esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Solution for Reconstructing More Details High Dynamic Range Image
## 2 Related Work
### Deblurring  
Although in recent years many scholars have made great progress in reconstructing HDR images and overcoming artifacts by using deep learning methods [10; 11; 12; 13; 14; 15; 16], it is easy to cause details to disappear and the image to be blurred if the process of fusing multi-exposure LDR images to generate HDR images uses smoothing to suppress artifacts. Scholars realized that in order to solve the problem of reconstructing HDR images, it is not only necessary to remove artifacts but also to deal with the blur associated with the reconstructed HDR images [46; 47; 48; 49; 50]. In 2018, [PERSON] et al. reconstructed the missing details of an input LDR image in the HDR domain and performed tone mapping on the predicted HDR material to produce an output image with restored details [46]. In 2018, [PERSON] et al. yielded a high-quality image by harnessing the complementarity present, both the sensor exposure and blur [47]. In 2022, [PERSON] et al. provided an approach to improve not only the quality but also dynamic range of reconstructed HDR video by capturing mid exposure as a spatial and temporal reference to perform denoising, deblurring, and upsampling tasks and by merging learned optical flow to handle saturated/disoccluded image regions [48]. In 2023, [PERSON] et al. addressed mosaiced multiple exposures and registered spatial displacements of exposures and colors by a feature-extraction module and a multiscale transformer, respectively, to achieve high-quality HDR images [49]. In 2023, [PERSON] et al. proposed LPIENet to handle the noise, blur, and overexposure for perceptual image enhancement [50].
# A Comprehensive Review on Clay Soil Stabilization Using Rice Husk Ash and Lime Sludge
## 1 Introduction
### Stabilization Methods  
The specific soil stabilization technique deployed in every project depends on the soil's characteristics and the project's requirements. Mechanical, chemical, and geosynthetic stabilization and soil mixing are the most common soil stabilization methods [25; 26]. Different types of soils require specific soil stabilization techniques to address their unique properties and challenges. Some common soil stabilization techniques for different soil types are reviewed: clay soils can be stabilized using chemical additives such as lime, cement, or fly ash [27]. These additives react with the clay particles, reducing their plasticity, improving their strength, and increasing their stability [28]. Sandy soils can be stabilized through chemical additives, like cement, and mechanical methods, such as compaction or densification [29]. Compaction increases soil density, while techniques like vibro-compaction or dynamic compaction can enhance load-bearing capacity [30]. Also, geosynthetic materials, such as geotextiles or geogrids, can reinforce sandy soils, improving their stability, preventing erosion, and enhancing slope stability [31]. Like clay soils, silty soils can be stabilized using chemical additives like lime or cement, improving their strength and reducing their compressibility [32]. Soil mixing with additives can also be efficient for silty soils to generate a more stable and homogenous soil mixture [33]. For organic soils, preloading and surcharging can be deployed: preloading with the placement of temporary surcharges can be used to consolidate and improve strength over time. The weight of the surcharge compresses the soil and expels water, reducing settlement potential [34]. Geosynthetic materials can also be used in organic soils to provide reinforcement and prevent excessive deformations [35]. Expansive soils, which are prone to swell and shrink with moisture changes, can be treated with chemical additives like lime, cement, or fly ash to reduce their volume change potential [36]. Proper moisture control techniques, such as drainage systems or moisture barriers, can be implemented to minimize moisture fluctuations in expansive soils [37].
# Integrated Design of Moon-to-Earth Transfer Trajectory Considering Re-Entry Constraints
## 4 Precise Three-Impulse Transfer Trajectory Determination  
In the previous section, the opportunity for Moon-to-Earth transfer in which the daily trajectory can be reversely integrated to the vicinity of the Moon was obtained. This section aims at the design of the precise transfer trajectory whose states at the perilune and the re-entry point precisely meet the constraints on a certain day, which is included in the opportunity.  
The transfer strategy proposed in this paper is to apply correction maneuvers at the boundary of the SOI of the Moon and one day before re-entry, so that the transfer trajectory can accurately meet the constraints of Moon departure and Earth re-entry under the high-precision dynamic model. This strategy also provides a more powerful guarantee for the transfer in case of an emergency.  
The perilune altitudes of the daily optimal transfer trajectories vary from one hundred to several thousand kilometers, which means different magnitudes of maneuvers are acquired to ensure a high precision transfer.  
The B-plane shooting method is applied in the design of the two maneuvers, while different correction parameters are chosen for the smoothness and fuel saving of the transfer trajectory. Three orbital elements of the semi-major axis, radius of ascending node, and argument of perigee are selected to adjust the position and velocity of the probe at the boundary of the SOI of the Moon. Compared to the velocity at the boundary of the SOI as the correction, this strategy may require smaller maneuvers for those cases where the feasible perilune heights are large. Usually, the positional offset of the trajectory at the boundary of the SOI after correcting is positively correlated with the initial perilune altitude deviation. Finally, a modified maneuver is applied the day before re-entry, choosing the initial velocity as the correcting parameter to precisely splice the whole transfer trajectory.
# Overrelaxed Sinkhorn-Knopp Algorithm for Regularized Optimal Transport
## 4 Sinkhorn-Knopp with Successive Overrelaxation
### Proposed Algorithm
Define the functions \(\Theta^{*}\) and \(\Theta\) as:  
\[\gamma^{\ell+1} =P_{\mathcal{C}_{1}}^{\Theta_{1}(\gamma^{\ell})}(\gamma^{\ell})\]0 \[\gamma^{\ell+1} =P_{\mathcal{C}_{1}}^{\Theta_{1}(\gamma^{\ell})}(\gamma^{\ell})\]1  
where \(\min w\) denotes the smallest coordinate of the vector \(w\). **Proposition 1**. : _The function:_  
\[\gamma^{\ell+1} =P_{\mathcal{C}_{1}}^{\Theta_{1}(\gamma^{\ell})}(\gamma^{\ell})\]2  
_is continuous and verifies the descent condition (26)._  
Proof. : Looking at Figure 5 can help understand this proof. Since the partial derivative of \(\partial_{\omega}\varphi_{\omega}(x)\) is nonzero for any \(x<1\), the implicit function theorem proves the continuity of \(\Theta^{*}\). The function \(\Theta^{*}((A_{k}\gamma)\oslash\mu_{k}))\) is such that every term in Relation (24) is non-negative. Therefore, by Lemma 3, using this parameter minus \(\delta\) ensures the strong decrease (26) of the Lyapunov function. Constraining the parameter to \([1,\theta_{0}]\) preserves this property. This construction, which is often an excellent choice in practice, has several advantages:  
* it allows choosing arbitrarily the parameter \(\theta_{0}\) that will be used eventually when the algorithm is close to convergence (we motivate what are good choices for \(\theta_{0}\) in Section 4.4);
* it is also an easy approach to having an adaptive method, as the approximation of \(\Theta^{*}\) has a negligible cost (it only requires solving a one-dimensional problem that depends on the smallest value of \((A_{k}\gamma)\odot\mu_{k}\), which can be done in a few iterations of [PERSON]'s method). The resulting algorithm, which is proven to be convergent by Theorem 1, is written in pseudocode in Algorithm 2. The implementation in the log domain is also given in Algorithm 3. Both processes use the function \(\Theta\) defined implicitly in (29). The evaluation of \(\Theta\) is approximated in practice with a few iterations of [PERSON]'s method on the function \(\omega\mapsto\varphi_{\omega}(\min u)\), which is decreasing, as can be seen in \[\gamma^{\ell+1} =P_{\mathcal{C}_{1}}^{\Theta_{1}(\gamma^{\ell})}(\gamma^{\ell})\]3 With the choice \(\theta_{0}=1\), one recovers exactly the original SK algorithm. ```
0:\(\mu_{1}\in\mathbb{R}^{n_{1}}\), \(\mu_{2}\in\mathbb{R}^{n_{2}}\), \(c\in\mathbb{R}^{n_{1}\times n_{2}}_{+}\)  Set \(u=\mathbf{1}_{n_{1}}\), \(v=\mathbf{1}_{n_{2}}\), \(\gamma^{0}=e^{-c/\epsilon}\), \(\theta_{0}\in[1;2)\) and \(\eta>0\) while\(||u\otimes\gamma^{0}\gamma-\mu_{1}||>\eta\)do \(\bar{u}=\mu_{1}\odot(\gamma^{0}\gamma)\), \(\omega=\Theta(u\odot\bar{u})\) \(u=u^{1-\omega}\otimes\bar{u}^{\omega}\) \(\tilde{v}=\mu_{2}\odot(\gamma^{0}u)\) \(\omega=\Theta(v\odot\tilde{v})\) \(v=v^{1-\omega}\otimes\tilde{v}^{\omega}\) endwhile return\(\gamma=\text{diag}(u)\gamma^{0}\,\text{diag}(v)\)
```  
**Algorithm 2** Overrelaxed SK Algorithm (SK-SOR).
## 3 DST-LEO Satellite-Traffic Prediction Model
### Traffic Data Processing  
Since the authors only focused on the magnitude of the traffic, which in turn helps to better analyze issues such as network resource optimization, capacity planning and expansion, and the quality of service assurance in subsequent research, the authors did not consider the traffic flow direction. Instead, the authors only selected the magnitudes of the data flowing in and out at a certain moment on a single link and added them together. At the same time, the connection and disconnection status of the link at the same moment was extracted. The processed data were provided for model training. It is necessary to ensure that the magnitudes of the traffic and the connection and disconnection status of the links are the same in quantity and correspond one-to-one. Figure 4 shows the processed data format of two inter-satellite links among them. The data included the magnitudes of  
Figure 3: Flowchart architecture of the overall traffic prediction for LEO constellations.  
the traffic on the two links within the corresponding time period as well as the connection and disconnection status of the links. 0 means that the inter-satellite link is disconnected, and 1 represents that the link is in normal communication. The dataset was constructed according to this format, and the magnitudes of the traffic in the first N columns need to correspond to the connection and disconnection status of the links in the subsequent N columns.
# Characterization of a Mechanical Antenna Based on Rotating Permanent Magnets
## 3 Materials, Methodology and Experimental Setup
### Measurement Setup and Equipment  
Figure 7 presents the experimental setup that was used for measuring the static and dynamic magnetic field (70 Hz) generated by the two configurations versus distance, up to 100 mm.  
The equipment presented in Figure 7 are given in Table 2.  
For determining harmonics and interharmonics, the experimental setup shown in Figure 8 was used.  
It can be seen from the figure above that the two sensors used for measuring the magnetic field were symmetrically placed about the rotating magnet. The characteristics of these sensors are given in Table 3.  
Figure 4: The numerical model of the permanent magnet and the mesh distribution.  
Figure 5: The domain and the mesh distribution of the model.  
Figure 6: The magnetic field values were extracted from the red line.  
The magnetic field received by the Narda EHP-50F sensor (Narda Safety Test Solutions GmbH, Pfullingen, Germany) is displayed on a laptop by means of the specialized software, and the signal given by the magnetic sensor coil is received with the spectrum analyzer embedded in a Tektronix MSO58 oscilloscope (Tektronix, Inc., Beaverton, OR, USA).  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**LakeShore Hall Probe Model** & **LakeShore 460** & \\
**MMZ-2508-UH** & **Gaussmeter** & \\
**(Lake Shore Cryotronics, Inc.,** & **(Lake Shore Cryotronics, Inc.,** & \\
**Westerville, OH, USA)** & **Multi Axis Positioning System** \\ \hline Frequency range: DC and 10 Hz, to 400 Hz & Frequency range: DC and 10 Hz, to 400 Hz & Three axis of coordinated motion \\ Operating temperature: 10 \({}^{\circ}\)C to 40 \({}^{\circ}\)C & DC accuracy: \(\pm\)0.10\% of reading \(\pm\)0.005\% of range & Resolution: 0.1 mm \\ Accuracy (\% of reading at 25 \({}^{\circ}\)C): 0.25\% & DC temperature coefficient: \(\pm\)0.05\% of reading \(\pm\)0.003\% of range per \({}^{\circ}\)C & USB computer interface \\ from 0 to 20 kG; 0.5\% from 20 kG to 30 kG; & AC RMS accuracy: \(\pm\)2\% of reading (50 to 60 Hz); & AC peak accuracy: \(\pm\)5\% typical \\ \hline \hline \end{tabular}
\end{table}
Table 2: Equipment used for measuring the magnetic field vs. distance.  
Figure 8: Experimental setup for determining harmonics and interharmonics for the two configurations of rotating permanent magnet antennas in an anechoic chamber.  
Figure 7: Experimental setup used for measuring the static and dynamic magnetic field vs. distance between 2 and 100 mm.  
In order to determine the variation of magnetic field with distance in the case of the CRRM, between 1 m and 25 m, as well as for determining the maximum distance at which the signal generated by the rotating magnet can be detected in a suburban environment where the 50 Hz component is less than 1 nT, the same field sensors mentioned in Table 3 were used; for measuring the signal given by the magnetic sensor coil, a Rohde&Schwarz NF-Geraschspannungsmesser Psophometer UPGS microvoltmeter was used (frequency range 15 Hz-20 kHz, sensitivity 1 uV; Rohde & Schwarz GmbH & Co. KG, Munich, Germany).
###### Abstract  
Newly developed formulas of ready-to-eat (RTE) products, despite conforming to the latest nutritional trends involving low-processed and high-protein products, may pose a risk of promoting the growth of _Listeria monocytogenes_ during shelf life. Food safety experts recommend challenge tests to assess the growth potential of _L. monocytogenes_, which will ultimately provide microbiological evidence to determine the food safety limit. The present study evaluated whether RTE uncured vacuum-packed pork bars with dried plasma met the 100 CFU/g safety level at the end of shelf life under certain storage conditions and aimed to develop predictive models for the growth of _L. monocytogenes_ in the above product. The bars were artificially inoculated with a mixture of three strains of _L. monocytogenes_ at two different inoculum densities of 2 log CFU/g and 5 log CFU/g and stored at three different temperatures (2, 4, and 6 \({}^{\circ}\)C) and then subjected to microbiological evaluation at specific time intervals up to 21 storage days. The growth potential (\(\Delta\)-value) for RTE pork bars contaminated with 2 log CFU/g _L. monocytogenes_ inoculum was 0.36, 0.14, and 0.91 log CFU/g at 2, 4, and 6 \({}^{\circ}\)C, respectively, while for bars contaminated with 5 log CFU/g inoculum, they were \(-\)0.36, \(-\)0.40, and 0.64 at 2, 4, and 6 \({}^{\circ}\)C, respectively. Statistically higher growth potential (\(p<0.05\)) was detected for RTE bars contaminated with 2 log CFU/g inoculum than with 5 log CFU/g. The results indicate that this type of product must be classified as a food category: _\"Ready-to-eat foods able to support the growth of _L. monocytogenes_, _other than those intended for infants and for special medical purposes_\" (Category 1.2. according to EU Regulation 2073/2005). The newly created models can also describe _L. monocytogenes_ growth in an environment where factors, such as temperature, pH, and \(\mathrm{a_{w}}\), change with time. The results showed that a higher inoculum density statistically reduced the growth potential values of _L. monocytogenes_ compared to a lower density.  
RTE food; _L. monocytogenes_; challenge test; pork meat bar; dried plasma; high protein; vacuum packaging; uncured food
## 3 Results and Discussion
### Characterization of the Setup (Phantom Measurements)
This resulted in a decrease of the noise (which was also visible from the errorbar in the contrast graph) and in an increase of the CNR in the case of gated measurements. Considering as a conservative threshold the 0.05 contrast and a CNR higher than 10, using the gated technique we could detect the inclusion until 2.6 cm and 3 cm in depth, with 5 and 30 mm source-detector distance, respectively. This conclusion is in contrast with theoretical findings predicting a contrast that is always higher when using a small source-detector distance [26]. Such a disagreement can be due to both the effect of the \"diffusion tail\" of the detector and to the so-called \"memory effect\". The former effect [19] is due to the non-perfectly efficient time-gating mechanism that causes a \"leakage\" of early photons in a temporal region of the curve where only the contribution of late photons are expected. This effect is as high as the source-detector distance decreases, since the peak of early photons increases. On the other side, the memory effect [27] causes a boost in the background level at longer delays that can hide the late photons, thus decreasing the maximum contrast achievable. Since the memory effect is proportional to the number of photons impinging on the detector when it is in the OFF state, its consequences are more evident when using a small source-detector distance. Figure 2: Contrast (left column) and contrast-to-noise ratio (CNR) (right column) computed at two different source-detector distances (5 mm, top row; 30 mm, bottom row) with and without the exploitation of the fast-gating technique (blue and red curves, respectively).
## 3 Data Sets
### Water Volume Variations  
A combination of satellite products was used to estimate the change in the lake volume from both the variation in the water level and the surface area. This is an empirical relationship between the lake surface (that can be derived from optical images) and water levels (that can be derived from satellite radar altimetry). The volume of a pyramidal frustum was derived 2000 years ago by the Greek mathematician [PERSON], native of Alexandria (10-70 AD), as follows:  
\[\Delta V=\frac{\left|L^{1}-L^{2}\right|}{3}\left(A^{1}+A^{2}+\sqrt{A^{1}A^{2}}\right) \tag{1}\]  
where \(L_{1}\) and \(L_{2}\) are two water levels with corresponding surface areas. \(A_{1}\) and \(A_{2}\) denote the difference in the water levels and the volume between two dates t\({}_{1}\) and t\({}_{2}\), respectively [38]. This is still the basic formula used by limnologists to estimate the volume of lakes since 2000 years ago. To this end, the lake of interest can be modeled as a simple geometric shape, for which the variation in the water volume (\(\Delta\)V) between two dates (t\({}_{1}\) and t\({}_{2}\), respectively) depends on L\({}_{1}\) and L\({}_{2}\) values taken from altimetry data or in situ gauges; A\({}_{1}\) and A\({}_{2}\) can be computed as described in Section 4.2. Equation (1) is applicable when the area increases linearly with change in water level; this assumption is valid for small intervals in water level but is not necessarily true over large changes in water level [26].
###### Abstract  
This paper presents a novel approach to designing beam codebooks for downlink multiuser hybrid multiple-input-multiple-output (MIMO) wireless communication systems, leveraging multi-agent reinforcement learning (MARL). The primary objective is to develop an environment-specific beam codebook composed of non-interfering beams, learned by cooperative agents within the MARL framework. Machine learning (ML)-based beam codebook design for downlink communications have been based on channel state information (CSI) feedback or only reference signal received power (RSRP), consisting of an offline training and user clustering phase. In massive MIMO, the full CSI feedback data is of large size and is resource-intensive to process, making it challenging to implement efficiently. RSRP alone for a stand-alone base station is not a good marker of the position of a receiver. Hence, in this work, uplink CSI estimated at the base station along with feedback of RSRP and binary acknowledgment of the accuracy of received data is utilized to design the beamforming codebook at the base station. Simulations using sub-array antenna and ray-tracing channel demonstrate the proposed system's ability to learn topography-aware beam codebook for arbitrary beams serving multiple user groups simultaneously. The proposed method extends beyond mono-lobe and fixed beam architectures by dynamically adapting arbitrary shaped beams to avoid inter-beam interference, enhancing the overall system performance. This work leverages MARL's potential in creating efficient beam codebooks for hybrid MIMO systems, paving the way for enhanced multiuser communication in future wireless networks.  
multi-agent reinforcement learning; massive MIMO; millimeter wave; hybrid beamforming 2024
## 4 Real-Time Prediction
### KF-FFT-SVM Model Building  
#### 4.2.1 KF Predicting  
To build the KF model of the displacement data, we chose the displacement, displacement velocity, and displacement acceleration as the state vectors, which are \(S_{k}\), \(V_{k}\), and \(A_{k}\), respectively. The relations between them are:  
\[\begin{array}{c}X_{k}=[S_{k},V_{k},A_{k}]^{T},\\ \{S_{k}=S_{k-1}+V_{k-1}\cdot T_{s}+w_{k-1}^{1},\\ V_{k}=V_{k-1}+A_{k-1}\cdot T_{s}+w_{k-1}^{2},\\ A_{k}=A_{k-1}+w_{k-1}^{3}\end{array} \tag{23}\]  
where \(T_{s}\) is the data sampling interval, and \(w_{k}^{1}\), \(w_{k}^{2}\), and \(w_{k}^{3}\) are the random errors. Let \(T_{s}=1\), then the stochastic difference equation of the system state is:  
\[X_{k}=\left[\begin{array}{ccc}1&1&0\\ 0&1&0\\ 0&0&1\end{array}\right]X_{k-1}+\left[w_{k-1}^{1},w_{k-1}^{2},w_{k-1}^{3}\right] ^{T} \tag{24}\]  
The observation formula is described as Formula (25):  
\[Z_{k}=[1\;1\;0]X_{k-1}+v_{k-1} \tag{25}\]  
Figure 13: The Kalman filtering results.  
where \(v_{k}\) is the random error, \(\text{A}=\left[\begin{array}{ccc}1&1&0\\ 0&1&0\\ 0&0&1\end{array}\right]\) and H = [1,1,0]. The random errors \(w_{k}\) and \(v_{k}\) are unknown, and our purpose was to use Formulas (5)-(11) to determine them. To solve this problem, we set \(Q\) and \(R\) with random values, and used the pre-processing data in Section 4.1 to find the values of \(Q\) and \(R\) that made the KF model converge to the optimal solution. Figure 13 shows the estimated result of BD1, BD2, BD3, and BD4, which are the red curves. In this KF model, \(Q=[5,3,3]^{T}\), and \(R=3\). The maximum estimated error is 5.73 mm, which indicates that the KF model we built had a good prediction and filtering result.