esa-sceva/satcom-chunk-collection huggingface dataset:
# Status Quo of Glycoslation in Cancer: What Is, What Is Not and What Is to Be
## 2 Milestones of Glycoslation in Cancer
### Analytical Milestones on Glycoslation for Cancer
[PERSON] et al., used MALDI- time of flight-MS (MALDI-TOF-MS) to detect N-glycan structures of AFP in HCC patients [32]. The [PERSON] group combined HILIC-UPLC with capillary electrophoresis-laser-induced fluorescence (CE-LIF) for characterizing N-glycan serum samples rapidly within 20 min [33]. The same group used HILIC-UPLC, assigning 140 N-glycans in human serum. They reported that increased sialylation, branching, and outer-arm fucosylation and decreased high-mannosylated and bi-antennary core-fucosylated glycans could help in identifying breast cancer patients from normal patients [34]. [PERSON] et al. designed a UPLC method that analyzed 864 plasma samples per day, facilitating serum-based glyco-biomarker discovery [35]. An alternative N-glycomic profiler combined DNA sequencer method was developed by [PERSON] et al. [36; 37], for characterizing 9-aminopyrene-1,4,6-trisulfonic acid (APTS) labeled N-glycome in bio-fluids [38; 39; 40] and purified proteins [41]. [PERSON] et al., modified this method to study the N-glycan-based biomarkers in HCC, CRC, multiple myeloma, gastric cancer and extrahepatic cholangiocarcinoma [42; 43; 44; 45; 46]. They have constructed diagnostic models for the same. Several analytical techniques based on lectins including lectin blot [47; 48] lectin histochemistry/cytochemisty [49; 50], lectin-antibody sandwich enzyme-linked immunosorbent assay (ELISA) [51; 52; 53], lectin affinity chromatography [54; 55], lectin microarray [56; 57; 58] and lectin microfluidics [59] have been used to capture specific glycans of glycoproteins [56; 60; 61; 62]. The [PERSON] group analyzed the diagnostic and prognostic application of fucosylated fetuin A in HCC patients using aleuria aurantia lectin (AAL)-based ELISA [63]. The [PERSON] group reported abnormal sialylation levels in the serum of ovarian cancer patients using sambucus nigra lectin (SNA)-based ELISA [64] and using reverse AAL-based ELISA, [PERSON] et al., evaluated fucosylated Hp combined CA125 in ovarian cancers, to distinguish early-stage ovarian cancer [53]. This was more accurate than CA 125 standalone. This is a brief overview of the cancer milestones attained availing of the various analytical expertise available. It is evident that the reported analytical techniques have impacted this area of research significantly and have led to noteworthy achievements. Figure 1 gives an overall outlook of the mass spectrometry-based variants that have been used for biomarker analysis.
## 8 Future Directions
### Exploring Liveness Detection and Mitigating Vulnerability to Spoofing Attacks  
Liveness detection and susceptibility to spoofing attacks are significant concerns in palmprint biometrics. Despite the advantages of palmprint recognition, it is not immune to deceptive practices, similar to other biometric modalities. Spoofing attacks, involving the presentation of fake biometric data such as displayed or printed images, pose challenges to verifying the authenticity of presented data, jeopardizing the privacy and security of palmprint biometrics. Despite the issue's importance, limited attention has been given to spoofing attacks on palmprint biometrics, as evident from the sparse existing studies [99; 100; 101; 102].  
Addressing this gap is crucial and necessitates research in robust presentation attack detection algorithms tailored to palmprint-based recognition systems. The lack of suitable anti-spoofing databases compounds this challenge, underscoring the urgency to fortify the security of palmprint recognition systems against adversarial activities.
# Safety Analysis of Secondary Lining of Yulinzi Tunnel Based on Field Monitoring
## 4 Analysis of Test Results
### Safety Evaluation of the Secondary Lining  
Based on the bending moment axial force data obtained above, we can calculate the safety factor of the secondary lining structure of the tunnel according to the safety factor calculation method introduced above. Through the safety factor, we can determine the dangerous area on the secondary lining section. At the same time, a reasonable evaluation of the long-term safety of the secondary lining can be further carried out according to the development of the safety factor, as well as a more accurate evaluation of whether the secondary lining is in a safe state.  
It was found through a calculation that the structure in the vault area was in a state of large eccentric compression. Therefore, the safe state of the section was mainly controlled by the yield limit tensile stress of the steel bars in the tension area. Equation (7) was used to calculate the value of the safety factor. The sections in the arch shoulder area and the arch foot area were in a small eccentric compression state, and the safe state of the section was mainly controlled by the ultimate compressive strength value of the concrete in the compression area. Therefore, Equation (9) is used to calculate the safety of the section in the area coefficient. The safety factor at different time points of the seven measuring points selected above during the monitoring period was calculated, and the change curve concerning time was drawn, as shown in Figure 17.  
As shown in Figure 17, the safety factor of seven representative measuring points in the entire section of the secondary lining structure changed with time. The safety factor value of the curve in the figure decreased rapidly from the maximum value at the beginning of monitoring in a short period. The vault area was in a state of large eccentric compression, with a safety factor limit value of 2.4, and the rest of the sections are in a small eccentric compression state, with a safety factor limit value of 2.0.  
According to the calculated value of the safety factor, it can be seen that the safety factor value of the measuring point D11 at the arch shoulder was the largest, and the safety factor was still 7.68 at the end of the monitoring, which is still far higher than the limit value of the safety factor of 2.0. The safety factor of D1 and D7 were small, and the safety factors at the end were 2.49 and 2.37, which are closer to the critical value of the safety factor. Therefore, it is necessary to focus on the secondary lining safety inspection at the end of the monitoring period. During the monitoring period, the safety factor value of each section is always decreasing, and the values all approach the limit value of the safety factor required by the code. However, within the time frame of the structural monitoring for all cross-sections, the safety degree of the structure is within the range required by the code, indicating that the structure was still in a safe state during the monitoring stage.
## 3 Results and Discussion
### Comparison of Raman and MIR Spectroscopy
The influence of yeast cells and other particles on the Raman signal due to light scattering can be reduced by using an especially opaque media-optimized Raman probe with a short focal length [34; 45]. Furthermore, laser instabilities and the decrease of laser output power over the operational life directly influence measuring sensitivity, since Raman spectroscopy is an absolute method (measured analyte concentration is proportional to intensity signal). Such variations in laser output power can be compensated by the use of internal or external standards [46]. In contrast to Raman spectroscopy, long-term intensity fluctuations of the black-body radiators in MIR setups have no impact on the stability of calibration models, if background measurements (e.g., intensity spectra against ambient air) are performed at regular intervals. Based on the presented results, MIR-ATR spectroscopy has slight advantages over Raman spectroscopy for bioprocess monitoring in laboratories. Furthermore, the price of the MIR measurement setup (US$25,000) used is considerably lower than the price for the Raman system (US$70,000). An important advantage of Raman spectroscopy is the use of quartz fibers. This enables bridging distances of over 100 m between the Raman probe and spectrometer or laser and simplifies the implementation in an industrial process environment.
# An Improved Channel Estimation Technique for IEEE 802.11p Standard in Vehicular Communications
## 2 System Model of the IEEE 802.11p  
### OFDM Frame of IEEE 802.11p  
Figure 1 shows the structure of an OFDM frame of the IEEE 802.11p. The IEEE 802.11p physical layer is nearly the same as IEEE 802.11a. The symbol duration in 802.11p is twice that of 802.11a. The data carried by the closely spaced orthogonal frequency subcarriers helps in improving the spectrum efficiency under harsh conditions of the vehicular data transmission channel. It can support various data rates from 3 to 27 Mbps depending on different modulation and coding schemes, as summarized in Table 1. The Fast Fourier Transform (FFT) size is of 64 points. The packet starts with a preamble that includes Short Training Symbols (STSs) and LTSs, the Signal Field, and the Data Section. The \"Signal Field\" provides information related to the type of modulation, coding rate, etc. The Data Section contains information about the transmitted data.  
Figure 1: Orthogonal frequency division multiplexing (OFDM) frame format of the IEEE 802.11p.  
Coarse synchronization is achieved by the ten STSs which are placed at the beginning of each packet. The two LTSs that follow Guard Interval (GI) of STSs are used for the channel estimation and fine synchronization. The GI is placed at this location to cope with the InterSymbol Interference ISI. The Signal Section has only one OFDM symbol. There is no specific limit on the number of OFDM symbols in the Data Section. The block diagram of the IEEE 802.11p transmitter model is shown in Figure 2.
# Topological Map-Based Autonomous Exploration in Large-Scale Scenes for Unmanned Vehicles
## 3 Methods
### PCATSP-Based Global Path Planning
PCATSP solver provides a path _tour_ composed of input nodes. By removing \(A\) from _tour_, we obtain a global exploration path \(tour_{explore}=\{vp_{1}^{i},vp_{1}^{j},\dots,vp_{1}^{j}\}\) that satisfies global exploration strategy \(O\), as shown in Figure 2b [27]. However, \(tour_{explore}\) cannot guarantee that the robot with the priority will explore areas outside prior information, i.e., Objective 2. To address this objective, frontier clusters that guide the robot towards global  
Figure 2: The basic scheme for autonomous exploration based on a prior topological map. (**a**) Extracting an agent for global target \(s_{j}\) that is in unknown space. (**b**) Obtain the exploration path \(tour_{explore}\) from _tour_ provided by the PCATSP solver. targets will be recognized, and movement cost between these frontier clusters and the robot will be increased. Details are discussed in Section 3.3. Figure 3 is a schematic diagram of the robot exploration process. The blue arrows represent the global exploration strategy, and green arrows are basic exert programs for global exploration paths. Based on our method, the robot explores the given region according to the global exploration strategy but prioritizes exploring scenarios outside prior information. Finally, following the global exploration strategy, the robot actively loops close and completes the exploration. Figure 3: The schematic diagram of the exploration process. (**a,b**) Robot prioritizes exploring scenarios outside prior information. (**c,d**) After exploring scenarios outside prior information, the robot explores the unknown environment according to a global exploration strategy. (**e,f**) Robot actively conducts loop exploration according to the global exploration strategy and finally covers the frontier clusters near the lowest-priority agent.
# Rethinking the Operation Pattern for Anomaly Detection in Industrial Cyber-Physical Systems
## 7 Discussion
### Insights  
In this study, we proposed an improved operation pattern and an unsupervised anomaly detection model. This model was evaluated with a coal mine ICPS and seven attack types and compared with nine state-of-the-art works. The evaluation and comparison results confirm that, by leveraging the improved features, the proposed model could also achieve a good performance and interpretability without requiring complex \"black-box\" algorithms. This work showed the importance of operation pattern features in anomaly detection models. This work suggests that future researchers should consider investigating the nature of ICPSs and using them to build interpretive models, rather than pursuing more complex ML and DL algorithms.
## 1 Introduction  
The possibility of calculating the slope and elevation using level curves has practical applicability in several fields of activity, such as civil engineering [1], architecture [2, 3], railway [4], road and bridge engineering [5, 6, 7, 8], topography [9], etc. Although the results found can be used in various areas, this article is limited to solving two problems related to civil engineering and to railways, roads, and bridges, both ultimately related to the embarnkments optimization. The problem is well known, but it was not closely examined by design engineers, who preferred an intuitive, approximate solution. The current article presents a rigorous and mathematically accurate solution to find the optimum between excavations and fillings, resulting in a reduction of the embarnkments related costs. The article presents in detail the solutions of two concrete problems in two different fields of activity.  
The first problem investigated is in the field of civil engineering and consists of determining the optimal elevation corresponding to the finished floor of the ground level, when the building has to be positioned on a slope [1, 2, 3]. The second problem investigated is in the design of a new railway or road when there are several fixed points on the topographic map through which the new road [10, 11, 12, 13] or the new railway [4, 11] will pass.  
The designer has to know the slope and elevation in each of these points as accurately as possible. Using the methods proposed in this paper, the two parameters (slope and elevation) can be determined fast and with a high level of accuracy using the level curves of the map, without the need for direct measurement on the spot. Furthermore, when designing roads or railways, the risk of failure, the frictional resistance, the safety factor against sliding or failure, the translational fill failure, or the cut volume and the road fill per meter require knowing the slopes (side slope, cut slope, or fill slope).  
The paper is organized as follows: an algorithm to calculate the ground slope at a given point, using level curves, is deduced in Section 2. This problem is reduced to estimating the shortest segment that connects two lines through a point. Two methods are proposed, a fast and approximate one and a more exact one, respectively. The two methods are compared, and it is shown that the errors of the first method are low. In Section 3, the optimum excavation/filling plane is calculated when a new house is built. Two methods are proposed and discussed in Section 4, where they are compared. To finalize the paper, conclusions will be drawn based on the arguments presented.
## 2 Calculation Methods
### Heat Loss Calculation  
In the heat collection system, the receivers include cavity receivers and tube receivers. For heat collection systems equipped with tube receivers, the selected conditions are consistent with the experimental conditions of the Solar Energy Generating System (SEGS), with an operating temperature of 400 \({}^{\circ}\)C [31]. To simplify calculations, for trough solar systems using cavity receivers, when determining the operating temperature, the heat loss rate per unit of receiver surface opening area can be considered a constant [32]. This value changes for cavity receivers with or without a glass cover. According to the new linear cavity receiver proposed by [PERSON] [19], at an operating temperature of around 400 \({}^{\circ}\)C, the heat loss of the cavity receiver is similar to that of the tube receiver. The net energy absorbed by the receiver at any given time of the day can be calculated using the following formula:  
\[q_{net}(t)=I_{in}(t)\eta_{0}-q_{loss}(t) \tag{28}\]  
\(I_{in}\) is the amount of incident solar radiation, \(\theta_{\parallel}\) is a longitudinal angular distribution, \(\eta_{0}\) is the optical efficiency, \(q_{loss}\) is the energy lost for the system, and \(t\) is time measured in days. These data depend on the structure, material and operating temperature of the receiver used in the system. The calculation for vacuum tube receivers with a glass casing is as follows [31]:  
\[q_{loss}=a_{0}+a_{1}T+a_{2}T^{2}+a_{3}T^{3}+DNI(b_{0}+b1T^{2}) \tag{29}\]  
where \(T\) is the operating temperature, and \(DNI\) is the direct incident ray. This method also takes into account the impact of the sun's incident angle on the efficiency of the parabolic trough.
# Ten Years of TerraSAR-X Operations
## 8 Performance Monitoring  
The quality of the delivered data is the most important benchmark of a SAR mission. Strong effort has to be undertaken, in order to ensure a stable quality of the system and resulting data. For this purpose, a verification and monitoring system was implemented in TerraSAR-X and TanDEM-X. With the short term analysis of instrument telemetry data and all data takes executed during the mission, malfunctions in the satellite systems, and especially in the SAR instruments are detected. Short-term analysis is performed in two different ways: instrument monitoring and data take verification. A long-term monitoring aims on detecting trends and evolutions that may lead to a malfunction in future. These analyses are explained in detail and with examples below.  
Figure 14: F10.7 cm solar flux (**top**), TSX in-plane maneuver activity (**middle**), and achieved cross-track deviation from the reference orbit (**bottom**) in period 2008–2017.
## 3 Proposed DNN Based UC in NOMA
### Description of UC Dataset  
To train the proposed model, the dataset which consists of transmit powers, channel gains, and the optimal cluster formation is generated using the B-FS-based UC developed in [15]. Specifically, the B-FS approach exhaustively searches through all the possible combinations of cluster formations obtained from (1) and examines their corresponding throughput performance by applying (6) to find the best cluster formation which could lead to the highest throughput. In \(K\)-user NOMA systems, there will be a total of \(3K\) attributes, which comprises the \(K\) channel gain values, the \(K\) transmit power values, and the \(K\) cluster information values. Despite the fact that the computational complexity of the B-FS is prohibitively high which makes it impractical for real-world implementation, it plays a crucial role in establishing the throughput theoretic upper bound for NOMA systems and enabling the DNN-UC to learn from the best UC.  
Once the best cluster formation is obtained, the clusters need to be systematically numbered to facilitate the training of DNN-UC. To this end, the smallest channel gains in each of the clusters are first determined, the clusters are then sorted in ascending order based on their smallest channel gains and numbered accordingly. Further, to ensure that the proposed DNN-UC is capable of adapting to network dynamics and channel variations without re-training the model, the dataset for the NOMA with a different number of users at random positions and a different number of subcarriers experiencing different fading levels is collected to train the DNN-UC.
## 2 Related Work and Motivations
### Motivation and Contributions  
Since the sinkhole attack causes various issues in VANETs, we propose the secure routing protocol to avoid the sinkhole attack. Different from the related works [19; 20; 21; 26; 27; 28; 29; 30; 31; 32], in this paper, we propose a hybrid-price auction-based secure routing protocol (HPA-SR) to avoid the sinkhole attack without detection. The proposed secure routing protocol employs the Markov decision process (MDP) to select the next nodes as well as sinkhole node avoiding, where the MDP can switch from first price auction to second price auction based on the number of routes to avoid the sinkhole attack adaptively. In addition, we propose the mobility-based clustering protocol, called advanced speed and cosine similarity-based clustering (ASCS), to enhance the route stability and reduce the control overhead, which is modified by our previous work [33]. The proposed ASCS clustering protocol can enhance the route connectivity. The main contributions of this paper can be summarized as follows:  
* We propose a novel hybrid price auction-based secure routing (HPA-SR) protocol to avoid sinkhole attacks. More specifically, the proposed HPA-SR protocol contains the first- and second-price auction. Each node employs the Markov decision process to conditionally select which kind of auction method used to establish the secure route against the sinkhole attack without detection.
* We further propose an advanced clustering protocol, called advanced speed and cosine similarity-based clustering (ASCS) protocol as underlying structure, to improve the route connectivity and reduce the control overhead in VANETs. The proposed clustering protocol consider node speed and direction as cosine similarity and cosine distance to form the clusters. In addition, the ASCS clustering protocol elects gateway nodes to support the communication between CHs when the next CHs is out of the transmission coverages.
* The performance evaluations show that the proposed routing protocol can establish more robust route against the sinkhole attack compared to that of AODV. Besides, the proposed ASCS clustering supports more strong connectivity since the clustering transforms the network topology hierarchically.  
The rest of the paper is arranged as follows. Section 3 introduces the background theorem that consists of the background of auction theory, first-price auction, and second-price auction. Section 4 introduces the proposed routing protocol that consists of the basic concept of the proposed routing protocol, the proposed clustering protocol (ASCS) and the proposed hybrid-price auction-based secure routing protocol (HPA-SR). Section 5 presents the performance evaluation that consists of simulation environments and parameters, performance metrics, and numerical results. Section 6 concludes the paper.
# Locating the Parameters of RBF Networks Using a Hybrid Particle Swarm Optimization Method
## 2 Method Description
### Preliminaries  
In order to perform interval arithmetic on RBF networks, the following definitions are introduced:  
1. The comparison of two intervals \(W=[w_{1},w_{2}]\), \(Z=[z_{1},z_{2}]\) is performed through the function: \[L^{*}(W,Z) = \begin{cases}\text{TRUE},&w_{1}<z_{1},\text{OR }(w_{1}=z_{1}\text{ AND }w_{2}<z_{2})\\ \text{FALSE},&\text{OTHERWISE};\end{cases}\] (6)
2. The function \(E(y)\) (Equation (3)) is modified to an interval one \(\big{[}E_{\text{min}}(y),E_{\text{max}}(y)\big{]}\) calculated with the procedure given in Algorithm 2.  
In the proposed algorithm, the RBF network contains \(n\) variables, where  
\[n=(d+2)\times k \tag{7}\]  
The value of \(n\) is calculated as follows:  
1. Every center \(\overrightarrow{c_{i}}^{\prime},\ i=1,..,k\) has \(d\) variables, which means \(d\times k\) variables.
2. For every center, a separate value \(\sigma_{i}\) is used for the Gaussian processing unit, which means \(k\) variables.
3. The output weight \(\overrightarrow{w}\) also has \(k\) variables.  
The fitness calculation for a given particle \(g\) is as follows:  
1. **Take**\(N_{S}\) random samples in \(g\).
2. **Calculate**\(E_{\text{min}}(g)=\min_{g_{i}\in N_{S}}\bigg{(}\Big{(}\sum_{j=1}^{M}y\big{(}x_{ j},g_{i}\big{)}-t_{j}\Big{)}^{2}\bigg{)}\).
3. **Calculate**\(E_{max}(g)=\max_{g_{i}\in N_{S}}\bigg{(}\Big{(}\sum_{j=1}^{M}y\big{(}x_{j},g_{i} \big{)}-y_{j}\Big{)}^{2}\bigg{)}\).
4. **Return**\(f_{g}=[E_{min}(g),E_{max}(g)]\).  
**Algorithm 2** Fitness calculation for the modified PSO algorithm.