esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Related Works
### Dynamic Time Warping  
The classification methods of time series can be roughly divided into four categories, namely, deep learning (e.g., fully convolutional networks [30]), feature learning method (e.g., time series forest [31]), ensemble methods (e.g., elastic ensemble [32]), and distance learning methods [19; 33; 34; 35; 36] with dynamic time warping (DTW) as a development basis.  
DTW [19] was originally used in the field of speech recognition, elongating or shortening (compresses) the unknown speech until it is consistent with the length of the reference template. In this process, the time axis of the unknown speech will be distorted or bent so that it can correspond to the standard pattern. This is given two time series \(T_{1}=\{a_{1},\cdots,a_{L_{1}}\}\) and \(T_{2}=\{b_{1},\cdots,b_{L_{2}}\}\) with lengths of \(L_{1}\) and \(L_{2}\), respectively. A pair of warping paths \(\beta=(\beta_{1},\beta_{2})\) between \(T_{1}\) and \(T_{2}\) need to meet the following constraints:  
\[1 =\beta_{1}(1)\leq\cdots\leq\beta_{1}(q)=L_{1}, \tag{17}\] \[1 =\beta_{2}(1)\leq\cdots\leq\beta_{2}(q)=L_{2},\] (18) \[q \leq max(L_{1},L_{2})\leq L_{1}+L_{2}-1. \tag{19}\]  
The optimal path between \(T_{1}\) and \(T_{2}\) is given by  
\[\beta^{*}=\textit{arg min}\hskip-14.226378 pt\sum_{\beta\in\Gamma =1}^{|\beta|}\delta\Big{(}T_{1\beta_{1}(i)},T_{2\beta_{2}(i)}\Big{)}\, \tag{20}\]  
where \(\Gamma\) is the set of all possible paths and \(\delta(\cdot,\cdot)\) is a distance metric. The DTW distance is given by  
\[\delta_{DTW}(T_{1},T_{2})=\sum_{i=1}^{|\beta^{*}|}\delta\Big{(}T_{1 \beta^{*}_{1}(i)},T_{2\beta^{*}_{2}(i)}\Big{)}. \tag{21}\]  
To sum up, searching for an optimal DTW path \(\beta^{*}\) is equivalent to finding the optimal solution from all possible warping paths according to minimizing the cumulative distance cost. The recurrence of cumulative distance matrix \(\Pi\) with \(\Pi(0,0)=0\) in DTW can be written as  
\[\Pi(i,j)=\delta(a_{i},b_{j})+min\{\Pi(i-1,j-1),\Pi(i-1,j),\Pi(i,j-1)\}. \tag{22}\]  
Developed from DTW, the 1 NN-DTW model [19; 33], which is the combination of 1 NN classifier and DTW, variant DDDTW [34], which is based on derivative distance, and constructing kernel function using DTW distance [35; 36] are widely used in time series classification. Utilizing DTW distance instead of Euclidean distance for calculating Gaussian RBF kernel, [PERSON] et al. proposed the Gaussian DTW (GDTW) kernel [35]. However, since DTW distance is not symmetric, the GDTW kernel is also not a symmetric kernel. To overcome this limitation, global alignment kernels (GAK) [36] used in [16] need to calculate all the alignments, despite the huge computational cost.
# Volatile Metabolome and Aroma Differences of Six Cultivars of _Prunus mume_ Blossoms
###### Abstract  
_Prunus mume_ is a traditional Chinese plant with high ornamental and application values due to its very early blooming and unique fragrance. Long-term breeding and cultivation have resulted in a variety of _P. mume_ blossoms and have made their exploitation more possible. Existing studies on the volatile metabolome and aroma of _P. mume_ blossoms are limited. In this study, six extensively planted cultivars of _P. mume_ blossoms, including Guilhong (GLH), Yudie (YD), LvE (LE), Dongfang Zhusha (DFZS), Jiangmei (JM), and Gongfen (GF), were investigated for their differences in terms of volatile metabolome, as well as their aroma characteristics based on the strategies and methods of metabolomics. The volatile metabolites were analyzed using HS-SPME-GC-MS technique. A total of eighty-nine compounds were detected and sixty-five of them were tentatively identified, including thirty-seven phenylpropanoids/benzenes, seventeen fatty acid derivatives, ten terpenoids, and one other compound. YD contains the most volatile metabolites in terms of number and amounts, which impart more abundant aromas to this cultivar. Fifteen differential compounds were screened through the untargeted metabolic analysis of twenty-nine samples by principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA), while nine compounds were screened based on the odor activity value (OAV) analysis of the sixty-five identified compounds. GLH and GF, JM and LE were found to be more similar to each other based on chemometrics analysis of both volatile contents and OAVs, while YD and DFZS were markedly different from other cultivars. Six main metabolites, including benzaldehyde, methyl benzoate, benzyl acetate, eugenol, (E)-cinnamic alcohol, and 4-allylphenol, together with 2-nonenal, 3,4-dimethoxytoluene, and trans-\(\beta\)-Ionone were screened as differential compounds, owing to their higher contents and/or lower olfactory threshold, which endow an almond, cherry, phenolic, wintergreen, cananga odorata, floral, jasmine, hyacinth, cinnamon, clove, woody, medicinal, and violet fragrance to each variety, and greatly contribute to the aroma differences of six cultivars of _P. mume_ blossom.  
a 2023 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).  
Prunus mume blossoms; untargeted metabolomics; volatile metabolome 4.0
# Generating Loop Patterns with a Genetic Algorithm and a Probabilistic Cellular Automata Rule
## 2 Generating Loop Patterns with a Genetic Algorithm
### Tiles for Generating Loop Patterns  
Now, we want to generate a loop pattern using the GA. A set of 12 tiles was designed specifying such patterns (Figure 3a). The size of the tiles was restricted to \(3\times 3\). The tiles were designed in the following way: (1) possible loop patterns were constructed on a grid (the _aimed patterns_). (2) The aimed patterns were scanned by small windows at several relevant positions. Such _window patterns_ were stored or remembered. (3) Tiles were designed that can cover the window patterns and that can overlap.  
The tiles A (Figure 3) define corners, B define straight continuations, and C define possible turns. The null pixels are necessary in order to enforce connections. Simply speaking, the tiles' surfaces need properly structured interfaces in order to aggregate. At the moment it is not clear how the interfaces can be defined in a methodical way.  
Figure 3b,c shows how certain small loop patterns can be assembled by tiles. A small open square (Figure 3b) can be formed as shown by (A0 + A1 + A2 + A3) + (B0 + B1 + B2 + B3) where '+' means adding a tile or compound tile that overlays without conflict. The resulting composition can fit into a cyclic field of size \(4\times 4\) or \(5\times 5\). A larger open square (Figure 3c) can be formed as shown fitting into a cyclic field of size \(5\times 5\) or \(6\times 6\). Figure 4 shows (a) another small loop and (b) how it can be assembled by tiles.  
Until now, it seems difficult to automate this process, and therefore, it is a topic of further research. It is also a question of what is a minimal set of \(m_{1}\times m_{2}\) tiles, or what is the smallest size of the tiles in order to produce the same class of patterns. Note that there are different classes of loop patterns that differ in certain details like allowed crossings (and how) or allowed diagonal connections. We discuss here only the class of loop patterns that are defined by the tile set shown in Figure 3a.  
Figure 1: The three tiles used for the first pattern generation by GA: (**a**) point; (**b1**) a square (11/11) with upper-left zeroes; and (**b2**) with the lower-right zeroes. The tile centers are marked by \".\". The tiles b1 and b2 can overlap forming a “full” square surrounded by 10 zeroes (at relevant positions), where “1” marks an overlap.  
Figure 2: \(9\times 9\) patterns generated by a Genetic Algorithm using three tiles, a point, and two overlapping partial squares that yield together a full square. The number of tile matches was optimized. The positions where tile centers match are marked. (**a**) 18 matches. (**b**) 18 matches. (**c**) 17 matches. (**d**) 16 matches.
## 3 Dataset and Features
### Synthetic Network Generation  
The limited size of our real dataset (eight networks) presented challenges for robust statistical analysis. To address this limitation, we generated 100 synthetic networks with controlled properties. This approach allowed us to test information diffusion across diverse network structures.  
We implemented a network generation function that created five distinct network types:  
* Random (Erdos-Renyi) networks with varying density parameters;
* Scale-Free (Barabasi-Albert) networks with preferential attachment;
* Small-World (Watts-Strogatz) networks with controlled clustering;  
Figure 6: Selected features by importance score.  
* Tree networks with hierarchical structures;
* Community networks with modular organization.  
The synthetic networks showed diverse structural properties (Figures 7 and 8). Random networks had the highest edge density (mean edges = 1050), while Tree networks maintained minimal connectivity (mean edges \(\approx\) nodes \(-1\)). Community networks showed distinct modular structures with high clustering coefficients.  
For each synthetic network, we calculated 30+ structural metrics including the following (Figures 9-12):  
* Basic measures: nodes, edges, density;
* Degree statistics: min/max/avg degree, degree heterogeneity;
* Centralization metrics: various centrality measures;  
Figure 8: Distribution of network sizes (edges).  
Figure 7: Distribution of network sizes (nodes).  
* Connectivity metrics: diameter, average path length, components;
* Community structure: modularity, community size;
* Spectral properties: algebraic connectivity, spectral radius.  
These comprehensive features allowed us to identify which structural properties most strongly influenced information diffusion. The synthetic dataset provided a robust foundation for testing diffusion models across diverse topological structures.  
Figure 10: Average clustering coefficient by graph type. Box plots show standard statistical summaries with outliers marked as circles.  
Figure 9: Density by graph type. Box plots show standard statistical summaries with outliers marked as circles.
## 7 Conclusions  
In real-time applications of wireless sensor networks, the time used to deliver sensor-streams from source to sink nodes is a major concern. The amount of data in transit through these constrained networks has a great impact on the delay. In this work, we presented a data-centric strategy to meet deadlines in soft real-time applications for wireless sensor networks. This work represents shows how to deal with time constraints at lower network levels in a data-centric way.  
With our data-centric strategy we met application deadlines in several scenarios. In additional, we showed how to design real-time sensor-stream reduction applications and a analytical model used to found the ideal sample size. Results showed the efficiency of the strategy by reducing the delay without losing data representativeness. If the application is not strongly dependent on data accuracy, or the network operates in exception situation (e.g., few resources remaining or urgent situation detection), then data reduction algorithms are powerful tool for real-time applications for resource-constrained networks.  
Figure 16: \(\mathbf{\Phi}\)-error considering the delay caused by relay nodes with concurrent traffic.  
As future work, we intend to match the proposed application-level solution with lower-level ones, for example, by considering some real-time-enabled signal processing method. In this case, not only data from a source is reduced, but similar data from different sources is also reduced, resulting in a more efficient solution. Another future work is to use feedback information to enable the source nodes to perform the reduction sooner. However, we intend to improve the central sampling algorithm complexity to \(O(n)\), by considering some rank selection algorithms.
## 2 Methodology and Site Description
### Data Collection from LoRaWAN-Based Sensor Device  
LoRaWAN is one of the most widely used Low Power Wide Area technologies that aims to collect and communicate data from an end sensor device at a large distance, making it perfect for a scenario in which sensor devices collect wind and barometric data and convey them to a centralized system. LoRaWAN employs a typical star-of-star topology where end devices communicate data in a single hop to one or more gateway devices. These messages are further forwarded to the network and application server for further processing, allowing authorized data to be forwarded to other external services (e.g., using MQTT message forwarding). LoRaWAN allows battery-operated devices to periodically transmit sensor data over large distances, while minimizing consumption during inactive periods. LoRaWAN technology finds its application in smart city/smart agriculture environments where there is no need for real-time (every second) transmission from end devices. During the inactive period, end devices simply cut off the consumption, allowing a battery lifetime up to a couple of years without any external power source.  
Figure 6 depicts the architecture of the MeteoHelix weather station of [PERSON] design that utilizes LoRaWAN as a radio technology. MeteoHelix [35] is an automatic all-in-one microwave station which is solar powered and can be active for up to 6 months without sun. It measures air temperature to WMO accuracy, air humidity to WMO accuracy with dew and frost point output, barometric pressure and solar irradiation (pyranometer). Another sensor of [PERSON] design was also installed that utilizes LoRaWAN communication--MeteoWind IoT PRO. MeteoWind [36] is used for wind monitoring and employs two sensors: a separate wind vane and anemometer. MeteoWind allows 4+ months of battery life without sun and a maintenance-free service life with long-term measurement stability due to its elliptical cup and metal construction. As depicted in Figure 7, both devices are placed at a 10 m height without any object around within 150 m so that both the wind speed and wind direction are not distorted, while both are located in location of Kastela Bay. Since both MeteoHelix and MeteoWind IoT PRO employ LoRaWAN communications to convey data over the air to the centralized system, The Things Network as a service provider was used to collect data for further processing. As a LoRaWAN gateway, an indoor Sentirius RG1 xx LoRaWAN gateway device placed around 150 m from the sensor devices was employed that forward messages to The Things Network (TTN) cloud infrastructure. Once the message arrives at the gateway, it is forwarded to the TTN Network and Application server. Furthermore, TTN allows message forwarding from TTN infrastructure to our  
Figure 5: Shoreline reflection coefficient along the coastline.  
dedicated Waves Seafront Monitoring app using MQTT protocol, which is described more in detail in the following section.
# ErrorExplainer: Automated Extraction of Error Contexts from Smart Contracts
## 5 Source Code Analysis
### Structure Analysis and Initialization
#### 5.1.2 Trace Analysis  
Errors can occur at any point along an execution path. Hence, ErrorExplainer groups candidate error contexts by feasible execution traces to narrow the search space. In Solidity, a trace starts at an external function and proceeds through internal calls. We call the ordered sequence of these functions a _trace_.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Pattern** & **Storage Slot** \\ \hline EIP-1967 & 0x360894a13 ba1a3210667c828492 db98 dca3e2076 cc3735a920a3 ca505d382 bbc \\ \hline Transparent Proxy & 0x5c60 da1b  
The transaction data reveal the entry function only, leaving all internal steps implicit. By computing which internal functions are reachable from which entry points, ErrorExplainer can pre-filter irrelevant errors. The reachability algorithm operates in three steps:  
1. **Call Graph Construction**: Creates the call graph based on the function call relationships in the source code. The call graph is represented as \(G_{f}=(\mathbb{F},\mathbb{E})\) where \(\mathbb{F}\) is the set of functions and \(\mathbb{E}\subseteq\mathbb{F}\times\mathbb{F}\) represents call relationships. An edge \((f_{1},f_{2})\in\mathbb{E}\) indicates that \(f_{1}\) invokes \(f_{2}\).
2. **Graph Reversal**: Reverses the call graph to show reachability relationships explicitly. In the reversed call graph \(G_{f}^{-1}\), every edge \((f_{1},f_{2})\in\mathbb{E}\) is reversed to become \((f_{2},f_{1})\).
3. **Entry Point Identification**: For each function \(f\in\mathbb{F}\), ErrorExplainer traverses \(G_{f}^{-1}\) to identify all external functions, denoted as \(\mathbb{F}_{ext}\), that can reach the given function \(f\). The reachability mapping \(R_{c}:\mathbb{F}\to\mathbb{Z}^{ext}\) from each function to its set of reachable entry-point functions is given by \[R_{c}(f)=\{f_{ext}\mid f_{ext}\in\mathbb{F}_{ext},\ f_{ext}\xrightarrow{*}_{G _{f}}f\},\] where \(f_{ext}\xrightarrow{*}_{G_{f}}f\) denotes that function \(f\) is reachable from function \(f_{ext}\) in the original call graph \(G_{f}\) via zero or more intermediate calls.
## 2 Ultra-High-Speed MCF-Based ICS OTN Prototype System
All the transmission equipment, including OTUs, WSSs, and EDFAs, are connected to the network management system, which enables the online parameter configuration of each device. Moreover, the real-time BER values and alarms for detected environmental disturbances can also be directly obtained from the network management system.
# A DMAIC Integrated Fuzzy FMEA Model: A Case Study in the Automotive Industry
## 3 Proposed Method  
The developed fuzzy FMEA system follows a basic architecture of the fuzzy FMEA inference system consists of three main modules: input interface module (fuzzification), knowledge base module (rules base), and output interface model (defuzzification), as shown in Figure 1, adapted from [10].  
As it can be observed in Figure 1, the input variables correspond to the parameters S, P, and D that are used in FMEA [23], whereas the output variable corresponds to the RPN. S, P, and D are fuzzified using membership functions to determine the degree of membership in each input class. The resulting fuzzy inputs are evaluated on the FIS, which uses a well-defined rule base. These rules are of the \"IF-THEN\" type and together with fuzzy logic operations are used to determine the level of risk of failure. The fuzzy conclusion is then defuzzified to obtain the RPN (output). The higher the value of the RPN, the greater the risk and vice versa.  
In the proposed methodology the fuzzy FMEA system is implemented in the DMAIC cycle. Figure 2 summarizes the main aspects of the methodology.  
Figure 1: Fuzzy inference system (adapted from [10]).  
Figure 2: Fuzzy FMEA system integration in DMAIC cycle.  
DMAIC methodology is employed for problem solving, being used in Six Sigma projects to improve the quality of manufacturing processes. The objective is to identify and solve the different root causes that cause defects through stages already defined. The name DMAIC comes from the initials of each stage, define, measure, analyze, improve, and control, and that each phase is essential for the Six Sigma project to be a success [24; 25; 26]. In order to correct a specific quality problem, DMAIC cycle was applied in this study.  
In the next section we explain the application of the proposed fuzzy FMEA system according to each DMAIC stage.
## 3 Technological Advancements in Bee-Based Biosensors/Biomonitors
### Light Detection and Ranging (LIDAR) Tracking and Mapping of Honey Bees  
With the short range issues for RFID and the focus on individual bees, and since our primary methods rely on mass conditioning of bees, we re-directed our efforts to developing image subtraction, acoustic detection, and laser for locating, tracking, and mapping bees. Related to our DARPA project, our collaborators at Sandia National Laboratories showed that a LIDAR system could be used to track honey bees, providing both range and coordinates of the bees at a distance of 1 mile (1.6 km) [14]. However, Sandia's instrument was intended for atmospheric studies and, as such, was large, and the LIDAR beam was not eye-safe for humans who might be down-range from the instrument.  
Having established that LIDAR could be used for locating, tracking, and mapping bees, our research team, representing UM, Montana State University (MSU), and Sandia Laboratory, obtained patents and published a series of studies to develop and use both visual monitoring and LIDAR to map bee locations [14; 15; 16; 70; 71; 72; 73]. The majority of the verification research was conducted using an Army test field in Missouri. The instruments used for these studies were mostly research prototypes.  
Figure 5: Current size of miniature RFID tags used by investigators [69].  
Since 2012 we have been using and testing two commercial prototype bee-mapping LIDAR instruments. These instruments are relatively compact, ruggedized, decimeter resolution LIDARs with built-in image mapping and both 2-D and 3-D visualization capabilities (Figure 6a-c). The LIDAR software includes modules for visualization, contouring, and surface modeling software from Golden Software in Colorado [74]. LIDAR management, display, and visualization software runs under Microsoft Windows.  
Figure 6: (**a**) Commercial prototype of bee-mapping LIDAR; (**b**) Instrument display screen. The computer screen display provides real-time detection of individual bees (_i.e._, dots shown in the sweep). Charts at the bottom characterize signal duration, shape, and frequency for each detected insect. Banner at the top gives instrument settings and controls. Most recent bee detection appears on screen in red; (**c**) Three-dimensional density map of actual bee locations across a field from one of our trials using bees conditioned to find a unique scent. The larger and taller the vertical “cone”, the more bees at that spot, and concurrently, the higher the vapor concentration of the subject chemical.
## 6 Results and Discussion
### Comparison with the Application's QoS Requirements  
This section provides a comprehensive assessment of the AA approach. We investigate the impact of MCR and DTR values on network performance. For each case, we consider up to four applications running concurrently.
## 5 Evaluation
### Evaluation Result
#### 5.2.3 Latency Iteration  
We use an iterative algorithm to gradually obtain an effective partitioning and deployment strategy to shorten a CNN model's inference latency. To study the decline curve of inference latency during the iteration process, we record the inference latency values during one iteration process, as shown in Figure 11. From Figure 11a, it can be seen that for VGG16, compared to the network layers located at the front, our method believes that the network layers located near the end are more suitable as cutting points for model partitioning. From Figure 11b, we can see that our method does not have the same preference for cutting points for AlexNet as for VGG16.