Delivery Delay Performance.** Simulation results of [13] show that the delay performance of RDV is considerably worse than SADV. This is mainly because of the property of the deployed routing metric. That is, SADV uses the routing metric based on delivery delay, whereas RDV's metric is designed based on the volume of vehicle traffic on roads. As a result, in exchange for better delivery ratio, RDV's delay performance is lower than SADV. The countermeasure for this problem is twofold. First, we extend RDV to use multiple paths, which not only improves the load balancing performance but may also improve delivery delay because some of the multiple paths may have smaller delay than the shortest path. Second, we try to make paths selection based on delivery delay. Since delay metric does not work simply in the RDV scheme, we design a mechanism to measure delivery delay of each link in cooperation with related nodes so that the delay metric values are advertised in the distance-vector messages in RDV (as well as MP-RDV).
## 4 System Performance Analysis
### The Analysis of HHT Protocol
#### 4.2.2 System Performance Analysis of the HHT Protocol Scheme  
The system reliability has been defined in (14). We can re-deduce the definition of the system reliability for HHT as follows:  
\[\mathcal{R}^{(v)}=F_{T_{\text{min}}^{(v)}}(t_{out})=\prod_{k=1}^{K}F_{T_{k}^{(v )}}(t_{\text{out}}). \tag{32}\]  
Similarly, using the definition of outage probability in (14), we can rewrite the outage probability of HHT protocol as  
\[\mathcal{O}_{\text{sys}}^{(v)} =1-F_{T_{\text{min}}^{(v)}(t_{out})} \tag{33}\] \[=\prod_{k=1}^{K}\bigg{(}1-F_{T_{k}^{(v)}}(t_{\text{out}})\bigg{)},\quad v\in\{MRC,SC\}.\]
## 3 Results  
### Model Parameter Optimization  
The undetermined parameters \(g_{0}\) and \(g_{1}\) were required for running Medlyn-PM. They were determined by fitting the analytical \(Gs\) equation, \(Gs=1.6*\frac{\text{GP}}{\text{Ca}}*\left(\frac{g_{1}}{\sqrt{\text{D}}}+1\right) +g_{0}\), and we obtained that \(g_{0}=0.06\) and \(g_{1}=3.94\). The variations in RMSE/MAE/R\({}^{2}\) with the change of the numbers of hidden layers and neurons for the ANN-PM model with training and validation datasets are presented in Figure 4.  
The figure shows that the RMSE and MAE of ANN-PM with the training dataset decrease gradually as the number of hidden layers (HL) and the number of neurons increase. The RMSE and MAE of ANN-PM with the validation dataset decrease as the numbers of hidden layers (HL) and neurons increase from 1 (the number of HL) \(-1\) (the number of neurons) to 10-48 but increase after the number of the two parameters become larger than 1-48. As the number of hidden layers and the number of neurons increase to 10-128, the R\({}^{2}\) of the training dataset reaches a maximum value (0.94), and the R\({}^{2}\) of the validation dataset is concentrated around 0.80. Then, considering the AIC values, we identified the best architectures of ANN-PM (AIC = \(-0.76\)) and Medlyn-PM (AIC = \(-0.55\)) models and the key parameters are shown in Table 5. The ANN-PM model has an ANN structure with two hidden layers and 48 neurons in each layer. The AIC index is also used to select the ANN-based GPP model in Medlyn-PM, and the optimal model has two hidden layers and one neuron in each layer.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **ANN-PM Model** & **Medlyn-PM Model** \\ \hline The number of hidden layers & 2 & 2 \\ The number of neurons & 48 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The key parameters of the two models. ANN is artificial neural network and PM is the Penman-Monteith.
_applied_  
_sciences_  
_Article_  
**Anodic Alumina Membranes: From Electrochemical Growth to Use as Template for Fabrication of Nanostructured Electrodes**  
**[PERSON], [PERSON], [PERSON] and [PERSON]\({}^{\ast}\)\({}^{\copyright}\)**  
Dipartimento di Ingegneria, Universita degli Studi di Palermo, 90128 Palermo, Italy;  
[EMAIL_ADDRESS] (B.P.); [EMAIL_ADDRESS] (S.P.); [EMAIL_ADDRESS] (C.S.)  
\({}^{\ast}\) Correspondence: [EMAIL_ADDRESS]  
**Featured Application: The high order degree of the porous structure of anodic alumina membrane (consisting of a close-packed array of columnar hexagonal cells, each containing a central cylindrical pore normal to the surface), makes it an ideal template for fabrication of nanostructured materials, suitable for applications in optoelectronics, sensors, magnetic memories and electronic circuits. A variety of nanostructures (metals, alloys, semiconductors, oxides and polymers) with different morphologies (tubules, wires, rods) were fabricated by utilizing an anodic alumina membrane as a template using a sol-gel process, microwave plasma chemical vapor deposition, electrodeposition and electroless deposition.**  
**Abstract: The great success of anodic alumina membranes is due to their morphological features coupled to both thermal and chemical stability. The electrochemical fabrication allows accurate control of the porous structure: in fact, the membrane morphological characteristics (pore length, pore diameter and cell density) can be controlled by adjusting the anodizing parameters (bath, temperature, voltage and time). This article deals with both the fabrication and use of anodic alumina membranes. In particular, we will show the specific role of the addition of aluminum ions to phosphoric acid-based anodizing solution in modifying the morphology of anodic alumina membranes. Anodic alumina membranes were obtained at \(-1\)\({}^{\circ}\)C in aqueous solutions of 0.4 M H\({}_{3}\)PO\({}_{4}\) added with different amounts of Al(OH)\({}_{3}\). For sake of completeness, the formation of PAA in pure 0.4 M H\({}_{3}\)PO\({}_{4}\) in otherwise identical conditions was also investigated. We found that the presence of Al(OH)\({}_{3}\) in solution highly affects the morphology of the porous layer. In particular, at high Al(OH)\({}_{3}\) concentration (close to saturation) more compact porous layers were formed with narrow pores separated by thick oxide. The increase in the electric charge from 20 to 160 C cm\({}^{-2}\) also contributes to modifying the morphology of porous oxide. The obtained anodic alumina membranes were used as a template to fabricate a regular array of PdCo alloy nanowires that is a valid alternative to Pt for hydrogen evolution reaction. The PdCo alloy was obtained by electrodeposition and we found that the composition of the nanowires depends on the concentration of two metals in the deposition solution.**  
**Keywords: porous anodic alumina; alumina membranes; aluminum anodizing; nanotechnology; template; nanostructures; nanowires; nanotubes; PdCo alloy**
# Bridgeless Buck-Boost PFC Rectifier with Positive Output Voltage
###### Abstract  
A bridgeless buck-boost power-factor-correction (PFC) rectifier with positive output voltage is proposed herein. This PFC rectifier operates in the discontinuous conduction mode (DCM). Owing to the DCM, a good performance on PF is easily achieved as well as no reverse recovery currents being generated from the diodes. By means of output voltage sensing along with the traditional voltage-follower control, a proper control force is created to drive the switches. By doing so, not only the output voltage is stably controlled at a given value, but also the input current tracks the input voltage as tightly as possible. In addition, the accompanying harmonic distortion meets the requirements of the IEC6100-3-2 Class C harmonics standard, and accordingly, the proposed rectifier is suitable for the AC-DC LED driver. Finally, via mathematical deductions and experimental results, the effectiveness of the proposed bridgeless buck-boost PFC rectifier is verified.  
bridgeless; buck-boost; BCM; DCM; PFC rectifier; voltage-follower control +
Footnote †: journal: Applied Physics Letters  
_Article_
## 4 Numerical Analysis with Results
### Link Utilization and Data Transmission  
Link utilization during CFP is calculated in percentage and it is the ratio of the used bandwidth by all GTS allocated nodes to the total allocated bandwidth for all these nodes in a superframe. The results shown in Figure 8 verify that GTS utilization in \(ESS_{ADC}\) is more than \(ESS\) and the standard, when nodes have random data requests in the range of 25 to 50 bytes. \(SO\) and \(BO\) values chosen for these results for both \(ESS\) and the standard are 3 and 4 respectively.  
GTS utilization of \(ESS_{ADC}\) is evaluated against three different values of \(SO\) of \(ESS\) and IEEE 802.15.4 standard in Figure 9. The results show that link utilization of \(ESS_{ADC}\) varies on varying data requests because it adjusts its slot size according to the requesting nodes to accommodate a maximum number of nodes. As increment of 1 in \(SO\) value doubles the GTS duration, hence less will be the utilization. Smaller \(SO\) value increases the GTS utilization as a majority of its slots are filled. However, data transmission and node allocation are not optimal in smaller CFP slots.  
Figure 8: GTS utilization of the network for random data requesting nodes.  
Figure 7: Accumulated delay for random data traffic.  
A comparative analysis of the proposed scheme with the other two schemes in respect of data transmission, when both have SO = 3 and BO = 4 for random data range is shown in Figure 10. \(ESS_{ADC}\) adjusts its slot size according to the data traffic to accommodate 14 GTS requesting nodes. Results show that the amount of data transmitted in \(ESS_{ADC}\) is similar to the \(ESS\) in different beacon intervals but greater than IEEE 802.15.4 standard. This is due to the larger slot duration in \(ESS\) and IEEE 802.15.4 standard with SO = 3, which allocates a separate slot to each data requesting node, consequently capacity nodes are entertained. Maximum GTS capacity in a superframe duration in \(ESS\) and the standard is 14 and 7 respectively, that's why \(ESS\) allows more data transmission as compared to the standard.  
Figure 10: Data transmission by all nodes for random data requesting nodes.  
Figure 9: GTS utilization of the network for varying range of fixed data requesting nodes.  
Figure 11 shows, that \(ESS_{ADC}\) allows nodes to send more data as compared to \(ESS\) and IEEE 802.15.4 standard for different values of \(SO\), when requested data of each node increases from 240 bytes. However, when each node has less amount of data, \(ESS_{ADC}\) accommodates same amount of data as allowed by \(ESS\). This is due to the reason that duty cycle adjustment of the proposed scheme is similar to the value of \(ESS\).