esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Problem Formulation
### Network Configuration  
Suppose a WSN is a hybrid network with a BS having additional processing power and \(n\) remote sensor nodes deployed in an \(L_{x}\times L_{y}\) monitoring area. There are \(m\) points of interest (abbreviated as POI) in the monitoring area. The location of the sensor node is assumed to be known a priori. Thus, the network is represented by the Euclidean graph \(G\), and \(G=(V,\,E)\), as depicted in Figure 1, with the following properties:  
\(V\) is a set of nodes in the network and \(V=\{S,\,BS\}\), where \(S\) is a set of sensor nodes with a circular sensing range \(r_{s}\) and \(S=\{s_{1},\,s_{2},\,\ldots,s_{n}\}\), \(BS\) is the base station, and \(n\) is the number of sensor nodes.  
\(\succ\) Sensor nodes in \(V\) of the network know their location information.  
\(\succ\)\(<\)\(s_{i}\), \(s_{j}\)\(\in E\), where \(s_{i}\)\(\
eq\)\(s_{j}\). It is sustainable if the distance between \(s_{i}\) and \(s_{j}\) is shorter than the communication range of the sensor nodes in \(V\).  
\(\succ\) All sensor nodes in \(S\) are homogeneous, _i.e._, their sensing range, wireless communication capability, and initial power are identical.  
\(\succ\) All nodes in \(V\) are stationary after the deployment.  
Figure 1: Example for the coverage model of sensor node.  
All nodes in \(V\) have the power management capability; their radio power can be dynamically adjusted according to the transmission distance.
* \(BS\) can be deployed at any location inside or outside of the monitoring area.
## 2 Light Clock  
### Principle of Light Clock Operation  
Figure 1 shows a clock, which we will call a signal clock. This clock uses a signal that propagates at a constant speed \(v_{0}\) in a homogeneous medium. The universal frame of reference \(U\) is connected with the medium in which the signal propagates. The clock can move relative to the medium in which the signal is propagated (at \(v\) speed). The clock is connected to the inertial system \(U^{\prime}\).  
The clock consists of a signal source and a mirror that can reflect such a signal. The signal source and the mirror are rigidly connected at \(D^{\prime}\) distance and form the arm of a clock. The signal is sent from the source and travels a distance of \(D^{\prime}\) length. Then it is reflected by the mirror and returns to the source along the same path. When the signal returns to the source, another signal is sent immediately. This means that the signal clock continuously generates further signals. The total time of one signal passing to the mirror and back is a time standard for an observer in the same inertial system \(U^{\prime}\) in which the clock is located. The duration of one cycle of a signal clock is the smallest unit of time that a signal clock can measure.  
If the signal used in a signal clock is a light impulse (or any other electromagnetic impulse), then the clock will be called a light clock. In this paper, we will analyze the properties of a light clock, assuming that light propagates in the universal frame of reference (ether), i.e., according to the Special Theory of Ether [3; 4; 5]. On this basis, conclusions will also be drawn from the Special Theory of Relativity.  
Every observer measures his time (proper time) with his own light clock, which is motionless in his inertial reference system.
# Design and Performance Analysis of Hardware Realization of 3 GPP Physical Layer for 5G Cell Search
## 2 Specifications of 5G CS and Literature Review
### 5g Cs PHY

Table 1: Execution Time and Resource Utilization Comparison for PSS Searchoccupied by a physical broadcast control channel (PBCH) carrying MIS and DMRS. The DMRS occupies 25% of the remaining SC, i.e., 144 SC. Since DMRS is quadrature phase shift keying (QPSK) demodulated, it consists of 288 bits which are generated using 3 GPP defined pseudo-random sequence generator using two parameters: (1) PCI, \(N_{ID}^{Cell}\), and (2) SS index, \(SS_{i}\), of the SS in SSB, \(SS_{i}\)\(i\in\{1,2,\ldots,8\}\)[16,18]. At UE, DMRS is detected after PSS and SSS detections which means DMRS allows estimation of the SS index which is critical for a frame, sub-frame, slot, and symbol synchronization. DMRS also allows channel estimation for MIS reception via PBCH [11,12,23]. The location of all SS in SSB among 4096 SCs is not fixed in 5G and it is decided by the GSCN value. Furthermore, gNB can dynamically change the GSCN of the SSB. As per 3 GPP specifications, there is a one-to-one mapping between the GSCN value and corresponding SSB center frequency [16,18]. In n78, the GSCN range is from 7711-8051 and the frequency resolution is 1.44 MHz. For example, the carrier frequency of 3305.28 Mhz is assigned a GSCN of 7711. Then, the GSCN of 7712 corresponds to the carrier frequency of 3305.28 + 1.44 = 3306.72 MHz. The last GSCN of the n78 band is 8051 and it corresponds to 3794.88 MHZ [16,18]. Such a GSCN raster of 1.44 MHz resolution allows UE to quickly detect SS by limiting the search over a limited number of center frequencies compared to the search over carrier frequencies with a channel raster of 15 kHz resolution. Figure 1: Details of 5G SSB scheduling and SS build blocks [16,18].
# A Comprehensive Review of Milk Components: Recent Developments on Extraction and Analysis Methods
## 2 Milk Macro-Constituents  
### Lipids  
Lipids are one of the most abundant classes of milk constituents. In general, they account for 6% of the total mass content of milk and mainly include triacylglycerlycerides [15]. Indeed, 98% of milk fatty acids are found in the form of triesters of glycerol, while the remaining 2% comprises other glycerol esters (mono- and di-), phospholipids, glycolipids and free fatty acids [16]. About half of the fatty acids occurring in milk have a full saturated chain made of 18, 16 or 14 carbon atoms, i.e., C18:0, C16:0 and C14:0 [17], while unsaturated fatty acids are generally minor components compared to the total.  
Unsaturated _trans_ fatty acids have at least one unsaturation which is in _trans_ configuration [18]. A particular class of trans unsaturated milk fatty acids is represented by the conjugated linoleic acids (CLAs). They are a group of geometric and positional isomers of C18 linoleic acid containing conjugated double bonds [19], usually found in positions 8 and 10, or 9 and 11, 10 and 12 and 11 and 13. Since the double bonds are not separated by a methylene group, geometric or _cis-trans_ isomers could be distinguished (Figure 1) [20].  
Saturated fatty acids and unsaturated _trans_ fatty acids are responsible for the decrease of HDL (high-density lipoprotein) values in the blood [21], which is considered one of the main causes of cardiovascular diseases resulting from hypercholesterolemia [22; 23].  
Another important group of milk lipids is represented by branched-chain fatty acids (BCFAs), which include fatty acids with a methyl group placed on the C2 or C3 carbon, numbering starting from the \(\omega\)-carbon of the chain. At least 56 isomers of branched fatty acids have been identified in milk, with a carbon chain length ranging from 4 to 26 carbon atoms [24]. Table 1 reports the mean content of BCFAs in f
# The Artificial Intelligence-Driven Intelligent Laboratory for Organic Chemistry Synthesis
## 3 AI-Driven Intelligent Chemical Experiments
### Artificial Intelligence-Driven Organic Chemistry Research
#### 3.2.1 Product Prediction
Thin-layer chromatography (TLC) is an important tool for dynamically tracking chemical transformation processes and analyzing the interfaces of multi-component systems, enabling precise visual evaluations of the separation efficiency of complex mixtures. [PERSON] et al. combined TLC technology with automated artificial intelligence, aiming to use specialized desktop robots to collect large amounts of TLC data, laying the foundation for complex steps designed in a subsequent ELC analysis [52]. High-performance liquid chromatography (HPLC) is a technique used for separating and identifying components in sample mixtures based on differences in adsorption capacity. [PERSON]'s research team introduced machine learning methods into chromatography, using CMRT data to construct HPLC columns driven by the synergistic effect of dual substrates and seven substituents. The team innovatively integrated symbolic regression with chromatographic thermodynamics knowledge graphs, breaking through the dimensional limitations of traditional single-column predictions and establishing a new computational screening paradigm for enantiomer separation, significantly enhancing the cross-platform generalization ability of separation predictions in complex systems [53]. [PERSON] et al. [54] proposed an innovative approach to solve forward reaction prediction problems by treating reaction prediction as a translation problem, predicting the generated products from known starting materials. They employed a data-driven sequence-to-sequence (seq2 seq) model based on attention mechanisms; the model is template-free and can accurately predict reaction outcomes without relying on manually encoded reaction templates or explicit atomic features. The model uses the Simplified Molecular Input Line Entry System (SMILES) to represent molecules and trains deep neural networks to learn how to transform \"reactants > reagents\" into the SMILES representation of products.
# Polysaccharide Hydrogels Based on Cellulose and Chitosan for Drug Sustained-Release Applications
## 2 Experimental
### In Vitro Release Tests
#### 2.4.2 Study on the Release Performance of Polysaccharide Hydrogel  
In accordance with the Chinese Pharmacopoeia and the study by [PERSON] et al. [32], CAA-CS polysaccharide hydrogel was immersed in various release media, including 0.9% (\(w/v\)) normal saline and phosphate-buffered saline (PBS, pH = 7.34), to evaluate drug release. By constructing standard curves for each release medium and measuring the release rate at different time points, the relationship between time and cumulative drug release rate was established. Additionally, a specific concentration of the drug was loaded into the CAA-CS hydrogel, and its release behavior was investigated in the corresponding medium.
# Driver Behavior Classification System Analysis Using Machine Learning Methods
## 4 Proposed Classification Methods
### Gradient Boosting Classifier  
Among the ensemble techniques based on decision trees method, the gradient boosting method is based on the competition-winning concept in which weak learners are iteratively boosted by optimizing a loss function. Generally, this approach tends to focus on observations that were difficult to predict in previous iterations and synthesizes an ensemble of weak learners. This approach has the ability to generate a model by optimizing a differential loss function. The gradient boosting classifier can be summarized by the following elements [52; 53; 54].  
* A loss function that depends on the problem type is formulated and used to create the prediction model. Note that a logarithmic loss function will be used in the case of building a classifier model. Besides, this boosting algorithm focuses on optimizing, at each stage, the unexplained loss from prior iterations;
* As one of its basic elements, the gradient boosting classifiers use decision trees as a weak learner to make predictions;
* Finally, the concept of additive models is used, in which trees are added one at a time (add weak learners) while the existing trees in the model are not altered. Then, a gradient descent procedure is used to minimize the loss function when adding trees. The gradient boosting algorithm can be summarized by the following steps: 1. Initialization: \[f_{0}(x)=\gamma\sum_{i=1}^{N}L(y_{i},\gamma)\] (1) 2. For \(m=1,2,\dots,M\):
* For \(i=1,2,\dots,N\): \[r_{im}=-[\frac{\partial L(y_{i},f(x_{i}))}{\partial f(x_{i})}]_{f=f_{m-1}}\] (2)
* Fit a regression tree to the targets \(r_{im}\) giving terminal regions \(R_{j_{m}},j=1,2,\dots,f_{m}\)
* For \(j=1,2,\dots,J_{m}\): \[\gamma_{jm}=argmin_{\gamma}\sum_{x_{i}\in R_{jm}}L(y_{i},f_{m-1}(x_{i})+\gamma)\] (3)
\[f_{m}(x)=f_{m-1}(x)+\sum_{j=1}^{J_{m}}\gamma_{jm}I(x_{jm}) \tag{4}\]  
Output:  
\[\hat{f}(x)=f_{M}(x) \tag{5}\]
## 4 Simulation Results
### Impact of the Location of Vent Opening  
Regarding the correlation among the location of the vent opening, the location of the blast point, and the impact of the location of the vent opening on leakage pressure, the leakage pressures at the blast heights of 10 cm (at the center of the vent), 20 cm (at the edge of the vent), or 30 cm (on the top of the vent) are discussed, respectively, as based on a 20 \(\times\) 20 cm vent opening and a distance of 30 cm from the 1 pound of TNT blast point to the vent opening. The computer simulation results are shown in Tables 8, 11, and 12. The correlation among blast height, the location of the vent opening, and the impact of blast height on blast pressure are shown in Figure 24. The results show that at the blast height of 20 cm (at the edge of the vent), while the blast pressure is subject to the inference of walls at 10 cm outside the vent opening, and then decreases, the blast pressure is consistent with that at the blast height of 10 cm (at the center of the vent) beyond 20 cm outside the vent opening. At the blast height of 30 cm (on the top of the vent), while the blast pressure is subject to the inference of walls at 20 cm outside the vent opening, and then decreases, the blast pressure decreases slightly, as compared with that at the blast height of 10 cm (at the center of the vent) beyond 30 cm outside the vent opening. In other words, while the location of the vent opening has an impact on blast pressure, the impact is not considered in the UFC 3-340-02 Specification at present. However, a relatively conservative design will be implemented by ignoring this effect.
## 4 Results and Discussion
### C-SAR/01  
(1) QPSI mode localization accuracy analysis  
The C-SAR/01 satellite QPSI model contained data of October 15 and October 19, and the positioning accuracy obtained is shown in Figure 10.  
Figure 10 illustrates that the images of October 15 and 19 contain 12- and 13-point targets, respectively. In the case of the QPSI mode images of C-SAR/01, the positioning accuracy falls within the range of \(-14\) to \(-4\) pixels in the range direction and between \(-19\) and \(13\) p  
Figure 10: Localization accuracy of C\(-\)SAR/01 QPSI mode data.  
Figure 9: Impulse response function of CR-4 in the GF-3 UFS mode image acquired on November 21. (**a**) Range impulse response function and (**b**) azimuthal impulse response function.  
(2) FSII mode localization accuracy analysis  
The C-SAR/01 FSII data contained data of November 20 and November 22, and the positioning accuracy obtained is shown in Figure 11.  
Figure 11 indicates that both the images of November 20 and 22 contain 14-point targets. In the case of the FSII mode images of C-SAR/01, the positioning accuracy varies from \(-24\) to \(-10\) pixels in the range direction and from \(-13\) to \(26\) pixels in the azimuth direction.
## 3 Materials and Methods
### Small Target Detection Model  
The STD module is designed upon the DETR detection framework, comprising a backbone network, neck network, and decoder. The backbone employs a CNN architecture, which greatly improves real-time performance. The backbone's output is handled by the encoder, performing intra-scale interaction and cross-scale fusion to transform multi-scale features into a sequence of image features. An IoU-aware query selection mechanism  
Figure 5: Details regarding Group Attention Refine Module (GARM): Depth-wise Conv, Batch Norm, ReLU, and grouped attention calculation for feature enhancement.  
is employed to select a fixed number of image features from the encoder's output. The decoder, with an auxiliary prediction head, iteratively refines object queries for efficient target detection.  
In optical satellite images, ship targets are extremely small, often only a few pixels in size, making target detection highly susceptible to noise interference. To better represent these targets, we introduce a Compact Inverted Block (CCIB) to enhance the pyramid feature representation capability within the network. The detailed structure of CCIB is shown in Figure 6. CCIB utilizes low-cost depth-wise convolutions for spatial integration and low-budget point-wise convolutions for channel fusion. While reducing the computational burden of the model and improving operational speed, it ensures that the model's accuracy remains unaffected and enhances the representation of both local and global information.
## 5 Discussion
### The Comparability of Eigenvalues  
The method proposed in this paper only possesses the scalar feature of geometric information content. Node degree, as the number of adjacent objects for areal objects, primarily serves the role of accumulating information content and does not affect the dimension of the eigenvalue. Both the visual distance factor and adjacency strength factor have a value range of [0, 1], essentially serving as a weight factor, and they also do not affect the dimension of the eigenvalue. Therefore, the spatial information content in this paper's method is a scalar feature value based on geometric information content as the basic unit, with the visual distance factor and adjacency strength factor as weights. Its magnitude is positively correlated with the amount of geometric information content, the size of visual distance factor and adjacency strength factor, and the number of node degrees. [PERSON]'s paper further discusses the advantages of consistency and comparability in the value range of the eigenvalues possessed by the proposed geometric information content measurement method [27]. Consequently, this paper also inherits these advantages. Combined with the construction of the convex hull adjacency graph, the measurement results in this paper are not limited by the map extent, thus ensuring widespread comparability of the measurement values.
# The Immobilization of Lipases on Porous Support by Adsorption and Hydrophobic Interaction Method
Besides, the enzyme immobilization method and enzyme carriers used for immobilization studies were reviewed [16; 17; 18]. Some advantages of immobilized lipase applications include reduced energy costs, shortened process times, the prevention of volatile compounds loss, and reduced risk of contamination [19; 20]. All types of immobilization methods, such as adsorption, encapsulation, covalent bonding, entrapment, and crosslinking, were discussed in previous studies [16]. Carriers for enzyme immobilization are divided into inorganic materials, metal-organic frameworks (MOFs), DNA origami, and polymers [17]. Most of the review papers more focused on different types of enzymes used for various types of immobilization methods and supports [19; 20]. The lipase immobilization on hydrophobic supports and the mechanism of lipase immobilization on hydrophobic supports using different types of immobilization techniques were reviewed [20]. However, among industrial applications, the adsorption method on porous supports is the most popular. Therefore, this review focuses only on lipase's immobilization using the adsorption and hydrophobic interaction method on a porous support.
## 3 Results and Discussion
### Plasma Diagnostics
Figure 5 shows an example of the time-resolved n\({}_{\rm e}\) evolution for the main current peak in the Me-Me system at a plasma power of 20 \(\pm\) 2 W, a gas flow rate of 4 L min\({}^{-1}\), and T\({}_{\rm g}\) of 1000 K. Under this experimental condition, the peak electron density reaches 1.4 \(\times\) 10\({}^{16}\) \(\pm\) 0.2 \(\times\) 10\({}^{16}\) cm\({}^{-3}\) and follows the structure of the current profile. Following the same approach, n\({}_{\rm e}\) was calculated for all experimental conditions, and the results are shown in the SI, Figure S6. It was found that n\({}_{\rm e}\) shows relatively constant values in both Me-Me and Me-L reactor configurations and varies between 1.2-2.1 \(\times\) 10\({}^{16}\)\(\pm\) 0.4 \(\times\) 10\({}^{16}\) cm\({}^{-3}\) and 0.8-2.4 \(\times\) 10\({}^{16}\)\(\pm\) 0.2 \(\times\) 10\({}^{16}\) cm\({}^{-3}\), respectively. These values correspond well to the so-called transient spark mode where a plasma is typically initiated by a streamer, followed by a short (\(\sim\)100 ns) high current (\(\sim\)10 A) pulse. In this case, the transition to a \"full\" spark discharge is limited by the voltage duration [36]. However, the used method is highly sensitive to the plasma channel diameter and strongly depends on the gas phase composition. Therefore, considering possible errors of the used method, it is hard, if even possible, to make a conclusion about the n\({}_{\rm e}\) behavior at different gas flow rates and plasma powers. To summarize, it can be stated that the interchange between the metal and the liquid ground electrodes has a negligible impact on plasma power and discharge mode. However, the plasma power is directly proportional to the number of plasma filaments and the plasma volume (higher power \(\rightarrow\) more filaments \(\rightarrow\) higher plasma volume). OES indicates that the main difference between the Me-Me and Me-L reactors is the presence of OH radicals in the latter case. Moreover, the gas temperature measurements show that the presence of a plasma/liquid interface results in plasma thermalization (although only of \(\sim\)400 K) that might have a negative cause on plasma-based nitrogen fixation energy efficiency. The following section is dedicated to the determination of the produced stable nitrogen-containing products in both gas and liquid phases.
# A Model-Agnostic Algorithm for Bayes Error Determination in Binary Classification
## 3 ILD Algorithm Mathematical Framework
### Accuracy  
In a binary classification problem, the accuracy is defined [22] as  
\[a=\frac{TP+TN}{M}. \tag{11}\]  
Using Equations (7) and (8), the accuracy can be rewritten as  
\[a=\frac{1}{M}\sum_{i=1}^{N_{B}}\left[p^{[i]}(m_{1}^{[i]}-m_{0}^{[i]})+m_{0}^{[ i]}\right] \tag{12}\]  
The maximum value of the accuracy is obtained if the model predicts \(p^{[i]}=1\) as soon as \(m_{1}^{[i]}>m_{0}^{[i]}\). This can be stated as  
**Theorem 1**: _The accuracy for an aggregated categorical dataset \(B\), expressed as Equation (12), is maximized by choosing \(p^{[i]}=1\) when \(m_{1}^{[i]}>m_{0}^{[i]}\) and \(p^{[i]}=0\) when \(m_{1}^{[i]}\leq m_{0}^{[i]}\)._  
Proof.: The proof is given by considering each bucket separately. Let's consider a bucket \(i\) that has \(m_{1}^{[i]}>m_{0}^{[i]}\). In this case, there are two possibilities:  
\[p^{[i]}=1\rightarrow p^{[i]}(m_{1}^{[i]}-m_{0}^{[i]})+m_{0}^{[i]}=m_{1}^{[i]} \tag{13}\] \[p^{[i]}=0\rightarrow p^{[i]}(m_{1}^{[i]}-m_{0}^{[i]})+m_{0}^{[i]}=m_{0}^{[i]}\]  
Therefore, the \(i\)th contribution to the accuracy in Equation (12) is maximized by choosing \(p^{[i]}=1\) for those buckets where \(m_{1}^{[i]}\geq m_{0}^{[i]}\). With a similar reasoning, the contribution to the accuracy for those buckets where \(m_{1}^{[i]}<m_{0}^{[i]}\) is maximized by choosing \(p^{[i]}=0\). This concludes the proof.