esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This paper presents the use of Google's simultaneous localization and mapping (SLAM) technique, namely Cartographer, and adaptive multistage distance scheduler (AMDS) to improve the processing speed. This approach optimizes the processing speed of SLAM which is known to have performance degradation as the map grows due to a larger scan matcher. In this proposed work, the adaptive method was successfully tested in an actual vehicle to map roads in real time. The AMDS performs a local pose correction by controlling the LiDAR sensor scan range and scan matcher search window with the help of scheduling algorithms. The scheduling algorithms manage the SLAM that swaps between short and long distances during map data collection. As a result, the algorithms efficiently improved performance speed similar to short distance LiDAR scans while maintaining the accuracy of the full distance of LiDAR. By swapping the scan distance of the sensor, and adaptively limiting the search size of the scan matcher to handle difference scan sizes, the pose's generation performance time is improved by approximately 16% as compared with a fixed scan distance, while maintaining similar accuracy.  
Cartographer; simultaneous localization and mapping (SLAM); LiDAR; scheduler +
Footnote †: journal: Applied Sciences
## 3 Evaluation with Expected Total Costs
### Probability Analysis
#### 3.1.1 Decision to Implement Countermeasures  
The decision to implement CMs is based on whether the reliability index after piling exceeds that of the target.  
\[P_{c}=\text{Prob}(\beta_{ob}<\beta_{T}) \tag{7}\]  
where subscript \(ob\) stands for the value after the observation of the piling data.  
All uncertainties (i.e., mean, variance, and probability distribution) are assumed to be known based on detailed database building and analysis, including the estimated variability after obtaining the piling data. As \(\beta_{T}\) is consistent with the value when designing based on geotechnical investigations alone, \(P_{c}\) is calculated as follows:  
\[\begin{split} P_{c}&=\text{Prob}\left(\frac{\ln(m_{ obj})}{\sqrt{\ln\left[\left(1+\text{COV}_{\text{R}\text{P}}^{\alpha}\right)\left(1+ \text{COV}_{\text{Q}}^{2}\right)\right]}}<\frac{\ln(m_{T})}{\sqrt{\ln\left[ \left(1+\text{COV}_{\text{R}\text{P}}^{2}\right)\left(1+\text{COV}_{\text{Q}}^ {2}\right)\right]}}\right)\\ P_{c}&=\text{Prob}(m_{obj}<m_{T}{}^{a})\\ a&=\frac{\sqrt{\ln\left[\left(1+\text{COV}_{\text{R} \text{P}}^{2}\right)\left(1+\text{COV}_{\text{Q}}^{2}\right)\right]}}{\sqrt{\ln \left[\left(1+\text{COV}_{\text{R}\text{P}}^{2}\right)\left(1+\text{COV}_{ \text{Q}}^{2}\right)\right]}}\end{split} \tag{8}\]  
where we call this characteristic value, \(m_{T}{}^{a}\), the trigger, and subscripts \(p\) and \(c\) in the \(\text{COV}_{\text{R}}\) stand for the proposed and conventional methods, respectively.  
A smaller \(\text{COV}_{\text{R}\text{P}}\) and larger \(\text{COV}_{\text{R}\text{C}}\) lead to higher triggers and fewer countermeasures. The observed values, \(m_{obj}\), follow a lognormal distribution with the median \(m_{i}\) and coefficient of variation \(\text{COV}_{\text{R}\text{C}}\), respectively.  
\[P_{c}=F\Big{(}x=\ln(m_{T}{}^{a});\text{mean}=\ln(m_{i}),\text{var}=s_{i}{}^{2} \Big{)} \tag{9}\]  
where \(F(0)\) is the normal CDF and subscript \(i\) stands for the initial.  
Figure 4 shows the probability of the CM based on Equation (9) when \(\text{COV}_{\text{R}\text{C}}\) = 0.35, \(\text{COV}_{\text{Q}}\) = 0.10, and \(\beta_{T}\) = 3.0, with reference to the findings [32; 44; 49], respectively. \(\text{COV}_{\text{R}\text{P}}\) = 0.35 requires 60% of the countermeasures even at SF = 3. This is because safety measures were taken due to the misinformation from highly varied estimates. In the paper below, the optimized SF ranges between 2 and 3, wherein it can be observed that the \(P_{c}\) decreases rapidly from a \(\text{COV}_{\text{R}\text{P}}\) of 0.25 to 0.15.
# A Deep Learning Approach for MIMO-NOMA Downlink Signal Detection
## 3 Deep-Learning Scheme Based on the MIMO-NOMA System
### Mimo-Noma-Dl System  
In this subsection, we propose a novel DL detector for MIMO-NOMA signal detection. Without any extra signal processing, the signal from the receiving antennas can be sent directly to the MIMO-NOMA-DL detector. Therefore, MIMO-NOMA-DL is an easier and more efficient scheme to replace the SIC receiver.  
Overall, the MIMO-NOMA-DL system is composed of three components: training block, testing block, and DNN detecting block. The construction of the MIMO-NOMA-DL model is illustrated in Figure 7.  
The training block is responsible for producing the MIMO-NOMA signal and providing the labels to the DNN. In this block, to acquire the MIMO-NOMA signal for \(N_{t}\) antennas, we should produce two training sequences of UE1 and UE2 for each antenna. They are then modulated by superposition coding with different allocation power factors. After impairment by the fading channel and AWGN channel,the receive signal is acquired at the receiver. Meanwhile, these sequences are known by the receiver as labels, and it is similar to the pilot sequence.  
The testing block is used to simulate real-time MIMO-NOMA transmission. In this block, we first produce the MIMO-NOMA signal. Labels are not required in this part. The testing data are used to assess the performance of the DNN detection. Notably, in order to avoid a perfect match, the channel models and the generated data in training blocks and testing block are i.i.d. to ensure that the DNN performs well in both the training and testing process. The SNR in training block is generated randomly with the data time slot varying over the range of interest, whereas the SNR in the testing block is fixed so that the error performance of the DNN can be evaluated in certain SNR conditions.  
The DNN block is the main detection block for decoding the received signal. The channel characteristics and MIMO-NOMA decoding algorithm can be studied by optimizing the hyperparameters of the deep neural network. In this block, multiple parameters, including the number of layers, activation function, loss function, and optimization criteria iteration algorithm, must be designed; the details are discussed in the next subsection.  
The first two blocks provide labels and signals polluted by channel, and the last block recovers the original data. Accordingly, the detection process can be divided into two steps:  
* _Step 1: Training mode_ In training mode, the offline training block is active while the online deployment block remains inactive. The input of the DNN training system includes two components: the received MIMO-NOMA signal as the input layer of the DNN system, and the labels as supervised data to help the DNN to optimize the parameters.
* _Step 2: Testing mode_ The testing mode is activated after the DNN has been trained. In Step 2, the offline block is suspended, and the online block accesses the DNN system. The system performance is evaluated in this step, and the results of the simulation are presented in Section 4.
###### Abstract  
The head, efficiency, and cavitation characteristics of centrifugal pumps are highly dependent on the velocity field in front of the impeller inlet. In multistage pumps, the velocity field in front of the second and each subsequent stage is determined by the shape (design) of the diffuser return guide vanes. This current work presents the results obtained by performing a numerical study using ANSYS CFX 14.0 to determine the impact of the shape (design) of diffuser return guide vanes on the head and coefficient of efficiency of one stage of a multistage centrifugal pump. Three RGVs with different Outlet angles are studied: \(\alpha_{6}\)--original RGV with \(\alpha_{6}=90\) deg, RGV1 with \(\alpha_{6}=110\) deg and RGV2 with \(\alpha_{6}=128\) deg. The results obtained after performing CFD modeling indicate that with one of the studied RGVs, the pump stage head increases by nearly 20%, while the hydraulic coefficient of efficiency remains almost constant. Applying entropy production theory is used to determine the impact of the various components of entropy production on the total head loss in the studied pump stage. The impact of the Outlet angle of the RGV on the velocity field of the flow in front of the next impeller (stage) as well as the RGV head is also analyzed. The numerical results of the original RGV are compared with the experimental data obtained from large-scale studies of pumps performed at the Laboratory of Hydraulic Machines of the University \"Angel Kanchev\" of Ruse, Bulgaria. When using the modified RGVs, the head curve of the original pump can be obtained by operating at a lower speed or with a smaller impeller diameter. This may lead to an overall increase in the energy efficiency of the machine, which could be explored as a future task.  
centrifugal pump; return guide vanes (RGVs); computational fluid dynamic (CFD)
## 3 Results and Discussions
### Antibacterial Analysis
Comparing the results presented in Table 1 and Figure 8 for the silanized and non-silanized samples, different behaviors could be noticed, depending on the tested bacterial strain. Thus, against four bacterial strains, the non-silanized samples showed a greater or a similar antibacterial effect with the silanized ones. Instead, the silanized GO samples functionalized with Ag and ZnO presented higher antibacterial effects against _Porphyromonas gingivalis_ and _Enterococcus faecalis_, respectively, as compared to the non-silanized samples. The differences in the antimicrobial activity between the non-silanized and silanized powders could be due to the physicochemical interaction between the graphene-derived materials and the microorganisms with which they come into contact [63; 64]. The limitation of this study is related to the fact that the antibacterial effect was tested against bacterial strains from the endowment of the biology laboratory. It is recommended to perform tests on other classes of bacteria or fungi, which are mostly identified on the walls of the heritage buildings. It would be also interesting to conduct a study on the evolution of the antibacterial effect over the time, in various environmental conditions, using different investigation methods.
## 1 Introduction
### Lignosulfonate-Based Admixtures  
The worldwide production of lignosulfonates is about 1 million tons/year [37]. Lignosulfonate is obtained from the black liquor generated during the paper manufacturing process. It is composed of 20-30% lignin and contains free sulfurous acid and sulfates, decomposition products of cellulose and lignin, different carbohydrates, and a complex mixture of sulfonation products of lignin [38]. In the construction industry, lignosulfonates are widely commercialized as 1 st generation admixtures. Among the different types of lignosulfonate-based admixtures available are those composed of sodium, potassium, calcium, and magnesium salts [39]. Depending on the associated cation (Ca\({}^{+2}\), Na\({}^{+2}\), Mg\({}^{+2}\), etc.), pH values may vary among these admixtures (pH between 4.0 and 9.0), although their performance is not affected [40].  
The molecular structure of lignosulfonates has a significant influence on their plasticizing properties. When incorporated into cement-based composites, their polar chains with anionic functional groups, such as sulfonic acids (SO\({}_{3}\)H), are adsorbed onto the cement particles. As a result, the polar end of the lignosulfonate is directed towards the water, which reduces its surface tension and makes the cement particle hydrophilic [41]. Although electrostatic repulsive force is the primary mechanism of lignosulfonate-based admixtures, their performance may also be associated with steric hindrance in some cases [35]. On the other hand, 3 rd generation chemical admixtures, such as polycarboxylate-based superplasticizers, have a high steric repulsion effect that enhances their plasticizing abilities [42]. Figure 1 illustrates the mechanism of action of lignosulfonate-based and other surfactant admixtures.  
Besides being classified as plasticizers or water reducers, lignosulfonate-based admixtures can act as air-entraining and set-retarding agents in cement-based composites [39]. In most cases, their influence on different properties of cement matrices can be seen as a negative point for their application [11; 39; 40]. The retarding effect of lignosulfonates is related to the presence of sugars (such as glucose and sucrose) in their chemical composition, which may interfere with cement hydration [43]. Other disadvantages of lignosulfonate-based admixtures are related to commercial aspects: (1) the Kraft pulping process is more applied than the Sulfite process, limiting the global production of lignosulfonate [44]; (2) Lignosulfonates have high variability in composition and purity [11; 33]; (3) Lignosulfonates have a limited amount of sulfonate groups that interfere with the anionic charge density of their polymer chains [12].  
Figure 1: (**a**) Adsorption of the polar chains of a surfactant admixture on the surface of the cement particle; (**b**) Floc formation by cement particles before the incorporation of a surfactant admixture, and dispersion of flocs after the incorporation (adapted from [PERSON] and [PERSON] [41]).
#### Novelty and Contributions  
The following are the significant contributions of this work:  
1. The efficacious and simple UWB monopole design achieves a reflection coefficient curve of less than \(-17\) dB throughout the entire working frequency range.
2. The controllable dual notch frequencies are achieved at the WLAN and complete X-band (uplink and downlink) satellite frequency bands.
3. To construct the two-port dual-notch UWB antenna, the intended dual-notched UWB antenna is reproduced with a minimum 3.8 mm separation parallelly.
4. The proposed MIMO antenna is evaluated for the characteristic mode analysis (CMA), MIMO diversity features, and time-domain characterization.  
The remainder of the study is structured as follows: The detailed antenna design and its evolution are presented in Section 2. Section 3 outlines the decoupling mechanism designed to achieve the optimal isolation of the projected antenna. Section 4 provides the findings of the CMA. The performance characteristics of the projected design are illustrated in Section 5. Section 6 provides concluding remarks.
# Accurate Determination of Geographical Origin of Tea Based on Terahertz Spectroscopy
## 2 Experiment  
### Experimental Setup  
A THz-TDS detection system (Advanced Photonix, Inc., T-Ray 5000, Roanoke, VA, USA) configured in transmission mode was adopted for the purposes of our experiment. The system includes a Ti-sapphire laser that produces a central wavelength of 1064 nm. A femtosecond laser pulse with repetition frequency of 100 MHz provides THz at an average power of 130 nW. A schematic diagram of the experimental setup is shown in Figure 1. The chamber containing the sample and the entire THz paths was filled with pure nitrogen to avoid the strong absorption of THz by water. The humidity and temperature of the chamber were maintained below 4% and at 21 \({}^{\circ}\)C, respectively.
# Evaluation of BLE Star Network for Wireless Wearable Prosthesis/Orthosis Controller +
## 5 Discussion
Of course, increasing the transmission strength will increase the power consumption, so developers must take caution when making these design decisions. Finally, to reduce the system latency, one could investigate the reduction of, or elimination of, some of the data processing or functionality on the microcontroller. This approach may also provide power savings. Future works may also consider the use of different development environments. As many wireless biosensors are designed to pair with existing devices such as smartphones or PCs, future works could measure the performance of the Nordic Semiconductor nRF52840 when paired with central nodes from different manufacturers or from different ecosystems (iOS or Android). Future works may also consider conducting similar comparisons using alternative MCUs, since each has its own unique design trade-offs that might influence the interplay between connectivity, power consumption, data throughput, latency, etc.
## 2 Methodology  
The methodology employed in this study combines the principles of multi-criteria decision-making (MCDM) with artificial intelligence (AI) techniques to address the challenges of cyberattacks in smart grids and facilitate the selection of effective cybersecurity options.  
First, a comprehensive analysis of the cybersecurity landscape in smart grids is conducted to identify the key challenges and potential threats. This analysis includes an examination of the different types of cyberattacks, their potential impacts on smart-grid infrastructure, and the specific vulnerabilities that need to be addressed (Section 3.1).  
Next, a set of relevant criteria is identified to evaluate the cybersecurity options available for smart grids. These criteria encompass factors such as security effectiveness, scalability, integration, and compatibility; performance impact; cost-effectiveness; manageability and usability; compliance with regulatory requirements; resilience and redundancy; vendor support and regulation; and future readiness. The analytical hierarchy process (AHP) (Section 2.2) is then used as the core methodology for the MCDM process. AHP allows for the systematic comparison and prioritization of the cybersecurity options based on their performance against each criterion (Section 3.2).  
To incorporate AI techniques, MCDM-AHP is applied to choose between different AI options. These options include machine learning, deep learning, swarm intelligence, Bayesian networks, etc. (Section 3.3).  
Overall, the methodology employed in this study combines MCDM principles, and AI techniques to create a robust framework for evaluating and selecting cybersecurity options in smart grids, thereby mitigating the risks associated with cyberattacks and safeguarding the integrity and security of smart grid systems.
# Detection of Thermal Strain in Steel Rails with BOTDA
###### Abstract  
Rail transportation is one of the most important and efficient forms of transportation. Large thermal strain can develop in the rail steel due to extreme climatic conditions resulting in safety related issues. We carried out a thermal-strain monitoring test on the rail specimen over a large temperature range from \(-40\)\({}^{\circ}\)C to \(+50\)\({}^{\circ}\)C using a Brillouin optical time-domain analyzer (BOTDA) for the first time, to the best of our knowledge. Two jacketed fibers and small-diameter carbon/polyimide-coating single-mode fiber were used for the purpose of investigating the jacket effect of thermal-strain detection on the rail. Although a nonlinear response to the temperature of the loose jacketed fiber was found, it was applicable for thermal strain monitoring when glued on the surface of the rail sample. The measured thermal strain in the rail specimen was validated by the results obtained by the strain gauge. The thermally induced strain from the large rail specimen was found to have suppressed the nonlinear impact of the fiber jacket.  
Keywords:Brillouin optical time domain analyzer; thermal strain; fiber jacket +
Footnote †: journal: Applied Sciences
## 4 Design of TLQoS
### Temperature Module  
This module focuses on route selection for regular packet type. Since the \(Rg\) traffic has no QoS constraints, this module chooses a forwarder with lower temperature among the neighbors. The route selection procedure used in this module is depicted in Algorithm 3.  
**INPUT** (a packet \(p\in Rg\), NT)  
```
1.if\(p.HC>HC^{max}\){Condition-2} then
2.\(\gamma\)=\(p.\gamma\)
3.\(\gamma=\gamma-0.1\)
4.\(p.\gamma\)=\(\gamma\)
5.else
6.\(\gamma\) =\(p.\gamma\)
7.endif
8.forall\(n_{j}\) in NT do
9.if\(T_{n_{j}}^{avg}<T^{thr}\){Hotspot Avoidance} then
10.if\(n_{j}\
otin RS\){Condition-1} then
11.\(c_{n_{i},n_{j}}=dst_{n_{i},n_{j}}\)
12. Calculate \(P_{n_{i}}^{t}\) and \(P_{n_{j}}^{t}\) according to Equations (9) and (10)
13. Calculate \(P_{n_{j}}^{Ht}\) according to Equation (14)
14. Calculate \(F_{h}(n_{i},n_{j})\) according to Equation (3)
15. Calculate \(F_{t}(n_{i},n_{j})\) according to Equation (11)
16. Calculate \(F_{Ht}(n_{i},n_{j})\) according to Equation (20)
17.endif
18.endif
19.endfor
20. Select \(n_{j}\) with MAX-\(F_{Ht}(n_{i},n_{j})\), MIN-\(P_{n_{j}}^{Ht}\), Random in turn
21. Call Queuing Manager with \(p\in Rg\) as parameter
```  
**Algorithm 3** Route selection algorithm at temperature module  
Similar to the other module, temperature module also executes the condition-1, condition-2 and hotspot avoidance mechanism following the same procedure. The difference lies in measuring the parameter values that considers temperature related potential and forces as depicted in line (12-16) of the algorithm. The potential forwarder is chosen having the maximum hybrid temperature force. If more than one neighbor has the same maximum value of hybrid potential force, minimum hybrid temperature potential is used to select the potential forwarder. If the potential forwarded is still not determined, it will be chosen randomly. At the end, queuing manager will be called with \(Rg\) packet as a parameter.
# MALDI-Based Mass Spectrometry in Clinical Testing: Focus on Bacterial Identification
## 6 Direct Identification from Other Matrices (Cerebrospinal Fluid, Urine)
### Direct Identification from Urine Samples  
In the diagnosis of urinary tract infections, direct identification is possible if the quantity of bacteria exceeds a certain level (10\({}^{4}\)-10\({}^{5}\) or more) [75]. However, in the future, handling multiple bacteria will be an issue.
## 4 Results and Discussion  
### Performance of the Baseline DNNs and Transductive Analysis  
The individual performance of the baseline DNNs (\(DNN_{\text{Train:80K}_{\text{11}}}\) and \(DNN_{\text{Train:80K}_{\text{2}}}^{\text{Test:20K}_{\text{2}}}\) for both \(K_{t}\)s is shown in Figure 12a,b [27]. Both networks show a balanced performance for healthy and cracked classes with over 95% sensitivity and specificity. This performance shows a marginal improvement over other DNN architectures [21] and a significant improvement (\(\sim\)10%) as compared to the symbolic analysis-based approach on similar data [31; 37], which also emphasizes the excellence of DNNs for such problems. The accuracies for each \(K_{t}\) serve as a benchmark to compare against all subsequent modifications to the DNNs. The performance of the transductive analysis (by switching testing data between \(K_{t}\)s) is shown in Figure 12c,d [27]. Incredibly, the analysis shows a significant loss in performance with an overall accuracy of 60.9% and 56.99%, respectively, for \(K_{\text{11}}\) and \(K_{\text{2}}\). Owing to different stress concentrations, the specimens are expected to exhibit different fatigue lives. With this insight, intuitively, one would, therefore, assume that the difference between the signals from both specimens would be predominantly observed after crack initiation. However, the poor performance in Figure 12c,d is balanced across both the classes with low magnitudes of sensitivity and specificity, thereby indicating a variation in the data across both classes. These results from the transductive analysis further motivate the need to develop better learning methods to enable unified damage detection.