esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Perception, Optimization and Localization  
Autonomous forklifts rely on accurate perception and sensing capabilities to understand their environment. Scientific contributions in this area, such as [19; 27; 28; 29; 30], focus on improving object detection. Important articles that explain the most prominent challenges of object detection in the autonomous vehicle domain are as follows: refs. [31; 32; 33; 22] discusses localization; refs. [34; 35] elucidates mapping; and refs. [36; 37] explores sensor fusion techniques. This may include developing new sensor technologies, designing robust perception algorithms and addressing challenges related to occlusions, dynamic environments and varying lighting conditions. Articles such as [38] contributed to this domain of perception and sensing. Additionally, in [39], an important survey was conducted to investigate the perception and sensing capabilities of autonomous vehicles in challenging weather conditions.  
In scenarios where multiple autonomous forklifts work together, scientific contributions focus on optimizing coordination and task allocation. Research such as that by [PERSON] et al. in [40] explores the predominant challenges linked to autonomous order picking systems in warehouses, shedding light on the constraints in speed, safety and accuracy.  
Also, as explained in [41], the authors designed routing systems for intelligent warehouses, where the router addresses real-world operational challenges by ensuring that the operations are conflict-free.It adeptly handles traffic congestion, creates optimized trajectories for autonomous forklifts, and dynamically adjusts priorities and routes to prevent collisions. In [42], the authors formulated a deep learning approach for task selection within a warehouse setting for a fleet of autonomous vehicles. Moreover, the study of [43] explores an optimization challenge within the inbound warehouse process, focusing on the coordination of multiple resources in a specific type of automated warehouse system.  
As it became evident, the contributions in this field encompass a wide range of diverse approaches, involving the formulation of algorithms and techniques for tasks such as scheduling, load balancing and resource allocation. These endeavors aim to enhance the overall effectiveness and productivity.  
Additional research about autonomous vehicles (AVs) can be found in [44]. The document provides an extensive examination of the cutting-edge techniques employed to enhance the effectiveness of AV systems in close-range or nearby automobile surroundings. It concentrates on recent investigations that employ deep learning sensor fusion algorithms for perception, localization and mapping.  
Additionally, notable studies including those referenced below, have concentrated on the localization and picking aspects of industrial forklifts. These research works, including [22; 45], emphasize the development of a robust range and visual method to comprehend their surroundings.
# LESS: Link Estimation with Sparse Sampling in Intertidal WSNs
## 7 Performance Evaluation
### Performance Metrics  
**Link quality reconstruction accuracy** is calculated by \((1-\frac{\sum|\emph{lq}_{\_}est.-\emph{lq}_{\_}orig.}{\sum\emph{lq}_{\_}orig.} )\times 100\%\), where \(\emph{lq}_{\_}est.\) and \(\emph{lq}_{\_}orig.\) are the estimated and original values of link quality. In LESS, we partially sample the link quality in matrix \(X\), then reconstruct the entire link quality matrix based on the sampled values. To evaluate the performance of LESS, only unsampled points in \(X\) are considered in link quality reconstruction accuracy.  
**Routing path reconstruction accuracy** is the ratio of correctly reconstructed routing paths given the estimated link quality matrix, i.e., \(\frac{\sum\emph{correctly reconstructed routing paths}}{\sum\emph{routing paths}}\times 100\%\). We obtain the ground truth routing paths with CTP from the original link quality matrix. We then reconstruct routing paths from the link quality matrix with CTP likewise. Finally, we calculate the routing path reconstruction accuracy. The result of the routing path reconstruction accuracy is a direct indicator of the performance of LESS.  
**Topology reconstruction accuracy** is defined as the ratio of correctly labeled links in the network, i.e., \(\frac{\sum\emph{correctly labeled links}}{\sum\emph{labeled links}}\times 100\%\). Each link in the topology is labeled with \"0\" or \"1\" to indicate its existence (\"1\" for yes and \"0\" for no). A link exists if its link quality value is larger than a threshold (i.e., the noise floor) and vice versa. Topology reflects connectivity of nodes in the network, and provides candidate routes for routing path selection.  
The groundtruth of link quality, routing path and topology is from the meta-data of collected packets. The routing protocol used in our systems is CTP.
# Multi-Access Channel Based on Quantum Detection in Wireless Optical Communication
## 3 Results and Analysis
### Numerical Result of Two-User Detection in Absence of Noise  
Section 2.3 theoretically gives the effect of using ideal USD measurement to achieve error-free inter-user detection in a noise-free situation, while MED measurement under the same conditions can only give detection results with a certain error probability.  
In this section, numerical experiments are given to the above results to investigate the maximum channel capacity when the coherent-state signals of two users are measured by MED and USD without any noise background. As well as the inter-user detection error probability and the maximum channel capacity measured by USD in the presence of thermal noise will be presented in the next section.  
Based on the calculation of Equations (23) and (27), Figure 5 depicts the maximum channel capacity curve measured by MED and USD, and the gray area between the two curves is the channel capacity area that is reachable by MED but not by USD. This shows that with regard to the multi-user detection problem, the USD measurement can obtain a better inter-user detection error probability (ideally no error) than the MED measurement, while the reachable area of the channel capacity is smaller than that of the MED measurement.  
As can be seen from Figure 5, the difference between the two is more obvious in the part where the average photon number of the signal is small. The maximum channel capacity for single-bit two-user communication is 2. The MED already exceeds 1.8 at an average photon number of 1.5 and is close to 2 at an average photon number of 3. For the USD case, although its channel capacity is lower than that of the MED, it is close to 2 when  
Figure 5: The maximum capacities of MED and USD for two-user channel, respectively.  
the average photon number exceeds five. This is efficient for multi-user communication, as theoretically they only need less power for coherent-state signals.  
On the other hand, as a theoretical limit of the channel capacity, it is practically difficult to reach, because there is always noise. In fact, for MED, Helstrom gives in [14] the theoretical limit of binary quantum detection, which is exactly the MED bound in Figure 5. Additionally, the actual receiver can overcome the effect of noise close to the performance of the Helstrom bound [36] when the signal power is large enough. So, the typical range of capacity is achievable up to the gray area close to the MED bound in Figure 5.  
For USD, what we know is that the typical range of capacity should be reachable in the area below the USD bound in Figure 5. However, the exact details of how to do this are much more complicated because the case of USD under noise for a general quantum mixed state has not been sufficiently studied [31; 32]. However, based on the symmetric coherent-state signal used in this paper, the noise problem of USD can be solved to some extent by using some mathematical tricks. This part will be developed in the next section.
## 1 Introduction  
Enzymes play a crucial role in numerous biotechnological applications, including biosensors, biocatalysis, biomedicine, food processing, bioenergy, and agrochemicals [1]. However, natural enzymes face challenges including poor stability, solvent sensitivity, high costs, and limited reuse. Furthermore, the activities of natural enzymes are often influenced by environmental factors like temperature, pH, metal ions, and organic solvents, which restricts their wide application in industrial processes [2].  
Enzyme immobilization has emerged as promising solution to address these challenges. This technique not only facilitates the easy separation and reuse of enzymes, thus reducing operational costs, but also enhances enzyme stability, allowing for more efficient and sustainable biocatalytic processes. The immobilization technique was initially reported in the early 1900s and was popularized in the 1960s [3].  
Recently, numerous immobilization approaches, such as adsorption, entrapment, covalent binding, and cross-linking, have been examined in the literature [1, 4, 5, 6]. These traditional immobilization techniques are often plagued by issues like enzyme leakage, instability, mass transfer limitations, and high operational costs. Additionally, these methods may result in enzyme inactivation, distortion of the enzyme shape, and the use of non-recyclable supports, complicating waste disposal. Steric hindrance and diffusional barriers can also impair the overall performance of the immobilized enzymes [7].  
Recent advancements in enzyme immobilization technologies have focused on overcoming these limitations. Innovations such as mesoporous supports, hydrogels, cross-linked enzyme aggregates, microfluidics, 3D printing, and nanoparticle-based techniques have shown promise in enhancing enzyme activity, stability, and loading capacity, thus reducing the overall costs of biocatalysts [8]. The integration of machine learning into these methods further enhances their efficacy [9]. Nanomaterials, in particular, have proven effective in improving enzyme performance, while 3D printing allows for the precise design of immobilization carriers, offering eco-friendlier and cost-effective biocatalysts. The growing interest in enzyme immobilization, coupled with the push for sustainable industrial practices, has accelerated the research into novel carriers and immobilization techniques.  
This review aims to provide a comprehensive overview of recent trends in enzyme immobilization research, highlighting the development of new carrier materials and innovative immobilization methods, especially AI-derived immobilization strategies. It will also explore the industrial applications of these advancements, focusing on their potential to improve biocatalytic processes and contribute to the achievement of global sustainability goals.
## 2 Modal Analysis and Natural Frequencies
### Formulation  
A vibrating system's motion is governed by the following basic equation [11]:  
\[M\cdot\ddot{x}(t)+C\cdot\dot{x}(t)+K\cdot x(t)=F(t) \tag{1}\]  
where M is the mass matrix, C is the damping matrix, K is the stiffness matrix, and x(t), \(\dot{x}\), and \(\ddot{x}\) are the displacement, velocity, and acceleration vectors, respectively. Finally F(t) is the external force.  
In modal analysis, in which no external force is applied, Equation (1) is reduced to  
\[M\cdot\ddot{x}(t)+K\cdot x(t)=0 \tag{2}\]  
To solve for the natural frequencies and mode shapes, a harmonic solution of the form below is considered:  
\[x(t)=\phi\cdot e^{i\omega t} \tag{3}\]  
where \(\omega\) describes the natural frequencies and \(\phi\) represents the mode shape. When this answer is entered into the simplified Equation (2) of motion, the following happens:  
\[(M\cdot\omega^{2}-K)\cdot\phi=0 \tag{4}\]  
Therefore, by solving Equation (4), natural frequencies and corresponding mode shapes are obtained, which serve as the foundation for evaluating the system's vibrational characteristics and identifying potential resonance issues.
# [PERSON] SOM Architecture for Intrusion Detection on In-Vehicle Communication Networks
## 5 Experimentation
### Evaluation Metrics  
In order to evaluate the performances of the supervised Kohonen network implemented on different datasets, we computed the traditional classification metrics as accuracy, detection rate, false positive rate (FPR), precision, and F1-score [55].  
We also computed the false negative rate (FNR) which represents the fraction of undetected attacks as follows:  
\[FNR=\frac{FN}{TP+FN}, \tag{6}\]  
where TP (true positive) and TN (true negative) are the number of messages correctly classified, respectively, as attack or normal, and FP (false positive) and FN (false negative) are the number of messages incorrectly classified, respectively, as attack or normal.  
Since even a very small number of undetected attacks can determine tampering in the vehicle, undermining its safety, the FNR measure has great importance in attack detection for in-vehicle CAN buses and its value should be very small.
# Random Noise Suppression Method of Micro-Seismic Data Based on CEEMDAN-FE-TFPF
## 1 Introduction
In order to avoid the problem of modal aliasing of EMD, CEEMDAN is used to decompose the original signal with random noise into several IMFs. 2. The IMFs which need to be denoised are distinguished by discriminant factors. This paper focuses on the selection, comparison, and analysis of discriminant factors. 3. To overcome the drawback of TFPF, the IMFs obtained by CEEMDAN can flexibly select different window lengths for TFPF denoising. 4. The denoising result is reconstructed through calculating the sum of the processed and residual IMFs. In this way, it can improve the signal SNR. The paper is organized as follows. Following the introduction, Section 2 describes the principle of the proposed method in this paper. Section 3 describes experiments conducted on synthetic and actual data to show the effectiveness of the method. The last section discusses the the results and gives conclusions.
# Path Planning and Collision Avoidance in Unknown Environments for USVs Based on an Improved D* Lite
## 3 Improved D* Lite
### Setting Safe Distance  
As we know, the path planned by D* Lite is very close to obstacles, which increases the collision risk when a USV sails through these obstacles. To avoid obstacles, multi-criteria decision making (MCDM) can be used to identify risky areas around obstacles before path planning [29]. Another simple and widely used approach is to expand the size of obstacles to maintain a certain distance between the path and obstacles. Here, we also use an expansion approach to enlarge the boundary of obstacles in order to keep a safe distance between the USV and obstacles. The pseudocode in Algorithm 1 shows the main expansion procedure.  
We let \(map\) be the environmental map in Algorithm 1; _rows_ and _cols_ are the width and height of the map, and _distance_ represents a safe distance between the USV and obstacles. If node \(map[i][j]\) is an obstacle (\(map[i][j]=0\)), all its surrounding nodes within the _distance_ will be set as \"virtual\" obstacles (\(new\_map[x][y]=0\)). By this approach, we can expand the area of obstacles and set a safe distance between the planned path and obstacles.  
```
0:\(map\), rows, cols, distance
0:\(new\_map\)
1:functionSetSafeDis(\(map\), rows, cols, distance)
2: set\(new\_map=map\);
3:for\(i=0;i<rows;i++\)do
4:for\(j=0;j<rows;j++\)do
5:if\(map[i][j]=0\)then/* node \(map[i][j]\) is an obstacle */
6:for\(x=i-distance;x<i+distance;x++\)do
7:for\(y=j-distance;y<j+distance;y++\)do
8:if\(0\leq x<rows\) and \(0\leq y<cols\)then/* node \(x,y\) is in the map boundary */
9: set\(new\_map[x][y]=0\); /* set node \(new\_map[x][y]\) as an obstacle */
10:else/* out of the boundary */
11: continue;
12:endif
13:endfor
14:endfor
15:endif
16:endfor
17:endfor
18:return\((new\_map)\);
19:endfunction
```  
**Algorithm 1** Set safe distance.
# Experimental Demonstration of a Multichannel Elastic Wave Filter in a Phononic Crystal Slab
###### Abstract  
With the aim of selecting particular frequencies of interest and rejecting others, the waveguiding and filtering properties of a two-dimensional phononic crystal slab are investigated in the context of a filtering application. To this end, we designed and manufactured a metallic device that consists of a square lattice of cylindrical pillars mounted on the top of a plate by using 3D printing technology. We respectively explored the theoretical and experimental characteristics of the device by using finite element method, a Micro System Analyzer (MSA) and a scanning laser Doppler vibrometer. The proposed device shows a complete band gap for Lamb wave around 0.3 MHz with a relative band-width of 30%. Tailorable waveguides are realized inside this phononic crystal by inserting several space gaps to achieve a demultiplexing effect through the splitting of an acoustic signal towards three different bandpass frequency channels. The demultiplexing performance has been experimentally demonstrated by achieving rejection levels up to 60 dB. The proposed phononic platform can have a significant impact in signal processing as well as droplet manipulation for biological applications.  
phononic crystal slabs; phononic waveguiding; lamb waves; wave demultiplexing Article
###### Abstract  
The aim of this study was to examine the relationship between the presurgical maxillolam- mandibular transverse index and post-surgical stability one year after mandibular advancement. For the material and methods, twenty-two subjects who were treated with mandibular advancement were enrolled in this study. Postsurgical stability was defined as the horizontal mandibular position change of <2 mm in lateral cephalogram 1 year after surgery. Subjects were divided into two groups according to the maintenance of postsurgical stability: a stable group (group S) and a less stable group (group LS). Presurgical maxillomandibular transverse index was determined as Yonsei transverse index (YTI) one month before surgery. A logistic analysis was performed on the postsurgical stability according to the YTI value. The presurgical, post-expansion target YTI value was obtained using receiver operating characteristic (ROC) curve. There were no notable differences in the baseline characteristics of the two groups except for vertical positions of point A, B, and gender distribution. Before surgery, however, there was a significant difference in YTI at both the fossa and CR level between the groups. The amount of mandibular advancement did not show a significant difference. The odds ratio for YTI was 0.35 (\(p\) = 0.024). The prediction of stability of presurgical YTI yielded an area under the ROC curve of 0.88. The cut-of-vil value for YTI was 1.45 mm. It can thus be concluded that presurgical transverse index showed a correlation with postsurgical stability, and correcting it in the presurgical phase to a certain level appears to aid in securing postsurgical stability.  
d pacs: date 000
## 4 Design Overview  
### Problem Formulation and Analysis  
Consider \(K\) function requests: \(R=\{R_{1},R_{2},\ldots,R_{k}\}\), where each request \(R_{i}\) of \(N\) different types belongs to a function type \(T_{j\in\{1,\ldots,N\}}\) and arrive at time \(t\). Each function type has unique characteristics, including execution time, cold start penalty, and resource requirements. Each request can be assigned to a warm container or a cold container to be processed. We define the variable \(x_{jk}\in\{0,1\}:1\) if the request \(k\) of type \(T_{j}\) assigned to warm container and variable \(y_{jk}\in\{0,1\}:1\) if the request \(k\) of type \(T_{j}\) assigned to a warm container. The problem of scheduling those requests under SLA latency constraints can be formulated as a Multiple Knapsack Problem (MKP) with additional constraints. The total knapsacks in our problem is the provider container limit \(C_{max}\) and the constraints can be summarized as:  
1. all requests must be assigned.
2. pre-warmed containers cannot exceed predictions.
3. the total containers that have been spawned is less than the provider resources limit in terms of CPU capacity and available memory (represented by total containers allowed). Those constraints are formulated as follows, respectively: \[\left\{\begin{array}{c}\sum_{k=1}^{K_{j}}\left(x_{jk}+y_{jk}\right)=K_{j}\; \forall j\in\{1,\ldots,N\}\\ \sum_{k=1}^{K_{j}}x_{jk}\leq C_{j}^{\text{warm}}\;\forall j\in\{1,\ldots,N\} \\ \sum_{j=1}^{N}\left(C_{j}^{\text{warm}}+\sum_{k=1}^{K_{j}}y_{jk}\right)\leq C _{max}\end{array}\right.\] (2) We define the SLA violation indicator as follows: \[\forall_{j}=\left\{\begin{array}{c}y_{j}\;if\;L_{j}^{cold}>S_{j},\\ 0\;otherwise.\end{array}\right.\] (3) where \(L_{j}^{cold}\) is the latency for cold containers of function type \(T_{j}\) and \(S_{j}\) is the latency threshold for type \(T_{j}\). Based on the assumptions above, the scheduling problem in serverless computing is formulated as maximizing the number of SLA-compliant requests: \[\text{Maximize}\sum_{j=1}^{N}\sum_{k=1}^{K_{j}}\left(x_{jk}+y_{jk}\cdot\;\| \;(L_{j}^{cold}\leq S_{j})\right)\] (4)
## 3 Ionospheric Model Quality
### Ionospheric Models in Positioning Domain
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Ionosphere** & \(\mathbf{\sigma_{3D}}\)**, **m** & \(\mathbf{<\Delta I>}\)**, **TECU** & \(\mathbf{<\Delta I/I>}\)**, \%** &
\begin{tabular}{c} \(\mathbf{<\Delta I\,I>}\)**, \\ **TECU** \\ \end{tabular} & \(\mathbf{<\Delta I\,I}\)**, \%** \\ \hline GIM IGSG & \(1.838\) & - & - & - & - \\ BDGIM & \(1.995\) & \(-0.67\) & \(17.81\) & \(2.89\) & \(44.00\) \\ NeQuick2 & \(2.032\) & \(-1.22\) & \(-9.20\) & \(3.00\) & \(30.00\) \\ GEMTEC & \(2.042\) & \(-0.28\) & \(4.79\) & \(2.36\) & \(26.90\) \\ IRI-2016 & \(2.042\) & \(-1.77\) & \(-17.06\) & \(3.15\) & \(30.48\) \\ NeQuickG & \(2.044\) & \(-1.53\) & \(-8.95\) & \(3.49\) & \(37.31\) \\ Klobuchar & \(2.082\) & \(1.24\) & \(75.98\) & \(4.84\) & \(90.81\) \\ IRI-2012 & \(2.083\) & \(-1.47\) & \(-15.16\) & \(3.15\) & \(30.35\) \\ IRI-Plas & \(2.168\) & \(3.06\) & \(39.27\) & \(4.24\) & \(47.20\) \\ GLONASS & \(2.185\) & \(-1.91\) & \(-9.20\) & \(4.81\) & \(52.32\) \\ No correction & \(2.356\) & - & - & - & - \\ \hline \end{tabular}
\end{table} 
Table 2: Mean 3D error of single-frequency positioning involving different models for ionosphere correction and mean errors of TEC. The data when all the models appeared (mostly 2015–2020). Figure 6: Statistics of positioning errors. The cumulative distribution function of 3D error of single-frequency positioning obtained without (**a**,**b**, grey) and with ionospheric correction by different models: GLONASS (**a**, red), Klobuchar (**a**, blue), NeQuickG (**a**, magenta), BDGIM (**a**, black), GIM IGSG (**a**,**b**, forest green), GEMTEC (**b**, orange), NeQuick2 (**b**, purple), IRI-2012 (**b**, sea green), IRI-2016 (**b**, light green), and IRI-Plas (**b**, green). The horizontal light grey line shows a 0.5 level. All the models feature high errors during magnetic storms. It is not only due to unexpected TEC dynamics but also due to irregularities influence that deteriorates positioning. Up to \(Kp=6\), geomagnetic activities faintly influences single-frequency positioning.
## 2 Performance Support System
### Definitions, Principles, and Theoretical Foundations
#### 2.1.1 Standalone Dedicated Control Channel (SDCCH) Signaling Channel  
This channel is dedicated to subscriber signaling activities. Among these activities, we can mention the basic steps to making a call, updating the physical location of subscribers, and sending and receiving short messages. Each subscriber occupies a capacity of the signaling channel before making a call in the network.  
Figure 2: An overall framework of the conducted works in this paper.