esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Rotational Speed Variation on the Flow Characteristics in the Rotor-Stator System Cavity
## 4 Conclusions  
In this study, the clearance inside a fluid machinery was simplified into a rotor-stator system cavity, and the experimental torque was compared with the numerical results. The numerical calculation model was expressed in a cylindrical coordinate system. The rotor \(Re_{\Phi}\) was gradually increased from 2.12 \(\times\) 10\({}^{6}\) to 4.23 \(\times\) 10\({}^{6}\), and air with a constant inlet flow rate was selected to flow in from the axial direction and flow out freely in the circumferential direction of the cavity. The main conclusions of this research are as follows:  
1. In a rotor-stator system, the torque on the rotor wall is mainly produced by shear stress. With an increase in the Reynolds number, the shear stress on the rotor wall increases, and the torque coefficient \(C_{\rm m}\) decreases.
2. In the range of the rotating Reynolds number selected for calculation, the main vortex separation occurs because of the inertial force of the fluid in the cavity, and the critical rotating Reynolds number at which separation begins is \(Re_{\Phi}\) = 3.53 \(\times\) 10\({}^{6}\).
3. The radial change in the shear strain rate in the rotor turbulent boundary layer is affected by different factors, and there is a turning point at \(r^{*}\) = 0.5-0.7. At this point, the change in the shear strain rate is transformed from the leading role of the inertial force of the inlet flow to the leading role of the rotor rotation on the fluid adhesion.
4. The order of torque on the rotor under different rotating Reynolds numbers is as follows: Case 4 \(>\) Case 3 \(>\) Case 2 \(>\) Case 1. In addition to fluid adhesion, the Reynolds stress also contributes partly to the rotor wall torque.
5. The vortex structure of the rotor-stator system cavity mainly includes the main vortex, return vortex, secondary vortex, and vortex in the turbulent boundary layer. With an increase in the rotating Reynolds number, the intensity of the vortex in the turbulent boundary layer decreases, but the number of vortex cores increases.  
Conceptualization, Y.J. and W.S.; methodology, Z.Z.; software, Z.Z.; validation, J.L. and W.S.; formal analysis, Y.J.; investigation, W.S.; resources, Y.J.; data curation, Z.Z.; writing--original draft preparation, Y.J.; writing--review and editing, Z.Z.; visualization, W.S.; supervision, W.S.; project administration, W.S.; funding acquisition, Y.J. and J.L. All authors have read and agreed to the published version of the manuscript.  
Not applicable.  
The study does not report any data.  
The authors express their gratitude and appreciation to [PERSON] et al. for providing available data needed to carry out this research project.
## 3 Coherent Feature Extraction and Evaluation
### Simulation Analysis  
In order to analyze the difference of coherent characteristics between the coherent and noncoherent pulse trains, three simulation experiments were performed based on time-frequency correlation. We took two sets of pulse trains: the first one was a coherent pulse train and the second a noncoherent pulse train. The signal type and the parameters of both pulse trains were the same. It is noteworthy that the number of pulses used for emitter identification must be greater than or equal to 4.  
Simulation Experiment 1: Signal modulation type is continuous wave (CW), CF is 30 MHz, sampling frequency (Fs) is 100 MHz, PRI is 60 \(\upmu\)s, PW is 10 \(\upmu\)s, pulse number is 10. Autocorrelation results of the two groups, without noise, are shown in Figure 2.  
Simulation Experiment 2: Signal modulation type is linear frequency modulation (LFM), CF is 30 MHz, bandwidth (BW) is 10 MHz, FS is 100 MHz, PRI is 150 \(\upmu\)s, PW is 15 \(\upmu\)s, pulse number is 8. Autocorrelation results of the two groups, without noise, are shown in Figure 3.  
Simulation Experiment 3: Signal modulation type is nonlinear frequency modulation (NLFM), CF is 100 MHz, BW is 20 MHz, FS is 200 MHz, PRI is 50 \(\upmu\)s, PW is 5 \(\upmu\)s, pulse number is 15. Autocorrelation results of the two groups, without noise, are shown in Figure 4.  
Figure 3: Autocorrelation result of coherent and noncoherent pulse trains; signal type is linear frequency modulation (LFM).  
Figure 2: Autocorrelation result of coherent and noncoherent pulse trains; signal type is continuous wave (CW).  
As shown in Figures 2-4, it can be seen that the three experiments of different modulation types have similar rules: the correlation degree between the coherent and noncoherent pulse trains is almost the same near the zero point, while outside the zero point, the correlation of the coherent signals is much better than that of the noncoherent signals. With increasing time delay, the correlation of the coherent signals decreases periodically and steadily, while it changes irregularly in the noncoherent signals. This is due to the strong correlation and continuity of the phase information of the coherent pulse train, while that of the noncoherent pulse train is random. Therefore, we can use this difference to distinguish the coherent and noncoherent signals. The next task is to define a feature parameter to describe this difference.
## 1 Introduction
### Emergency and Acute Treatment of SCI  
At an accident scene where SCI is suspected, emergency responders prioritize immobilization. A rigid collar is placed around the neck, and the patient is carefully positioned on a backboard [18]. Sedatives may be used to minimize movement, and a breathing tube may be inserted if needed. At a trauma center, interventions include spinal realignment using a brace or mechanical force. Surgery may remove fractured vertebrae, bone fragments, or herniated discs. Early spinal decompression surgery improves functional recovery. SCI often leads to complications requiring treatment [19]. Respiratory issues are common; one-third of SCI patients need temporary or permanent breathing assistance. Injuries at the C1-C4 levels can impair diaphragm function, requiring mechanical ventilation. Pneumonia is a leading cause of death, particularly in patients on ventilators. Circulatory complications (unstable blood pressure, blood clots, and abnormal heart rhythms) necessitate anticoagulants and compression stockings. SCI can also lead to muscle tone changes (spasticity and atrophy). Autonomic dysreflexia, a life-threatening condition in individuals with upper spinal injuries, can cause sudden hypertension, headaches, sweating, and vision disturbances. Positioning the patient upright can help reduce blood pressure. Other complications include pressure ulcers, neurogenic pain, bladder and bowel dysfunction, and sexual health issues, all requiring specialized treatment. SCI also has a psychological impact; many individuals experience depression. Therapy and medication can help manage these mental health challenges [20].
# Results and Achievements of the ALLIANCE Project: New Network Solutions for 5G and Beyond
## 7 The RINA Domain
### Research Activity  
We started collaborating in RINA development back in 2014. Recently, we extended the concept of Degradation of Quality (\(\Delta\)Q) [81] proposed for IP networks and designed the new Quantitative Timeliness Agreement Multiplexor (QTA-Mux) scheduling policy [82] for RINA. In contrast to simpler QoS differentiation solutions, where QoS services are provided in a priority order, QTA-Mux provides a way for an application and a network to negotiate performance in terms of bandwidth, urgency and cherish. The QoS class (called QoS Cubes in RINA) differentiation is enforced by a Cherish/Urgency (C/U) matrix which enables an inter-flow resource contention based on both losses and delay and by a Policer/Shaper module which addresses the intra-flow contention. Since a common API is enforced between the DIFs, specific QoS Cubes can be requested by an IPC to the underlayer DIF during the flow allocation.  
In ALLIANCE, our goal was to evaluate in a prototype the RINA QoS support by means of this QTA-Mux scheduler. Therefore, we set up a RINA network infrastructure shown in Figure 7a, consisting of 10 nodes in total, spanning from the end-user terminal to the server where applications run in a DC. This scenario is composed by 2 Internal Routers (IRs) interconnecting 3 Provider Edge (PE) routers, one of them providing connectivity to two Home Routers (HRs), another one to the DC where the VLC VideoLAN server runs, and a last one providing connectivity to another Service Provider.  
Figure 7b depicts the configuration of DIFs. An Ethernet Shim DIF has been configured over the Ethernet links interconnecting the physical nodes, allowing the use of RINA over this legacy communication technology. On top of these Shim DIFs, a Metro Provider Network (MPN) DIF interconnecting PE routers has been configured, as well as a Service Provider Network (SPN) DIF extending the communication between HRs and the DC BR, a DC Network (DCN) DIF inside the DC, a Home DIF inside the end-user home, and an upper-level Video Streaming Application DIF supporting the delivery of the video streaming sessions to end-users across the VLC VideoLAN Server, the DC BR and the HRs. As VLC VideoLAN is an application that runs over IP, ipornid is required on the video streaming session endpoints, to create IP point to point tunnels over the RINA network.  
Two sets of experiments were conducted. The goal of the first experiment was to evaluate the ability of RINA to guarantee QoS-specific requirements between the layers and compare it with a case where it cannot be provided as in TCP/IP. The second experiment focused on demonstrating a successful transmission of a HD video streaming in highly congested network with perfect users' QoE. More detailed explanation of these experiments and the results obtained are reported in [83].
## 3 Others
### ALBI and mALBI Grading as Part of Total Staging Scoring System and Treatment Algorithms  
The Japan integrated staging score (JIS) [4; 5], which is simply calculated using only the TNM stage of the Liver Cancer Study Group of Japan (LCSGJ) [71] and Child-Pugh classification, has been proposed as an easy total stage scoring system. Recently, the ALBI-TNM score (ALBI-T), calculated with the TNM of LCSGJ and the ALBI grade instead of the Child-Pugh classification, has been developed as a modified JIS [17; 18; 72; 73]. Another method, the Modified Cancer of the Liver Italian Program (CLIP) score with the ALBI grade, has also been reported [74; 75; 76]. It was noted that when the ALBI grade was used instead of the Child-Pugh classification with the evidence-based clinical practice guidelines for HCC of the LCSGJ (\(n=3495\)), there was only a small difference with regard to the therapeutic selection and the AIC of ALBI-T was better than that of JIS (21989.4 vs. 22075.1) [18]. In a multicenter analysis (\(n=6649\)), the AIC of mALBI-T, calculated with the mALBI grade and TNM of LCSGJ, was superior to that calculated with ALBI-T and JIS (45327.1 vs. 45467.7 and 45555.8) [17]. [PERSON] et al. reported that the AUCs for predicting prognosis of ALBI-T and mALBI-T were better than the other tools (e.g., Child-Pugh classification, BCLC stage, ALBI grade, ALBI-based BCLC) as a validation study (\(n=1910\)) [77]. Thus, the development of not only ALBI but also mALBI grading is thought to provide matching tools for contemporary clinical practice for the treatment of HCC cases (Table 3).  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline  & **Authors** & **Area** & **No. of Patients** & **Results for Predicting OS** \\ \hline Modified CLIP & & & & AIC and c-index: ALBI-CLIP vs. \\ with ALBI grade & Shao [74] & Taiwan & 142 & CLIP = 995.0 vs. 1001.1, and 0.724 \\  & & & & vs. 0.703, respectively \\ \hline Modified CLIP & & & & AIC and c-index: ALBI-CLIP vs. \\ with ALBI grade & & & & CLIP = 15493.5 vs. 15534.3, and \\  & & & & 0.789 vs. 0.785, respectively \\ \hline Modified CLIP & & & & AIC: ALBI-CLIP vs. CLIP = 2620.2 \\ with ALBI grade & & & 389 HBV-related & \\ with ALBI grade & & & & patients treated with TACE & AUCs for one- and two-year \\  & & & & survival: ALBI-CLIP vs. CLIP = \\  & & & & 0.697 and 0.618 vs. 0.687 and \\  & & & & 0.612, respectively \\ \hline \hline \end{tabular}
\end{table}
Table 3: Total scoring staging systems using ALBI and mALBI grading.
# Fully Metallic Additively Manufactured Monopulse Horn Array Antenna in Ka-Band
## 5 Future Research Lines and Applications
### Complex Structure Fabrication Using AM  
AM offers unique advantages for the construction of intricate geometries that are challenging to achieve with conventional subtractive techniques such as CNC milling. In high-frequency applications, precise geometrical configurations are critical for achieving optimal performance. The LPBF technique used in this study provides a streamlined process for creating these complex structures in a single, monolithic piece, which directly addresses limitations encountered in other conventional methods. Components such as the twisted waveguide sections, which play a crucial role in phase-shifting, or the septum, which enhances impedance matching within the array, are practically unfeasible to monolithically produce through standard machining due to their curved and rotated surfaces with a compact form factor.  
By allowing for fully monolithic fabrication, AM removes the need for joint assemblies, which in turn minimizes potential signal losses at connection points, ensures superior structural integrity, and enhances the robustness of the antenna. Future research could explore optimizing new RF devices to integrate into the feeding network, such as filters or OMT, to operate at different frequency bands or dual polarization. In addition, AM facilitates the fabrication of antennas with graded geometries and variable material properties, enabling novel designs that combine mechanical robustness with minimized weight, particularly beneficial for aerospace and satellite applications.
## 3 Parameter Analysis and Optimization Design
### Parameter Analysis
#### 3.1.2 Impact of Beam's Bending Line Stiffness  
This section expounds the law of \(f_{E}\) and \(f_{d}\) as the beam's bending line stiffness changes. In Figure 8, \(i_{b1}\) refers to the beam's bending stiffness with the cross-section of 400 mm \(\times\) 800 mm. When the intermediate column's position \(\beta\) changes, \(f_{d}\) and \(f_{E}\) do not change in the same way with \(i_{b}\). As is shown in Figure 8a, when \(\beta=0.1\), \(\gamma\) and \(\eta_{d}\) increase with the increase in \(i_{b}\). Hence, \(f_{d}\) and \(f_{E}\) increase as \(i_{b}\) increases. As is revealed in Figure 8b, when \(\beta=0.5\), with the increase in \(i_{b}\), \(\gamma\) slowly decreases, and \(\eta_{d}\) first increases and then decreases. Therefore, as \(i_{b}\) increases, \(f_{d}\) and \(f_{E}\) first rapidly increase and then decrease. For different \(\beta\), the impact law of \(i_{b}\) on \(f_{E}\) is shown in Figure 8c. From Figure 8a-c, it is concluded that CLNVD located at the mid span can achieve higher \(f_{E}\) with a smaller beam cross-section.
# Multifeature Fusion Neural Network for Oceanic Phenomena Detection in SAR Images
## 5 Discussion  
### Influence of Network Structure on the Detection Results  
After many experiments, we only use the features from partial levels obtained in ResNet-50 when designing the network structure, and multiscale features extraction is only carried out for the highest-level features. Using different combinations of features from different levels will have different detection effects. Multiscale feature extraction for multiple-level features also has an impact on the detection effect. So we will further explore new structures and try to introduce new structures to utilize other low-level information, which may have different effects.  
When MFNN detects multiple types of oceanic phenomena coexisting or superimposing in one image, some phenomena cannot be detected. Therefore, improving the network structure is necessary in order to improve detection accuracy and extract information capable of distinguishing more phenomena.
## 6 Future Research Directions
### Mobility in NOMA  
NOMA is used to enable the 5G and beyond 5G technologies. In IoT, V2V and M2M communications mobility are the key parameters. Most of the research in NOMA systems is based on static systems. The power allocation, pairing and SIC receiver algorithms are proposed based on the static behavior of the users. But dynamic power allocation, pairing and SIC algorithms are required in future communications. As the user is moving from one location to another location, then the channel gains vary with respect to the user's location. Therefore, proposing dynamic algorithms for NOMA systems is one of the future research areas.
# Skin Lesion Segmentation Using Image Bit-Plane Multilayer Approach
## 4 Implemented Method
### Segmentation Phase  
The RGB dermoscopic images adopted as test bench used 8 bit planes for each color component, so that 24 bit planes were extracted from each image under test and used for the skin lesion segmentation aim. A three-dimensional binary data set was achieved by stacking the afore mentioned bit planes on each other. This stack of bit planes, named BPS, represents the raw data of this segmentation procedure. Every bit plane of BPS contained local specific information about a distinctive pattern occurring at a certain reflectance level on the image.  
Since BPS provides a lot of redundant data, a redundancy reduction was carried out using a specific procedure to obtain a sort of bit plane orthogonalization. The proposed procedure aimed to detect a partition of the image into areas containing pixel levels which were both mutually strongly correlated and uncorrelated/lowly correlated with pixels in different areas.  
To enhance the inherent bit planes' correlation, the gray encoding was used. Because each bit plane of BPS was in a binary form, simple Boolean operators were used. In particular, the proposed binary orthogonalization was implemented by logical AND (&), OR ( ), and NOT (1-U) operators adopting the proposed pseudo code in which _U(i)_ is the \(i\)th bit plane in BPS:  
for (i, j), with (i \(\
eq\) j)  
// definitions  
AND = U(j) & U(k);  
AnotB = (1-U(j)) & U(k);  
notAB = (1-U(k)) & U(j);  
Figure 1: Singular values of a generic skin lesion image under test. The three curves refer to Red, Green, and Blue color component in the image.  
// operations  
U(j) = AND \(|\) AnotB;  
U(k) = (AND \(|\) notAB);  
end  
Several new transformations of bit planes containing mainly color plane coherent areas were consequently generated. Because many of such areas were overlapping and duplicated, a procedure to reduce the number of segmented areas was carried out. Sorting the segmented areas in accordance with their dimensions, a multilevel image description of coherent color areas was performed by selecting a number of slices. Figure 2 shows an example of the segmentation phase output: On the Cartesian axes, pixel number of the tested image is reported and yellow areas show the segmented regions obtained by the proposed binary approach.
## 2 Related Work
### Physical Layer Security  
Physical layer security refers to the use of the characteristics of physical layer wireless signals to protect the security of computer equipment and data. It is the lowest barrier of wireless network security, which can accurately identify authorized and unauthorized users. In the process of research on individual identification, many scholars have proved that it is an effective method of identity authentication based on characteristics of the wireless signals [20; 21; 22]. Since the signals transmitted by each device imply its individual characteristics, such as signal envelope fluctuations, steepness, modulated instantaneous frequencies, and wireless transmission paths, these physical layer features are unique and unclonable. Thus, we can use wireless signals to identify \"legal\" or \"illegal\" IoT devices, thereby ensuring IoT security.
## 2 Case Study Area  
Perito Moreno Glacier, in the Los Glaciares National Park (Argentina), is an eastward outlet glacier of the Southern Patagonian Ice field (SPI, Figure 1), with an area of 254 km\({}^{2}\)[27]. The glacier length is about 30 km, from the continental divide East of the _Lago Argentino_ (Argentinian Lake). The average height of the ice divide is about 2200 m, with the highest point at _Cerro Pietrobelli_ (2950 m a.s.l.). The accumulation and ablation areas cover about 70% (i.e., 178 km\({}^{2}\)) and 30% of the total area, respectively [10]. The ablation area is made by a valley-type glacier tongue, about 15 km long and about 4 km wide. The front terminates with calving cliffs rising, until at 50-80 m a.s.l., above the South-Western arm of the _Lago Argentino_, namely, into the _Canal de los Tempanos_ and _Brazo Rico_, at an altitude of about 180 m a.s.l. [20]. The ice front reaches the opposite bank at the Magallanes Peninsula, with a drainage ice tunnel at 50\({}^{\circ}\)28.5\({}^{\prime}\) S, 73\({}^{\circ}\)02\({}^{\prime}\) W [8]. According to [PERSON] et al. [28], accumulation on the PMG is among the highest worldwide, i.e., about 5540 \(\pm\) 500 mm y\({}^{-1}\)
# C-V2X Centralized Resource Allocation with Spectrum Re-Partitioning in Highway Scenario
## 3 Bandwidth Re-Partitioning Techniques among RSUs
### Full Frequency Reuse Technique (FFR)  
The FFR technique protects the packets broadcast in the network from interference signals. Each RSU deployed along the highway partially utilizes a given frequency band. In other words, the given band is divided into two sub-bands dedicated to vehicles located in the two adjacent RSU coverage areas. Each sub-band is applied again by the RSU, which is adjacent to the RSU neighboring to the current one. In this technique, the distance between  
Figure 2: \(BCZ_{area}\) of two vehicles located in different RSUs coverage area.  
the two nearest RSUs using the same sub-band \(h_{s\_band}\) is equal to the diameter \(RSU_{diameter}\) of the coverage area of the center RSU.  
\[h_{s\_band}=RSU_{diameter} \tag{21}\]  
The \(h_{reuse}\) will be lower than \(h\) (see Figure 2) if the \(RSU_{diameter}\) is higher than the broadcasting range of the vehicles. Otherwise, if two vehicles are using the same frequency resource and are located on the edge of their RSU coverage (i.e., RSUs X and Z in Figure 2) adjacent to their common RSU (i.e., RSU Y), BCZ will be observed in the middle of the RSU Y coverage area. The vehicles in this area will not be able to decode the packets from both transmitters A and B. The failure to increase the RSU coverage area is the limitation of resources in each \(RSU_{band}\) for the number of vehicles. Decreasing the number of vehicles in an RSU area is disadvantageous and unacceptable. Therefore, to eliminate this possibility, the PFR technique has been proposed to solve this drawback.