esa-sceva/satcom-chunk-collection huggingface dataset:
# v-Mapper: An Application-Aware Resource Consolidation Scheme for Cloud Data Centers +
## 2 Related Work
### VM Management in Cloud Datacenters  
Applications in cloud infrastructures acquire virtual machines (VMs) from providers when necessary. The current interface for acquiring VMs from most providers, however, can be too limiting for tenants. Data-intensive applications [11; 12; 13] need to communicate with datacenters frequently, and therefore, the traffic loads among these VMs are particularly heavy. This can also increase system overhead and potentially degrade the network performance. Energy efficient placements seek to consolidate VMs for resource use, which can also greatly impact network performance. This can lead to situations in which VM pairs are placed on host machines whose traffic volumes are already large.  
VMs typically tend to follow patterns in their resource demands. A study [14] of a large number of CPU traces from production servers showed that most of the demand traces had a high correlation with a periodic behavior. Statistical multiplexing can also be used to exploit possible correlations among VMs. A continuous monitoring approach to study VM resource usage patterns on all running VMs has been presented in [15; 16].  
In recent years, VM placement concepts have been exploited to address important data center infrastructure related challenges. However, supporting tenant-controlled VM placement makes it difficult, if not impossible, to perform simple resource management tasks. We believe that if application-awareness concepts were to be employed, it would help provide a simple and clean interface for datacenter administrators.
## 2 Manoeuvre Prediction Model for Moving Targets at Sea
### Manoeuvring Prediction Model of Moving Target in Two-Dimensional Space Coordinate System  
In reality, the environment of the moving target at sea is the Earth's surface; therefore, it is necessary to extend the prediction model to the three-dimensional coordinate system. As shown in Figure 1, set the initial position at \(t_{n}\) as \(A(x_{n},y_{n},z_{n})\) and the position at \(t_{n+1}\) as \(B(x_{n+1},y_{n+1},z_{n+1})\). The spherical angle \(\angle CAD\) is the mean course angle \(\beta\); arc length \(\AA\) is the mean distance of the moving target \(\AA C=V(t_{n+1}-t_{n})\). The target deviates from the mean distance \(\Delta l\) and deviation from the mean position angle \(\Delta\theta\) are determined by geometric relations:  
\[\Delta l=\left|\AA\right|-\left|\AA C\right|=R_{\text{C}}\text{arccos}\left( \frac{x_{n+1}x_{n}+y_{n+1}y_{n}+z_{n+1}z_{n}}{R_{\text{C}}^{2}}\right)-\text{ V}(t_{n+1}-t_{n}) \tag{7}\]  
\[\Delta\theta=\angle CAD-\angle\text{BAD} \tag{8}\]  
Therefore, in the three-dimensional space coordinate system, the probability that the moving target moves from position \(A\) at \(t_{n}\) to position \(B\) at \(t_{n+1}\) is established as follows:  
\[P_{r}(A,B,t_{n+1}-t_{n})=\frac{1}{2\pi\sigma_{r}\sigma_{\beta}}\exp\left(- \frac{\Delta l^{2}}{2\sigma_{r}^{2}}-\frac{\Delta\theta^{2}}{2\sigma_{\beta} ^{2}}\right) \tag{9}\]  
The probability of target distribution transferred to a certain region can be solved using integration.
# Deep-Learning-Based Stream-Sensing Method for Detecting Asynchronous Multiple Signals
## 6 Evaluation  
This section evaluates the ASensNet, which is compared to the existing mathematical schemes in [21]. The compared schemes are the Akaike information criterion (AIC) and minimum description length (MDL). Note that both schemes are meant for a scenario in which a received signal is constructed by synchronously transmitted signals. Since there is no work on asynchronous stream sensing, those two schemes are sufficient as comparison schemes. In the synchronous scenario, the precision and recall of AIC and MDL are greater than 0.9.  
We have evaluated our ASensNet, AIC, and MDL for two scenarios. The first scenario is that transmitted signals of all mobile devices have almost the same SNR value at the receiver of a rescue UAV. Through evaluation, we investigate the performance of those schemes for each SNR value. The second scenario is that the transmitted signals of all mobile devices have different SNR values. This is more like a real-world scenario, from which we can assess how well the ASensNet will work in reality.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Mini batch size & 10 \\ Initial learning rate & \(1\times 10^{-3}\) \\ Scheduler & CosineArnealingLR \\ Optimizer & RMSprop \\ Trainable parameters & 260 M \\ Model Parameter size & 1032 M \\ \hline \hline \end{tabular}
\end{table}
Table 1: Model complexity and hyperparameters.  
For the two comparison schemes, the maximum detectable number of signals depends on \(N_{\text{R}}\). Specifically, both can detect up to \((N_{\text{R}}-1)\) transmitted signals. Therefore, ASensNet, AIC, and MDL are compared for 0 to \((N_{\text{R}}-1)\) transmitted signals. (In theory, for the synchronous scenario, a receiver equipped with \(N_{\text{R}}\) antennas can detect up to \((N_{\text{R}}-1)\) transmitted signals from a received signal. Since this is due to a theoretical limitation of mathematical frameworks MDL and AIC, the maximum number of devices is set to \((N_{\text{R}}-1)\) in this work.)
# Authentication and Billing for Dynamic Wireless EV Charging in an Internet of Electric Vehicles
## 5 Proposed Authentication and Billing Scheme
### Man in the Middle (MITM) Attack  
This happens when an attacker secretly relays and possibly alters the communication between two parties who believe that they are directly communicating with one another. In addition to using the hash function to ensure message integrity, asymmetric key cryptography is utilized in this work to provide mutual authentication between the EV, the CSC and the PO, and help in protecting against MITM attacks. That is, by encrypting the messages using the public key of the receiver, only the intended receiver can decrypt the message using his private key. Assuming the private key of the receiver is not compromised, this encryption scheme provides protection against MITM attacks.
## 2 Dual-Port MIMO Antenna
### Diversity Parameters
#### 2.5.3 Total Active Reflection Coefficient (TARC)  
It evaluates the complete power reflected when all ports are excited simultaneously with identical amplitude and phase. For the developed 2-port MIMO configuration, TARC is determined through Equation (4) [25; 26]. Figure 12 illustrates the TARC outcomes obtained from both simulation and measurement. The TARC remains under \(-10\) dB  
Figure 10: Comparison of simulated and measured ECC.  
within UWB spectrum for both cases, indicating superior diversity performance of the developed antenna.  
\[\textit{TARC}=\sqrt{\frac{\textbf{1}}{\textbf{N}}{\sum_{i=1}^{N}{1\sum_{j=1}^{N}{S_ {ij}}}^{2}}} \tag{4}\]
# Ground Based Hyperspectral Imaging to Characterize Canopy-Level Photosynthetic Activities
## 2 Materials and Methods
### Data Acquisition
#### 2.2.2 Fluorometry Measurement  
Active chlorophyll fluorescence measurements were conducted simultaneously with hyperspectral imaging collection. A human operator followed the GPhenoVision system and used a portable pulse-amplitude-modulation (PAM) fluorometer (OS5p+, Opti-Sciences, Inc., Hudson, NH, USA) to measure the uppermost fully expanded leaf at approximately the fourth mainstem node below  
Figure 1: Illustration of data collection system: (**a**) picture of the “GPhenoVision” system and (**b**) diagram of the system configuration and sensor installation.  
the plant terminal. Three leaves per plot were measured. At each diurnal sampling time, the leaf blade was clipped so that the orientation of the exposed adaxial surface relative to incoming solar radiation was left unchanged, and steady state fluorescence (\(F_{s}^{{}^{\prime}}\)) was measured under ambient light conditions using a 660 nm modulation measuring beam under naturally occurring solar irradiance as our actinic light source. While measuring fluorescence, PAR at the leaf surface was estimated using a PAR sensor integrated into the leaf clip. Subsequently, maximal fluorescence intensity (\(F_{m}^{{}^{\prime}}\)) was estimated using a multi-phase flash (provided by a 35 W halogen bulb) approach comparable to the methods described in [45], wherein relative fluorescence intensity is plotted versus the reciprocal of PAR following exposure of the leaf sample to a sequence of flashes with increasing intensity (2850, 5700, and 8550 \(\upmu\) mol/m\({}^{2}\)/s) for a total duration of 0.95 s. A linear function was fit to the resulting data set to estimate \(F_{m}^{{}^{\prime}}\) at infinite light intensity. This represents fluorescence intensity when all reaction centers are closed.
# A Feasibility Study of the Drive-By Method for Damage Detection in Railway Bridges
## 5 Conclusions  
The paper analyzed the feasibility of a drive-by damage detection method for railway bridges, based on acceleration signals measured on-board the train. The analysis is carried out by numerical simulations of the train-track-bridge interaction, comparing different scenarios with several bridge span lengths, damage levels, damage positions, train speeds, and typologies of bogie arrangement. The identification of a defect in the bridge is carried out by comparison between the signal measured on the train crossing the damaged bridge, and a reference signal corresponding the healthy structure.  
Two main criteria are considered to analyze the feasibility of the proposed technique: the magnitude of the acceleration to be measured to discern between a healthy and a damage structure, (i.e., the so-called damage component), and the accuracy of the damage localization. The method proved to be in principle applicable to the velocity range that is typical of a commuter train, with a rather good accuracy in localizing the defect. However, the damage component to be measured is in the order of \(10^{-2}\) m/s\({}^{2}\) or even \(10^{-3}\) m/s\({}^{2}\), which is an issue to be considered for implementation in a real railway environment, where track irregularities can decrease the signal-to-noise ratio.  
The acceleration measured on the leading bogie is the most appropriate for damage detection, and the algorithm analyzing the quasi-static response of the bridge shows in general a better performance, allowing a more accurate localization of the damage and a lower sensitivity of the results to an evolution of track irregularity. The highest values of damage components are obtained for the shortest bridges. The damage component increases with train speeds, in the range up to 140 km/h, and with the damage level, whereas the best localization accuracy is obtained for lower train speeds and lower damage levels. This might result in a conflict between the two prerequisites.  
Further investigations are needed before that the method can be tested in practice. The major one is related to the possibility of installing high-sensitive accelerometers on the train bogie, with the adoption of mechanical suspensions (i.e., silicon or rubber layers) to filter the frequencies out of the range of interest, and to consequently reduce the full-scale range that is required for the sensors.  
The software used was developed internally at Department of Mechanics of Politecnico di Milano. It is named ADTRES. A.C. supervised all the works and drove the implementation of all the numerical models. [PERSON] developed the algorithms for damage identification, and performed the analysis related to track irregularities. [PERSON] performed the simulations without track irregularities and the data analysis, as part of his Master Thesis at Polimi. The paper writing was mainly carried out by [PERSON] and [PERSON]. This research received no external funding.
## 3 Study Area  
The Incheon Free Economic Zone (IFEZ), located in Incheon, Korea, is a Korean Free Economic Zone officially designated by the Korean government in August 2003 that consists of three regions: Songdo, Cheongna, and the island of Yeongjong, with a total area of 209.38 km\({}^{2}\). The IFEZ is planned to become a self-contained living and business district by transforming these three areas into hubs for logistics, international business, leisure, and tourism in the Northeast Asian region. Thus, the IFEZ will become one of the future top three economic zones in the world.  
For this research, the selected study area is Cheongna International City located on the mainland adjacent to Yeongjong Island. Cheongna is a development project built on 7.27 km\({}^{2}\) with a population of 90,000 people, constituting the Incheon Free Economic Zone along with Songdo and Yeongjong. It is being developed as an international business and leisure center, and is as a coastal city with major transportation axes connecting Yeongjong and Seoul, including Incheon International Airport Expressway, Gyeonggin Expressway, and Gyeonggin Ara Waterway.  
Cheongna territory was built by reclamation of tidal flats carried out from 1979 to 1989 and was used as farmland in the 1990s and 2000s. In fact, the original reason for reclaiming this area was to use it as agricultural land [21]. Figure 1 is shows the location of study area.  
According to the master plan, the extension is approximately 17.8 km\({}^{2}\). The planned population was 90,000, but already surpassed 100,000 in April 2019, and is expected to increase to 120,000-130,000 in the future if only apartment houses, officetels (common Korean multi-purpose buildings with residential and commercial units), townhouses, and detached houses that are currently on sale are counted. According to the June 2021 resident registration figures, the population is approximately 112,000 [21].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Formula** & **Resistance Coefficient (A)** & **Flow Exponent (B)** \\ \hline [PERSON]–[PERSON] & 4.727\(C^{-1.852}d^{-4.871}L\) & 1.852 \\ [PERSON]–[PERSON] & 0.0252\(f(e,d,q)d^{-5}L\) & 2 \\ [PERSON]–[PERSON] & 4.66\(n^{2}d^{-5.33}L\) & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Resistance coefficient and flow exponent values for each head loss formula in EPANET.
## 3 Recent Literature Analysis
### Cyber Security  
#### 3.1.1 Cyber crime  
[PERSON] et al. [17] provided a novel perspective on cybercrimes within the context of the COVID-19 pandemic, plotting timelines of historical cyber-attacks. These attacks tended to fall into two categories: cyber-dependent crime, such as malware and denial-of-service attacks, or cyber-enabled crime, including financial fraud and phishing. The research highlights how unique scenarios, triggers, and circumstances can contribute to the techniques employed by cybercriminals [17]. This subsequently raises questions regarding how other historical events or world contexts may influence cybercrime trends and tactics.  
Cybercrime was also considered through historical use cases, including the COVID-19 outbreak. One analysis focused on the correlation between cybercrime-related activities and the progression of COVID-19 lockdown policies [18]. The research highlights the importance of understanding complex cyber-social relationships and the effects of security measures, while also acknowledging that human factors, such as the willingness to report crimes, may influence these statistics [18].
## 3 Results  
### ASA and Anti-Fas Ab Influenced the Diameter of HCT116 and HT29-Derived Colonospheres  
Cancer cells of two human CRC lines were treated with the combination of anti-Fas agonistic antibody (200 ng/mL) and 2.2 mM and 1.8 mM ASA for HCT116 and HT29 cell lines, respectively. After 10 days of treatment colonospheres sizes, phenotype and apoptosis were measured.  
In order to establish the proper working concentrations of ASA in our cell lines, we determined the IC50 of ASA using a cytotoxicity assay after 24 h-incubation and ASA concentrations based on the previously published results [24; 25; 26]. Our analysis shown an IC50 2.2 mM and 1.8 mM of ASA for HCT116 and HT29, respectively. The concentration of anti-Fas antibody (200 ng/mL) was evaluated in our previous study [20].  
Following the combined stimulation with anti-Fas Ab and ASA spheres were statistically significantly smaller compared to the size of spheres after incubation with ASA only and control, untreated colonospheres (Figures 1 and 2). Similarly, colonospheres after stimulation with anti-Fas Ab were relevantly bigger than those after combined treatment, and these differences were statistically significant. This observation confirmed our previous results showing that Fas signaling may play a pro-survival role for cancer cells [20].  
Figure 1: Sizes of colonospheres. Colonospheres were formed from HCT116 or HT29 cells following 10-day incubation with agonistic anti-Fas antibody (200 ng/mL) and/or aspirin (ASA) (2.2 mM or 1.8 mM for HCT116 or HT29, respectively). Statistically significant differences were assessed by Kruskal-Wallis test followed by Dunn’s test as a post hoc procedure. Bars and whiskers represent median \(\pm\) interquartile range (\({}^{*}p<0.05\) vs control unstimulated cells, \(\bullet\)\(p<0.05\) ASA vs ASA/ anti-Fas antibody, \(n=50\)–\(60\)).  
Figure 2: Morphology of colonospheres. Representative colonospheres were derived from HCT116 and HT29 cells following 10-day incubation with agonistic anti-Fas antibody (200 ng/mL) and/or aspirin (ASA) (2.2 mM or 1.8 mM for HCT116 or HT29, respectively). Scale bar, 100 μm.
# Modeling Soil Temperature with Fuzzy Logic and Supervised Learning Methods
## 2 Materials and Methods
### Model Performance Criteria  
In this study, the root mean square error (RMSE), the mean absolute error (MAE), and the coefficient of determination (R\({}^{2}\)) were employed, as they have been widely used in the previous studies in different areas of agricultural science to assess model performance [55; 56; 57].  
\[R^{2}=1-\frac{\sum\limits_{i=1}^{n}\left(z_{mea}-z_{pre}\right)^{2}}{\sum \limits_{i=1}^{n}\left(z_{mea}-z_{avg}\right)^{2}} \tag{2}\]  
\[RMSE=\sqrt{\frac{\sum\limits_{i=1}^{n}\left(z_{mea}-z_{pre}\right)^{2}}{n}} \tag{3}\]  
\[MAE=\frac{\sum\limits_{i=1}^{n}\left|z_{mea}-z_{pre}\right|}{n} \tag{4}\]  
where \(z_{mea}\) denotes the actual value, \(z_{pre}\) symbolizes the predicted value, \(z_{avg}\) is the average actual value, and n is the total data number.
# Application of Digital Twin in Handling and Transportation of Hazardous Chemicals
###### Abstract  
In China, the Ministry of Transport stressed the need to \"Strengthen the application of active safety technology\". The transportation of hazardous chemicals represented by LNG, LFG, and liquefied hydrocarbons is the weak link in traffic safety. The aim of this study is to apply digital twin (hereinafter referred to as DT) technology to the whole process of handling (including loading and unloading) and transportation of hazardous chemicals to help improve the anti-risk ability of road networks at all levels. The method is intended to design a monitoring system covering operation visualization, information fusion, cargo tracking, and hazard source monitoring that is based on DT technology and multi-source data acquisition technology. First, DT technology in the areas of hazardous chemicals handling and transportation is discussed. Then, the DT system is designed, including the system construction, functions, and the means of achieving these functions. Finally, taking the procedure in LNG road transportation as an example, we illustrate the application of DT in its four stages. This system is used to present the evolutionary path of accidents that occur in different links and assist in testing the rationality of the comprehensive disposal plan.  
digital twin; hazardous chemicals; handling; transportation; active safety +
Footnote †: journal: Applied sciences
## 1 Introduction  
Q-switched solid-state lasers manifest the capability of generating nanosecond-level pulses and high peak power light sources. Since the concept of Q-switching was proposed by [PERSON] in 1958 and the first Q-switched device of ruby laser was demonstrated by [PERSON] and [PERSON] in 1961 [1, 2], there has been a wide range of applications to the fields of lidar, rangefinder, medicine, material processing, holography, and nonlinear optics [3, 4, 5, 6, 7]. The technique is achieved by tuning the intracavity losses of the resonator and can be further classified into active Q-switched (AQS) lasers and passive Q-switched (PQS) lasers. The operation of AQS lasers requires active control devices such as spinning mirrors, and acousto-optic attenuators to modulate the cavity losses. For PQS lasers, the saturable absorbers like Cr\({}^{4+}\):YAG for 1-\(\upmu\)m lasers and V\({}^{3+}\):YAG for 1.3-\(\upmu\)m lasers can be used to modulate the cavity losses; the crystals of these material are much lighter in comparison with the active control devices. Thus, PQS lasers possess the advantages of compactness, reliability, and light weight in the cavity design. Owing to the concern of reliability and cost reduction, PQS had been evaluated to be more applicable for the space mission in GLAS and MLA of NASA [8, 9].  
Since the coupled rate equations of passive Q-switching were proposed [10], several models and approaches of optimization have been developed. Many works have shown the dependence of performance on several factors in the cavity, such as the selection of gain medium, initial transmission of the absorber, reflectivity of output coupler, and intracavity mode area [11, 12, 13, 14, 15]. The transverse intensities of the pump and laser might affect the performance as well. In the classic PQS model, the transverse mode of lasing was assumed to be uniform. However, the mode of a resonator is more like a Hermite-Gaussiandistribution in real cases instead; thus, the influences of the spatial effect must be considered into the coupled rate equations.  
In this article, we start from considering the lasing photon density to be a constant TEM\({}_{00}\) Gaussian distribution along the optical direction and the pump density of a laser diode to be a top-hat distribution which diffracts along the optical direction, respectively, then incorporate the spatial dependence into the coupled rate equations for four-level PQS lasers. A transcendental equation of energy utilization is derived and approximated by an analytical function for calculating the output energy in the condition of no saturation in the gain crystal. To validate the developed spatially-dependent model, an end-pumped Nd:YVO\({}_{4}\)/Cr\({}^{4+}\):YAG laser in a concave-plano cavity with output coupling reflectivity ranging from 10% to 92% was demonstrated, and two different pumping spot sizes of laser diode were adopted.
# Hybrid Satellite-Terrestrial Networks toward 6G: Key Technologies and Open Issues
## 6 Open Issues
### Resource Management  
The basic communication architecture of the terrestrial network and the satellite network differs, resulting in resource configurations that differ between the two networks. While spectrum and power resources are shared by both networks, orbit and gateway resources are unique to the satellite network due to its top-down nature. To meet the QoS requirements of various users and traffic, all distinct resources in HSTN must be managed collaboratively. If we implement resource allocation for each network separately or simply apply traditional resource management strategies to the integrated network, network performance will suffer greatly due to inefficient resource utilization. Given the differences in resource characteristics between satellite and terrestrial networks, novel resource management mechanisms must be developed.