esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 CCA-YOLO Network Model
### Channel Coordinate Attention Mechanism  
Coordinate attention encodes channel relationships and long-term dependencies through precise position information. This encoding is divided into two steps: the coordination of information embedding and coordinate attention generation [32]. As shown in Figure 4, one-dimensional adaptive average pooling of input features in the x-axis and y-axis directions was performed to obtain independent directional perception features with x-axis and y-axis information retained, respectively. One spatial direction captures long-range dependencies, whereas the other retains accurate position information.  
As shown in Figure 4 and Formula (1), the feature map is changed from [C, H, W] to [C, 1, 1] using the global average pooling method, and the information is subsequently processed using two \(1\times 1\times 1\) convolutions, resulting in a C-dimensional vector. The addition join does not add new dimensions without preserving the characteristics of the previous layer. It is generally believed that the input mapping features \(\mathbf{U}\) as a combination and embedding of the global spatial information in vector z, encoding the channel-related dependencies:  
\[\mathbf{z}_{k}=\frac{1}{\mathrm{H}\times\mathrm{W}}\mathbf{\sum_{i}^{H} \mathbf{\sum_{j}^{W}u_{k}}} \tag{1}\]  
Using a sigmoid function to pass \(\sigma(\hat{z})\) for normalization, \(\hat{z}\) is brought to the interval \([0,1]\) to obtain the corresponding mask, and channel-wise multiplication is finally per  
Figure 4: Network diagram of the CCA mechanism.  
formed to obtain an information-calibrated feature map, emphasizing the useful features to suppress the useless ones. The resulting vector is used to recalibrate or excite \(\mathbf{U}\) to  
\[\hat{\mathbf{U}}_{\text{CCA}}=\text{F}_{\text{CCA}}(\mathbf{U})=[\sigma(\hat{ \mathbf{z}}_{1})\mathbf{u}_{1},\sigma(\hat{\mathbf{z}}_{2})\mathbf{u}_{2}, \ldots,\sigma(\hat{\mathbf{z}}_{\text{C}})\mathbf{u}_{\text{C}}] \tag{2}\]  
\(\sigma(\hat{\mathbf{z}}_{i})\) indicates the importance of the channel being rescaled. As the network learns, this activation is adaptively adjusted to ignore less important channels and emphasize the important ones, improving the network performance by focusing on the key locations of the image.  
After optimizing the channel dimension, the two obtained one-dimensional features are spliced in the \(W\) dimension and a convolution and nonlinear activation function are passed; subsequently, the features are split in the channel dimension [31]. Two feature graphs with long-range dependencies of specific spatial direction are obtained through convolution and the sigmoid activation function. These two feature maps can be complementarily applied to the input feature map to enhance the target of interest. A feature graph with attention weight in the width and height directions is obtained through feature fusion. The experimental results show that CCA can effectively improve the accuracy of the model while slightly increasing the number of computations.
# A Novel Approach to Multi-Provider Network Slice Selector for 5G and Future Communication Systems
###### Abstract  
The Network Slice Selection Function (NSSF) in heterogeneous technology environments is a complex problem, which still does not have a fully acceptable solution. Thus, the implementation of new network selection strategies represents an important issue in development, mainly due to the growing demand for applications and scenarios involving 5G and future networks. This work presents an integrated solution for the NSSF problem, called the Network Slice Selection Function Decision-Aid Framework (NSSF DAF), which consists of a distributed solution in which a part is executed on the user's equipment (for example, smartphones, Unmanned Aerial Vehicles, IoT brokers) functioning as a transparent service, and another at the Edge of the operator or service provider. It requires a low consumption of computing resources from mobile devices and offers complete independence from the network operator. For this purpose, protocols and software tools are used to classify slices, employing the following four multicriteria methods to aid decision making: VIKOR (Visekriteriumska Optimizacija i Kompromisno Resenje), COPRAS (Complex Proportional Assessment), TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) and Promethee II (Preference Ranking Organization Method for Enrichment Evaluations). The general objective is to verify the similarity among these methods and applications to the slice classification and selection process, considering a specific scenario in the framework. It also uses machine learning through the K-means clustering algorithm, adopting a hybrid solution in the implementation and operation of the NSSF service in multi-domain slicing environments of heterogeneous mobile networks. Testbeds were conducted to validate the proposed framework, mapping the adequate quality of service requirements. The results indicate a real possibility of offering a complete solution to the NSSF problem that can be implemented in Edge, in Core, or even in the 5G Radio Base Station itself, without the incremental computational cost of the end user's equipment, allowing for an adequate quality of experience.  
5G; beyond 5G; networks softwarization; multi-criteria decision methods; Network Slice Selection Function (NSSF) +
Footnote †: journal: _Sensors_
# Targeting KRAS in PDAC: A New Way to Cure It?
## 4 KRAS Inhibitors for PDAC
### \(\text{Kras}^{\text{G12D}}\) Inhibitors  
#### 4.2.1 MRTX1133  
While sotorasib has been approved by the FDA for the treatment of KRAS\({}^{\text{G12C}}\) mutation-containing NSCLC [51], the development of other KRAS mutation inhibitors has come to a standstill. One of the main reasons hindering the development of KRAS\({}^{\text{G12D}}\) inhibitors, which has been mentioned previously, is the low rate of intrinsic GTP hydrolysis in the KRAS\({}^{\text{G12D}}\) mutant [60]. KRAS mutations lead to a decrease in intrinsic GTPase activ  
Figure 2: Structures of RAS proteins and inhibitors. Protein is indicated by surface representation, and compounds and nucleotides are shown in stick models. The carbon and hydrogen atoms of the inhibitor are marked in yellow to highlight them. (**a**) KRAS\({}^{\text{G12C}}\) and AMG510 (Protein Data Bank (PDB); 60 IM). (**b**) KRAS\({}^{\text{G12C}}\) and MRTX849 (PDB: 6 UT0). (**c**) KRAS\({}^{\text{G12D}}\) and MRTX1133 (PDB: 7 RPZ). (**d**) HRMS\({}^{\text{G60A}}\) and NSC290956 [14].  
ity, which further decreases the rate of GTP hydrolysis and ultimately continues to activate downstream pathways and produce carcinogenesis [61]. The intrinsic hydrolysis rate of the KRAS\({}^{\text{G12C}}\) mutation is equivalent to approximately 70% of that of the wild-type KRAS, while the intrinsic hydrolysis rate of the KRAS\({}^{\text{G12D}}\) mutation is only less than 30% [60]. This disadvantage poses a challenge for the design of KRAS\({}^{\text{G12D}}\) inhibitors. It is also challenging to determine whether the inhibitor has sufficient affinity for 12-aspartate involved in the KRAS\({}^{\text{G12D}}\) mutant to avoid binding to wild-type KRAS. In February 2022, Mirati Therapeutics announced a selective non-covalent inhibitor, MRTX1133 of KRAS\({}^{\text{G12D}}\) (Figure 2). The structure of MRTX1133 is based on MRTX849, a KRAS\({}^{\text{G12C}}\) inhibitor developed by Mirati Therapeutics. The investigators introduced a salt bridge between the inhibitor and 12-aspartate to enhance the reversible affinity for KRAS\({}^{\text{G12D}}\). This strengthened the selectivity of the inhibitor for KRAS\({}^{\text{G12D}}\) through a series of modifications to avoid binding to wild-type KRAS. Compared to several KRAS\({}^{\text{G12C}}\) inhibitors whose reversible affinity for the target is in the micromolar range [50; 75; 76], MRTX1133 has a picomolar range of reversible affinity for KRAS\({}^{\text{G12D}}\). Although MRTX1133 binds weakly to KRAS proteins in the GDP state, it also has the ability to bind to KRAS proteins in the GTP state [77]. This will lead to new ideas for combination therapy studies of KRAS inhibitors. In a previous study, MRTX1133 achieved excellent results in a mouse xenograft model of pancreatic cancer, with a 94% reduction in tumor volume at 3 mg/kg BID (IP) compared to the control group [13].
# Performance Evaluation of rPPG Approaches with and without the Region-of-Interest Localization Step
###### Abstract
[8] studied performance of four different face detection and tracking algorithms applied in rPPG: (1) landmarks detection [14]on all frames and (2) landmarks detection on the first frame only in combination with (a) KLT tracker, (b) Circulant Structure with Kernels (CSK) tracker [28], and (c) Sum of Template and Pixel-Wise Learners (STAPLE) tracker [29]. Two different publicly available data sets (PURE [30], UBFC-RPPG [6]) and one private data set (Self-RPPG) were used and Plane-Orthogonal-To-Skin (POS) [31] algorithm was applied for final rPPG signal extraction. The best overall result (in terms of SNR, MAE, and RMSE) for PURE was achieved by landmarks detection on each frame, for UBFC-RPPG by an approach relying on KLT tracking, and for Self-RPPG by the approach relying on CSK tracking. Recently, [PERSON] et al. [32] indirectly studied the performance of the following ROI localization approaches: (1) detecting a face on the first frame using [PERSON] frontal face detector and keeping the position of the bounding box fixed throughout the entire recording, (2) detecting a face on the first frame using [PERSON] frontal face detector and then tracking it using KLT tracker, and (3) the same approach as the previous one with an addition of statistical skin classifier proposed by [PERSON] and [PERSON] [33]. Their performance in terms of accuracy and MAE was tested on two data sets containing a total of 1000 ten-seconds-long, lossy compressed video recordings recorded under challenging illumination conditions. The authors reported that, in the case of using the green channel signal as the final pulse waveform signal, the best overall result was achieved by the first approach (accuracy - defined as the ratio between the number of true positives [i.e., PRs within the range of \(\pm 5\) BPM around the reference PR] - of 0.1709 and MAE of 23.53 BPM), in the case of applying CHROM [21] rPPG algorithm, by the third approach (accuracy of 0.3411 and MAE of 16.77 BPM) and in the case of applying POS [31] algorithm, by the second approach (accuracy of 0.3455 and 15.95 BPM). The purpose of this work is to study the effect of approaches with and without the ROI localization step (i.e., with and without the image processing front-end), on the (1) success rate of extracting rPPG signals from video recordings, (2) SNR and (3) RMSE of average PRs estimated from video recordings. Performance evaluation is to be carried out on the publicly available data sets offering uncompressed and compressed (both lossy and lossless) videos covering various scenarios. Extraction of final rPPG signals will be extracted using two state-of-the-art rPPG algorithms relying on distinct principles and differing in sensitivity to the presence of non-skin pixels within ROI: model-based POS [31] and non-model-based (i.e., data-driven) Spatial Subspace Rotation (2 SR) [34].
# A Real-Time Simulator for Navigation in GNSS-Denied Environments of UAV Swarms
## 4 Simulation Result  
### Simulation for ISMN  
The ISMN is tested at Beijing University of Science and Technology, based on the 4546 \(\times\) 3912 resolution Google civil and commercial satellite map. We aim to demonstrate the effectiveness of the simulation environment and localization algorithm by comparing the output of the ISMN with the truth values provided by the simulation environment. The ISMN outputs the longitude and latitude of the UAV. The simulation environment can provide true values of longitude and latitude for comparison. The mapping relationship between the real-time continuous images captured by the UAV and the satellite reference images is shown in Figure 8.  
The result, shown in Figures 9 and 10, indicates that when the UAV's altitude is 800 m, the optimized reference map area and the input image overlap >90%, and the average positioning error is 3.1 m (<0.387% of the height) when the UAV flies smoothly. Moreover, when the UAV turns at a large angle (the attitude is 30\({}^{\circ}\)), the position error is 13.1 m. The experimental results indicate that the use of ISMN can achieve navigation in GNSS-deni  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Description** \\ \hline center & The map center: latitude and longitude \\ \hline World\_size & The desired size of the world target to be covered \\ \hline Model\_name & The name of the map model \\ \hline pose & The pose of the map model in the simulation \\ \hline zoom & The zoom level \\ \hline Map\_type & The map type to be used, which can be a roadmap, satellite, \\  & terrain or hybrid. By default, it is set to satellite \\ \hline Tile\_size & The size of map tiles in pixels. The maximum limit for standard \\  & Google Static Maps is 640 pixels \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of Google map.  
Figure 8: Real-time mapping between images captured by the UAV and the base map.
## 1 Introduction  
Thyroid eye disease (TED), also known as Graves ophthalmopathy, is an autoimmune disease affecting the orbital tissue. About 25-50% of patients with [PERSON] disease will have TED [1, 2]. Histologically, TED presents as infiltration of various cytokines and inflammatory mediators in the orbit, with soft tissue swelling in the active stage and tissue atrophic fibrosis in the late phase. Clinical manifestations of TED include periodontal edema, eyelid recession, incomplete closure of the eyelid, and restrictive strabismus [3]. About 5.0% of TED patients develop dysthyroid optic neuropathy (DON), a vision-threatening ocular change [4]. Early diagnosis of DON can therefore help prevent the reduced quality of life resulting from low vision.  
The diagnosis of the disease is currently based on a combination of clinical signs and symptoms, laboratory tests, and imaging examinations. Computed tomography (CT) is a routine imaging modality for the diagnosis, treatment evaluation, and postoperative follow-up of TED, and has been widely used in clinical practice. The advantage of CT lies in its high resolution and can clearly display the bony structure, soft tissue, and apical structure of the orbit. Currently, imaging diagnosis lacks a standard quantitative indicator and is quite subjective, being essentially based on the ophthalmologist's personal experience. A combination of CT and three-dimensional (3D) reconstruction techniques assists in the spatial localization and parameter measurements. Therefore, CT enables quantitative methods to be used for clinical diagnosis. The quantitative relationship between the volumes of orbital tissues based on CT analysis and clinical features of TED has been explored in several studies [5; 6]. However, the assessment of eyeball movement and compressive optic neuropathy caused by TED using geometric and volumetric analysis of orbital soft tissue has not been widely studied, especially in Asians.  
Therefore, this study aimed to examine the usefulness of both geometric and volumetric measurements of orbital soft tissues on CT scans to provide quantitative diagnostic guidance in TED image reading.
# Condition-Based Monitoring and Maintenance: State of the Art Review
## 2 Literature Review
### Reliability-Centered Predictive Maintenance  
Reliability-centered maintenance (RCM) is a systematic approach to ensure effective and efficient use of the assets in the designed operating conditions. It focuses on effective and cost-efficient preventive and predictive maintenance programs. A reliability-centered predictive maintenance (CBPM) policy is proposed for a continuously monitored system subject to degradation due to the imperfect maintenance [127]. It is assumed that the system hazard rate is a known function of the system condition and then can be derived directly through CBPM. A hybrid hazard rate recursion rule based on the concept of age reduction factor and hazard rate increase factor is built up to predict the evolution of the system reliability in different maintenance cycles. The optimal reliability threshold is determined by minimizing the cumulative maintenance cost per unit time in the residual life of the system, which is based on simulation. In another study, a reliability-centered maintenance strategy was used on the basis of maintenance-free operating period philosophy and total lifetime operating cost analysis for the aero industry [128]. RCM can also address the life cycle cost. A design of operational vehicle maintenance program based on life cycle cost and reliability-centered maintenance is proposed in military applications [129]. The cost-based performance measured was used in another study wherein a prototype cost model of functional check decisions was used in reliability-centered maintenance [130]. RCM is more commonly applied, being oriented with large number of details of personal involvement.
# Agriculture 5.0 in Colombia: Opportunities Through the Emerging 6G Network
## 3 Materials and Methods
### Selection Processes  
Subsequent to the conclusion of the selection process, the relevant texts were analyzed. In this phase, a qualitative evaluation framework was applied, adapted from international tools such as the Digital Agriculture Readiness Assessment Tool [52]. Three key aspects were established, namely: rural digital infrastructure, regulatory framework for spectrum management, and public investment in agricultural innovation and technological adoption. Each dimension was assessed qualitatively based on objective indicators drawn from official sources.  
The findings that were obtained are reported in this study, and they concern the present development of 6G technology in Colombia, its possible applications in different productive sectors, and, in particular,, its projection in the agricultural sector. Furthermore, the study identified elements for its prospective implementation in this sector, and scenarios for the utilization of the IoT and 6G in national agriculture were proposed.
## 3 Results
### Data Sets  
We show results for four data sets that encompass a typical sky at the UAB campus, and different peculiar atmospheric conditions at the ORM. We characterize the pBRL performance based on these nights, which somewhat bracket extreme cases of good and bad atmospheric environments for the science operations of CTAO. The data sets are as follows:  
* D-I The night of 5 July 2018 at the UAB campus.
* D-II The night of 18 March 2022 at ORM, with clean atmospheric conditions. This should resemble the standard for CTAO operations.
* D-III During the last two weeks of August 2021, an approximately ten-day-long Saharan dust intrusion event occurred, the so-called calima [21]. Calima breached the usually stable inversion layer and significantly degraded air quality above the observatory. In the first days, the mineral dust concentration was so high that multiple scatterings made an accurate analysis of the constituent aerosols almost impossible. In the last days of the event, when Saharan dust spread over a large part of the Atlantic Ocean (see Figure 21), the concentration of scatterers decreased. The analysis presented here refers to data collected in the evenings of 25 and 26 of August 2021.
* D-IV On 19 September 2021, the Tajogaite volcano on the Cumbre Vieja mountain ridge erupted in the southern part of the La Palma island. The volcano was located at a distance of about 14 km toward the south-south-east of the ORM. In the following days, a dust plume spread over the whole island. During the measurements in the evening of 22 September 2021, a vertical scan of the sky was performed. Figure 22 shows satellite data taken during that event.  
Figure 20: Location of the pBRL test sites for the validation campaigns. **Left**: The UAB campus (41.5 N, 2.1 E, 134 m a.s.l.). **Right**: ORM (28.8 N, 17.9 W, 2185 m a.s.l.). The final location of the BRL, as well as the location of several nearby optical telescopes and the approximate area covered by the future CTAO-N are also shown in the figure.
## 3 Results and Discussion
### Resiprometric Tests  
Ammonia Uptake rate tests (AUR) tests were carried out to evaluate the nitrifying activity of the thermophilic biomass. In respiratory tests, no nitrification activity was  
Figure 4: Multivariate linear regressions: comparison between normalized observed value and modelized values Figures from (**a**–**k**) show the trend of the measured values and of the modeled values considering both the concentrations of pollutants in input and their removal efficiency.  
detected. This study showed that uncommon ammonia removal was caused by stripping due to pure oxygen injection rather than microbiological transformation. The microbiological analyses highlighted the absence of nitrifying biomass (see Section 3.3). Indeed, these results completely agree with previous literature findings. At thermophilic temperatures, above 45 \({}^{\circ}\)C, nitrification processes do not take place and the bacteria due to this process are totally inhibited [31].  
The nitrogen uptake rate (NUR) findings confirm the ability of the thermophilic biomass in denitrifying nitrogen in thermophilic conditions (Figure 5). Data from the monitoring of the system suggested nitrates removal higher than 95%.  
The potential of thermophilic bacteria to decrease nitrates over time was confirmed by the findings. Tests carried out under standard conditions have very high NUR kinetics (1-1.7 mgN-NOX gVS\({}^{-1}\) h\({}^{-1}\)). The tests carried out to keep the test reports equal to the real plant ratio showed definitely lower values, equal to 0.21 mgN-NOX gVS\({}^{-1}\) h\({}^{-1}\). The two values are lower than the value found in the literature, relating to mesophilic biomass with carbonaceous source methanol, an excellent substrate according to the literature [28].  
To obtain feedback on the very high nitrate abatement performance, it is necessary to pay attention to the concentration of solids in the tank. In fact, as can be seen in Figure 5, the denitrification kinetics of the thermophilic biomass taken under the real plant conditions is equal to 0.21 mgN-NOX gSV\({}^{-1}\) h\({}^{-1}\). The very high removal yields are, therefore, guaranteed by the high concentration of VS in the TBR (50 kg m\({}^{-3}\)). Considering the obtained kinetics and the content of VS, the biological system was able to remove almost 1680 kg of nitrates in the HRT of the system, which was 94% of the fed N-NOx at the same time 1791 kg. This aspect confirms the results obtained by monitoring of the full-scale system.