esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Engineering Applications
### Analysis of the Effect of Grouting Curtain  
The primary objective of constructing a grouting curtain is to address the issue of flooding during tunnel excavation. The magnitude of water seepage from the surrounding rock of the tunnel can be utilized to assess the effectiveness of the curtain. To assess the effectiveness of grouting, it is important to record the changes in water volume in the drill holes before and after the grouting process. These data can then be shown for evaluation purposes.  
Figure 20 displays the water inflow in the exploratory boreholes both before and after grouting. Out of the holes mentioned, holes 1 to 8 are used to probe for water and have a water influx rate of approximately 30 m\({}^{3}\)/h before grouting. Holes 9 and 10 are designated as check holes and, following the grouting process, exhibit a water influx rate of less than 1 m\({}^{3}\)/h. This is performed to ensure that the cumulative water ingress while digging does not exceed 5 m\({}^{3}\)/h. This suggests that the slurry has the ability to create a reliable barrier for grouting when used with this specific arrangement of water-probing holes, resulting in an optimal water-plugging effect.  
The roadway meets the necessary conditions for resuming excavation. However, it is possible that there may be loose water in the rock body, and the rock-mineral contact zone may be exposed during excavation. Therefore, it is important to strengthen the  
Figure 19: Grouting process flow chart.  
Figure 20: Bar graph of borehole water influx records before and after grouting.  
construction process with the complementary measures of \"short exploration and short injection + short excavation and short support\". Enforce the efficient execution of the brief survey and excavation process and adopt appropriate support measures based on the revealed lithology. To ensure construction safety, we adopt a method of short exploration and excavation in addition to long exploration. Each cycle involves digging 5-8 short exploration holes, with a depth of 5-8 m and a maximum excavation depth of 1.6-3 m. During construction, if the cumulative amount of water in the short exploration holes exceeds 5 m\({}^{3}\)/h but is less than 10 m\({}^{3}\)/h, the excavation will be halted. Grouting and water-blocking will be carried out before resuming construction. If the cumulative amount of water in the short exploration holes exceeds 10 m\({}^{3}\)/h, a long exploration of the working face will be conducted. If the total volume of water in the short probe hole exceeds 10 cubic meters per hour, the long probe construction will be conducted at the working face.
## 4 Hybrid Action Space SAC-Based MLLM Task Offloading and Resource Allocation Scheme  
In this section, we address the challenges of optimizing task offloading and resource allocation for MLLMs in the SEC environment. Specifically, we consider MLLM tasks that involve processing inputs from the MMMU dataset [32], which includes multimodal inputs consisting of text sequences of varying lengths and images. The MLLMs deployed on the servers include VILA1.5 (3B, 8B, 13B parameters) [33], Obsidian 3B [34], and Llava1.6 (7B, 13B parameters) [35]. To accommodate different computational constraints and accuracy requirements, these models are deployed with various quantization levels ranging from 4-bit to semi-precision, enabling flexible trade-offs between inference speed and model accuracy.  
DRL is particularly effective for tackling the long-term optimization objectives of the identified challenges, despite the complications arising from complex hybrid discrete-continuous action spaces. Traditional methods, such as discretizing continuous actions or converting discrete actions to a continuous format, often result in excessively large action spaces or significant exploration demands. To address these challenges, we propose a hybrid action space framework that decouples actions and allocates them to separate agents for collaborative learning, thereby effectively integrating discrete and continuous policies.
## 4 Results
### Results of Polarization Analysis  
We applied polarization analysis to the three-component, hour-long continuous data for the YL array and four AU stations. Firstly, the instrument response was removed, and two differentiations were performed to obtain the ground acceleration. Then the hour-long data was divided into 27 sliding windows, each with a length of 8192 samples (204.8 s) and a sliding volume of 5096 points (127.4 s). The overlap rate between two sub-windows was 62%. Each sub-window was detrended, filtered with a Hanning window, and applied fast Fourier-transform (FFT) to perform a 3 \(\times\) 3 spectral covariance matrix. Finally, an overall spectral covariance matrix of the hour-long data was obtained by averaging 27 spectral covariance matrices. The eigenvalue and eigenvector of the overall spectral covariance matrix represented the polarization of the noise over the one-hour period.  
The results of the polarization analysis showed that the secondary microseism energies from the YL array were significantly higher than those from four AU stations, regardless of the level of the cyclone wind speed or the distance between the station and the cyclone center (Figure 4). A similar result had been found for the comparison between the ocean bottom seismometers and land stations [25; 26; 27]. This could be due to the different location of the instrument. Since the YL array was in the water, it was exposed to swell propagating from multiple source regions and ended in a broader secondary microseism frequency range and higher energies [28]. As the secondary microseism energy is mainly radiated as Rayleigh wave, which attenuates very quickly when it travels through the shoreline to the continent's crust, four AU stations, located on land approximately 3000 km away from the YL array, exhibited substantially lower secondary microseism energy than the hydrophones.  
With the north-south motion of TOMAS, the direction of the secondary microseism from four AU stations slightly shifted from northeast to southeast (Figure 4). In contrast, none of the directions from the YL array showed this change. Several peaks in the secondary microseism period caused by the swell propagating from multiple source regions would have an impact on the direction measurement. The vibrating and tilting of the hydrophone caused by the turbulence could be another factor contributing to the inaccurate direction measurement of the polarization analysis since the effect of this noise had not been removed by the method [29].
## 6 Current Innovations in the Medical Device Industry Using PEEK and PEKK  
Numerous companies manufacture spinal implants using PAEK polymers, capitalizing on their biocompatibility, mechanical strength, and imaging transparency. The key manufacturers are summarized in Table 7 below.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  
These companies employ cutting-edge materials and advanced manufacturing technologies such as 3D printing to develop spinal implants optimized for individual patients' needs. Their innovative approaches play a pivotal role in advancing orthopedic and spinal care. By integrating PEEK and PEKK polymers, these manufacturers create implants that not only comply with rigorous regulatory requirements, but also enhance patient outcomes through an exceptional performance, biocompatibility, and customization. The unique combination of these polymers' properties has solidified their status as preferred materials for the development of next-generation medical devices.  
Compared to titanium and Co-Cr alloys, which have long been the preferred materials for spinal implants, PAEK materials offer several key advantages. While titanium and Co-Cr alloys are valued for their strength and durability, they can present challenges with imaging, often causing artifacts in X-rays, CT scans, and MRIs [22]. In contrast, PEEK and PEKK polymers are transparent in these images, allowing for clearer and more accurate diagnostics and monitoring. Moreover, titanium and Co-Cr alloys are relatively stiff, which can lead to stress shielding and negatively impact bone tissue [21; 34]. PAEK materials, however, have mechanical properties similar to those of bone, fostering better integration and reducing the risk of implant failure [29]. Their superior biocompatibility further improves patient outcomes by lowering the likelihood of adverse reactions or inflammation, making them the increasingly favored choice in spinal implant and other orthopaedic development. PAEK/HA composites promise a long-term performance, offer excellent biocompatibility, and promote bone integration due to the HA component. The low density and fatigue resistance of PAEK make it suitable for orthopedic applications, particularly in medium-load situations [61; 139]. However, its mechanical properties such as stiffness are worse than those of titanium, which may limit its use in high-load-bearing areas. Despite this, PAEK/HA implants generally exhibit good osseointegration and stability over time, with minimal wear or degradation under stress [63; 91]. This makes them a viable option for applications requiring reduced weight and enhanced osteointegration, though titanium remains the preferred material for high-load implants.
## 4 Parameter Setting and Analysis
### Slope Threshold  
The slope threshold was determined based on the maximum values of the road's cross and longitudinal slopes. Typically, highway cross slopes range from 2.5% to 4%; therefore, this study focused on four different slope threshold values. From Figure 11a, it can be observed that the slope threshold has a minimal impact on the accuracy of flat road surfaces. Similar results were obtained for sloped road surfaces when the slope threshold exceeded 2.5% (Figure 11b). However, the accuracy is poor when the slope threshold is 2% because of the excessively large longitudinal slopes, causing some road points to not be accurately identified. Therefore, to satisfy the threshold requirements for both experimental scenarios, in this study, 5% was chosen.
## 7 Conclusions  
This paper proposes a reconfigurable satellite constellation (ReCon) for Earth observation. Its physical feasibility is demonstrated using the payload aperture and chemical propellant database from past missions. On top of that, a ReCon is shown to provide better performance-to-mass ratios than static constellations for uncertain ground targets. Its high responsiveness can be useful in particular, for example, to observe extreme weather events now occurring more often and unexpectedly across the globe. Its complexity in constellation management is currently overcome by active research in academia and industry [19; 20; 21].  
Systems engineering approaches with heuristic optimization methods are exploited, whose procedures and results are discussed in detail. The optimization goal is to balance coverage, response time, and constellation mass which is a proxy of cost. Genetic algorithm yields more optimal solutions than simulated annealing and steepest gradient methods, due to the nonlinearity of the problem.  
To realize a ReCon which is a complex federated satellite system, political and policy aspects must also be considered [58; 59; 60]. Multi-sensor satellite constellations may be integrated by multinational agencies (A-Train) or operated by several entities (Disaster Monitoring Constellation) [61]. Future work will address these issues in addition to the technical expansion of the current ReCon framework. Areas of further technical research includes, but not limited to, investigation of low-thrust (electric propulsion), radar observation and GPS radio occultation, and satellite constellations for planetary observation [62; 63].  
Conceptualization, Software, Validation, Visualization and Writing--original draft, S.W.P.; Methodology, Software, Data curation, Formal analysis and Writing--review and editing, S.K.; Investigation, Resources, Project administration and Supervision, O.d.W.  
This research received no external funding.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Symbol** & **Description** & \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} Design \\ variable \\ \end{tabular} } & _nk_ & RGT ratio & 29/2 \\  & _delta\_alt_ & Altitude difference & \(-\)19.9 km \\  & _n\_planes_ & Number of orbital planes & 5 \\  & _n\_sats_ & Number of satellites per plane & 3 \\  & _regard_ & Field of regard & 41.4\({}^{\circ}\) \\ \hline \multirow{4}{*}{
\begin{tabular}{c} Performance \\ metrics \\ \end{tabular} } & \(J_{1}\) & GOM area coverage & 4.71 \% \\  & \(J_{2}\) & ROM revisit time & 1173 sec \\ \cline{1-1}  & \(J_{3}\) & Constellation mass & 41796 kg \\ \cline{1-1}  & \(J_{4}\) & Reconfiguration time & 13.6 days \\ \hline \hline \end{tabular}
\end{table}
Table 21: Sun-synchronous orbits (SSO) ReCon optimization (SA).
## 3 Methods
### Variogram Model Parameters from TM Data  
When using tower observation to validate the remote sensing albedo product, the spatial representativeness of the observation footprint was investigated on semivariogram models [37; 39; 47; 54]. In this study, the method of deriving variogram functions to analyze surface albedo with TM data was used [36]. The variogram estimator \(\gamma(h)\) was used to obtain the half-average squared difference between albedo values within a certain distance. According to [PERSON] et al. [47], the isotropic spherical variogram model [55] was used to fit the variogram model parameters--the range (a), the sill (c), and the nugget effect (\(\epsilon_{0}\)), as below:  
\[\gamma_{sph}(h)=\left\{\begin{array}{c}c_{0}+c\bigg{(}1.5\frac{h}{a}-0.5 \bigg{(}\frac{h}{a}\bigg{)}^{3}\bigg{)}for\ 0\leq h\leq a\\ c_{0}+c\ \ for\ h>a\end{array}\right. \tag{1}\]  
The range is the distance from a point beyond which there is no further correlation of the albedo associated with that point. It is the average patch size of the landscape in landscape ecology, which represents a region that differs from its surroundings, but is not necessarily inter-homogeneous. The sill is the maximum semivariance, and is the ordinate value of the range at which the variogram levels off to an asymptote. The non-zero value of the variogram when h = 0 is called the nugget effect. It depends on the variance associated with small-scale variation, measurement errors, or their combination [56]. The range, the sill, and the nugget effect all reveal the spatial variation of the land surface and the scale effect associated with remote sensing data [47; 57]. It has been suggested that  
Figure 1: Field data comparison with Landsat Thematic Mapper (TM) data, which is the average of \(3\times 3\) pixels. The fitting function is then used for TM albedo data calibration.  
the land surface is homogeneous (representative) when the sill value is less than 0.001 [37]. In this paper, the semivariogram model was used as well. The 30 m Landsat albedo was first re-projected to a sinusoidal projection and the Landsat pixel located at the center of the MODIS 500 m pixel was determined. The semivariogram was calculated from Landsat data. The model parameters were fitted according to the spherical model.
# Low-Light Mine Image Enhancement Algorithm Based on Improved Retinex
###### Abstract  
Aiming at solving the problems of local halo blurring, insufficient edge detail preservation, and serious noise in traditional image enhancement algorithms, an improved Retinex algorithm for low-light mine image enhancement is proposed. Firstly, in HSV color space, the hue component remains unmodified, and the improved multi-scale guided filtering and Retinex algorithm are combined to estimate the illumination and reflection components from the brightness component. Secondly, the illumination component is equalized using the Weber-Fechner law, and the contrast limited adaptive histogram equalization (CLAHE) is fused with the improved guided filtering for the brightness enhancement and denoising of reflection component. Then, the saturation component is adaptively stretched. Finally, it is converted back to RGB space to obtain the enhanced image. By comparing with single-scale Retinex (SSR) algorithm and multi-scale Retinex (MSR) algorithm, the mean, standard deviation, information entropy, average gradient, peak signal-to-noise ratio (PSNR), and structural similarity (SSIM) are improved by an average of 50.55%, 19.32%, 3.08%, 28.34%, 29.10%, and 22.97%. The experimental dates demonstrate that the algorithm improves image brightness, prevents halo artifacts while retaining edge details, reduces the effect of noise, and provides some theoretical references for low-light image enhancement.  
guided filtering; low-light; Retinex; image enhancement; HSV +
Footnote †: journal: Medical Image Analysis  
1
## 4 Analysis  
### Comparison with Existing System  
Attribute-based encryption has been studied, focusing on malicious conspiracy attacks rather than conventional encryption algorithms. In addition, attribute-based encryption capable of attribute withdrawal can be further developed to delegate or revoke attributes. In this study, the safety against attribute withdrawal and modulation attacks is analyzed and compared with previous studies. Attribute withdrawal is to remove or disable the delegated attribute. For example, when the attribute value \"a\" is withdrawn, the certification authority notifies the proxy not to perform an operation related to \"a\" and to remove \"a\" from the system attribute set in which all attributes are integrated. Thereafter, a new secret key is issued and transmitted to all users who have a secret key related to \"a\". It also regenerates the re-encryption key.  
The function of delegating and withdrawing user attributes is suitable for a cyber-physical system environment. The delegation of authority is the delegation of authority to the proxy to provide their data. The proxy can determine whether the delegate's access to the delegate's data is legitimate. In the proposed method, the attribute conversion key was transmitted to the policy management agency to enable justification.  
In this study, we propose a safe method for modulating the attacks of attribute-based encryption capable of attribute withdrawal. For authority management and delegation, the proxy determines the legitimacy of access and provides access authority management. Here, justification is determined using the dynamic threshold password. This can solve the problem of modulation attacks. Table 2 shows a comparison between the proposed system and existing frameworks.
# Real-Time Guitar Amplifier Emulation with Deep Learning +
## 3 Recurrent Neural Network Model
### Long Short-Term Memory  
A key difference between a feedforward network, such as the WaveNet-style model, and an RNN model, is that the RNN model has a state. The state is used and updated at each time step. This means that the RNN can operate with just a single sample as input at each time step, whilst still using information from previous time steps.  
In this RNN model, the state is part of an LSTM unit. The LSTM was first proposed in [35], its unit state is made up of two vectors, the _hidden state_, \(h\), and the _cell state_, \(c\). At each time step the input sample, \(x[n]\), is used along with the initial unit states, \(h[n-1]\) and \(c[n-1]\), to produce the LSTM outputs, which are the updated unit states, \(h[n]\) and \(c[n]\). The hidden state, \(h[n]\), is the LSTM's output which in our model is input to the fully connected layer, to produce the RNN's predicted output for that time step. The following functions describe how the input and initial states are used to update the LSTM unit's state.  
\[i[n]=\sigma(W_{ii}x[n]+b_{ii}+W_{hi}h[n-1]+b_{hi}), \tag{12}\]  
\[f[n]=\sigma(W_{if}x[n]+b_{if}+W_{hf}h[n-1]+b_{hf}), \tag{13}\]  
\[\tilde{c}[n]=\tanh(W_{ic}x[n]+b_{ic}+W_{hc}h[n-1]+b_{hc}), \tag{14}\]  
\[o[n]=\sigma(W_{io}x[n]+b_{io}+W_{ho}h[n-1]+b_{ho}), \tag{15}\]  
\[c[n]=f[n]c[n-1]+i[n]\tilde{c}[n], \tag{16}\]  
\[h[n]=o[n]\tanh(c[n]), \tag{17}\]  
where \(i[n]\) is the input gate, \(f[n]\) is the forget gate, \(\tilde{c}[n]\) is the candidate cell state, \(o[n]\) is the output gate, \(\tanh(.)\) is the hyperbolic tangent function and \(\sigma(.)\) is the logistic sigmoid function. The PyTorch machine learning library was used to implement the RNN model described in this paper, and (12)-(17) have been reproduced from the PyTorch documentation [36]. The weight matrices and bias vectors in these equations contain the LSTM learnable parameters, which are learned during training.  
The size of both the hidden and cell states is equal to the LSTM's _hidden size_, which is a hyperparameter chosen by the user. Increasing the hidden size generally results in the model being more accurate, however it increases the number of learnable parameters in the network, as well as the processing power required to run it.
# Tamed Warping Network for High-Resolution Semantic Video Segmentation
## 4 Tamed Warping Network
### Core Networks: The Key-Frame CNN (KFC) and the Non-Key-Frame CNN (NKFC)  
TWNet contains two core networks: the key-frame CNN (KFC) and the non-key frame CNN (NKFC). As the name suggests, while TWNet is processing a video clip, if the coming frame is a key frame, it will be sent to KFC; otherwise, it will be sent to NKFC. Technical details of key frame selection are discussed in Section 4.5.1.  
Figure 3: The framework of TWNet. A group of frames consists of one key frame followed by many non-key frames. Key frames are sent to the key-frame CNN (KFC) and non-key frames to the non-key-frame CNN (NKFC), where the warped context features from the preceding frame are corrected by the CRF and RGA modules. Both CNNs output the result label maps and the interior context feature maps. Dashed arrows and boxes in NKFC indicate the operations to be skipped.  
KFC is built on an encoder-decoder architecture, e.g., FPN [47] and U-Net [35]. It behaves like a regular semantic segmentation model except that one layer of KFC is chosen to send its features to the next frame, which is also the first non-key frame in the current group of frames. Also, KFC is used to guide the learning of NKFC as described in Section 4.4.  
NKFC has the same network structure as KFC and also sends the features from a chosen layer to the next frame, but it has two additional functions. First, NKFC receives the layer features from the preceding frame (it can be either a key frame or a non-key frame) and performs feature warping at the same layer. Formally, let \(t\) be the subscript of the current frame and let \(t-1\) be that of the preceding frame. Given features \(\mathcal{F}_{t-1}\), we can first resize the motion vectors \(\widehat{\mathcal{M}v_{t}}\) to \(\widehat{\mathcal{M}v_{t}}\) to match the size of \(\mathcal{F}_{t-1}\) and then predict the features of the current frame \(\widehat{\mathcal{F}_{t}}\) by  
\[\widehat{\mathcal{F}_{t}}=\text{warp}(\mathcal{F}_{t-1},\widehat{\mathcal{M} v_{t}}). \tag{3}\]  
The motion vectors we use for warping are readily available from the compressed domain, sparing one from time-consuming motion estimation such as optical flows. In this way, NKFC boosts the speed of segmentation because it skips several layers of CNN operations. The trade-off of speed and accuracy are discussed in Section 4.5.2.  
The second additional function NKFC has is that it performs feature correction. Although the warping in Equation (3) speeds up the video segmentation, it also introduces errors. To correct the warped features, we propose two modules: the context feature rectification (CFR) module and the residual-guided attention (RGA) module. The details of these two modules are presented in Section 4.3.  
Unlike previous warping methods, NKFC fuses features from _head_ layers to those of _tail_ layers to enhance spatial details. Compared to direct lateral connections such as FPN [47] and U-Net [35], the spatial features from head layers are first fed into the correction modules as described in Section 4.3.