esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Scaffold 1
#### 3.1.1 Ligand-Based Scaffold Hopping  
A total of 10,000 core-hopping structures were obtained. Of those, 1115 hits were obtained that exhibited a synthesizability score >0. We docked these compounds into the binding pocket of FmlH. Nine compounds showed a Glide score of <-6 kcal/mol (Figure 3; Table 1). The three-dimensional structures of the hits from the molecular docking are shown in Figure 3. The binding poses of these compounds are shown in Figure 3 (bottom panel). We observed three distinct binding modes, in which the ligand's aglycone end occupied three different sub-pockets of the main FmlH binding pocket. This suggests that there can be three exit vectors, which can be utilized in the design of novel compounds by Scaffold 1 replacement. For all the ligands, the GalNAc moiety occupied the same part of the main binding pocket (Figure 3). The synthesizability scores for all the molecular docking hits are summarized in Table 1.  
Figure 3: Matched molecular pairs obtained from in silico ligand-based Scaffold 1 hopping. Three-dimensional structures of the obtained hits. The unreplaced scaffolds are shown with green carbons and the novel scaffolds after replacement are shown with gray carbons. The three distinct binding sub-pockets of the main FmlH binding pocket that are occupied by the aglycone of the hits are shown in the bottom row panels. The orange arrows point to adjacent sub-pockets, which highlight possible exit vectors to facilitate the ligand design. The FmlH binding pocket is shown in the surface representation.
## 2 Related Background
### Prior Works  
In prior works, ML methods have been introduced into the numerical weather prediction cycle [45], and attempts were made to use ML methods in different stages of numerical weather prediction. Some examples include improving remote sensing of the environment [46], data assimilation [47], and subgrid scaling of physical processes that were previously performed using approximation formulas [48]. In 2019, the first attempt was made to completely abandon physical simulation and use an ML model for weather prediction [36].  
One of the earliest implementations of ML in the field of numerical weather prediction involved Kalman filters [49; 50]. The filter, which improves forecasting in non-linear forecasting by performing non-Gaussian estimation of the system, was used to improve the observation data and data assimilation [51; 52]. Later, when multimodel ensemble forecasting was developed, ensemble Kalman filters nearly became the norm for the representation of forecast uncertainty.  
Recent examples of ML methods for weather forecasting include analyzing the wind signal of the prior minute by using a neural network to predict wind shifts during the following minute in order to support competitive sailors [53]. Precise short-term wind forecasting was performed using a measured wind signal decomposed using a wavelet transform as an input for a fuzzy ARTMAP neural network (which is superior to generic neural networks in learning new features while forecasting); the forecasted signal was then recomposed to wind signal forecast using wavelet transform [54]. Estimation of a nondirect model output (road surface temperature) by using quantile regression forests on multiple prediction models' output was suggested by [PERSON] et al. [55].  
These earlier implementations of machine learning in weather predictions caught the attention of both AI moguls such as Google and Huawei and weather prediction agencies such as the ECMWF, which recently published their versions of an AI weather forecast. Google implemented a unique data-interpolation method on 0.25\({}^{\circ}\) weather reanalysis data to create the input for a 16-layer graph neural network in an encoder-decoder configuration to create a six-hour global forecast (which can then be used as input to create longer-range forecasts). That approach achieved more accurate forecasts than prior operational forecasts both in terms of everyday prediction and in terms of extreme weather prediction [56]. Huawei implemented a standard encoder-decoder design for vision transformers on reanalysis data with an Earth-specific positional bias version of the shifting window that also performed better than prior NWP methods [57]. Both Google and Huawei were also able to significantly reduce computer resources when performing their forecasts.
## 1 Introduction
Therefore, the influence of HHP treatment on proteins can also be evaluated by measuring their content [22; 25; 27; 28]. Figure 1: Myoglobin redox in fresh meats [24]. High hydrostatic pressure significantly impacts the structural and functional properties of muscle proteins; aside from denaturation a change in solubility and gel formation can commonly occur [23; 29; 30; 31]. Under pressure, sarcoplasm proteins are highly susceptible to denaturation [23], and these changes can be tracked through the application of gel electrophoretic methods (SDS-PAGE and native-PAGE) [32]. Myofibrillar proteins, which account for approximately 55% of all muscle proteins, can also be affected by HHP treatment. Differential scanning calorimetry (DSC) is the most straightforward procedure and effective method to register the process of myofibrillar protein denaturation [33]. HHP can be responsible for the dissociation of non-covalent, ionic, hydrophobic, and hydrogen bonds to change the secondary, tertiary, and quaternary structures of myofibrillar proteins. The pressure level induces either local or global changes in protein, and eventually leads to denaturation by altering the balance of the interactions that stabilize the conformation of native proteins [29]. The aim of this study was to investigate the effects of high hydrostatic pressure (HHP) treatment on the quality characteristics and the protein structure of vacuum-packed fresh pork and wild boar means. It was also intended to determine an optimal pressure level at which the sensory properties of pork and wild boar meats (e.g., color) are not changed to a remarkable extent compared to fresh meat but have a longer shelf life.
# Multi-Media and Multi-Band Based Adaptation Layer Techniques for Underwater Sensor Networks
## 1 Introduction
### Need of Multi-Media Communication Techniques Underwater  
In order to get reliable communication underwater, multi-media techniques can be used. The network architecture of underwater communication includes an underwater area network, a surface area network, and a terrestrial area network. The underwater area network supports acoustic, optical, and MFAN communication; the terrestrial and surface area network consists of RF communication and the internal communication portal is TCP/UDP. In order to avoid interference and data loss, and to decrease the error rate and increase reliability, a four-in-one adaptable modem should be designed. So, different channels can be adapted in underwater communication systems. Thus, the communication system can be more effective and reliable. Also, multi-media communication systems can avoid collisions.
# Predictive Quantization and Symbolic Dynamics
## 1 Introduction
This lossy encoding helps remove noise from the continuous latent vectors by allocating fewer bits or even completely removing part of the encoding dimensions that carry little information about the data. During the symbolization step, the amount of predictive information depends on a threshold parameter that is used for distinguishing between the similar versus distinct latent vectors. Analogous to the threshold-crossing method described above, the information rate dependence on a threshold parameter is a non-monotonic function, with a zero information rate occurring at very low and very high thresholds and with several local maxima possible at intermediate threshold values. We use a threshold value that gives the global maximum of information rate as a symbolization choice. An additional use of lossy encoding is in exploring transfer entropy between simultaneous time series. The intuition is that causal relations between separate data streams that originate from related sources might be better revealed by reducing the fidelity of their data representation. To summarize the concepts and methods presented in the paper, in Section 2, we define the general framework of information dynamics; in Section 3, we describe the predictive quantization and time series symbolization using information rate; in Section 4, we discuss representation learning and the use of bit allocation for rate reduction; in Section 5, we develop a symmetric transfer entropy estimator using information rate and mutual information measures; and in Section 6, we describe applications for motif finding and a symbolic version of recurrent quantification analysis.
# Investigating Master-Slave Architecture for Underwater Wireless Sensor Network
## 4 System Model
### Impact of Acoustic Signal Velocity in the Underwater Environment  
The velocity of the sound is affected by various factors including temperature, pressure, and the salinity level of the seawater. Mathematically, it can be elaborated as follows [40]:  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Symbols/Abbreviations** & **Description** \\ \hline UWSN & Underwater Sensor Network \\ TWSN & Terrestrial Wireless sensor network \\ MSF & Master Selector Function \\ NR & Packet for Neighbor request \\ DATA & Data packet \\ ACK & Acknowledgment \\ CTRL & Control Packet \\ NTAB & Neighbor Table \\ MTAB & Table for the master nodes \\ PFZ & Potential Forwarding Zone \\ PFZ1 & cap of the upper hemisphere \\ PFZ2 & lower Part of upper hemisphere \\ PFN & Potential Forwarding Node \\ AT\({}_{\text{Mf}}\) & activation time for Mi i = 1,2,3. \\ HT\({}_{\text{Mi}}\) & Holding time for Mi i = 1,2,3. \\ PDC & Propagation delay for control packet \\ APD & Accumulated Propagation Distance \\ PDR & Packet Delivery Ratio \\ En & Eligible Neighbor \\ E\({}_{\text{th}}\) & Threshold energy for data transmission \\ N\({}_{\text{i}}\).E & Energy of node i (i = 1,2,3,4...) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Symbols/Abbreviations.  
Figure 2: Network architecture.  
\[c =1446.961+4.591T-5.305\times 10^{-2}T^{2}+2.374\times 10^{-2}T^{3}\] \[\quad+1.340(S-35)+1.63\times 10^{-1}D+1.6750\times 10^{-7}D^{2}-\] \[\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad 1.025\times 10^{-2}T(S- 35)-7.1390\times 10^{-13}TD^{3}\]  
where \(c\) is the velocity of the sound signal measured in m/s, \(T\) shows the temperature whose unit is Celsius, \(S\) indicates the salinity of water with the unit of Part Per Thousand i.e., PPT and \(D\) pertains to the depth measured in meters. The above equation qualifies for \(0\) C \(\leq T\leq 30\) C, \(30\leq S\leq 40\) PPT, \(0\leq D\leq 8000\) m.
# Hands-On Deformation of Volumetric Anatomical Images on a Touchscreen
## 2 Related Work
### Volume Image Deformation  
The deformation of volume images has been addressed using two major families of methods. One family is based on volume rendering with 3D texture mapping, and encoding the deformation as the mapping function. The other family is based on resampling the original volume into a new regular grid using the deformation.  
Early methods performed volume rendering using slicing planes, and transformed voxels to the original dataset using GPU-based 3D texture mapping (Steinpresented a method for volume rendering high-resolution irregular tetrahedral meshes, which enables arbitrary encoding of deformations within the mesh. [PERSON] et al. [41] also used a mapping function to render deformable volume images. They designed displacement fields with editing capabilities, subject to feature preservation constraints, to ease illustrative visualization. [PERSON] et al. [42], on the other hand, reformatted volume images into planar views to aid in diagnostic explorations.  
Thanks to the increase of performance of graphics hardware, real-time resampling of the full volume image is now possible. This enables the use of richer volume rendering methods, while handling the deformation as part of a resampling step. The approach was first used with deformations defined on coarse tetrahedral meshes and voxel-level parallelization [43], and it was then accelerated through tetrahedron-level parallelization [2]. Further extensions have considered different deformation models, such as a chain-mail algorithm [44], the corotational coarsening model used in our work [1], or the addition of respiratory models [45].  
As discussed in the introduction, the classical approach to simulating anatomical deformation is to segment and mesh the individual anatomical elements. Selection and manipulation actions are then largely simplified, as they can leverage the explicit boundaries of the elements. The simulation of deformations is solved using state-of-the-art contact mechanics formulations, and there are even open frameworks that facilitate this task [46]. A sample of diverse representative medical applications can be found in the review of [PERSON] et al. [47]. The classical approach is largely complementary to our work, as we can leverage segmented data when available. However, in the classical approach all relevant anatomical elements must be individually segmented. Due to the complexity of this task, the approach is either rejected or combined with laborious manual intervention. In our approach, instead, all anatomical elements remain present, as all interaction is defined on the original volume data.
## 2 Materials and Methods
### Data Collection and Processing
Examples of asystole, PEA, VF, pVT and pulse-generating rhythm in both compression-affected and non-compression ECG segments are presented in Figure 1.  
Figure 1: Representative ECG examples of asystole, pulseless electrical activity, ventricular fibrillation, pulseless ventricular tachycardia and pulse-generating rhythm in compression-affected and non-compression electrocardiogram (ECG) segments. These labels were cross-verified with electronic medical records (EMRs). Pulse-generating rhythms in compression-affected ECG segments were identified based on EMRs, and we specifically selected the rhythm immediately after ROSC declaration. For pulse-generating rhythms in non-compression ECG segments, we chose segments showing organized electrical activity interspersed between compression artifacts, just prior to ROSC declaration. Organized electrical activity refers to regular, discernible QRS complexes, indicative of coordinated ventricular depolarization. Shockable rhythms, which require defibrillation, included VF and pVT. For these shockable rhythms, additional ECG segments were extracted when different patterns were observed within the same patient, acknowledging the variability in these rhythms.
## 2 Methodology to Complete Missing Spatial and Geometrical Attributes
### Detailed Methodology and Illustrative Examples
#### 2.2.2 Model Completion Strategy  
The model completion stage _\"Complete\"_ analyses existing data, identifies missing attributes needed for geometrical representation and applies completion rules. Rules are based on the generic rule representation model [25], confidence levels are assigned to completed attributes.  
For the tree root object class, the SITG database only provides the planimetric position of the roots. Diameter and depth attributes have to be completed for a 3D representation and are derived by hypotheses. The depth, e.g., is assumed to be 0.2 \(\times\) Total_Tree_Height.  
When attributes of subsurface objects are completed, confidence levels are assigned. In the case of the depth completion, the maximum level of confidence is set to 80%. The confidence level is associated with a stair type function, which rallies 80% of the surface of the cumulative distribution until 0.2 \(\times\) Total_Tree_Height (Figure 7).  
Figure 5: Data work flow from Geographical Information System (GIS) store to resource description framework (RDF) triple store.  
Figure 6: Geometry model selection for the tree root object-class in the taxonomy.  
The Dirac probability density function represents the confidence for completion rules of single empirical values. These are, for example, a standard height and quantity for basement floors of buildings, a standard diameter for utility pipelines, etc. Figure 8 presents the model of the Dirac primitive for a gas network node. The maximum level of confidence for such a completion is again set to 80%.  
The third example for a completion rule is the spatial depth positioning of natural gas pipeline networks. Two distinct strategies are applied, when the \"z\" attribute is missing:  
1. No depth data is available (Figure 9, left): the standard depth of SGWA [30] is used (in this case 0.8 m).
2. Depth data of the same network type close to the one with missing data exists (Figure 9, right): in this case, a \"Pert\" distribution is established using available data and applied to obtain missing \"z\" attributes.  
Figure 8: Use of a Dirac probability density function along the X-axis for a diameter completion of a gas network node.  
Figure 7: Primary geometry, depth completion and stair type probability density function along the Z-axis.
# Linking and Cutting Spanning Trees
## 2 The Challenge  
We start by describing an intuitive process for generating spanning trees that does not obtain a uniform distribution. It produces some trees with a higher probability than others. This serves to illustrate that the problem is more difficult than it may seem at first glance. Moreover, we explain why this process is biased, using a counting argument.  
A simple procedure to build \(A\) consists in using a union-find data structure [4] to guarantee that \(A\) does not contain a cycle. Note that these structures are strictly incremental, meaning that they can be used to detect cycles but can not be used to remove an edge from the cycle. Therefore the only possible action is to discard the edge that creates the cycle.  
Let us analyze a concrete example of the resulting distribution of spanning trees. We shall show that this distribution is not uniform. First, generate a permutation \(p\) of \(E\) and then process the edges in this order. Each edge that does not produce a cycle is added to \(A\), edges that would otherwise produce cycles are discarded, and the procedure continues with the next edge in the permutation.  
Consider the complete graph on four vertices, \(K_{4}\), and focus on the probability of generating a star graph, centered at the vertex labeled \(1\). Figure 2 illustrates the star graph. The \(K_{4}\) graph has \(6\) edges, hence there are \(6!=720\) different permutations. To produce the star graph, from one such permutation, it is necessary that the edges \((1,2)\) and \((1,3)\) are selected before the edge \((2,3)\) appears; in general the edges \((1,u)\) and \((1,v)\) must occur before \((u,v)\). One permutation that generates the star graph is \((1,2)\), \((1,3)\), \((2,3)\), \((1,4)\), \((2,4)\), \((3,4)\). Now, \((2,3)\) can be moved to the right to any of three different locations, so we know four sequences that generate the star graph. The same reasoning can be applied to \((2,4)\) which can be moved once to the right. In total we count \(8\) different sequences that generate the star graph, centered at \(1\). For each of these sequences it is possible to permute the vertices \(2\), \(3\), \(4\), amongst themselves. Hence, we multiply the previous count by \(3!=6\). In total we count \(48=8\times 6\) sequences that generate the star graph, and therefore the total probability of obtaining a star graph is \(48/6!=1/15\). According to [PERSON]'s formula, the probability of obtain the star graph centered at \(1\) should be \(1/4^{2}=1/16\). Hence, too many sequences generate the star graph centered at \(1\).  
In the next section we fix this bias by discarding some edge in the potential cycle, not necessarily the edge that creates it.
## 4 Results and Discussion
### Meso-Analysis of Blocking Behavior
#### 4.1.4 The Three Stages of the Blockage Process  
After repeated rain washing and drying cycles, it can be ascertained by observing the template that as the number of clogging cycles increases, the largest clogging occurs in the first three clogging processes. During the subsequent 4-7 th blockage process, only local gaps were blocked or washed away by water flow, or continued to be blocked. Finally, after the 8 th cycle, the congestion situation remained basically unchanged. In summary, the clogging process can be roughly divided into three parts.  
1. Rapid blockage stage. The clogging is the most obvious in the early stage, the fine aggregate is blocked in the lower part of the mold, and the coarse aggregate is blocked in the upper part of the mold. The main reason is that the coarse particles block the upper connecting gap, making the gap width smaller. Most blockages cannot move downwards, only small aggregates can move downwards through narrow gaps. The clogging in this process mainly comes from the clogging effect of the coarse aggregate, which occurs relatively quickly.  
Figure 9: (**a**) Blockage with dry blockage. (**b**) Blockage with impact of water flow.  
2. Slow blockage stage. After the initial clogging is finalized, the rainwater will pass through the narrow void structure to wash away the small clogged large particles and most of the clogged small particles. As a result, a few clogged large particles are loosened, and the shape of the narrow gap changes slightly. The blocked small particles are continuously washed away under the impact of the water flow, and some are deposited in the gaps, turns, and bumps. In the process of repeated drying and scouring, they are hardened together to form a \"stubborn blockage\". The clogging in this process mainly comes from the clogging effect of the fine aggregate, and the occurrence process is slow.  
3. The final blockage stage. After many clogging cycles, the lower void structure was also blocked by the compacted fine aggregate, and could not be washed away by the water flow. The upper part of the mold is blocked by coarse aggregates, and the lower part is blocked by hardened materials. The blockage is basically shaped.
# Disentangled Feature Learning for Noise-Invariant Speech Enhancement
###### Abstract  
Most of the recently proposed deep learning-based speech enhancement techniques have focused on designing the neural network architectures as a black box. However, it is often beneficial to understand what kinds of hidden representations the model has learned. Since the real-world speech data are drawn from a generative process involving multiple entangled factors, disentangling the speech factor can encourage the trained model to result in better performance for speech enhancement. With the recent success in learning disentangled representation using neural networks, we explore a framework for disentangling speech and noise, which has not been exploited in the conventional speech enhancement algorithms. In this work, we propose a novel noise-invariant speech enhancement method which manipulates the latent features to distinguish between the speech and noise features in the intermediate layers using adversarial training scheme. To compare the performance of the proposed method with other conventional algorithms, we conducted experiments in both the matched and mismatched noise conditions using TIMIT and TSPspeech datasets. Experimental results show that our model successfully disentangles the speech and noise latent features. Consequently, the proposed model not only achieves better enhancement performance but also offers more robust noise-invariant property than the conventional speech enhancement techniques.  
Keywords:noise-invariant speech enhancement; disentangled feature learning; adversarial training; deep neural networks; noise reduction +
Footnote †: journal: Applied