esa-sceva/satcom-chunk-collection huggingface dataset:
# Using Connected Vehicle Data to Evaluate National Trip Trends
## 7 Trip Lengths by Vehicle Type  
Using the vehicle classification code attribute from Table 1, each CV trip was classified as an EV, HV, or ICEV trip. Correspondingly, summary trip statistics by vehicle type for December 2022 were computed at the state level. Figure 4 shows an alphabetically sorted two-column view of mean trip lengths categorized by vehicle type and US state for December 2022 with states from Alabama to Mississippi shown in Figure 4a and Missouri through Wyoming depicted in Figure 4b. Individual pareto-sorted visuals of these mean trip lengths by vehicle type are shown in Figure 5 for easier comparisons among the various states.  
Figure 4: Mean Trip Lengths Measured from CV data by Vehicle Type for December 2022 (alphabetically sorted) (**a**) States AL through MS (**b**) States MO through WY.  
CV trips in Mississippi show the highest mean trip length among HVs and ICEVs, while Oklahoma depicts the highest mean trip length among EVs. Wyoming, Montana, and Hawaii depict the lowest mean trip lengths for EVs, HVs, and ICEVs, respectively. Overall, HVs show higher mean trip lengths, followed by ICEVs and EVs.  
These trends can potentially be explained by Mississippi being a primarily rural and low population density state, resulting in driving being the preferred mode of travel and thus leading to higher mean trip lengths. Correspondingly, the states of Alaska, Idaho, and Wyoming have two of the three lowest counts of alternative fueling stations nationwide [28], which may potentially help explain the low mean trip lengths for EVs in these states (Figure 5a), owing to range anxiety among motorists due to a lack of charging infrastructure resulting in shorter EV travel.  
Figure 5: Mean Trip Lengths by Vehicle Type for December 2022 (pareto sorted) (**a**) EV (**b**) HV (**c**) ICEV.
# Background and Recent Advances in the Locata Terrestrial Positioning and Timing Technology
## 2 Locata Core Technologies  
The Locata concept has been designed to overcome the limitations of GNSS, as well as other pseudolite-based positioning systems, with four key objectives: (1) Being available in all environments; (2) High reliability; (3) High accuracy; and, (4) Cost effectiveness [16].  
In the Locata concept, these objectives are achieved through a design based on a network (a \"LocataNet\") of ground-based _transceivers_ (\"LocataLites\") that cover a chosen area with strong signals, which is suitable for accurate positioning in classically difficult GNSS environments (Figure 1). Importantly, the positioning signals that are emitted by different LocataLites are time-synchronised (via \"TimeLoc\") to the order of 30 picoseconds, which allows a single mobile Locata receiver (\"Locata Rover\") to determine its position within the network in the same manner as GNSS. However, unlike GNSS, the sub-centimetre level of synchronisation between LocataLites allows for single-point positioning with GNSS Real-time Kinematic (RTK) level of accuracy, _without the use of a reference station, in a manner unlike GNSS Precise Point Positioning_. The Locata Rover can operate in an environment where GNSS signals augment the Locata system (and visa versa), or they can operate entirely independently of GNSS [16].
## 4 Simulation and Analysis of Transmission Lines Foreign Object Detection  
### Simulation Environment and Evaluation Indexes  
The experimental configuration of foreign object detection consists of the software environment of MATLAB 2021a and the hardware equipment with CPU of AMD (A) Ryzen 5600H and GPU of NVIDIA GeForce RTX 3050 Ti with 4 GB video memory, memory 16 GB and the Windows10 operation system.  
The confusion matrix was introduced to analyze the experimental results, which was obtained by the classification of the random forest. Meanwhile, in order to evaluate the performance of the proposed method, the commonly used indexes: precision (\(P_{i}\)), recall (\(R_{i}\)) and accuracy were introduced, where \(i\) = (1, 2, 3, 4) represents different categories of the foreign objects. The specific definitions of the indexes are  
\[P_{i}=\frac{TP}{TP+FP}\times 100\%,\quad R_{i}=\frac{TP}{TP+FN}\times 100\% \tag{5}\]  
\[Accuracy=\frac{TP}{TP+FP+FN+TN}\times 100\% \tag{6}\]  
where \(TP\) (true positive) represents the number of positive samples correctly predicted by the random forest; \(TP\) + \(FP\) represent the number of positive samples predicted by the random forest. \(TP\) + \(FN\) represent the number of actual positive samples. \(TP\) + \(TN\) + \(FP\) + \(FN\) represent the total number of samples.  
In addition, the \(macro\_F_{1}\) also was introduced to evaluate the performance of the foreign object detection model, which is commonly used for multiple classification problems and suits the multi-classification problems with unbalanced samples. The calculation equation is  
\[macro\_P=\frac{1}{m}\sum_{i=1}^{m}P_{i}\times 100\%,\quad\ macro\_R=\frac{1}{m }\sum_{i=1}^{m}R_{i}\times 100\% \tag{7}\]  
Figure 7: The schematic diagram of random forest.  
\[macro\_F_{1}=\frac{2\times macro\_P\times macro\_R}{macro\_P+macro\_R}\times 100\% \tag{8}\]  
where \(m\) equals four, i.e., the total number of foreign object categories. \(P_{i}\) is the precision of each category. \(R_{i}\) is the recall of each category.
# Future Generation 5G Wireless Networks for Smart Grid: A Comprehensive Review
## 4 Smart Grid and 5G
### Security in 5G Based Smart Grid Networks  
The security concerns involved in creating an effective communication system in a smart grid are an important issue to address before deploying an efficient energy system in the future [96; 97] Furthermore, the rapidly growing security threats should be solved in order to establish a reliable, advanced metering infrastructure in smart grids [98]. Security attacks in any communication system can be classified as active or passive attacks. The passive attack, such as eavesdropping or traffic analysis, involves learning and making use of information being transmitted in the communication medium. The active attacks involve interruption and modification of information, as in the case of a man in the middle attack, replay attack, and denial of service attack. Tackling the security attacks using cryptography analysis requires management and distribution of symmetric keys. The existence of heterogeneous network architecture in 5G imposes challenges to the management of symmetric keys, which demands better security mechanisms. Another security strategy that can be implemented in smart grids is the physical layer security approach. It exploits the unique wireless physical layer medium features, which provides advantages such as low computational complexity and high scalability [75]. Apart from the conventional cryptographic methods and physical layer approach, secure communication using 5G in a smart grid can be ensured by using an intrusion detection system [80]. Moreover, a fusion-based defense mechanism can be implemented in order to enhance the security in 5G [99]. Game theoretic approaches can bring new insight into network robustness and cyber security [100; 101; 102] The use of public communication infrastructure and Internet based protocols may lead to security threats [103]. The attacker can act in place of a legitimate user in  
Figure 5: Benefits of 5G networks in smart grids.  
an unauthorized manner. The public key infrastructures, the hashing schemes, and the trusted computing are effective key security technologies for smart grids [104; 105]. Therefore, a proper security based routing strategy [106] has to be implemented in order to obtain an effective and protective communication system.
## 2 Materials and Methods
### Case Studies  
Two paddocks located in the Namoi catchment were selected as case study areas (Figure 1). The Namoi catchment is a large catchment (44,000 km\({}^{2}\)) located in New South Wales, Australia. It is a region with intensive agriculture and large areas of cotton production. The selected study case areas are near the town of Narrabri in the middle of the catchment, and border the Namoi River, which functions as the main source for irrigation water. The study site **a** is located at approximate coordinates 149.9\({}^{\circ}\) and \(-\)30.4\({}^{\circ}\) (WGS 1984) at a mean elevation of 226.9 m and covers an area of 1032.57 ha. The site has a subtropical climate with no dry season, a mean annual rainfall of 629 mm, a mean temperature of 18.7 \({}^{\circ}\)C, with a mean minimum temperature of 11.4 \({}^{\circ}\)C and a mean maximum temperature of 26 \({}^{\circ}\)C. Site **b** is located nearby at approximate coordinates 150\({}^{\circ}\) and \(-\)30.5\({}^{\circ}\) at a mean elevation of 233.1 m and is 460.55 ha. The site has a similar climate, with a mean annual rainfall of 615 mm and a mean annual temperature of 18.6 \({}^{\circ}\)C, with a mean minimum of 11.3 \({}^{\circ}\)C and a mean maximum of 25.9 \({}^{\circ}\)C ([[http://www.bom.gov.au/climate/averages/maps.shtml](http://www.bom.gov.au/climate/averages/maps.shtml)]([http://www.bom.gov.au/climate/averages/maps.shtml](http://www.bom.gov.au/climate/averages/maps.shtml))) (accessed on 20 February 2022).
## 4 Results
### Grid-Level FRP  
#### 4.3.1 Missing MODIS FRP Due to Sampling Limitations  
MODIS misses a considerable number of fire observations (counts) and FRP (Figure 6). At a grid size of 0.05\({}^{\circ}\), Aqua MODIS has no fire detections in 76% of grids where VIIRS I-band senses at least one detection (Figure 6a) and fires detected by VIIRS in these grids account for 58% of daily VIIRS I-band fire detections (Figure 6b) and 39% of daily VIIRS sum FRP (Figure 6c) across Africa. Similarly, the VIIRS I-band observes no fires in 16% of grids where Aqua MODIS senses at least one fire detection (Figure 6a) and MODIS fire detections in these grids explain 12% of daily MODIS fire detections (Figure 6b) and 9% of daily MODIS sum FRP (Figure 6c). As grid size grows, the missing fire detections and FRP retrievals in the not-matched grids decrease sharply. For instance, when grid size grows to 0.25\({}^{\circ}\), the number of VIIRS I-band fire detections missed by Aqua MODIS in the not-matched grids reduces to 28% (Figure 6b) accounting for 20% of daily VIIRS sum FRP (Figure 6c), while VIIRS merely misses \(<\)1% of daily Aqua MODIS fire detections (Figure 6b) and \(<\)1% of daily Aqua MODIS sum FRP. When grid size further grows to 5\({}^{\circ}\), there are still 34% of grids where only VIIRS observes fires (Figure 6a) but these grids explain \(<\) 1% of daily VIIRS fire detections (Figure 6b) and \(<\) 1% of daily VIIRS sum FRP (Figure 6a).  
Missing MODIS fire detections increase along view angles and the missing fires generally have relatively small FRP values (Figure 7). At a 0.25\({}^{\circ}\) grid across Africa, the percent of VIIRS fire detections missed by Aqua MODIS in every 5\({}^{\circ}\) VZA increases from 14% to 52% as the MODIS view angle increases from nadir to the scan edge, with 79% of all the missing fire detections located in grids with MODIS VZAs \(>\) 30\({}^{\circ}\) (Figure 7a). The VIIRS fire detections missed by Aqua MODIS have an average FRP of 6.8 MW per fire pixel, which is much smaller than the average FRP (12.4 MW per fire pixel) of VIIRS fire detections observed by Aqua MODIS. Furthermore, the VIIRS FRP frequency density distributions indicate that MODIS observed and missed a similar number of VIIRS fires with FRP less than 2 MW but MODIS observed more VIIRS fires than missed ones with an FRP larger than 2 MW than it missed (Figure 7b).  
Figure 6: Percentages of the daily not-matched grids (**a**) and fire detections (**b**) and sum FRP (**c**) in these grids between Aqua MODIS (blue) and VIIRS I-band (red) at seven different resolutions across Africa. The solid cycles represent median values and the error bars indicate values at the 5 th and 95 th percentiles.
## 3 Results and Discussion
### Influence of Mass Transport  
When the potential of bulk copper electrode was held at +0.18 V versus Ag/Ag\({}^{+}\) for 10 min in choline chloride-based IL, the surface initially darkened and a green film slowly formed on the electrode's surface, as presented in the previous work [21]. At this potential, the most likely salt is CuCl, which is only sparingly soluble in the electrolyte. The light green colour indicates that the further oxidation of Cu(II) occurs; however, given the applied electrode potential, the oxidation would also be caused by the existence of dissolved oxygen.  
After the green film was washed off, the electrode metal was darkened quite considerably owing to surface roughening. To study the morphology of the surface, it is important to determine the role of mass transport; the experiment was repeated using a rotating disc electrode, as presented in Figure 7. The morphology of the surface before and after anodic polarisation can be seen in later section for a bulk copper electrode with and without stirring.  
The manipulation of mass transportation was tested using a rotating disc electrode, as presented in Figure 7. It is important that in the absence of stirring, passivation occurs, even at stirring rates of up to 500 rpm. Only at rotation speeds above 1000 rpm, the passivation response was lost. As the rotation speed increased, so did the current, due to the anions provided to the surface by which more reactions occurred. The current did not, however, reach a steady state value as would be predicted for a solution based species, so it must be limited by the diffusion of oxidised copper from the electrode's surface rather than a diffusion of chloride from the bulk electrolyte region to the electrode's surface.  
Figure 6: The influence of sweep rate on the equilibrium passivation potential; square-dotted and circle-dotted lines correspond to passivation time and passivation potential, respectively.  
The formation of films on the electrode's surface is known to influence the surface morphology of the dissolved copper at the surface. The process of electropolishing to small extent, i.e., surface levelling, is thought to occur because of film formation which restricts metal ions diffusing away from the electrode's surface as a consequence of compact film formation, i.e., resistive film.
## 1 Introduction
The unified approach mentioned above led to a significant reduction in the computational time required to solve various problems. Applying some algorithms and changing their parameters may reduce computational time. The objective of this paper, given the importance of capacity as a criterion in various problems and in constructing realistic conditions, is to develop the VAOMP model with a capacity criterion, so that this model becomes the VAOCMP (Vector Assignment Ordered Capacitated Median Problem). This is because, in the real world, each facility has a specific capacity that must be considered in the simulation of objects. This study then investigates the application of the VAOMP and VAOCMP models to the specific study area of fire stations in Tehran (in new areas with large populations), with a demand capacity of 50,000 people and the goal of minimizing the arrival time of fire engines to the incident site to 5 min. Using the Tabu Search and Simulated Annealing algorithms in a GIS environment (previously examined by [PERSON] et al. and [PERSON] et al. [14,15]), this paper compares VAOMP with VAOCMP and evaluates the implementation of the two different algorithms in solving large-sizeproblems and then it tests the models in a larger set to make sure that the models on the big data also deliver acceptable results. GIS is also used in this study to collect and organize data, to introduce the candidate locations, and to produce the graphical outputs. The rest of this paper is organized as follows. The following section defines the problem definition, before the subsequent section reviews the literature, to identify which similar studies have already been undertaken. Section 4 presents the development of the VAOMP model, considering the capacity criterion and the formulation of VAOCMP. Section 3 presents the methodology or the workflow of the VAOCMP model with the Tabu and Simulated Annealing algorithms, and Section 4 presents the implementation of the model in two case study scenarios (with and without the capacity criterion). We then conclude with a summary of findings and a discussion of necessary future work.
# Prediction of Cell-Penetrating Peptides Using a Novel HSIC-Based Multiview TSK Fuzzy System
###### Abstract  
Cell-penetrating peptides (CPPs) are short peptides that can carry cargo into cells. CPPs are widely utilized due to their powerful loading capacity and transduction efficiency. Identifying CPPs is the basis for studying their functions and mechanisms; however, experimental methods to identify CPPs are expensive and time-consuming. Recently, CPP predictors based on machine learning methods have become a research hotspot. Although considerable progress has been made, some challenges remain unresolved. First, most predictors employ a variety of feature descriptors to transform an original sequence into multiview data; however, extant methods ignore the relationships between different views, limiting further performance improvement. Second, most machine learning models are actually black boxes and cannot offer insightful advice. In this paper, a novel Hilbert-Schmidt independence criterion (HSIC)-based multiview TSK fuzzy system is proposed. Compared with other machine learning methods, TSK fuzzy systems have better interpretability, and the introduction of multiview mechanisms provides comprehensive insight into the intrinsic laws of the data. HSIC is utilized here to measure the independence and enhance the complementarity between different views. Notably, the proposed method attained prediction accuracy results of 92.2% and 96.2% for the training and independent test sets, respectively. The empirical results show that our promising approach features greater recognition performance than the state-of-the-art method.  
cell-penetrating peptides; machine learning; TSK fuzzy system; multiview learning; HSIC
# Low Temperature Plasma Strategies for
## 3 Results
### Inactivation of Suspension of X. fastidiosa by Plasma Activated Water  
It has been shown that the effect of the active species produced in PAW on bacteria depends on the bacterial species. In general, Gram-negative bacteria, such as XfDD, were found to be more sensitive to PAW than Gram-positive bacteria due to significant differences in cell wall structures, physiological state and ultimately planktonic or biofilm status [67]. To test the efficacy of plasma treatment and its potential for use in vivo, bacteria were suspended in PAW previously treated in the SDBD discharge chamber for 15 min. To decipher the exact mode of action of the plasma treatment, its effect on XfDD was monitored using viability assays with fluorescence live/dead staining.  
Fluorescent probes were used to assess cell membrane integrity and XfDD viability after incubation in PAW. The untreated control cells were almost all stained green with SYTO 9, indicating that they were viable. A small number of cells were stained red with propidium iodide (PI), indicating that they were probably dead (Figure 8A). The number of cells stained red with PI increased dramatically after treatment with PAW, while cells stained with SYTO 9 decreased as expected, and only a few fluorescent cells were visible after treatment (Figure 8B). This indicates that the integrity of the cell membrane of the bacteria was damaged by the treatment, affecting their viability.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**NT** & \(10^{5}\) & \(1.1\times 10^{6}\pm 3.8\times 10^{6}\) & 9 & \(1.9\times 10^{6}\pm 8.7\times 10^{6}\) & 9.3 & NC & NC \\  & \(10^{4}\) & \(1.2\times 10^{6}\pm 3.8\times 10^{5}\) & 6.1 & \(6.5\times 10^{7}\pm 9.5\times 10^{5}\) & 7.8 & & \(7.5\times 10^{8}\pm 1.3\times 10^{6}\) & 8.9 \\  & \(10^{3}\) & 0.0 & & \(6.0\times 10^{5}\pm 8.7\times 10^{4}\) & 5.8 & & \(2.9\times 10^{8}\pm 6.3\times 10^{4}\) & 8.5 \\ \hline
**TRIAL** & \(10^{5}\) & \(3.3\times 10^{6}\pm 1.4\times 10^{6}\) & 6.5 & \(4.2\times 10^{6}\pm 1.4\times 10^{6}\) & 6.6 & & & \\ \(\mathbf{(A)}\) & \(10^{4}\) & \(1.3\times 10^{4}\pm 0.7\times 10^{4}\) & 4.1 & \(1.7\times 10^{5}\pm 1.4\times 10^{5}\) & 5.2 & & & \\ \(\mathbf{1/DAY}\) & \(10^{3}\) & 0.0 & 0 & 0.0 & 0.0 & & & \\ \hline
**TRIAL** & \(10^{5}\) & & & & & \(3.4\times 10^{7}\pm 6.3\times 10^{6}\) & 7.5 & \(1.6\times 10^{8}\pm 5.2\times 10^{6}\) & 8.2 \\ \(\mathbf{(B)}\) & \(10^{4}\) & & & & \(3.3\times 10^{5}\pm 1.4\times 10^{5}\) & 5.5 & \(1.7\times 10^{6}\pm 1.4\times 10^{5}\) & 6.2 \\ \(\mathbf{1/WEEK}\) & \(10^{3}\) & & & & 0.0 & \(1.0\times 10^{5}\pm 5\times 10^{4}\) & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of the dose dependence experiment performed on BYCE agar surface-grown XfDD.  
Figure 8: Typical fluorescence images of XfDD suspension in water for the untreated (**A**), and PAW treated (**B**) samples.
## 4 Results
### Testing and Validation of the Platform-Based Calibration Data Exchange and Management Processes  
The operation of the features of the platform defined in Table 2 was examined based on the sufficiency of the implemented functionalities per feature and the technological readiness of the features. The results of the testing and validation are presented in Table 3. The testing indicated that the platform met its requirements and was mostly working as intended. Some of the features would benefit from further development and refinement. This was expected since the platform was developed as a POC and not a finalized system for industrial use as such.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Universal Features** & **Functionality Readiness** & **Technological Readiness** \\ \hline User management with Azure AD & 2 & 2 \\ DCC import and export APIs & 2 & 2 \\ UI for manual use & 1\({}^{(1)}\) & 2 \\ Listview for DCCs & 2 & 1\({}^{(2)}\) \\ \hline Features necessary for calibration provider & \\ \hline DCC input form & 1\({}^{(3)}\) & 2 \\ DCC sealing & 2 & 2 \\ DCC sharing & 2 & 1\({}^{(4)}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Testing results of the features defined in Table 2. The functionality and technological readiness were evaluated as follows: 2 = pass, usable as is; 1 = pass, usable but improvable; 0 = fail.