esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Resolution and Semantic-Aware Bidirectional Adapter for Multi-Scale Object Detection
## 4 Experiments
### Ablation Studies  
#### 4.2.1 Ablation Studies on Three Components  
To assess the significance of the components within MSBA, we progressively integrate three modules into the model. For all our ablation studies, the baseline method employed is Faster R-CNN with FPN, based on ResNet-50. As indicated in Table 1, MCF enhances the baseline method by 1.2 AP, owing to the utilization of diverse-resolution images and a cascaded dilated convolution fusion strategy. Multi-resolution images encompass ample spatial object information, while the cascaded method provides diverse receptive field messages. MCF effectively furnishes adequate information for objects of varying scales--small, medium, and large. SRT contributes a 1.3 AP enhancement to the baseline method by refining long-range relationships within high-level features. The most substantial contribution to the superior performance stems from the enhancements in \(AP_{L}\) (+2.9 AP), facilitated by ample semantic information. The findings suggest a deficiency in semantic information within the high-level features of the baseline method. SRT rectifies this shortfall by refining semantic information and enhancing feature representation in the high-level layer. BFI boosts detection performance by 1.4 AP, with a noteworthy improvement in \(AP_{S}\). Evidently, robust interaction across various levels is conducive to mitigating scale variations. Furthermore, the fine-grained messages proficiently enhance detail and contour information across multi-scale features.  
Combining any two of these components results in significantly improved performance compared to the baseline method, underscoring the efficacy of their synergistic interaction. For instance, the simultaneous integration of MCF and SRT yields an AP improvement of 39.0, surpassing the enhancement achieved by either module individually.  
Furthermore, the incorporation of all three components with the baseline method results in an AP of 39.5. These ablation results substantiate the efficacy of the three individual components and their combined configurations, affirming their mutual complementarity.
# Adapting the GACT-X Aligner to Accelerate Minimap2 in an FPGA Cloud Instance
## 2 Materials and Methods
### Adapting GACT-X for Minimap2 on an FPGA Platform
#### 2.4.2 The FPGA Cloud Platform  
An AWS instance was created with the FPGA Developer AMI (Amazon Machine Image) [40], version 1.4.1, in the region \"US West (Oregon)\". The instance type is f1.2 xlarge, the smallest one that has an FPGA in a coupled board, which is enough for the purposes of this research. Moreover, it currently presents a higher performance/cost ratio as compared to instances with a larger number of FPGAs [32]. The instance provides a server under the Intel Xeon E5-2686 v4 processor, with 122 GiB of memory and 480 GB of SSD (solid-state drive) storage. The Puttygen, WinSCP, and Tmux [41] tools were used to control and manage the instance remotely from Windows. The Minimap2 version 2.18 was collected from GitHub [42] and installed on the server. The Minimap2 was configured to different settings according to experiments seen in the following sections.  
In the instance configuration, the \"aws-fpga\" library was cloned from [43], and its version was swapped to an older version since GACT-X was developed on the SDAccel environment, and, from 2020 on, the default environment changed to Vitis. The Darwin-WGA project was cloned from GitHub [44] (git committ 8e0894e). There were no other versions of it at the time of writing. The Amazon FPGA Image (AFI) was created for GACT-X execution following the recommendations by AWS.  
Part of the software written in OpenCL for GACT-X on the host had to be adapted for this implementation. Darwin-WGA is a whole genome aligner, so it sends the two genomes in their entirety to the DRAM, to be aligned with each other by looping the pairs of extending positions. In contrast, in Minimap2, each pair of references and read sub-sequences resulting from the chaining process is streamed to the DRAM to calculate the extending results. Additionally, the process described in the paper of creating and managing tiles [30] was not implemented in the original GACT-X host deposited on GitHub [44]; thus, this was added as well.
# Rainfall Estimation from Tropospheric Attenuation Affecting Satellite Links
## 4 The Rainfall Rate Estimation Model  
The proposed rain rate estimation algorithm is a typical inverse model; specifically, it is a closed-form relationship that expresses the rain rate as a function of two physical quantities, namely the rain attenuation \(A_{R}\) (obtained from the link) and the equivalent rain height \(h_{R}\) (estimated from ECMWF data, as described more in detail in Section 4.3).  
The chosen model to be inverted is the prediction method that is described in Section 2.2.1.1 of recommendation ITU-R P.618 [19], which provides as output the rain attenuation \(A_{R,p}\) exceeded for an arbitrary time percentage \(p\) in a year, as a function \(g(R,h_{R},\ k,\ \alpha)\) of the rain rate \(R\) (mm/h), of the equivalent rain height \(h_{R}\) (km), and of the \(k\) and \(\alpha\) power-law coefficients of Table 1:  
\[A_{R,p}=g(R,h_{R},k,\alpha). \tag{2}\]  
The model, whose full mathematical framework can be found in [19] (and is not reported here for the sake of brevity), has proven to reliably predict rain attenuation on a statistical basis [20; 21; 22]: the idea that is put forth in this contribution (and verified in the remainder of the contribution) is that such a model can also be used to provide accurate results on an instantaneous basis. It is worth underlying that part of the good prediction accuracy of this model is ascribable to the inclusion of path reduction factors, analytical expressions that allow for taking the inhomogeneity of precipitation along the link in due account. As a result, the model offers an accurate prediction of the rain attenuation along the path from the sole knowledge of the rain rate measured at the receiver site; this means that it is possible to predict point rain rate values to be compared against reference precipitation data collected at the receiver site through the inversion of the model.  
As is clear from [19], the function \(g\) includes various non-linear relationships between \(A_{R}\) and \(R\): as a consequence, no analytical inversion for \(g(R,\ h_{R},\ k,\ \alpha)\) exists.
## 4 Discussion
Even if the correlation between free n-3 PUFAs plasma levels and EPA/DHA intakes was low, it still provides reliable data on how the short-term diets impact the plasma lipid profiles and directed us to evaluate the rs526126 activity separately by grouping the subjects according to the EPA and DHA intakes. Although no other relevant clinical outcome is associated with the presence of the low-frequency allele rs526126, its presence and the higher levels of free DHA in plasma could have long-term benefits. The ability of free DHA to readily cross the blood-brain barrier [28] and be incorporated in the cell membrane phospholipids [29] is considered an essential process for brain development in childhood and adolescence. Additionally, studies show that despite a higher dietary PUFAs intake, obese children show lower levels of DHA than normal-weight children, possibly due to the metabolic dysfunction in the synthetic pathway of the n-3 PUFAs [30]. An essential role of DHA as a modulator in the process of inflammation response is carried by resolvins, protectins, and maresins by inhibiting the neutrophil migration, enhancing the macrophage phagocytosis of apoptotic neutrophils, and by the pro-inflammatory cytokines and chemokines suppression [31]. Furthermore, DHA is involved in several signaling processes, translating into a better brain function throughout the lifespan [32]. In this study, DHA and EPA were not found to be associated with cholesterol, triglycerides, and HOMA-IR, as previously reported [33]. This finding may be due to the measurement of FFA in plasma instead of the whole blood. Nevertheless, we can conclude that rs526126 and free n-3 PUFAs were not associated with diabetes and insulin resistance in this cohort. In a previous study [34], we demonstrated that genetic signatures in _PEMT_ and _MTHFR_ genes are associated with fatty acids composition in red blood cells, including DHA. Therefore, this study adds to the knowledge that the blood's fatty acids composition is influenced by genetic variations at different stages in lipid metabolism. To further highlight the role of genetic influences on the complex lipid metabolism and to add to the existing evidence thereof, this study included the results from a separate analysis of free FAs in plasma. The main limitation of this study was represented by the absence of a comparison of relevant parameters in normal-weight children vs. overweight children. In addition, the underreporting in the assessment of the dietary intake using 24 h recalls could have brought limitations. Therefore, we cannot conclude that the _FADS2_ variation's related changes in n-3 PUFA levels are specific to overweight children. A longitudinal analysis of the status of free PUFAs over a longer period could have provided more insight.
###### Abstract  
In traditional 3D reconstruction using UAV images, only radiance information, which is treated as a geometric constraint, is used in feature matching, allowing for the restoration of the scene's structure. After introducing radiance supervision, NeRF can adjust the geometry in the fixed-ray direction, resulting in a smaller search space and higher robustness. Considering the lack of NeRF construction methods for aerial scenarios, we propose a new NeRF point sampling method, which is generated using a UAV imaging model, compatible with a global geographic coordinate system, and suitable for a UAV view. We found that NeRF is optimized entirely based on the radiance while ignoring the direct geometry constraint. Therefore, we designed a radiance correction strategy that considers the incidence angle. Our method can complete point sampling in a UAV imaging scene, as well as simultaneously perform digital surface model construction and ground radiance information recovery. When tested on self-acquired datasets, the NeRF variant proposed in this paper achieved better reconstruction accuracy than the original NeRF-based methods. It also reached a level of precision comparable to that of traditional photogrammetry methods, and it is capable of outputting a surface albedo that includes shadow information.  
neural radiance field (NeRF); DSM reconstruction; UAV imaging model; radiance acquisition 2024164168
# An Efficient Resource Allocation Algorithm for Device-To-Device Communications
## 4 Simulation Results
### Complexity and Performance Analysis
#### 4.2.1 Complexity Analysis  
The complexity analysis of the five resource allocation algorithms is as follows:  
1. The complexity of the random resource allocation algorithm increases linearly with the increase of the number of cellular users, that is, \(\boldsymbol{O}(\boldsymbol{C})\) (where \(\boldsymbol{C}\) is the number of macro cellular users), and the algorithm calculates the lowest amount.
2. The worst-case complexity of the DARA scheme [16] is \(\boldsymbol{O}\big{(}\boldsymbol{C}^{2}\big{)}\), and the average complexity is \(\boldsymbol{Clog}(\boldsymbol{C})\).
3. The optimal algorithm is an exhaustive search algorithm that satisfies the constraints, and needs to traverse all possible allocation methods with the highest computational amount.
4. The complexity of the CG algorithm [20] is \(\boldsymbol{O}(\boldsymbol{N})\) where \(\boldsymbol{N}\) is the number of channels.
5. The complexity of the proposed scheme is \(\boldsymbol{O}(\boldsymbol{N}-\boldsymbol{12})\), therefore, the calculation amount is significantly lower than the random allocation algorithm, and the optimal algorithm and the algorithms in references [16; 20].
# Reduced Overhead Routing in Short-Range Low-Power and Lossy Wireless Networks +
## 4 Reduced Overhead Routing Protocol
### Point to Multi-Point Reduced Overhead Routing  
In the non-storing MoP, RPL uses source routing for P2 MP communication. Source routing results in variable size IP packet header, the variable size header can result in higher packet processing delay. Moreover, as IP addresses or IEEE 802.15.4 MAC addresses of all the nodes along the path to the destination are present in the packet header, this approach also results in a higher control overhead. Typically, nodes in a low-power wireless network possess limited energy, and it is well known that wireless transmission consumes most of a node's energy. Therefore, source routing in such networks results in higher power consumption, hence shorter network lifetime and frequent replacement of nodes' energy sources.  
To circumvent the issues associated with RPL's source routing in P2 MP communication, we present a forwarding protocol that relies on a probabilistic data structure called a Bloom Filter (BF). BF is a time and space efficient m-bit array data structure. A BF uses \(k\) hash functions to insert an element into the BF. The \(k\) hash functions are individually applied to an element that needs to be inserted in the BF, and each hash function individually sets a bit in the m-bit array. To check whether an element is in the BF, the same \(k\) hash functions are applied to the element to obtain \(k\) positions in the m-bit array. If the corresponding \(k\) bits in the m-bit array are set, the element may exist in the BF, however if any of the \(k\) bits is not set, the element is certainly not in the BF. However, BFs can yield false positive results. A BF configuration includes the set \(\{k,m,n\}\). We have already discussed the set's elements \(k\) and \(m\) but have not yet discussed the element \(n\). \(n\) represents the maximum number of items that can be inserted in the BF's m-bit array. The probability of false positive depends on the values of the elements in the set \(\{k,m,n\}\). Equation (1) shows the probability of false positives for specific values of \(k\), \(m\), and \(n\). An effective and efficient network design for a short-range low-power and lossy wireless network use case, such as smart city, smart home, surveillance, etc., invariably does not result in a network in which every node is many hops away from the root. Therefore, in this scenario, if we replace source routing information with BF's m-bit array data structure, the value of \(n\) is certainly not large for the type of network considered here. In this research, we use \(n=12\) as it is a big enough value for the type of networks we are targeting here, and the same value is also suggested in [21]. For a false positive probability of approximately 1%, the following two configurations yield values of \(k\) equal to 4 and 2 respectively: \((i)\)\(n=12\) and \(m=128\) bits and \((ii)\)\(n=12\) and \(m=256\) bits.  
\[p=\left(1-\left(1-\frac{1}{m}\right)^{kn}\right)^{k} \tag{1}\]
# On Global Quantum Communication Networking
## 4 QCN under Development  
The terrestrial QCN to be developed at the University of Arizona is shown in Figure 6; it will exploit the existing NSF MRI INQUIRE quantum network, representing the quantum hub (QuHub) to share entangled photons and SPDs among different labs across the campus. The outdoor FSO bidirectional link, connecting the Electrical and Computer Engineering and Optical Sciences buildings, has already been established, with the FSO transceiver shown in Figure 7. We will also create the mesh  
Figure 5: Proposed hybrid QKD-PQC scheme against MDI-QKD and TF-QKD in terms of secret-key rate vs. distance, assuming that ultra-low loss fiber is used.  
network as well as the hybrid network composed of mesh, optical star, and ring network segments. The deployed heterogeneous QCNs will allow us to test novel quantum-networking theories and develop experimental tools for counteracting various channel impairments. To deal with atmospheric turbulence effects, the adaptive optics (AO) subsystem, composed of a wavefront sensor (WFS) and deformable mirror will be used. The AO will be combined with adaptive LDPC coding.  
To provide global coverage, we envision a scenario in which disconnected terrestrial QCNs, such as the one shown in Figure 6, are coupled through the LEO satellite quantum network. We have recently shown that a Bessel-Gaussian (BG) beam, carrying an orbital angular momentum mode, exhibits better tolerance to atmospheric turbulence effects compared to Gaussian beams for distances up to a few kilometers [31]. However, for LEO satellite-to-ground QuCom links, BG beams diffract much faster than Gaussian beams for such long-distance applications. Hence, we need to use pure Bessel beams to overcome this problem, as we have shown in our recent paper [32]. To enable robustness against turbulence encountered by FSO links, the envisioned quantum satellite QCN should communicate to ground nodes only through the LEO satellite-to-ground links, exhibiting a vertical downlink profile through vacuum followed by a turbulence layer with altitude-dependent strength. In principle, MEO/GEO satellite QCNs can be created above LEO QCNs to provide the planetary coverage.
## 4 Experimental Results and Analysis
### Comparison of Inference Times Across Methods  
Time efficiency plays a critical role in evaluating the effectiveness of steganalysis algorithms, particularly in scenarios requiring real-time detection. Rapid processing of each input sample is essential to ensure timely and responsive analysis. All experiments were conducted on a system configured with an Intel (R) Xeon (R) E5-2678 v3 @ 2.50 GHz CPU and an NVIDIA (Nvidia Corporation, California, America) GeForce GTX 3080 Ti GPU. The corresponding results are presented in Table 7.  
As shown in Table 7, DMSM [31] achieved the highest time efficiency among all the methods. Although our proposed approach did not achieve the fastest inference speed, it satisfied the real-time processing requirement for AMR-NB steganalysis. Given that each AMR-NB speech frame corresponds to 20 ms, the inference time is sufficiently low to support real-time deployment in practical scenarios.
# Reutilization of Recycled CDW Sand in Mortars, Paving Blocks, and Structural Concrete
## 2 Material and Methods
### Design, Preparation, and Characterization of Mortars and Paving Blocks
The paving blocks were cast in molds measuring 400 \(\times\) 400 \(\times\) 40 mm\({}^{3}\), each with eight cavities of 100 \(\times\) 200 \(\times\) 40 mm\({}^{3}\), as shown in Figure 1.  
Paving blocks were prepared using a mortar mixing rod, with a total mixing time of 9 min. Initially, the aggregates were mixed with cement, followed by the addition of water for 1 min. Mixing continued for 4 min before incorporating the fluidizing additive. The mixture was then mixed for a further 4 min. Finally, the samples were compacted on a vibrating table, as depicted in Figure 2.  
Both paving blocks and mortars were cured for up to 28 days at 20 \({}^{\circ}\)C and 100% relative humidity in a controlled temperature and humidity chamber until the testing age. The density of the mortar and paving block samples was determined by weighing and measuring them. Their workability was evaluated by adapting the procedure outlined in Standard UNE-EN 1015-3 [42], using Vicat test molds with a truncated cone shape and a bottom diameter of 80 mm. The molds were first moistened and filled in two layers. Each layer was compacted with 10 strokes, applied at a constant frequency of one stroke per second, using a circular tamper (40 mm diameter, 200 mm long, and a mass of 250 \(\pm\) 15 g). After 30 s, the mold was lifted vertically and slowly, and the plate was tapped 15 times, at a rate of one tap per second. Workability was assessed in two perpendicular directions by measuring the diameter of spread on a glass plate. For the mortars, workability was  
Figure 1: Paving blocks made with the NA and CDW aggregates: (**a**) molds; (**b**) developed paving blocks. Figure 2: Preparation of paving blocks made with NA and CDW sand: (**a**) mixing; (**b**) compaction. measured immediately after mixing (4 min and 30 s after mixing began). For the paving blocks, workability was measured immediately after mixing (10 min) and once again 20 min after mixing commenced. The water absorption of the paving blocks was assessed according to Annex E of Standard UNE EN 1338:2004 [43]. The specimen was first conditioned at a temperature of \(20\pm 5\)\({}^{\circ}\)C. It was then submerged in water for at least 3 days until it reached a constant mass. After this, the specimen was dried in an oven at \(105\pm 5\)\({}^{\circ}\)C for a minimum of 3 days, until a constant mass was attained. The water absorption of each specimen was calculated as the mass loss relative to the dry mass. The compressive strength of the mortar samples was assessed in a universal testing machine MEH-3000 PT/W by Ibertest (Madrid, Spain), after 7 and 28 curing days, in accordance with Standard UNE EN 196-1 [41]. Six samples per mix and curing age were tested. The splitting tensile strength of the paving blocks was measured after 3, 7, and 28 curing days using the same equipment, but following Annex F of Standard UNE EN 1338:2004 [43], as illustrated in Figure 3. Two samples per mix and curing age were tested.
## 5 Experimental Results  
In order to appraise the performances of the proposed control architecture, a series of numerical simulation experiments are carried out. In this section, a control system with the decoupled longitudinal and lateral is applied to contrast with the presented control approach. Linear quadratic regulator (LQR) control is one of the most representative control methods since it is a relatively mature control method. Hence, in this paper, LQR control is applied as a comparison to further verify the proposed control strategy. The optimal solution of LQR control can be written as a unified analytic expression, and the closed-loop optimal control system can be reconstructed simply by using the state linear feedback control law, which takes into account multiple performance indicators. Therefore, the lateral vehicle motion controller is selected as a traditional LQR controller. In terms of longitudinal control, the four-wheel drive is distributed evenly. An 11 degrees-of-freedom high fidelity vehicle model is established, which is used for simulation. The vehicle parameters are presented in Table 2.
## 3 Test Results
### Fresh Properties of RPC
#### 3.1.3 Relationship between Setting Time and Flowability  
Figure 5 shows the relationship between the relative flowability and the setting time of RPC. Setting time and \(\Gamma_{\rm m}\) are proportional to each other. In other words, the mixture with lower \(\Gamma_{\rm m}\) results in fast setting times, regardless of the initial and final setting times. For instance, while the initial and final setting times of the mixture 0.18 WB-20 SF with the lowest \(\Gamma_{\rm m}\) of 1.25 are 297 min and 453 min, respectively, those of the mixture 0.26 WB-20 SF with the highest \(\Gamma_{\rm m}\) of 5.25 are 420 min and 558 min, respectively.