esa-sceva/satcom-chunk-collection huggingface dataset:
# Back to the Metrics: Exploration of Distance Metrics in Anomaly Detection
## 2 Methodology
### Visualization Analysis of Different Metrics
#### 2.3.2 PatchCore Anomaly Score Calculation Function  
The function composition of \(\alpha_{1}\) resembles Softmax. As shown in Figure 4, \(\alpha_{1}\) has larger values at the boundaries of the sample coverage area. As \(k\) increases, the maximum value of \(\alpha_{1}\) first decreases rapidly and then decreases slowly, approximately inversely proportional  
Figure 3: Contour plots of Equation (6) for anomaly scores under a simulated co-reset with different values of \(k\). Orange dots represent outliers and blue dots represent normal samples.  
to \(k\). Combining Equations (11) and (12), we can observe that as \(k\) increases, the weight \(w\) tends to approach 1, and the influence of distance variance on the final anomaly score decreases.  
Figures 5 and 6 illustrate the behavior of \(s\) and \(w\) in Equation (11). \(w\) increases with the increase of \(k\), while its factor \(\alpha_{1}\) decreases significantly. Except for Figure 5a, which exhibits a bizarre distribution of anomalies due to excessive influence from \(\alpha_{1}\), assuming a randomly distributed sample set, both Figure 5b and Figure 5c are relatively reasonable: they conform to Assumption 2. If the outliers in the normal samples are marginal samples of this cluster, the gradient of anomaly scores on the outlier side is larger.  
In other words, \(w\) induces a subtle merging phenomenon in the distribution of anomaly scores for the \(k\) nearest neighbors of each point. This has adverse effects on the distribution of anomaly scores for clusters with a size less than or equal to \(k\).  
As shown in Figure 7b, when \(k=2\), the distribution of anomaly scores around the cluster composed of three sample points on the right side of the image does not exhibit significant inward shifting, forming a closed whole. Around the cluster composed of two sample points in the middle, a slight phenomenon of shifting towards the right side appears. While around the cluster composed of one sample point on the left side, there is a clear tendency for merging towards the right side. All clusters shown in Figure 7b exhibit slight merging phenomena. These merging or shifting phenomena are inconsistent with our assumptions.  
Figure 4: Contour plots of the \(\alpha_{1}\) of Equation (12) under a simulated co-reset with different values of \(k\). Orange dots represent outliers and blue dots represent normal samples.  
Figure 5: Contour plots of Equation (11) for anomaly scores under a simulated co-reset with different values of \(k\). Orange dots represent outliers and blue dots represent normal samples.
## 3 Results
### Identification of DEGs Related to Glucosinolate Biosynthesis  
We further investigated the DEGs related to glucosinolate biosynthesis and found that 17, 14 and 14 DEGs were involved in glucosinolate biosynthesis in the three plant parts, respectively (Figure 2D-F, Table S6). Among them, compared to low RAA content pools, all 17 DEGs were upregulated in the high RAA content pool of leaf (Figure 2D, Table S5). While 12 of 14 DEGs were upregulated in the high RAA content pool of stalk and floret, respectively (Figure 2E,F, Table S6). As an essential transcription factor, \(MYB\) plays a critical regulatory role in glucosinolate biosynthesis. In this study, 98, 110, and 136 \(MYB\) were found in leaf, stalk and floret, and 2, 6, and 2 had a significantly difference, respectively (Figure 2D-F, Table S6).  
Strikingly, six genes (_Bo5g113720, Bo2g011730, Bo2g161100, Bo7g098000, Bo4g018590, Bo5g021810_) were differentially expressed between the low and high RAA content pools in  
leaf and floret (Figure 2D,F, Table S6). _Bogg083740_ was differentially expressed between low and high RAA content pools in leaf and stalk (Figure 2D,E, Table S6). Phylogenetic analysis showed that _Bog5113720_ had high similarity with _BCAT4_ of _Arabidopsis thaliana_, which encodes a methionine-oxo-acid transaminase and is involved in the methionine chain elongation pathway that leads to the ultimate biosynthesis of methionine-derived GSLs (Figure S4A). _Bog2g161100_ and _Bogg08000_ had high similarity with _MAM1_ of Brassica rapa, and it catalyzed the aliphatic GSLs synthesis with short-chain (Figure S4C). _Bog5021810_ showed high similarity with _CYP79F1_ of _Brassica rapa_ and _Arabidopsis thaliana_ (Figure S4E), and it played a critical role in the formation of the core structure 6. Meanwhile, _Bog2g011730_ and _Bog018590_ were similar to _UNE12_ and _IPMI2_ of _Arabidopsis thaliana_ (Figure S4B,D). The results of RNA-Seq and qRT-PCR analysis all showed that all these genes were upregulated in the high RAA content pools compared to the low pools (Figure 3A, Table S7). In addition, we also selected six DEGs related GSLs biosynthesis to verify the results of RNA-seq gene expression in the stalk, and qRT-PCR analysis was well matched with RNA-seq analysis (Figure 3B, Table S7).
## 1 Introduction
Ultimately, spectral decomposition misses partials on the higher end of the spectrum because the separation is artificial, depending on the spectrum estimation method rather than the spectral characteristics of musical instrument sounds. We consider spectral decomposition to be a consequence of artifacts from previous sinusoidal models instead of an acoustic property of musical instruments. Therefore, we propose the full-band modeling of musical instrument sounds with adaptive sinusoids as an alternative to spectral decomposition. Adaptive sinusoids (AS) are nonstationary sinusoids estimated to fit the signal being analyzed usually via an iterative parameter re-estimation process. AS have been used to model speech [43; 44; 45; 46] and musical instrument sounds [25; 47]. [PERSON] [45; 48] developed the adaptive Quasi-Harmonic Model (aQHM), which iteratively adapts the frequency trajectories of all sinusoids at the same time based on the Quasi-Harmonic Model (QHM). Adaptation improves the fit of a spectral template via an iterative least-squares (LS) parameter estimation followed by frequency correction. Later, [PERSON] [43] devised the extended adaptive Quasi-Harmonic Model (eaQHM), capable of adapting both amplitude and frequency trajectories of all sinusoids iteratively. In eaQHM, adaptation is equivalent to the iterative projection of the original waveform onto nonstationary basis functions that are locally adapted to the time-varying characteristics of the sound, capable of modeling sudden changes such as sharp attacks, transients, and instrumental noise. In a previous work [47], we showed that eaQHM is capable of retaining the sharpness of the attack of percussive sounds. In this work, we propose full-band modeling with eaQHM for a high-quality analysis and synthesis of isolated musical instrument sounds with a single component. We compare our method to QSS estimated with the standard SM [20] and EDS estimated with ESPRIT [36]. In the next section, we discuss the differences in full-band spectral modeling and traditional decomposition for musical instrument sounds. Next, we describe the full-band quasi-harmonic adaptive sinusoidal modeling behind eaQHM. Then, we present the experimental setup, describe the musical instrument sound database used in this work and the analysis parameters. We proceed to the experiments, present the results, and evaluate the performance of QSS, EDS, and eaQHM in modeling musical instrument sounds. Finally, we discuss the results and present conclusions and perspectives for future work.
## 2 Materials and Methods
### Motion Configurations Tested and Design of the Simulations
#### 2.3.5 Fourth Set, S4: Common and Random Sources of Additive Noise on Markers' Trajectories  
In the S4 set, the effect of the different sources of accuracy errors was observed by tuning of the values \(\text{SNR}_{\text{cmm}}\) and \(\text{SNR}_{\text{md}}\) to evaluate the sensitivity of the AoR estimation algorithms to common (rigid) and random (non-rigid) additive noise on marker's trajectories. Four values were tested for each gain, that is, 100, 90, 80, and 70 dB. The simulations were performed for A and D configurations, with the default range of motion of flexion/extension equal to (+20, -30) deg. A default cycle duration equal to 1.2 s was considered.
## 7 Smartphones as Mobile M2M Gateways
The smartphone has to transmit this data through 3G and needs to append protocol headers and likely execute security mechanisms. We assume the use of CoAP application protocol, no header compression, and that each transmission includes a fixed overhead of 62 B to the useful data: 10 bytes for a common CoAP confirmable request [54], 13 bytes for DTLS per packet [54], 8 bytes for the UDP header, 20 bytes for the IP header, 2 bytes for the PDCP header, 5 bytes for the RLC and MAC Headers, and 4 bytes for CRC [82]. The data can be forwarded as 0.702 KB every second, 1.342 KB every two seconds, 6.462 KB every 10 s, and so forth, depending on the aggregation chosen. Figure 6 depicts the battery depletion of the smartphone for different transmission rates using 3G. Due to the scale in hours of  
Figure 5: Common three stages of a 3G transmission: the ramp and the tail are transitions to and from high-power states where the actual transmission takes place. Based on [79]. the \(x\) axis, it is not possible to visualize the changes in slope when transmissions occur. The results show that only for larger aggregations of transmitted data, \(64\) KB every 100 s, we can achieve a full day, e.g., 08:00 - 20:00, of battery operation. We conclude that frequent transmissions of small size data will have an undesired effect in the expected depletion time of a smartphone's battery in mobile M2M communications scenarios. In order to use smartphones as M2M gateways, it is a good advice to maximize the collection of data necessary to be forwarded from nearby sensors, and maximize the intervals between transmissions. More research is required to devise energy efficient transmission methods that enable the use of smartphones as mobile gateways. Techniques such as transmission scheduling, header compression, data aggregation, or leverage heterogeneous networks can enhance smartphones' capabilities acting as gateways while easing their energy consumption problems.
# Modeling and Assessment of GPS/BDS Combined Precise Point Positioning
## 3 Data Processing  
To assess GPS/BDS PPP performance under different processing models, and to validate the simplified GPS/BDS combined PPP model, daily static and kinematic PPP data analysis is performed. Data of 11 IGS MGEX stations in January 2014 is used. Figure 1 shows the geographic distribution of these tracking stations.  
For results evaluation and comparison, data processing is performed in the following four scenarios: GPS-only PPP, BDS-only PPP, and GPS/BDS combined PPP with traditional and simplified models, where station-wise ISB is estimated as a daily constant in a traditional model.  
Precise satellite orbits and satellite clocks from SHA [17; 30] are used for data analysis. We apply the IGS absolute antenna phase center model for GPS observations [31], the phase-wind up modeling [32] and the station displacement are modeled according to the IERS conventions 2010 [33]. A cut-off angle  
Figure 1: The geographic distribution of GPS/BDS stations used in the data analysis.  
of 7\({}^{\circ}\) is set for usable measurements and data sampling is set to 30 s. An elevation-dependent weighting strategy is applied to measurements at low elevations and the noise ratio between pseudo-range and phase observation is 500:1. Moreover, we estimate wet zenith path delay every hour by applying the most recently developed GPT2 empirical slant delay model [34]. An improved version of the LTW_BS software is used [35].
# A Review of Machine Learning and IoT in Smart Transportation
## 2 Machine Learning and IoT in Smart Transportation
### ML and IoT Applications in Smart Transportation
#### 2.2.4. Accident Detection/Prevention  
Accident detection and prevention is a domain of smart transportation and a crucial activity for every city since an operating prevention method can help save human lives. Road accidents can be prevented if the drivers stay more focused throughout the traveling period. An accident prevention system can notify the driver about critical situations and allow them to act in a timely manner. Accident detection can also reduce the accidents number and the traffic congestion, by identifying accident prone areas or accidents that have taken place in the live traffic network. ML has been proven very useful in detecting road accidents, or detect patterns that could lead in new accidents and notify drivers in order to avoid them.  
The authors in [22] developed a method for cooperative vehicle perception, using a CHMM method so that information can be shared among vehicles in a timely manner to avoid accidents. Real-time autonomous accident detection is performed in [29], by analyzing data obtained from road sensors with an FF-NN, a Regression Tree, and the \(k\)-NN algorithms. V2V communication together with the use of an RF method compared with an SVM and an ANN is used for accident detection in the roads [37]. A novel approach to detect objects in the blind spot of smart vehicles is used in [19], using an FCN method. The DRAM method developed in [25] can be also used for road accident detection since it can help with multiple object detection from image data. In the research [17], the drivers' consciousness is monitored with image data, processed by AdaBoost to prevent possible accidents. An Inception Neural Network is used in [32], to detect accident prone areas. Finally, the work of [24]to detect street elements could also support accident prevention. The research uses a combination of a DBN and a \(k\)-NN or SVM classifier.  
The authors in the research [72] propose an IoT cloud platform that will enable traffic visualization and early notifications about sudden slowdowns that can lead to accidents. The implemented setup would provide Infrastructure as a Service (IaaS), Platform as a Service (PaaS), Software as a Service (SaaS), and a novel approach called IoT as a Service (IoTaaS). GPS data are collected by devices placed in volunteer vehicles, and they are sent in a cloud server via a 4G network. The cloud server handles GPS data with the OpenGTS platform and enables traffic visualization with OpenStreetMaps. The data are stored in an SQL and a Distributed MongoDB to enable further analysis. The back end scalability is supported with the use of Docker containers. The system response time a key element in the success of the implementation, and the suggested systems achieves sending an alert for a 1 km distance in about 120 ms.
## 5 Discussion  
Researchers around the world and automotive companies are developing their own inventory forecasting techniques that consider various factors that affect the vehicle's operational condition. For automotive companies, it is very important that the level of customers' satisfaction meet their expectations, while not increasing inventory costs and related costs. Demand for spare parts is unstable, the moment of vehicles' faults has a stochastic character, and seasonal changes also affect the formation of stocks.  
Various methods are used to determine the required margin. Inventory management involves maintaining such a ratio of parts with frequent or irregular demand, so that at the same time there is a high turnover, and the cost of maintaining stocks is minimal. Most service centers use a warehouse management system based on the classical principles of inventory management, and it is relevant with an ideal logistics system and a minimum time factor. If these criteria cannot be met, service and repair times are extended, especially away from logistics centers.  
In this paper, we tried to solve the problems of demand uncertainty, failure moments, and other factors and the complexity of calculations and forecasting for a warehouse with a multi-item load using game theory. We have described two cases of decision making: under conditions of risk and uncertainty, the occurrence of which depends on the amount of information about the input flow of enters to the service center. In addition, since we had access to statistics at our disposal, we were able to check the result of the calculation using the first method. Existing methods also allow for solving the problem of determining the minimum stock in the warehouse. But the volume of data that is collected from service centers is very large and it is almost impossible to process them without automation. Thus, the proposed method, in contrast to solving the problem by representingthe process under consideration in the form of Markov chains, allows the use of a tabular form with subsequent automation of calculations. In addition, it allows us to consider when calculating the amount of penalties for unrepaired vehicles and the costs associated with illiquid stocks.
# Chloroplasts at the Crossroad of Photosynthesis, Pathogen Infection and Plant Defense
## 1 Introduction
Plants have developed elaborate constitutive and inducible defense mechanisms to protect themselves from pathogen infections. Constitutive defense includes non-specific antimicrobial toxins and preformed structural barriers (e.g., cell walls) [43]. Inducible defense is triggered by the recognition of pathogen-associated molecular patterns (PAMPs), or effector proteins released by the pathogen [44; 45; 46]. The recognition of PAMPs by pattern recognition receptors leads to PAMP-triggered immunity (PTI); the recognition of effectors by resistance (R) proteins leads to effector-triggered immunity (ETI) [44; 45; 46]. Early defense events include cytoskeletal reorganization, cell wall fortification, generation of ROS, stomatal closure, and synthesis of antimicrobial secondary metabolites [21; 29; 47; 48]. Later defense responses include transcription and translation of pathogenesis-related (PR) proteins and the development of the hypersensitive response (a type of programmed cell death [PCD] to minimize pathogen spread) [49; 50]. Plants develop the hypersensitive response, a hallmark of ETI, if the pathogen is able to suppress plant basal defense (i.e., the constitutive and inducible defense described above) [44; 45; 46]. These local defense responses require the participation of multiple defense-related hormones and non-hormone signaling molecules. For example, ABA, JA, SA, ET, ROS, nitric oxide (-NO) and Ca\({}^{2+}\) all function in PAMP-triggered stomatal closure [29]. In addition to local defense at or near the site of infection, plants may develop the systemic acquired resistance (SAR), a \"whole-plant\" resistance, after a localized exposure to a pathogen [51]. This process is associated with the accumulation of PR proteins and requires the participation of SA [51]. Elaborate interplays exist among photosynthesis, pathogen infection, and plant defense. Here, we review: (1) how defense-related signaling molecules or their precursors are generated in the chloroplast; (2) how these signals crosstalk and regulate photosynthesis and plant defense; (3) how chloroplast-targeted effectors and phytotoxins produced by bacterial pathogens manipulate chloroplastic functions, especially photosynthesis, to suppress plant defense and promote pathogenicity; and (4) why the chloroplast plays a central role in the interplay between photosynthesis, pathogen infection, and plant defense. Plant defense is also regulated by photorespiration and light. If readers are interested in the roles of photorespiration and photoreceptors in plant defense, they may refer to reviews on related topics, such as [PERSON] et al. [52] and [PERSON] [53].
## 2 Methodology
### MIC-FCBF Combined Feature Screening
#### 2.1.1 Maximum Mutual Information Coefficient  
The Maximum Mutual Information Coefficient (MIC) is a correlation analysis method based on the mutual information coefficient, which measures the dependency between two random variables [36]. MIC is computed by assessing mutual information across different dataset subsets and selecting the maximum value. It involves dividing the dataset, calculating mutual information for each subset, and choosing the highest value. This method quantifies linear or nonlinear correlations, preserving information for strongly correlated variables and addressing nonlinear relationships among weakly correlated ones. The MIC calculation equation is as follows:  
\[I[U;O]=\sum_{U,O}p(U,O)\log_{2}\frac{p(U,O)}{p(U)p(O)} \tag{1}\]  
\[MIC[U;O]=\max_{|U|\rangle|O\rangle<B}\frac{I(U,O)}{|\log_{2}(\min(|U|,|O|))|} \tag{2}\]  
In this equation, I represents mutual information, \(|\)U\(|\)\(|\)\(|\)O\(|\) denotes the number of large grids partitioned in the U and O directions, and B is the grid constraint variable, where B = n\({}^{0}\) (\(\theta\) = 0.55 or 0.6).
# Review of Traffic Assignment and Future Challenges
## 5 Discussion
### Promising Directions
However, current traffic assignment techniques do not allow for real-time route planning. Computation times increase when considering vehicle behavior and traffic regulation details with high resolution. Currently, traditional traffic assignment approaches fall short in delivering real-time itineraries. Simultaneously, there exists a lack of consensus in the literature regarding the impact of contemporary navigation systems. Several authors have underscored adverse effects, as indicated in refs. [183; 184; 185; 186]. This issue pertains to the challenge of efficiently assigning _CAV_ on a constantly evolving road network, taking into consideration real-time traffic conditions. One crucial aspect of the problem is determining optimal routes for vehicles based on real-time traffic information. The commonly used shortest path search method, which calculates the fastest routes between two points on a road network, does not effectively solve the real-time traffic assignment problem. The main drawback of this method is its lack of responsiveness to constantly changing traffic conditions. Figures 4 and 5 depict the adverse effects of such an approach on route selection. The first figure illustrates how vehicles can become trapped when new vehicles are rerouted to an alternative path with smoother traffic, highlighting the necessity for a thorough evaluation of intersection times. Meanwhile, the second figure demonstrates how a vehicle may be misdirected due to the absence of traffic that is not yet present, emphasizing the critical importance of accurately estimating upcoming traffic conditions. New strategies with simple rules must be defined to guide vehicles in real time. These rules should be capable of providing both efficient and fair routes while fostering smooth traffic flow. Some studies already address these issues by proposing itinerary reservations [187,188]. Among these studies, some focus on road booking in order to not exceed their capacity [189,190,191,192]. Others are inclined towards intersection reservations [149,193,194,195] to alleviate costs associated with conflicts arising from diversified routes. However, these approaches are relatively recent and deserve to garner broader attention within the community to receive more feedback on microscopic models of large cities incorporating innovative strategies for sharing road infrastructures with more transparency.