esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Vehicles will soon be connected and will be interacting directly with each other and with the road infrastructure, bringing substantial benefits in terms of safety and traffic efficiency. The past decade has seen the development of different wireless access technologies for vehicle-to-everything (V2X) communications and an extensive set of related use cases have been drafted, each with its own requirements. In this paper, focusing on short-range communications, we analyze the technical and economic motivations that are driving the development of new road users' connectivity, discussing the international intentions to mandate on board devices for V2X communication. We also go in depth with the enabling wireless access technologies, from IEEE 802.11p to short-range Cellular-V2X and other complementary technologies, such as visible light communication (VLC) and millimeterWaves, up to hybrid communication and 5G. We conclude our survey with some performance comparison in urban realistic scenarios, underlying that the choice of the future enabling technology is not so easy to predict and mostly depends on mandatory laws at the international level.  
connected vehicles; vehicular sensor networks; IEEE 802.11p; cellular-V2X; LTE-V2V; VLC; mmWave; 5G; performance comparison 1
# Common-Mode Voltage Analysis and Reduction for the Quasi-Z-Source Inverter with a Split Inductor
###### Abstract  
In transformerless grid-connected photovoltaic (PV) systems, leakage currents should be properly addressed. The voltage fluctuations between the neutral point of the grid and the PV array, i.e., common-mode voltage (CMV), will affect the value of the leakage currents. Therefore, the leakage currents can be attenuated through proper control of the CMV. The CMV depends on the converter topology and the modulation strategy. For the quasi-Z-source inverter (qZSI), the amplitude of the high-frequency components in the CMV increases due to the extra shoot-through (ST) state. The CMV reduction strategies for the conventional voltage source inverter (VSI) should be modified when applied to the qZSI. In this paper, an input-split-inductor qZSI is introduced to reduce the CMV, in which all the CMV reduction strategies for the VSI can be used directly with appropriate ST state insertion. Moreover, the proposed method can be extended to impedance source converters with a similar structure. Simulations and experimental tests demonstrate the effectiveness of the proposed strategy for the qZSI in terms of CMV reduction.  
photovoltaic (PV); quasi-Z-source inverter (qZSI); leakage current reduction (LCR); common-mode voltage (CMV); input split inductor
## 3 The Security Mechanism
### Local Detection
#### 3.1.2 LD-2 Mechanism  
In LD-2, nodes exchange neighbor lists which contain information of neighbor nodes when they encounter them in the network. For ease of description, we assume there are two nodes \(u\) and \(v\) in the network. When node \(u\) find node \(v\) in its communication range, it stores node \(v\)'s location information, ID and the current time denoted by \((LOC_{v}^{T_{v}}\parallel lo\,c_{v}^{t_{v}}\parallel ID_{v})\). At the same time, node \(v\) does the same thing like node \(u\) does. Thus nodes can collect enough information about their two-hop neighbors. If there exit contradictions, nodes would sign alert messages and broadcast them to the network. Other details such as authentication processes are similar to LD-1.
## 2 The Dynamic Traveling Salesman Problem  
### Problem Definition  
The TSP is a well-known combinatorial optimization problem proven to be \(\mathcal{NP}\)-hard [18]. The problem can be described as follows: a salesperson must visit multiple cities once and only once, starting from one city and returning finally, to the same city [19]. Typically, a fully connected weighted graph \(G=(N,A)\) is used to model the TSP, where \(N=\{1,\ldots,n\}\) is the set of \(n\) cities and \(A=\{(i,j)\in N:i\
eq j\}\) is a set of arcs connecting these cities. Each arc \((i,j)\) is associated with a non-negative value \(d_{ij}\), which is the distance between cities \(i\) and \(j\).  
The TSP solution \(s=[1,\ldots,n]\) is a permutation of city indices, and the objective function of the TSP is defined as follows:  
\[\phi(s)=\sum_{i=1}^{n-1}d_{s(i)(s(i+1)}+d_{s(n)s(1)}. \tag{1}\]
# Multiple-Mode Orthogonal Time Frequency Space with Index Modulation
## 5 Conclusions  
Aiming at the problem of grid resource waste in the OTFS-IM system, this paper proposed a multiple-mode OTFS-IM system, namely the MM-OTFS-IM system. In the proposed system, each grid was modulated by the constellation of different modes, and the combination of different mode constellations was used to transmit index bits implicitly. By comparing the SE, EE, and computational complexity of the proposed system with other competitors, the proposed system showed a higher SE performance. The performance comparisons between the MM-OTFS-IM system with that of the OTFS, OTFS-IM, and OTFS-DM-IM systems validated that the proposed MM-OTFS-IM system achieved better BER performance under the same SE in time-varying channels.  
Conceptualization, H.R. and W.X.; methodology, H.R., W.X. and L.W.; software, H.R. and W.X.; validation, H.R. and W.X.; formal analysis, H.R. and L.W.; writing--original draft preparation, H.R.; writing--review and editing, H.R., W.X. and L.W.; visualization, H.R.; supervision, W.X.; project administration, L.W.; funding acquisition, W.X. All authors have read and agreed to the published version of the manuscript.  
The National Natural Science Foundation of China (No. 61871337).  
**Institutional Review Board Statement**: Not applicable.  
**Informed Consent Statement**: Not applicable.  
**Data Availability Statement**: Not applicable.  
Figure 9: BER performance of the MM-OTFS-IM system under different UE speeds.
## 3 Results and Discussion
### 1L Scale Batch Fermentation  
Lactic acid is most commonly produced in the batch mode [40], but some examples of continuous culture exist [41], as well as several examples of fed-batch fermentation [42]. Batch strategy generally gives high lactic acid concentrations and yields, provided all carbon substrate is depleted. However, it provides lower productivity compared to continuous or fed-batch fermentation, due to unavoidable idle times (i.e., load and discharge) and consequently to a sub-optimal utilization of the bioreactor. In our experimental conditions, where simplicity and low managing cost were the main drives, batch mode was chosen for its simplicity. This closed system has the advantage to reduce the risk of contamination and to assure the complete consumption of the substrate. On the other hand, either substrate and/or product inhibition could limit the fermentation performance. Previous studies on lactic acid production have reported the highest lactic acid yield up to 96% in batch fermentation by LAB on glucose [43]. The fermentation mixture, constituted by a ratio of 25:75 of pear pomace and RCW, respectively, contained a total amount of fermentable sugars of 31.60 g/L, characterized by 2.10 g/L of glucose, 4.30 g/L of fructose, 24.20 g/L of lactose and 1.00 g/L of sorbitol.
## 2 Experimental Setup
### Indentation and Data Acquisition System  
A number of different sensors were used to measure various parameters during the tests. An LPSW-100K Universal Tension & Compression Shear Web Load Cell with a maximum capacity of 445 kN was placed between the indenter and the base plate to measure the ice load. Three MHR 500 A.C. linear variable displacement transducers (LVDTs) were used to measure the structural deflection of the 'compliant' beams. One LVDT was positioned at the centre of the beam and the other two were placed 560 mm from the centre on each side. The LVDTs were bolted on an aluminium block and clamped to the W12 \(\times\) 170 mild steel I-beam using C-lamps (Figure 4). These LVDTs had a stroke length of \(\pm\)12.7 mm, maximum non-linearity of \(<\)\(\pm\)0.25%, and an operating temperature range of \(-\)55 \({}^{\circ}\)C to 150 \({}^{\circ}\)C. A PCB piezoelectric accelerometer (model 352C33) was also used to measure the acceleration of the 'compliant' beam. The measurement range of the accelerometer was \(\pm\)50 g with a sensitivity of 100 mV/g. A series 330 LVDT was used to measure the distance travelled by the indenter in the ice sample which had a working range of \(\pm\)25.4 mm, maximum non-linearity of \(<\)\(\pm\)0.20%, and an operating temperature range of \(-\)20 \({}^{\circ}\)C to 80 \({}^{\circ}\)C. Resistance temperature detectors (RTDs) and a Mastercraft non-contact infrared digital thermometer were used to measure the temperature of the ice samples.  
An HBM QuantumX MX840B (Hottinger Bruel & Kjaer Inc., Marlboro, MA, USA) universal measuring amplifier with 8 connectors was used as the data acquisition system. Using the HBM CATMAN software v.5.3 ([[https://www.hbm.com/en/2290/catman-data-acquisition-software/](https://www.hbm.com/en/2290/catman-data-acquisition-software/)]([https://www.hbm.com/en/2290/catman-data-acquisition-software/](https://www.hbm.com/en/2290/catman-data-acquisition-software/))) easurements were recorded at a frequency of 4800 Hz. All the tests were recorded using GoPro cameras (GoPro, San Mateo, CA, USA).
## 4 Machine Learning (ML)-Based Internet of Vehicles
### Applications of Machine Learning-Based IoV System
\begin{table}
\begin{tabular}{c c c c} \hline
**References** & **IoV Application** & **Model Applied** & **Accuracy** \\ \hline
[124] & Traffic identification system & & Acc = 98:90 \\  & & Adaptive Boosting Classifier and Random Forest Algorithm (ABC-RFA) & \\  & & CNN, LSTM, and LSTM-CNN & \\
[25] & Traffic information detection & & FI = 0.9042 \\
[111] & Transportation network planning or routing & GA, SA, and AIS. & Gain of the total travel time SD = 3-5.5\% \\
[112] & ANN & R\({}^{2}\) = 0.76 \\
[126] & NLMR & R\({}^{2}\) = 0.78 \\
[127] & Accident forecasting and detection & TLBN & Acc = 90:94\% \\  & & GRU, RNN, and LSTM & Acc Dec = 94.2\% \\
[113] & & DC, RF, LR, and NB & Acc = 75.7\% \\ \hline
[113] & & ANN & RMSE = 2337 \\  & Traffic accident estimation & GA & RMSE = 4453 \\
[127] & Rare-events logistic regression & McFadden-R\({}^{2}\) = 100.6-106.9 \\ \hline
[128] & Incident detection & MLP & Detection rate = 90\% \\ \hline
[129] & & MTL & Acc = 81.53-91.74\% \\
[130] & Traffic flow prediction & SAE & Acc = 86.90\% \\
[131] & DNN & AC = 0.80-0.83 \\
[132] & ANN & Acc = 92.98 \\ \hline
[133] & Short-term speed prediction & BPNN, NARCNN, SVM-RRF, SVM-LIN, MLR & RFE = 88.26-95.46\% \\ \hline
[134] & Incident detection & NLP and SVM. & Acc = 88.28\% \\ \hline
[135] & Network traffic classification & & Acc = 96.89-99.29\% \\ \hline
[136] & & Acc = 96.89-99.29\% \\
[122] & Driver distraction evaluation & CNNs, BILSTM & Acc = 92.7\% \\ \hline \end{tabular}
\end{table} 
Table 6: Summary of application of ML based IoV.
# The Relationship between Process Capability and Quality of Measurement System
## 2 Results
### Measurement System Analysis
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**B** & **C** & **D** & **E** & **F** & **G** & **H** & \\ \hline
28.3 & **45.8** & **32.0** & 25.3 & 21.2 & **52.9** & **34.3** & A \\ \hline  & **53.8** & **44.19** & **30.06** & 24.19 & **51.97** & **32.39** & B \\ \cline{2-7}  & & 27.87 & **40.9** & **41.1** & **62.03** & **55.18** & C \\ \cline{2-7}  & & 25.23 & 27.47 & **50.48** & **38.87** & D \\ \cline{2-7}  & & & _15.35_ & **51.06** & **30.35** & E \\ \cline{2-7}  & & & & **48.32** & 24.3 & F \\ \cline{2-7}  & & & & & **57.19** & G \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Values of %GRR (%). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**B** & **C** & **D** & **E** & **F** & **G** & **H** & \\ \hline
16.9 & 29.5 & 25.5 & _6.1_ & _0.8_ & _0.0_ & _0.0_ & A \\ \hline  & **46.41** & **42.9** & 25.31 & 20.63 & _16.98_ & _19.54_ & B \\ \cline{2-7}  & & _5.53_ & 26.50 & **30.68** & 23.97 & **38.70** & C \\ \cline{2-7}  & & & 21.56 & 25.83 & 21.02 & **33.53** & D \\ \cline{2-7}  & & & _4.78_ & _0.0_ & _11.20_ & E \\ \cline{2-7}  & & & & _0.0_ & _5.15_ & F \\ \cline{2-7}  & & & & & _0.0_ & G \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Values of %AV (%). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**B** & **C** & **D** & **E** & **F** & **G** & **H** & \\ \hline
95.91 & 88.90 & _94.74_ & _96.74_ & _97.73_ & 84.84 & 93.93 & A \\ \hline  & 84.30 & 89.71 & _95.38_ & _97.03_ & 85.44 & _94.61_ & B \\ \cline{2-7}  & & _96.04_ & _91.25_ & _91.16_ & 78.44 & 83.40 & C \\ \cline{2-7}  & & & _96.76_ & _96.15_ & 86.32 & _92.14_ & D \\ \cline{2-7}  & & & & _98.81_ & 85.98 & _95.28_ & E \\ \cline{2-7}  & & & & & 87.55 & _97.00_ & F \\ \cline{2-7}  & & & & & & 82.03 & G \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Values of %PV (%). The %PV index represents the suitability of a measuring instrument that can distinguish the differences between the measured parts. The optimal value of the index is in the range of 90 to 99%. As can be seen from Table 9, an optimal degree of resolution was found in several pairs, especially the pair of metrological appraisers \(EF\). The %GRR index quantifies the capability of the measurement system. As can be seen from Table 10, the value of the index was higher than 10% for all pairs. This required corrective action. The best capability was shown by the pair \(EF\) (15.35%), and conditional capability was shown by the pairs AF, BF, FH, DE, AE, CD and AB. Pairs marked with bold text had an incapable measurement system.
## 1 Introduction
inhibitory protein; COX-2, cyclooxygenase-2; CREB, cAMP response element binding protein; DR, death receptor; EGFR, epidermal growth factor receptor; Egr-1, early growth response factor-1; ERK, extracellular signal-regulated kinase; FAK, focal adhesion kinase; FasL, Fas-ligand; FGF-1, fibroblast growth factor-1; GSK3\(\beta\), glycogen synthase kinase-3\(\beta\); HMG-CoA, 3-hydroxy-3-methylglutaryl-coenzyme A; Hsp, heat shock protein; IAP, inhibitor of apoptosis protein; ICAM-1, intercellular adhesion molecule-1; IFN-\(\gamma\), interferon-\(\gamma\); IL-1, interleukin-1; iNOS, inducible nitric oxide synthase; JNK, c-Jun N-terminal kinase; MAPK, mitogen-activated protein kinase; Mcl-1, myeloid cell leukemia-1; MCP, monocyte chemotactic protein; MEK, MAPK/ERK kinase, MIP-2, macrophage-inflammatory protein-2; MMP, matrix metalloproteinase; mTOR, mammalian target of rapamycin; NF-AT, nuclear factor of activated T-cells; NF-xB, nuclear factor-kappa B; PARP, poly (ADP-ribose) polymerase; PI3K, phosphoinositide-3 kinase; PKC, protein kinase C; PPAR, peroxisome proliferatorator-activated receptor; Sp1, specificity protein 1; STAT3, signal transducer and activator of transcription 3; TF, tissue factor; TLR2, Toll-like receptor-2; TNF-\(\alpha\), tumor necrosis factor-\(\alpha\); TRAF1, TNF receptor-associated factor-1; uPA, urokinase-type plasminogen activator; VCAM-1, vascular cell adhesion molecule-1; VEGF, vascular endothelial growth factor; VEGFR, VEGF receptor; XIAP, X-linked IAP.
## 2 Materials and Methods
### Numerical Schemes and Boundary Conditions
#### 2.4.1 Fluid and Structure Domains Mesh  
The fluid domain is a cubic box with dimensions of \(0.116\,\mathrm{m}\times 0.116\,\mathrm{m}\times 0.116\,\mathrm{m}\) along the x, y, and z axes, respectively. We employed a \(N\times N\times N\) Cartesian grid for the spatial discretization of the fluid region. The computational domain, which is based on Cartesian meshes with \(N\) selected as 78 and obtained through the mesh independence analysis presented in the following section, is shown in Figure 2. In order to improve the computational efficiency, a level of static mesh refinement was employed around the immersed structure, with a refinement ratio of four, resulting in a total grid size of approximately 4 million elements.  
The reference aortic housing and valve structures were discretized using unstructured, first-order tetrahedral elements. Figure 3a-e displays the detailed view of solid regions and their meshes for the reference housing and for each grade of calcified aortic valve. In the IBFE method we adopted, the solution's accuracy depends on the ratio between the Lagrangian element node spacing and the Eulerian grid spacing, referred to as the mesh factor or \(M_{\text{FAC}}\). Accordingly, we set the sizes of the structural mesh elements to achieve \(M_{\text{FAC}}\) values of approximately 1.0 near the wall and 0.5 around the valves [37].  
Figure 2: Cross-sectional view of the computational mesh for the fluid domain and native aortic valve model, demonstrating the inner structure and housing.  
The structural meshes consist of approximately 1.86 million elements for the native healthy aortic valve and 1.89, 1.88, 1.93, and 2.12 million elements for Grades 3 to 6, respectively.
## 3 Methodology
### Explanation of Notations  
* The range of \(i\) in \(V_{i}\) varies from \(1\) to \(n-1\), i.e., \(i:\{1,2,3,\ldots,n-1\}\to N\), but in other notations, it varies from \(0\) to \(n-1\) i.e., \(i:\{0,2,3,\ldots,n-1\}\to W\)
* MD5-based secure hash function 128-bit hash value ranges from \(h(.):\{0,1\}^{*}\times G\to Z_{q}^{*}\)
* \(r_{i}\) such that \(i:\{0,1,2,3\ldots,n-1\}\to W\) presents a set of random numbers generated using the LCG algorithm
* Linear congruential generators can be defined through recurrence relation as: \(X_{i+1}=(aX_{i}+c)modm\)
* \(SELECT\_RANDOM_{s}\) will generate a six-digit code.
* \(SVC_{s}\) is generated using the Fisher-Yates shuffle algorithm. Here is the pseudo representation of this algorithm: \(\circ\) function fisherYatesShuffle(array) \(\circ\)\(n=\) length(array)
* for i from \(n-1\) down to \(1\) do \(\circ\)\(j=\) random integer between \(0\) and \(i\) (inclusive) \(\circ\)swap \(array[i]\) and \(array[j]\) \(\circ\) return array  
**Explanation of Fisher-Yates Shuffle Algorithm:**  
* The fisherYatesShuffle function takes an array as input and returns the shuffled array.
* It initializes the variable n with the length of the array.
* The algorithm starts a loop from \(n-1\) down to \(1\). This loop iterates over the elements of the array in reverse order.
* A random integer \(j\) is generated inside the loop between \(0\) and \(i\)(inclusive), where \(i\) is the current iteration index.
* The elements at indices \(i\) and \(j\) are swapped using a temporary variable.
* After the loop completes, the shuffled array is returned.  
The Fisher-Yates shuffle algorithm guarantees a uniformly random shuffle and has a time complexity of \(O(n)\), where n is the number of elements in the array.  
* \(getLatitude()\) and \(getLongitude()\) are two extractor functions that extract latitude and longitude from the input location.
* \(MT_{i}\) contains message templates. These messages might contain the following commands: \(\circ\) Please give me the way. I am on your back! \(\circ\) Speed up! \(\circ\) Danger ahead! \(\circ\) Traffic is jammed on the road. Please adopt an alternative way. \(\circ\) I run short of fuel. Please help! \(\circ\) I need a mechanic. \(\circ\) The tire is punctured. \(\circ\) There is an accident on the road near my location. \(\circ\) There is a crowd protesting on the way. \(\circ\) Please give way to the ambulance! \(\circ\) Stop on the way. There is a check post. \(\circ\) The weather condition is bad.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Notation** & **Description** \\ \hline \(getLatitude()\) & The function that will extract latitude from the location \\ \(getLongitude()\) & The function that will extract longitude from the location \\ \(MP_{i}\) & Message Priority \\ \(MT_{i}\) & Message Template \\ \(1D_{rec}\) & Receiver ID \\ \(1D_{snd}\) & Sender ID \\ \hline \hline \end{tabular}
\end{table}
Table 4: _Cont._\(MP_{i}\) contains three values, including 0, 1, and 2. (0) means the priority of this message is nothing. It might be an informative message. 1 means normal priority, while 2 means a very high priority.
# Optimizing Non-Glare Zone Width of Adaptive Driving Beam (ADB) Using Fuzzy Logic Control
## 2 Modeling: Night Drive Simulator
### Driving Scenario Design  
Four S-curve driving scenarios are designed, which are shown in Figure 6. The designs are based on the road shown in Figure 1. All four S-curves are designed with a track transition curve [20] with a minimum curvature of 25 m, 50 m, 75 m, and 100 m. Those curvature designs are based on the speed of the ADB vehicle, calculated by the formula (1) of the curve design guideline provided by the AASHTO Green Book [21].  
\[R_{min}=\frac{V_{d}^{2}}{127(e+f_{max})} \tag{1}\]  
where:  
* \(R_{min}\) is the minimum curvature;
* \(V_{d}\) is the advisory speed;
* \(e\) is the superelevation, which is 0 in this case, as suggested by NHTSA for ADB testing in NPRM [22]; and
* \(f_{max}\) is the comfortable side friction factor referenced from the Green Book [23].  
The 100 m curvature corresponds to a genetic ADB activation speed of 45 km/h, which is also the minimum curvature that NHTSA proposed for ADB testing. The ADB function of the most current production vehicle will automatically be activated when the vehicle speed exceeds the activation speed. The deactivation speed of ADB, however, is usually lower than the activation speed. A study from NHTSA shows that ADB deactivation speed can be as low as 25 km/h [24]. Thus, if a vehicle traveling with ADB enabled encounters a curve with a curvature as low as 25 m, the ADB will not be deactivated. The 50 m and 75 m curves are included to study the influence of curvature on optimized non-glare zone width.  
The following distance between the two vehicles is calculated by the three-second rule, suggested by the National Safety Council of the United States [25], assuming there are safe driving conditions. The complete set of parameters of the four driving scenarios for this research are shown in Table 1.
## 4 Model Development
This bidirectional processing is particularly beneficial in traffic prediction, where future states (e.g., upcoming intersections or traffic signals) can influence current traffic conditions. For example, Melbourne's Hoddle Street's traffic flow can be impacted by prior and upcoming signal phases and intersections. The BiLSTM's ability to process bidirectionally allows it to leverage these dependencies, enhancing prediction accuracy in dynamic traffic environments. When selecting a traffic prediction model, there is often a trade-off between accuracy and computational complexity. While models such as BiLSTM provide high accuracy due to their ability to capture bidirectional dependencies in data, they require more computational resources and may present challenges for real-time processing, especially when handling large datasets or when quick decision-making is essential. For real-time applications, simpler models may be preferred for faster processing times, though they may have limitations in predictive accuracy. Future studies may focus on optimising BiLSTM and other complex models to balance accuracy with computational efficiency, enabling broader applications in real-time traffic management systems. Figure 5: LSTM Architecture (Matlab 2021b). Note: \(\text{X}_{\text{t}}\) represents the input, \(\text{h}_{\text{t}-1}\) represents the output at the previous time (t \(-\) 1). Ct and \(\text{C}(\text{t}-\) 1) represent the cell state at time step t and t \(-\) 1, respectively.