esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Modern Imaging Technologies
### Photon-Counting Computed Tomography  
Photon-Counting Computed Tomography (PCCT) represents a major advancement in dental imaging, offering enhanced spatial resolution, superior contrast, reduced image noise, intrinsic spectral data, and more efficient radiation dose usage. These features enable clearer visualization of anatomical structures, which is critical for accurate diagnosis and treatment planning in dentistry [15; 16]. PCCT uses photon-counting detectors (PCDs) that directly convert individual X-ray photons into digital signals with high precision. This technology significantly minimizes metal artifacts from implants using techniques such as Iterative Metal Artifact Reduction (iMAR), Virtual Monoenergetic Imaging (VMI), and sinogram inpainting, thereby improving diagnostic reliability in implant-related cases [17]. Thanks to its high-resolution imaging and artifact reduction, PCCT is especially well-suited for complex diagnostic scenarios in orthodontics, implantology, and oral surgery.
## 2 Materials and Methods
### Evapotranspiration Partitioning and Data Processing  
#### 2.2.1 The Double-Layer Eddy Covariance (DLEC) Method  
Double-layer eddy covariance (DLEC) measurements require the installation of EC systems at two height positions above and below the canopy to separate ecosystem evapotranspiration. Total ecosystem evapotranspiration observed above the canopy includes plant transpiration, canopy evaporation, and soil evaporation by source, while total understory evapotranspiration observed below the canopy includes transpiration from understory groundcover plants and soil evaporation [9]. Thus, for forest ecosystems with negligible canopy evaporation, ecosystem evapotranspiration approximates arboreal evapotranspiration at low E/ET, which can be derived using double-layer eddy correlation observations with the following equation:  
\[TR=ET_{above}-ET_{below} \tag{1}\]  
Before the separation process, it is necessary to exclude data for all periods with precipitation > 0.1 mm to exclude data with negative water vapor flux and subcanopy air temperature above the dew point temperature [9], most importantly, to determine the state of canopy vertical layer air mass coupling and the best diagnostic index for a specific site.  
Here, we choose several indicators to test the coupling state of airflow, including the friction velocity (u*) [27], the mean wind speed (U) [28], the standard deviation of vertical wind speed (ow) [29], and the integral turbulence characteristics ow/u* and ow/U [30; 31]. We used all the half-hour data of ow to determine the airflow coupling state qualitatively, and then the airflow coupling threshold was quantitatively analyzed by dividing the interval. We also investigated the airflow coupling between the canopy and atmosphere in detail. After finishing this process, we obtained the best indicator for our site and used it to ensure that airflow coupling occurred throughout the day to partition evapotranspiration.
# Extraction of Visible Boundaries for Cadastral Mapping Based on UAV Imagery
## 4 Discussion
### Quality Assessment
It is argued that manually delineated boundaries influence the overall results of the accuracy assessment since different human operators might digitize differently [26]. However, in the selected case study, most of the object boundaries were sharp and the presence of white stones at outlines of the agricultural field contributed to the objectivity of manual digitalization. In addition, the real cadastral data could not be used since they did not correspond with the object boundaries on the image (Figure 1a) and it would not have been possible to outline the potential of the ENVI FX. However, the approach of automatic extraction of visible boundaries is case dependent. To reliably avoid the influence of manually digitized reference data, the following studies should consider a case study where the cadastral map is up to date.
# Dependence of Dispersion on Metamaterial Structural Parameters and Dispersion Management
## 4 Light Pulse Propagation in the MM
### Impact of \(\beta_{2}\) and \(\beta_{3}\)
#### 4.1.1 Impact of \(\beta_{2}\) at \(\overline{\omega}_{D}\)
The major difference between Tables 1 and 2 is the sign of \(\beta_{2}\), but \(\beta_{2}\) always causes pulse broadening, no matter its sign. So, Figure 5 is similar to Figure 4. The difference is that the secondary peak appears at the rear edge of the pulse if \(\beta_{2}~{}>0\). It also illustrates that, to some extent, a larger \(|\beta_{2}|\) can inhibit the influence of the pulse splitting. We also noted that the pulse splitting in Figure 5d is not as serious as that in Figure 3, in spite of a higher \(\beta_{3}\) here. Again, this is due to the inhabitation of \(\beta_{2}\). \begin{table}
\begin{tabular}{c c c c c} \hline \(\overline{\omega}\) & \(\beta_{2}(\text{ps}^{2}/\text{km})\) & \(\beta_{3}(\text{ps}^{3}/\text{km})\) & \(L_{D}(\text{km})\) & \(L^{\prime}{}_{D}(\text{km})\) \\ \hline
0.70680 & \(-1.2589\) & 2.0676 & 19.86 & 60.46 \\
0.70675 & \(-2.6715\) & 2.0675 & 9.36 & 60.53 \\ \hline \end{tabular}
\end{table} 
Table 1: Two sets of dispersion data for \(\overline{\omega}<\overline{\omega}_{D}\). Figure 4: The pulse waveform comparison at different propagation distances. (**a**) The first case in Table 1; and (**b**) the second case in Table 1.
# Functional Safety Networks and Protocols in the Industrial Internet of Things Era
## 6 Conclusions  
Functional safety networks are expected to be increasingly used in IIoT ecosystems, particularly over wireless media. In this respect, in this paper, we addressed safety protocols, focusing on those described by the IEC 61784-3 International Standard, and investigated their suitability for IIoT applications. The provided analysis, as well as the results of an extensive experimental session carried out on a prototype implementation of FSoE over WiFi, allowed us to make some interesting considerations. First, the black channel principle can be successfully exploited to bring safety protocols over communication media different from those for which they were natively designed. Second, although the black channel approach in principle ensures feasibility, it is clear it might introduce limitations, particularly with respect to performance. This aspect derives from the undeniable fact that each protocol has been conceived for a specific network. As an example, referring to wired systems, functional safety protocols designed for Ethernet networks can be difficultly implemented on communication systems that use short payloads such as Controller Area Network. Even more evidently, referring to OPC UA Safety, SPDUs may reach large sizes, thus, they need adequate MAC and physical layers to be transferred. Possible performance limitations have been clearly evidenced by the experimental assessment of FSoE over WiFi provided in Section 4. Indeed, as we have seen, the behavior of FSoE is strictly related to the protocol stack included in the black channel. Nevertheless, it has also been shown that there is large room for improvement and future developments. Focusing on WiFi, performance might be further improved with respect to both reliability and reaction time. Indeed, rate adaptation algorithms specifically designed for industrial WiFi applications, as well as suitable network protocol tuning, may lead to significant benefits in terms of reliability and timeliness, as discussed in [36; 37]. In addition, new forthcoming WiFi versions, such as those based on the IEEE 802.11 ax standard, promise considerable performance improvements. Finally, we remark that the upcoming extension of TSN features to WiFi and 5G systems, thanks to its built-in mechanisms to improve reliability and timeliness, promises considerable performance improvements that could enforce rapid and extensive adoption of safety protocols in IIoT ecosystems.  
Conceptualization, G.P. and A.M.; data curation, G.P. and A.M.; funding acquisition, S.V.; investigation, F.T. and S.V.; methodology, F.T. and S.V.; supervision, F.T. and S.V.; visualization, G.P. and A.M.; writing--original draft, G.P., A.M., F.T. and S.V.; writing--review and editing, G.P., A.M., F.T. and S.V. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.
# Benefits of Advanced Air Mobility for Society and Environment: A Case Study of Ohio
## 4 Benefit Factors
### BF-6: Cost Savings in Agriculture
#### 4.6.2 Cost Savings in Crop Farming  
Drones can lower the labor and equipment cost significantly. According to Informa Economics and Measure, a drone service provider firm, corn farmers stand to benefit the most from aerial devices, with average savings of $11.58 per acre denoting \(d\), whereas thefarmers of wheat and soybean would save $2.57 and $2.28 per acre, respectively [72]. Here, the estimated cost savings \(C_{C}\) by drones in a corn field in year \(i\) is  
\[C_{C_{i}}=A_{S_{(i)}}\times d\times F_{ag}\qquad i\in\{2022,2023,...,2032(year)\}. \tag{40}\]  
Similarly, we calculated the yearly cost savings for soybean (\(C_{S_{(i)}}\)) and wheat (\(C_{W_{(i)}}\)). Using Equation (41), we computed \(CS_{i}^{crop}\) the total estimated value of cost savings in year \(i\) from soybean, corn, and wheat fields in Ohio:  
\[CS_{i}^{crop}=C_{S_{(i)}}+C_{C_{(i)}}+C_{W_{(i)}}\qquad i\in\{2022,2023,...,2032 (year)\} \tag{41}\]
## 4 Experiments and Discussion  
In this section, we detail several experiments we conducted to evaluate the performance of the proposed defect detection method both qualitatively and quantitatively. First, experimental data and parameter configuration are briefly described. Second, the effectiveness of the local difference module is shown. Finally, the inspection performance of the proposed method is compared with that of several other unsupervised methods.  
Figure 5: The effectiveness of local difference analysis. (**a–d**) The defective images; (**e–h**) The mask images corresponding to (**a–d**).
# A Construction Method for a Dynamic Weighted Protein Network Using Multi-Level Embedding
## 3 Experiments and Results
### Performance Comparison
#### 3.3.3 Methods Efficiency Analysis  
In this section, we separately analyze the efficiency of different methods for complex recognition. Combining the results shown in Figure 7, it can be found that ST-APIN+MCL has the longest running time on both DIP and BioGRID datasets, while FS-DPIN+ClusterONE has the shortest running time. However, DWPNMLE+ClusterONE on both datasets is between the former two, inferior to FS-DPIN+ClusterONE but superior to ST-APIN+MCL.  
Specifically, on the two datasets, when combining the four complex recognition algorithms of ClusterONE, COACH, MCL, and Core, DWPNMLE has an average efficiency of 9%, 5.9%, 4.9%, and 7.3% over ST-APIN, respectively. On the BioGRID dataset, the advantage of DWPNMLE is even more significant, corresponding to an efficiency improvement over ST-APIN of 11.1%, 11.5%, 3.7%, and 7.8%, respectively. Although DWPNMLE's overall efficiency is slightly lower than that of FS-DPIN's, mainly due to the additional computational overhead required to represent multiple protein subnetworks and perform multi-level embeddings, the method sacrifices time efficiency for higher complex identification accuracy.  
Figure 6: False negative analysis of different recognition methods.  
Figure 7: Efficiency analysis of different methods.  
Overall, DWPNMLE maintains high recognition accuracy while keeping the computational efficiency of the complex recognition task within an acceptable range, showing a good balance between time performance and recognition accuracy.
## 2 Materials and Methods
### Experimental Set-Up
#### 2.4.2 Stage 2--M-BGS Anaerobic Digestion  
The performance of the M-BGS anaerobic digestion was assessed using the volumetric gas production method in batch respirometric reactors (AMPTS II, BPC Instruments AB, Lund, Sweden). The digestion process was run at 38 \(\pm\) 1 \({}^{\circ}\)C. The bioreactors were equipped with a vertical agitator, which ran for 30 s every 10 min at 100 rpm. The active volume of the respirometers was 500 cm\({}^{3}\). Initial organic load rate (OLR) was 5.0 gVS/dm\({}^{3}\). To ensure anaerobic conditions in the respirometers prior to the measurements, the system was purged with 150 dm\({}^{3}\)/h pure nitrogen for 5 min. Digestion continued for 40 d. Biogas composition was monitored chromatographically. The measurement system was equipped with an ex-situ CO\({}_{2}\) adsorption unit, fixing the CO\({}_{2}\) from the biogas with 3M NaOH. A biomethane output report was logged once a day using a program that generates results for a normalized gas volume (standard atmospheric pressure of 101.3 kPa at 0 \({}^{\circ}\)C and zero humidity). Endogenous biogas generated by anaerobic sludge was excluded from the calculation. A diagram of the experimental equipment is presented in Figure 5.  
Figure 4: Experimental set-up for LF-DSS treatment (**a,b**) coupled with M-BGS production and a two-stage biomass separation system (**c**).
## 2 Architectural Framework of IBSN and Its Components
### Communication Strategies
#### 2.5.3 Network Topology  
The topology of IBSN can be in the following forms:  
* Star topology: A star topology-based IBSN consists of a central controller (namely the coordinator), which initiates, terminates, and manages the transmission within the network. The communication in a star topology network is either between the coordinator and device (downlink) or between device and the coordinator (uplink). Note that the peer-to-peer communication (device-to-device) is not considered here. The coordinator uses beacon commands to identify and manage (such as create, maintain and terminate) communication in an IBSN.
* Cluster-tree topology: The cluster-tree topology is a type of a multi-hop mesh network, in which there is always only one single path between two devices. The first device starting the network becomes the root of the tree. Another device can join the network as a \"child\" of the root node. It in turn allows other devices to join the network. Devices are aware of their \"parent\" node andany \"child\" nodes. This hierarchical topology reduces routing complexity. An advantage of the cluster-tree approach is that it enables low power consumption of leaf nodes which, in the case of IBSN, can be the implanted life-critical nodes.
* Star-mesh hybrid topology: This topology allows the connection of a mesh network with one or more star networks or several star networks with each other. A mixed star and mesh network topology combines the simplicity of the single-hop star topology with the scalability and flexibility of the multi-hop mesh topology.
# UAV-Based Privacy-Preserved Trustworthy Seamless Service Agility for NextG Cellular Networks
## 3 Preliminaries
### System Model  
In this work, we consider a NextG Non-Standalone (NSA) cellular infrastructure (with 4G/5G eNodeBs/gNodeBs towers) where UEs rely on the contemporary 5G Authentication procedure (EAP-AKA or EAP TLS) for connection establishment. We further assume a 4G/5G cellular infrastructure that enables cooperative service provisioning between different service provider networks. The UAVs are assisting with the attachment process to the UE in need of this service, as shown in Figure 2. The UAVs are considered the mediator party between the UEs and the guest network to perform authentication and initiate the connection toward the guest network while preserving user privacy. In this work, UAVs are considered a complementary eNB/gNB support node and not a relay node, and hence, the UAV is considered to be stationary during service-providing communication. We further assume a centralized model where the core network provides the UAVs with the best location of the service with a stable channel connection.  
We assume that the UE stores the public keys of the 5G core network during the UE initial attachment and authentication to the home-network cellular provider. Those keys are refreshed and updated securely by the 5G core network periodically. The UEs are pre-registered for the agility services as an extra paid feature for non-emergency circumstances. Nevertheless, all the UEs are directly eligible for the inter-network cooperation services in emergency circumstances. The home core network assigns a Token Key (TTK) to all the users registered for the service, which is refreshed periodically. Those token keys are presented to the assigned UAV to authenticate the UE as a valid user and to show eligibility for the service. The guest network receives a similar key token for authentication purposes and to avoid IMSI or SUCI catchers from posing as a legitimate guest network service provider. The home core network also provides the UE with a set of Concealed Identifications (CIDs) for communication over the guest network for privacy purposes. The CIDs are concealed user IDs to keep the UE identity anonymous to all the parties participating in the data trafficking process (i.e., UAVs and Guest eNB/gNB). This anonymous communication framework preserves the UE privacy during the service porting to a guest network and prevents user profiling attacks.  
Figure 1: 5G-AKA procedure for the UE authentication [41].
# Novel Multi-Level Dynamic Traffic Load-Balancing Protocol for Data Center
## 4 Proposed Multi-Level Traffic Load Balancing MDTLB
### Design Considerations
#### 4.2.2 Adaptive Parameters Settings  
To overcome the parameters setting sensitivity of Hermes, this article proposed two steps of improvement:  
1. The first is to increase the number of path levels to give the scheme more awareness of the path's status. Hence, the path is divided into five types {very good, good, gray, bad, and very bad}, as shown in Figure 4.
2. The second step is to make the judging dynamic by introducing static and dynamic TRTT thresholds, as shown in Figure 5 and pseudocode one. The user sets the static thresholds, while the dynamic thresholds are set by the system using an adaptive way to determine the best threshold value based on the network load. It is important to note that TRTT can be used as the main judging thresholds. This is because the measurements of TRTT are more accurate and can give a wider range than TECN to distinguish between path levels.  
The judging algorithm is illustrated in pseudocode one and Figure 6.  
If (good portion is small) or (good portion is medium and bad portion is big) or (good portion is big) then changeRatio = ChangeRatio + 10 Else if ( good portion is big and bad portion is not big ) then changeRatio = ChangeRatio \(-\) 10 End  
Figure 4: Block diagram of path judging logic.
# Surgical Tool Detection in Open Surgery Videos
## 2 Related Work
### Computer Vision Research in the Open Surgery Domain  
A small body of research exists in the open surgery domain. Most works have been related to recording techniques. [PERSON] et al. [26] proposed a novel surgical recording system using multiple cameras mounted on a surgical lump where computer vision-based region segmentation and recognition techniques are applied to automatically select the camera with the best view, hence producing a single video without occlusion. [PERSON] et al. [39] improved this camera selection algorithm using CNNs. [PERSON] et al. [40] presented self-supervised learning for camera selection by taking advantage of a first-person view. [PERSON] et al. [41] estimated the incision scenes in long-duration open surgery videos using learning gaze speed, hand movements, number of hands, and background movements in egocentric surgical videos.  
Few studies have focused on research related to surgical tool detection in open surgery videos [22; 23; 24; 25; 42]. Ref. [42] performs operating hands detection and tracking in open surgery videos. [PERSON]. [24] proposed detecting two similar surgical tools using hand information in an open surgery video recorded by an egocentric camera. [PERSON] et al. [22] performed surgical tool detection using a multi-camera setting that can help prevent the surgical tool from being totally invisible in open surgery videos, where occlusion often occurs. In the same approach as MIS, the multi-task framework has been often utilized in open surgery video analysis. [PERSON] et al. [25] proposed a multi-task network for action segmentation, tool detection, hands detection, and hand pose keypoints estimation. [PERSON] et al. [23] proposed a multi-task network that solves both tool localization and tool hand interaction detection task.
# Analysis of the Quantization Noise in Discrete Wavelet Transform Filters for 3D Medical Imaging
## 5 Conclusions  
The problem of analyzing the quantization noise effect in coefficients of DWT filters for 3D medical imaging was solved. The method was proposed for wavelet filters coefficients quantizing, which allows minimizing resources in hardware implementation. The method was developed for estimating the maximum error of 3D grayscale and color images DWT with various BPC. The derived Formula (5) allows determining the minimum quality of 3D medical images DWT depending on the wavelet used, bit-width of wavelet filters coefficients and BPC. We proved that Formulas (6)-(9) can be used to determine the minimum bit-width of wavelet filters coefficients for which the result of 3D images DWT reaches high (\(PSNR\geq 40\) dB for images with 8 BPC, \(PSNR\geq 60\) dB for images with 12 BPC and \(PSNR\geq 80\) dB for images with 16 BPC) and maximum (\(PSNR=\infty\)) quality respectively depending on the wavelet used. The experiments of the 3D tomographic images DWT showed that the bit-width of wavelet filters coefficients could be significantly reduced for high-quality medical imaging compared to theoretical analysis results. All data were presented in a fixed-point format and rounding operations were simplified in the proposed method of 3D images DWT.  
The proposed DWT method could be used in a wide range of applications for denoising and compression of 3D medical images. Given the need to improve the efficiency of medical visual data processing methods, further research can be expected in this direction.  
Conceptualization, P.L.; Data curation, P.L.; Formal analysis, N.C.; Investigation, N.N. and P.L.; Methodology, N.C.; Project administration, N.C.; Resources, N.C.; Software, N.N.; Supervision, N.C.; Validation, N.C.; Visualization, N.N.; Writing--original draft, N.N. and P.L.; Writing, review & editing, N.N., P.L. and N.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Russian Foundation for Basic Research (RFBR), grants numbers 19-07-00130 A and 18-37-20059 mol-a-ved, and the Council on grants of the President of the Russian Federation, grant number SP-2245.2018.5.  
We are thankful to the Stavropol Regional Clinical Advisory and Diagnostic Center for providing tomographic images.  
The authors declare no conflict of interest.