esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Early Injection Strategy in Premixed Combustion Engines
###### Abstract  
Due to the increasing awareness of environmental protection, limitations on exhaust emissions of diesel engines have become increasingly stringent. This challenges diesel engine manufacturers to find a new balance between engine performance and emissions. Advanced combustion modes for diesel engines, such as homogeneous charge compression ignition (HCCI) and premixed charge compression ignition (PCCI), which can simultaneously reduce exhaust emissions and substantially improve thermal efficiency, have drawn increasing attention. In order to allow enough time to prepare the homogeneous mixture, the early injection strategy has been utilized widely in HCCI and PCCI diesel engines. This paper is aimed at providing a comprehensive review of the effects of early injection parameters on the performance and emissions of HCCI and PCCI engines fueled by both diesel and alternative fuels. Various early injection parameters, including injection pressure, injection timing, and injection angle, are discussed. In addition, the effect of the blending ratio of alternative fuels is also summarized. Every change in parameters has its own advantages and disadvantages, which are explained in detail in order to help researchers choose the best early injection parameters for HCCI and PCCI engines.  
early injection; injection pressure; injection timing; injection angle; alternative fuel; blending ratio +
Footnote †: journal: Applied Sciences  
_Applied_  
_Keywords_: early injection; injection pressure; injection timing; injection angle; alternative fuel; blending ratio
## 3 Experimental Validation
### Prolate Spheroidal Modeling  
A few experimental results, which prove the efficacy of the NR NF/FFT with spherical spiral scanning for offset-mounted elongated AUTs modeled by a prolate spheroid, are shown in this subsection.  
The prolate spheroid considered to model the AUT has major and minor semi-axes equal to 6 \(\lambda\) and 1 \(\lambda\), respectively. The probe voltages have been acquired on a sphere of radius \(d=20\)\(\lambda\), and the reconstructions shown here have been attained by choosing \(\chi^{\prime}=1.30\), \(\chi=1.20\), and \(p=q=7\). It is noteworthy that, as previously stated, to avoid the local growth of the band-limitation error, the factor \(\chi^{\prime}\) has been properly augmented in the region of the spiral identified by the 20 samples close to each of the poles, thus acquiring some extra samples.  
To prove the validity and precision of the 2-D OSI expansion (13), the amplitudes and the phases of the reconstructed voltage \(V_{r}\) on the meridians at \(\varphi=0^{\circ}\) and \(\varphi=90^{\circ}\) are compared with those of the directly acquired one on the same meridians in Figures 4 and 5, respectively. As can be observed, the attained recoveries are very good everywhere and degrade only in the zones characterized by very low voltage levels (about \(-50\) dB and below), wherein the remaining reflections from the anechoic chamber walls and the noise tend to contribute to the reconstruction error. The FF cuts at \(\varphi=0^{\circ}\) and \(\vartheta=90^{\circ}\), obtained from the NR NF samples, are compared in Figure 6 with those (references) attained from the data directly acquired on the NF spherical grid prescribed by the standard NF/FFT [15]. Lastly, for completeness, the comparison relevant to the FF cut at \(\varphi=90^{\circ}\) is reported in Figure 7. As can be observed, also the FF reconstructions are accurate, demonstrating the efficacy of the devised NF/FFT technique with spherical spiral scanning for offset-mounted long antennas.  
It is worthwhile stressing that the number of utilized NR samples is 918 (comprehensive of the 160 extra samples nearby the poles), way lower than those 1860 and 7320 required by the MI software, which implements the classical NF/FFT [15] in the onset and offset mounting cases, respectively.  
Figure 4: Voltage amplitude (**a**) and phase (**b**) on the meridian at \(\varphi=0^{\circ}\). measured. ++++ obtained from the NR data.
## 5 Discussion  
### Influence of Landing Location on System Dynamics  
The primary objective of the simulation experiment was to investigate the dynamic response of the FPTLIR landing points on the PTL. The simulation results were carefully analyzed to determine the initial peak values and rise times of the system's dynamic parameters at different positions in Figure 19a. The peak Z-displacements and roll angle of the different points were all determined to be in the safe workplace.  
The relative peak Z-displacement varied between \(-0.82\) m and \(-0.28\) m, indicating a significant influence of the landing point on this parameter. The relative peak Z-displacement showed a progressive reduction from point 1, which was far from the tower, to point 11, which was close to the tower. The peak Z-displacement at landing point 11 was found to be 34.14% of the value observed at point 1. Regarding the FPTLIR's attitudes, the peaks showed variability in response to different landing points. Due to the smaller fluctuation amplitudes of the pitch and yaw angles compared to the roll angle, the current analysis focuses on the variation characteristics of the roll angle. Overall, the roll angle peaks increased as the FPTLIR approached the tower, ranging between 0.1\({}^{\circ}\) and 4.98\({}^{\circ}\), with the highest peak observed at 4.98\({}^{\circ}\) when landing at point 9. The roll angle at landing point 9 was found to be 3.7% of the value observed at point 1.  
The peak time for Z-displacement varied between 0.61 s and 2.02 s, while the roll angle's peak time ranged from 2.43 s to 4.49 s (Figure 19b). The roll angle's peak time consistently exceeded that of the Z-displacement, as gravity, being the sole external force, caused the FPTLIR to first reach its peak along the gravitational direction before deflecting. This trend arose from the strong coupling effect between the Z-displacement and the roll angle within the system. Despite varying landing points, the peak times remained relatively stable when the same mass was considered.  
Figure 19: Comparison of the peak values and rise times of FPTLIR Z-displacement and roll angle at different landing points on the PTL: (**a**) peak value; (**b**) rise time.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Z-Displacement** & **Pitch** & **Roll** & **Yaw** & **Contact Force** \\ \hline
0.004 & 2.14 & 0.044 & 21.1 & 0.079 \\ \hline \end{tabular}
\end{table}
Table 3: Comparison of MRE.
# Vulnerability Assessment of Ubiquitous Cities Using the Analytic Hierarchy Process
## 2 The Components of U-Cities and Hazards
### Services  
Some standard services offered by U-cities are supported by more than one area mentioned above. Some examples are listed as follows to outline the general idea of interconnective services across areas:  
U-life: Services that assist daily household tasks by utilizing applicants through remote control such as lighting, curtains, ovens, washing machines, dishwashers, and so forth. Even utility services on inspections and reading meters can be performed remotely. Systems such as ZigBee embrace their system of sensors which allow the management of physical environment parameters [28].  
U-business: Cost benefits and time efficiency services can be provided by using augmented reality (AR) and virtual reality (VR) or holographic projection multimedia for business conferences and meetings with clients and colleagues from offices around the globe [22].  
U-government: Detection of pollutants can be completed with ease simply by the addition of an array of sensors. Airborne, waterborne, and soil pollutants can be distinguished. Closed-circuit television (CCTV) cameras being utilized in public areas such as parks and squares add to the safety of the citizens. Specific technologies and instruments allow for the precise projection of public transport schedules, allowing for a smoother commute reducing traffic because of the greater incentive to use public transport [22]. The vision-impaired can be assisted greatly by the addition of location-based services. Agricultural uses have become prevalent in monitoring livestock both in location and heath.  
Location-based services (LBS): By utilizing mobile phone networks, LBS and information can be collected from users. GPS data can be gathered from mobile devices anonymously and received by third-party advertising agents. When GPS is unreachable, triangulation and trilateration are used to obtain data. This allows businesses such as restaurants to advertising within the vicinity. It also integral to emergency services for quicker responses and accurate location data hence reducing dispatch and call-out times [29].  
Intelligent Transportation Systems (ITS): It provides efficient public transportation systems by saving time, cost, and reducing traffic congestion and impacts [30].  
Smart and Intelligent Building: Smart buildings can automate and manage tasks by forecasting situations and scenarios in which it can preemptively respond with a significant reduction in resources [3].  
Teleport and Intelligent Building: A teleport dealing predominantly with a large scale of data and information. It receives, modifies, and relays information accordingly to the desired destination. The teleport generally incorporates a central processing unit which controls telecommunications between different areas [24].  
With the implementation of a combination of the technologies, these services help to create a ubiquitous city with an efficient and organized lifestyle for the occupants, by reducing pollution, promoting environmental sustainability, and ensuring security and safety.
# Single Trace Analysis on Constant Time CDT Sampler and Its Countermeasure
## 2 Preliminaries
### Lattice-Based Cryptography
(Search-LWE problem) Given \(n\) independent samples \((a_{i},b_{i})\in\mathbb{Z}_{q}^{n}\times\mathbb{Z}_{q}\) drawn from \(A_{s,X}\) for a uniformly random \(s\in\mathbb{Z}_{q}^{n}\) (fixed for all samples), find \(s\). (Decision-LWE problem) Given \(n\) independent samples \((a_{i},b_{i})\in\mathbb{Z}_{q}^{n}\times\mathbb{Z}_{q}\) where every sample is distributed according to either (1) \(A_{s,X}\) for a uniformly random \(s\in\mathbb{Z}_{q}^{n}\) (fixed for all samples), or (2) the uniform distribution, which distinguish which is the case (with non-negligible advantage). Note that, without errors, both problems are easy to solve using Gaussian elimination. Errors smudge the lattice points and somewhat erase the structure of the lattice, making problems challenging to solve. After [PERSON]'s work, most of the lattice-based cryptosystems are based on LWE. Below is the encryption algorithm proposed by [PERSON]. * **Key Generation:** Sample \(A\in\mathbb{Z}_{q}^{m\times n}\) and \(s\in\mathbb{Z}_{q}^{n}\), chosen uniformly at random. Sample \(e\in\chi^{m}\) from Gaussian distribution. Compute \(b=As+e\). The public-key is \((A,b)\in\mathbb{Z}_{q}^{(n+1)\times m}\) and the secret key is \(s\in\mathbb{Z}_{q}^{n}\). * **Encryption:** For given message \(M\), let \(t\leftarrow\{0,1\}^{m}\). Compute \(C_{1}=t^{T}A\) and \(C_{2}=t^{T}b+\left\lfloor\frac{q}{2}\right\rfloor\cdot M\). The ciphertexts are \((C_{1},C_{2})\). * **Decryption:** Compute \(d=C_{2}-C_{1}\cdot s\). If the result is close to 0, output 0, else output 1. As error vectors play an important role in the security of the LWE-based cryptosystem, the importance of implementing the Gaussian sampler has been raised.
## 1 Introduction  
The plant kingdom is a vast source of secondary metabolites, with many of these compounds possessing therapeutic activity. Although many of these compounds have been characterized and used as therapeutic agents, there are still many more that should be investigated as potential sources of novel, and easy-to-obtain therapeutic agents. Natural products are still a compelling lead in drug discovery. The traditional strategy used to discover therapeutic activity has been challenged in the last few decades, from the laborious and expensive methods under which bioactivity was studied only after the isolation of the components, to an Effect Directed Analysis (EDA) approach. EDA is a combined approach that directly links chromatographic separation with in vitro effect-directed (bio)assays and physico-chemical analysis for the bioactive characterization of detected compounds.  
Targeted Identification of Bioactive Compounds versus Nontarget Analysis and Effect-Directed Analysis (EDA)  
Complex samples such as plant extracts contain hundreds of compounds. In the past, only known target compounds were looked for by target analysis and in this way, most bioactive compounds were left undiscovered.  
In nontargeted analysis, many compounds present in the sample are identified by analyzing the peaks identities. Thus, the nontarget identification leads to a huge list of compounds. However, which peaks (present compounds) are responsible for biological activity will not be elucidated. In contrast, the objective of an EDA approach is to identify which compounds are responsible for a certain biological effect.
## 4 Numerical Simulation
### Analysis of Simulation Results  
#### 4.3.1 Water Pressure Distribution Patterns around the Tunnel  
To ensure the reliability of the numerical simulation results, the simulated water pressure at a 126 m water level around the tunnel is compared with the water pressure changes at measurement points on the tunnel surfaces, as monitored on-site and depicted in Figure 11.  
As shown in Figure 11, the pattern of the water pressure change law from numerical simulations aligns with the field test results. The water pressure at the inverted arch and arch foot is notably higher than above the arch waist. This is because sections RK51 + 590 and LK51 + 640 are at the edges of a fault fragmentation zone, where highly fractured peripheral rock allows atmospheric precipitation and reservoir water to accumulate through rock fissures. The convergence of atmospheric precipitation and reservoir water through rock fissures leads to significantly higher water pressures at sections RK51 + 590 and LK51 + 640 compared to sections RK51 + 620 and LK51 + 610. Field test data show that at section RK51 + 590, water pressure at position A3 (arch foot) is 42 kPa higher than at position A2 (arch waist), and it is 17 kPa higher than the same position at section RK51 + 620. Prolonged high water pressure increases the likelihood of cracking in the lining at the inverted arch and arch foot of sections RK51 + 590 and LK51 + 640, potentially leading to water seepage and leakage during tunnel operation. This necessitates further investigation into the deformation characteristics of the tunnel lining.  
Figure 12 shows a cloud diagram of water pressure distribution around the tunnel at various groundwater levels, as obtained from numerical simulations.  
Pressure decreases toward the tunnel with higher pressures at the inverted arch and arch foot compared to other areas. The tunnel structure experiences upward buoyancy; as water levels rise, so does the surrounding water pressure, emphasizing the need to monitor lining deformation near the inverted arch and arch foot. The side view of the water pressure contour map indicates that the water pressure contour lines directly below the tunnel outside the fault are nearly straight, while those influenced by the fault appear curved (see Figure 10 for the fault's position in the model). Because the fault sections in the numerical model have higher hydraulic conductivity, the water pressure on both sides of the fault at the same elevation is higher than that within the fault.  
Figure 11: Comparison of water pressure at different locations in numerical simulation and field test. (a) LK51 + 610. (b) LK51 + 640. (c) RK51 + 590. (d) RK51 + 620.  
Figure 12: Water pressure cloud at various groundwater levels. (**a**) 126 m. (**b**) 127 m. (**c**) 128 m. (**d**) 129 m.
# Escalator: An Autonomous Scheduling Scheme for Convergecast in TSCH
## 6 Evaluation
### Testbed Results  
Figure 10 represents the end-to-end delay and PDR for each scheduling scheme in the testbed. Figure 9(a) shows the end-to-end delay for each packet delivery hop counts in 20-s intervals. Experimental results show that Orchestra has the largest increase in end-to-end delay with increasing packet delivery hop counts. Orchestra can receive packets from multiple child nodes within a single slotframe, but only one packet can be sent to the parent. Thus, when a packet is transmitted across multiple hops, the end-to-end delay increases proportionally to the slotframe size. On the other hand, Escalator shows a smaller end-to-end delay than Orchestra because packet transmission from the source to sink is performed in one slotframe.  
Figure 9(b) shows the end-to-end delay for each packet transmission hop counts in 5-s intervals. In the experimental results, 6 TiSCH-minimal shows a higher end-to-end delay increase compared to  
Figure 10: Testbed results: (**a**) end-to-end delay by packet transmission hop when the packet transmission interval is 20 s; (**b**) end-to-end delay by packet transmission hop when the packet transmission interval is 5 s; (**c**) PDR per the packet transmission interval.  
the case of the 20-s interval. 6 TiSCH-minimal performs contention-based communication unlike the other two scheduling schemes. Therefore, if collision increases due to node traffic increase, end-to-end delay increases by packet retransmission.  
Figure 9(c) shows the PDR per packet transmission cycle. When the packet transmission interval is 20 s, all three scheduling schemes show a PDR of more than 98%. When the packet transmission interval is 5 s, the PDR of 6 TiSCH-minimal and Orchestra is reduced to 96%. Due to the increase of retransmission and the lack of timeslots for packet transmission, the time for the packet to remain in the buffer of the node becomes longer and the packet is dropped due to the lack of the buffer of the node. However, Escalator shows 99% or more PDR regardless of the packet transmission interval due to the collision-free schedule and the use of fewer buffers by consecutive timeslots.
# A New Method for Detecting Onset and Offset for Singing in Real-Time and Offline Environments
## 3 The Proposed Algorithm
### Calculating the Standard Deviation of the Local Slopes  
To define a trajectory change in the fundamental frequencies, the sample standard deviation of the local slopes is calculated as shown in Equation (5).  
\[STD(Point_{i})=\sqrt{\frac{\sum_{x=i-n}^{x=i}(Slope(Point_{x})-Mean(Point_{i}) )^{2}}{n-1}}, \tag{5}\]  
Figure 6: Box and whisker of the estimated notes’ duration in the SVNote1 and the Erkomaishvili datasets.  
The same window size (\(n\) value) as for calculating the mean was used for estimating the standard deviation.
## 3 System Profile and Overall Scheme
### Aggregation Policy and Intelligent Routing Design
After aggregating into data packets \(\text{AD}_{\text{i}}^{t1}(n)\) in the \(t1\) data type queue \(\text{Q}_{\text{i}}^{t1}(n)\) of node \(S_{i}\), they are sent to the next hop node \(S_{p}\) of the \(t1\) type. Similarly, the \(t2\) type data packets \(\text{AD}_{\text{k}}^{t1}2(n)\) from nodes \(S_{k}\) and \(S_{l}\), \(\text{AD}_{\text{i}}^{t2}(n)\) is in the \(t2\) data of node \(S_{i}\). The data packets \(\text{AD}_{\text{i}}^{t2}(n)\) are aggregated in the type queue \(\text{Q}_{\text{i}}^{t2}(n)\) and sent to the next-hop node \(S_{q}\) of type \(t2\). After this aggregation process, the data of the same type is first aggregated and sent to the next-hop node with the same data type until the data packet is sent to the sink node to complete the aggregation and transmission of the data.
## 2 Material and Methods
### Airborne Laser Scanning Processing Chain
#### 2.2.1 Classification of Green-LiDAR Data  
Statistical parameters for LiDAR point clouds were calculated for describing point attributes with the module opalsPointStats. OPALS calculates statistical features by first selecting points and then relating them to a reference model. The selection of points was based on an infinite vertical cylinder (searchMode = d2); the reference model was a \"horizontalPlane\". Since the laser footprint at ground level was around 0.4 m in diameter, the cylinder radius (searchRadius) was set to 0.2 m for the majority of calculations. In the model \"horizontalPlane\", the samples were reduced in relation to a plane passing through the feature point. Calculated statistics for identifying patterns in point clouds were ranked (rank) and point density (pdens). Rank statistic was calculated as the relative position (0 = local minimum and 100 = local maximum) of the feature point within its vertical neighbourhood. The feature pdens is the number of points in relation to the area/volume of the search cylinder. Since the usual measurement units for point density is points/m\({}^{2}\), a cylinder searchMode with a searchRadius of 0.5 m was selected as the most proximate value to a square meter (Figure 3). Filtering was crucial for the classification, since it allowed for points to be defined when a new attribute had to be calculated (processing filtering) in relation to the local neighbourhood (neighbourhood filtering). The classification was performed with the module opalsAddInfo. In order to define the threshold values, point clouds were inspected through cross-sections every metre with the module opalsSection. Special attention was placed on spatial distribution, density, and intensity of points. Cross-sections revealed that height and vertical distribution of points are important factors to differentiate dense and sparse need beds.
# An Explainable Deep Semantic Coding for Binary-Classification-Oriented Communication
## 2 System Model and Problem Formulation
### System Model  
The system model consists of three components: the mixed source, the semantic coding module, and the binary classification module, as depicted in Figure 1. The mixed source \(X\) consists of two subsources, \(X_{1}\) and \(X_{2}\), each of which corresponds to a distinct class attribute and is defined on the same support set \(\mathcal{X}\). In each time block, an independent and identically distributed (i.i.d.) source sequence of length \(m\), \(X_{1}^{m}\) or \(X_{2}^{m}\), is generated either with respect to the marginal distribution \(P_{X_{1}}\) or \(P_{X_{2}}\), with the prior probability \(\alpha_{1}\) or \(\alpha_{2}\). For \(i\in\{1,2\}\), the source sequence \(X_{i}^{m}\) is first processed by the semantic coding module.  
assuming a powerful channel coding scheme and error-free channel transmission, after encoding and decoding (hereinafter referred to as coding for brevity), the reconstructed sequence \(Y_{i}^{m}\) is subsequently fed into the classification module. The objective of the binary classification task is to determine from which subsource each source sequence originates.  
In the semantic coding module, we analyze the rate-distortion trade-off of a mixed source based on information theory and information spectrum methods. In the binary classification module, large deviation theory is utilized to evaluate the classification performance. Consequently, the overall performance of the BCOC is jointly determined by the source compression rate, reconstruction distortion, and classification accuracy. The next subsection provides an overview of the information-theoretical foundations.