esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 GPP Maritime Standardization and Use Cases
Legislation is advancing in different countries to enable autonomous and remote-controlled operations in national waters. For example, remote pilotage from shore or some other place outside a ship is allowed in Finnish waters since February 2019. National tests and experiments provide valuable information also for international legislation. There is a coastal area called Jaakonmeri test area in Finland that is open to all companies, research institutes and others wishing to test autonomous maritime traffic, vessels, or technologies related to it. Currently, 3 GPP is considering and developing a system specifically for maritime communications for Release 16 and beyond to support the needs of future maritime users [37; 40]. One of the requirements of this \"cellular-Maritime\" system is to support up to 100 km coverage. It will also support the interworking between the 3 GPP system and the existing/future maritime radio communication system for the seamless service of voice communication and data communication between users adhere and at sea or between vessels at sea. Use cases have been grouped into four main use case categories presented in Figure 1.  
_Mobile broadband services for users at sea_ include video streaming services for people onboard as well as positioning inside the vessel. Essentially this details that the future 3 GPP communication systems should also support the needs of people, not only to support the automated operations and logistics. _Machine-type communication services inside a vessel, between vessels and between UE at sea_ including, e.g., communications between wearable Internet of Things (IoT) services and maritime rescue systems for saving lives; also push to location services to report locations of smaller vessels to neighboring vessels to improve maritime safety are included in this category. _Maritime communication services between authorities and users at sea_ such as search and rescue communications, alarms and warning services, vessel traffic services (VTS) required in national waters, and tugs and pilotage services. Vessels will provide the navigational information and dynamic information of their speed and movements via the standardized system. _Interworking and harmonization with VHF and satellite access_ include a VHF data exchange system (VDES) to digitalize VHF communications. In addition, integrated satellite-terrestrial 5G systems [41] aim to provide services to maritime and other users globally in the future. Both direct access to satellite using the 5G interface as well as indirect access are included in the development [42].
# Safety Evaluation of Crossing Tunnel Engineering: A Case Study
## 3 Analysis of Highway Tunnel Safety
### Dynamic Response Analysis
#### 3.4.2 Vibration Speed Analysis  
Figure 11 shows the cloud speed of the highway tunnel at 10 ms, 16 ms and 20 ms. The peak vibration velocity occurs at the right tunnel vault of the highway tunnel; the right tunnel is 0.555 cm/s at 10 ms and 0.934 cm/s at 20 ms, and the second lining of the highway tunnel reaches 1.527 cm/s.  
Figure 12 shows the vibration time-history curve of a selected point located in the second lining of the right tunnel of the highway tunnel at the intersection of the railway and highway tunnels, and Figure 13 shows the vibration time-history curves of the arch apex mass point in different directions. From Figures 12 and 13, it can be seen that during the railway tunnel blasting construction process, the vibration velocity at the mass point of the highway tunnel arch apex is the highest, and it rapidly increases from 0 to 1.527 cm/s within 0-16 ms, then rapidly decreases, and has a small rebound at 24 ms, after which the  
Figure 10: The blasting load function.  
vibration velocity slowly decreases and reaching 0.02 cm/s. The peak vibration velocity at the mass point on the left and right walls of the highway tunnel is 0.606 cm/s, and the trend of vibration velocity with time is consistent with the arch apex vibration velocity curve. The vibration at the arch apex mainly vibrates along the Z direction, and the vibration velocity in the X and Y directions is very small.  
According to the \"Technical Regulations for Monitoring and Measurement of Highway Tunnels\" (DB42/T 900-2013) and the \"Blasting Safety Regulations\" (GB 6722-2014), the permissible safe vibration velocity for the existing highway tunnel, subject to the disturbance caused by the new railway tunnel construction, should be less than 10 cm/s. Considering the operation of the highway tunnel for a duration of 12 years and the resulting degradation of the lining, the allowable safe vibration velocity is reduced to less than 2 cm/s. Throughout the dynamic analysis process, it is evident that the vibration velocity of the existing highway tunnel remains below the allowable safe vibration velocity of 2 cm/s.  
Figure 11: Cloud diagram of two-lining vibration velocity of highway tunnel at different times (**a**) 10 ms, (**b**) 16 ms, and (**c**) 20 ms.  
Figure 12: Vibration time-history curve of road tunnel control point at intersection.  
Consequently, these findings indicate that the impact of railway tunnel blasting excavation on the existing highway tunnel is within manageable limits.
## 2 Materials and Methods
### Antimicrobial Activity  
#### 2.5.1 Microorganisms and Media  
The bacteria and fungi employed in this study were selected mainly on the basis of their significance as opportunistic pathogens of humans with diarrheal diseases. The bacterial strains include _Shigella flexneri_ KZN, _Proteus vulgaris, Klebsiella pneumonia_ ATCC 4352, _Staphylococcus aureus, Enterococcus faecalis_ ATCC 29212, _Streptococcus pyogenes_, _Pseudomonas aeruginosa_ ATCC 19582 and _Serratia merccescens_ ATCC 9986. The fungi strains of the American Type Culture Collection (ATCC) include _Triphophytonrubrum_ ATCC 28188, _Trichophytton tsunarus_ ATCC 28942, _Trichophytton mucoides_ ATCC 201382, _Microsporum canis_ ATCC 36299, _Microsporum gypseum_ ATCC 24102, _Aspergillus niger_ ATCC 16888, _Aspergillus fumigatus_ ATCC 204305, _Penicillium chrysogenum_ ATCC 10106 and _Penicillium aurantiogriesum_ ATCC 16025. The bacteria test organisms were obtained from the Department of Biochemistry and Microbiology, University of Fort Hare, South Africa and fungi test organisms were purchased from Davies Diagnostics (Pty) Ltd., Gauteng, South Africa, respectively.  
The Mueller-Hinton dextrose agar (MHA), Mueller-Hinton dextrose broth (MHB), potato dextrose agar (PDA) and Sabouraud dextrose broth (SDB) were prepared according to the instruction from the manufacturer. The nutrient agar was suspended in demineralized water, heated and stirred until it completely dissolved and then sterilised by autoclaving at 121 \({}^{\circ}\)C for 15 min. The fungi and bacteria were maintained at 4 \({}^{\circ}\)C on MHA and PDA. Scraped cell mass in 0.85% salt solution was diluted and adjusted to 0.5 McFarland standards and authenticated by spectrophotometric reading at 580 nm assays [12] to prepare the inoculums for the assays.  
Finally, the cell suspensions were diluted 1:100 in nutrient broth to present an estimated inoculum of 10\({}^{4}\) CFU\(\cdot\)mL\({}^{-1}\) in comparison with McFarland standard for use in the assays [12].
## 2 Materials and Methods
### Effects of Dosage on Harvesting Efficiency  
Two chemical flocculants were also assessed for their harvesting efficiencies in comparison to _M. oleifera_ seed powder (MP), aluminium sulphate (AS--Al\({}_{2}\)(SO\({}_{4}\))\({}_{3}\) 18H\({}_{2}\)O, Chem-Lab, Belgium), and iron (III) chloride (IC--FeCl\({}_{3}\) 6H\({}_{2}\)O, Merck, Rahway, NJ, USA). The three flocculants (MP, AS, and IC) were tested in nine different doses (g L\({}^{-1}\)), i.e., 0.01,0.1, 0.2, 0.3, 0.4, 0.5, 1, 2.5, and 5. The flocculant in a powder form was added to 100 mL flasks containing 100 mL of algae sample with a standard initial optical density (OD\({}_{680}\)) in the range of 1.2-1.4, corresponding to an algal dry matter of approximately 0.5 g L\({}^{-1}\). The samples were mixed in a shaker (GFL orbital shaker type 3015, Hannover, FRG) for 5 min at 150 rpm for the coagulation step, followed by a flocculation step at 70 rpm for 20 min, and then left to rest (sedimentation step). The used flocculation protocol was developed from preliminary experiments using AS. Samples were taken every 20 min during the resting phase from the middle of the liquid column in the flasks, and their OD\({}_{680}\) was measured in a spectrophotometer (Jenway 6400, Chelmsford, UK). The formula applied for the calculation of the harvesting efficiency is shown in Equation (1):  
\[\text{Harvesting efficiency}\ (\%)\ =\ \frac{\text{Abs}_{0}\ -\ \text{Abs}_{ \text{t}}}{\text{Abs}_{0}}\ \times\ 100 \tag{1}\]  
Abs\({}_{0}\) in the OD\({}_{680}\) of the algal culture was measured before flocculant addition, and Abs\({}_{\text{t}}\) is the OD\({}_{680}\) in the middle of the liquid column on time t of the harvesting process.
# VOC Removal from Manure Gaseous Emissions with UV Photolysis and UV-TiO\({}_{2}\) Photocatalysis
## 3. Materials and Methods
### Sampling and Analyses of Odorous VOCs and Odor  
The effectiveness of treatment was defined as the percent conversion and assessed as follows:  
\[R=(\text{Control-Treatment})(\text{Control}\times 100 \tag{1}\]  
where \(R\) is the percent conversion (or percent removal);  
Control, Treatment = measured gas or odor concentrations for the untreated and treated gas, respectively.  
Positive values of \(R\) signify removal (or conversion) while negative \(R\) signifies compound generation.  
An analytical method was developed for quantification of VOCs using SPME and gas chromatography-mass spectrometry (GC-MS) [55]. Carboxen/polydimethylsiloxane (CAR/PDMS) 85 \(\upmu\)m SPME fibers (Supelco), were used, and 5-min gas sampling time was selected based on the optimization of SPME conditions for target odorous VOCs. Untreated (control) and treated air was sampled and analyzed in triplicate to estimate the % conversion of target VOCs.  
VOCs from real manure atmosphere was sampled for 30 min on a 50/30 \(\upmu\)m divinylbenzene (DVB)/CAR/PDMS SPME (Supelco) and analyzed on a GC-MS system (GCD G1800B, Hewlett Packard) equipped with a 30 m \(\times\) 0.25 mm ID capillary column (Rt-bDEXsm; Restek) [9]. Specific VOCs were identified using two criteria: (1) matching of observed retention times with those of pure compounds run as standards; (2) matching mass spectra of unknown compounds using ChemStation (version D.00.0.38) from the Agilent and National Institute for Standards and Technology 98 MS spectral library. If no standard existed, identifications were arbitrarily made for matching quality \(\geq\) 70% [9; 18]. Identified compounds were semi-quantified based on peak area counts obtained by the integration of MS response with ChemStation. Untreated (before entering the reactor) and treated air (exiting the reactor) were sampled and analyzed in duplicates to estimate the % conversion of VOCs. Kitagawa colorimetric gas detection tubes and sampling pump system were used to measure concentrations of NH\({}_{3}\) (tubes range 5-260 ppm) and ozone (tube range 0.025-3 ppm) (Komyo Rikagaku Kogyo K.K., Kanagawa, Japan). Monitoring O\({}_{3}\) could provide additional insight into the contribution of three processes that potentially were involved: UV photocatalysis, UV photolysis, and UV-induced ozonation.  
Odor reduction was evaluated by forced-choice dynamic olfactometry. Air samples were collected with 10 L (Iowa, USA) or 60 L (ARO, Israel) Tedlar bags and analyzed within 24 h on AC'SCENT medical (AC'SCENT International, St. Croix Sensory Inc., Stillwater, MN, USA) following ASABE method using four trained panelists (Iowa, USA) or on Odile 2510 (Odotech Inc., Canada) [56], using six trained panelists (ARO, Israel). Detection thresholds were calculated following the ASTM [57], Iowa State, or following the European standard EN13725 [58].
## 2 Types of Flexible Wearable Antennas
### Microwave Imaging Antennas
Microwave imaging antennas have several medical diagnostic applications, as seen in Table 3. The compact, cheap patch antenna (3-10 GHz, C/X band) aids breast cancer diagnostics. The slot antenna (1-6 GHz, S/C band) detects brain strokes with high gain and directivity [30]. The array antenna (10-30 GHz, K/Ku band) delivers high-resolution 3D imaging while the metamaterial antenna (30-100 GHz) is compact and flexible for wearable cancer diagnosis [31; 32; 33; 34; 35; 36]. These antennas balance frequency, sensitivity, and integration to improve healthcare diagnostics. The breast tissue imaging Vivaldi antenna (1-10 GHz,  
Figure 2: Diagram of imaging system block. S/C/X band) has high sensitivity and a wide bandwidth. Deep penetration makes the horn antenna (0.3-3 GHz, UHF/L/S band) suitable for lung disease diagnosis [33].
# State-of-the-Art Review of Structural Vibration Control: Overview and Research Gaps
## 6 Optimized Control System Designed to Tackle Practical Implementation Challenges  
Upon reviewing the literature, it becomes evident that each control algorithm exhibits strengths and weaknesses. Researchers have consistently focused on enhancing control system performance by taking various factors into account [28, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 22, 23, 23, 24, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 82, 84, 86, 89, 87, 88, 89, 91, 80, 83, 85, 89, 92, 86, 87, 89, 93, 88, 88, 89, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 108, 109, 110, 111, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183]. The overall effectiveness of the control system can be improved by considering a range of factors, which are elaborated in this section.  
To successfully apply control systems to structures, a balance must be found between reliability and robustness. Reliability, as a crucial concept, depends on the control system's sustained efficiency over time, encompassing all aspects of frequent system use, regardless of whether it is used for controlling minor, major, near-field, or far-field earthquake ground motions. However, increasing the reliability of a system does not automatically ensure optimal performance under varying earthquake conditions.  
In contrast, system robustness is crucial for preventing failure, malfunction, or counterproductive effects in structural response during exposure to a wide range of ground motions, including extreme earthquakes. While enhancing reliability ensures consistent system operation, enhancing robustness guarantees effective performance under more substantial seismic events.  
The subsequent section discusses research-driven strategies to elevate seismic control performance. These strategies are tailored to enhance the robustness of semi-active control systems. Notable approaches include minimizing the impact of uncertainty, time delay, and the optimal placement of sensors and actuators. This paper aims to shed light on how advancements in robustness can strengthen the effectiveness of active and semi-active control systems under seismic conditions.
## 5 Security Analysis of a V2I Authentication Scheme
### RSU Captured Attacks  
Suppose the adversary captures a RSU and obtains the stored information \(\{ID_{TA}\), \((RID_{i},\alpha_{i})\}\). For the message \(m_{3}=\big{\{}RID_{i}^{*},VID_{i}^{*},Q_{3},Q_{4},Q_{5},Q_{6},C_{3},C_{4}\big{\}}\), \(N_{7}\oplus CK_{VT}=Q_{4}\oplus ID_{r}\oplus N_{4}\), where \(N_{7}\) and \(N_{4}\) are the random numbers selected by TA and RSU, respectively, \(ID_{r}\) is RSU's identity. RSU calculates \(N_{2}\oplus N_{7}\oplus ID_{v}=Q_{3}\oplus CK_{RT}\), which can be translated to \(N_{2}\oplus ID_{v}\oplus CK_{VT}=Q_{3}\oplus CK_{RT}\oplus Q_{4}\oplus ID_{r} \oplus N_{4}\), in 5.1, \(CK_{VT}=Q_{1}\oplus(N_{2}\|ID_{v})\); therefore, \(N_{2}\oplus ID_{v}\oplus CK_{VT}=N_{2}\oplus ID_{v}\oplus Q_{1}\oplus(N_{2}\|ID_ {v})\), \(N_{2}\oplus ID_{v}\oplus(N_{2}\|ID_{v})=Q_{3}\oplus CK_{RT}\oplus Q_{4}\oplus ID _{r}\oplus N_{4}\oplus Q_{1}\), where \(Q_{3}\), \(CK_{RT}\), \(Q_{4}\), \(ID_{r}\), \(N_{4}\), and \(Q_{1}\) are RSU's known parameters, and the length of identity \(ID_{v}\) is shorter than the random number \(N_{2}\), in the output of \(N_{2}\oplus ID_{v}\oplus(N_{2}\|ID_{v})\), \(ID_{v}\) is easy to be recovered. So, their scheme cannot achieve user anonymity.  
On the other hand, the adversary can also compute the session keys of all users authenticated with the captured RSU.  
**Remark 2**.: _The reason why their scheme suffers from this attack is because the secret values in RSU are not well protected due to, for instance, not using a physically unclonable function._
## 3 Methodology
### Statistical Metrics  
To evaluate the accuracy of the three machine learning upscaling methods, the coefficient of determination (R\({}^{2}\)), root mean square error (RMSE), and bias were selected as the evaluation metrics [72]. R\({}^{2}\), RMSE and bias can be written as:  
\[R^{2}=\frac{\left(\sum\limits_{i=1}^{N}\left(x_{i}-\overline{x}\right)\left(y _{i}-\overline{y}\right)\right)^{2}}{\sum\limits_{i=1}^{N}\left(x_{i}- \overline{x}\right)^{2}\sum\limits_{i=1}^{N}\left(y_{i}-\overline{y}\right)^ {2}} \tag{5}\]  
\[RMSE=\sqrt{\frac{1}{N}\sum\limits_{i=1}^{N}\left(x_{i}-y_{i}\right)^{2}} \tag{6}\]  
\[bias=\frac{\sum\limits_{i=1}^{N}\left(x_{i}-y_{i}\right)}{N} \tag{7}\]  
where \(N\) refers to the number of samples and \(x_{i}\) and \(y_{i}\) refer to the \(ith\) reference and estimated AGB, respectively. \(\overline{x}\) and \(\overline{y}\) refer to the average value of the reference and estimated AGB, respectively.  
The sequential Mann-Kendall test was introduced by [PERSON] [73] to discover abrupt potential turning points in long-term data. We used it to detect the turning point of long-term AGB variation. This test includes the calculation of two data series: one is progressive, and the other is retrogressive. When the two data series cross each other and deviate beyond the confidence limitation of 95%, then, that point will be a statistically significant turning point. It is calculated by comparing the values of \(x_{j}\) annual mean time series (\(j=1,\,\ldots,\,n\)) with \(x_{i}\), (\(i=1,\,\ldots,\,j-1\)) [74]. At the time of comparison, the cases where \(x_{j}>x_{i}\) is counted and denoted by \(n_{j}\).  
Then, test statistic t is calculated using the equation as follows:  
\[t_{j}=\sum\limits_{1}^{j}n_{j} \tag{8}\]  
The mean and variance of the test statistic are  
\[E(t)=\frac{n(n-1)}{4} \tag{9}\]  
And  
\[Var(t_{j})=[j(j-1)(2j+5)]/72 \tag{10}\]The sequential values of the statistic \(u(t)\) are  
\[u(t)=\frac{t_{j}-E(t)}{\sqrt{Var(t_{j})}} \tag{11}\]  
The values of \(u^{\prime}(t)\) retrogressive series are calculated similarly but starting from end of the series.  
Linear trends were applied to quantify the interannual and pixel trends of the AGB and other variables. The equation can be written as [75]:  
\[y_{t}=y_{0}+bx_{t} \tag{12}\]  
where \(y_{t}\) is the changed value; \(y_{0}\) is the initial value; \(x_{t}\) is the time interval; and \(b\) is the trend. Statistically significant differences were set as \(p<0.05\) unless otherwise stated.
# The Effect of Clamping Force on the Wear Behavior of a Steam Generator Tube
###### Abstract  
Anti-vibration bars (AVBs) are essential components of a steam generator (SG) and are used to prevent steam generator tubes (SGTs) from vibrating intensely because of flow-induced vibration. However, the contact force generated at contact surfaces between AVBs and tubes can change the natural frequency and wear behavior of the tube. Contact force is represented by clamping force in this study. Considering the effect of the clamping force on the natural frequency and sliding distance of SGT, dynamic wear behavior under different clamping forces was analyzed based on the finite element method, and the natural frequency of the tube was measured in the present work. Moreover, the wear experiment was conducted at room temperature to verify the conclusions of dynamic behavior analysis. The increase in clamping force reduces the sliding distance of SGT, and wear depth affected by both clamping force and sliding distance also decreases.  
steam generator tube; anti-vibration bar; clamping force; wear behavior; natural frequency
# Gray Image Denoising Based on Array Stochastic Resonance and Improved Whale Optimization Algorithm
## 4 Array Stochastic Resonance Strategy Based on Improved Whale Optimization Algorithm
### Image Denoising Method by Array Stochastic Resonance
#### 4.1.3 Array Saturation Stochastic Resonance Process  
The core of image processing is the array stochastic resonance structure, and each independent sub array can be regarded as an image channel. Its input is the noisy signal \(r(t)=s(t)+\eta(t)\) obtained from the original gray image through a series of changes such as dimension reduction, coding and modulation. The output of each bistable array subsystem is \(x_{n}(t),n=1,2,\cdots,L\), where \(n\) is the number of array elements. The overall output response of an array stochastic resonance system can be regarded as the arithmetic mean sum of each subarray, namely  
\[x(t)=\sum_{n=1}^{L}x_{n}(t)/L \tag{16}\]
## 3 Results  
### TRPV1 Antagonist Hit Prediction and Compound Selection  
From the in silico screening of natural bioactive compounds from the COCONUT database using the MT-DTI model (Section 2.1), we identified 10 candidates (Table 3). Regarding a potential safety issue, antibiotic class compounds, such as karamomycin C, fredericamycin (fdm) A, fdm E, or an experimental cytotoxic anticancer agent amamistatin A, were excluded in order to proceed with wet experiments. Among these, troxerutin had a 0.333 pharmacophore-Tanimoto similarity score (maximum of 1.0) to the reference TRPV1 antagonist JTS-653 [24] and was selected based on the screening criteria (Section 2.1). To the best of our knowledge, troxerutin, a derivative of the bioflavonoid rutin, has not been previously reported as a TRPV1 antagonist.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**COCONUT ID**} & \multirow{2}{*}{**Chemical Name**} & \multirow{2}{*}{**SMILES**} & **TRPV1** & **TRPV1** & \multirow{2}{*}{**Group**} & **Similarity** \\  & & & **Predicted \(K_{D}\)** & & **Predicted** & \\  & & & **(nM)** & **IC\({}_{50}\) (nM)** & & **Score** \\ \hline \multirow{4}{*}{CNP0145155} & \multirow{4}{*}{Karamomycin C} & CO1 cc(C2=NC & & & \\  & & (C3 SCC4C5 SCC(C) & & & \\  & & (C(=O)N43)N5C52)c(O) & & & & \\  & & & & & \\  & & & & & & \\  & & & & & & \\ \hline \multirow{4}{*}{CNP0303130} & \multirow{4}{*}{Amaristatin A} & CCCCCCCCC(OC=(O)C & & & \\  & & (CCCCN(O)C=O)NC & & & \\  & & (=O)cInc(<2 cc & & \\  & & (OC)ccc2O)oc1 CJC & & & \\  & & (C)(C)C(=O)NC1 CCCCCN & & & & \\  & & (O)C1=O & & & \\ \hline \multirow{4}{*}{CNP0115741} & \multirow{4}{*}{Fdm E} & CC=