esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of Biosynthetic Pathway and Engineered Biosynthesis of Alkaloids
## 6 Opiates
Subsequently, salutaridine was reduced by _P. bracetatum_ salutaridine reductase (SalR) to yield salutaridinol, which was then acetylated by _P. somniferum_ salutaridinol 7-O-acetyltransferase (SalAT) to form 7-O-acetylsalutaridinol. Finally, 7-O-acetylsalutaridinol was spontaneously converted into thebaine, completing the de novo biosynthesis of the key intermediate for morphine biosynthesis in yeast. Conversion of thebaine to hydrocodone was achieved by essentially the same approach used in the previous study (Figure 14) [73]. Overall, the complete biosynthesis of hydrocodone from simple sugar required introduction and engineering of a total of 23 genes from plants (11 genes from four plant species, including one extensively engineered SalSyn gene), mammal (one engineered gene from rat), bacteria (two genes from _P. putida_) and yeast (nine genes, of which four were modified). In another approach, [PERSON] et al. focused their efforts on establishing a BIA biosynthetic pathway in _E. coli_ and successfully developed production systems capable of biosynthesis (_R_,_S_)-THP, (_R_,_S_)-reticuline, thebaine and hydrocodone (Figure 16) [70; 81; 82; 83]. Their strategy relied on supplying the key intermediate reticline as a racemic mixture using an artificial biosynthetic pathway comprised of tyrosinase, L-3,4-dihydroxyphenylalanine (L-DOPA) decarboxylase and monoamine oxidase (MAO) obtained from various bacterial sources. These three enzymes work together to convert L-tyrosine into dopamine and 3,4-dihydroxyphenylacetaldehyde (3,4-DHPAA) [81]. Initially, an NCS gene was introduced to the system to combine dopamine and 3,4-DHPAA into (_S_)-THP for subsequent conversion into (_S_)-reticuline [70; 81]. However, NCS was later eliminated to rely on the spontaneous formation of a racemic mixture of THP from dopamine and 3,4-DHPAA via a Pictet-Spengler reaction inside the cells [82]. Then, (_R_,_S_)-THP was converted into (_R_,_S_)-reticuline by 6 OMT, CNMT and 4'OMT [83]. This was the same set of three enzymes that was used by [PERSON] et al. to prepare (_S_)-reticuline from (_R_,_S_)-THP (Figure 12) [71], except all of the genes used by [PERSON] et al. originatedfrom _C. japonica_. Once _E. coli_ strains for the production of reticuline were established, _P. somifierum_ genes that code for SalR, SalAT and engineered SalSyn having its \(N\)-terminal transmembrane region removed were added to the system to produce thebaine [83]. Lastly, addition of _P. somifierum T6 ODM_ and _P. putida morp_ genes accomplished the biosynthesis of hydrocodone [83]. This _E. coli_-based biosynthetic system developed by [PERSON] et al. employed a stepwise fermentation technique to avoid tyrosinase from degrading the early intermediate THP [82]. This method also helped minimize product inhibition of pathway enzymes and propagation of undesired byproducts through the biosynthetic steps. Those factors are thought to have contributed to the superior product yield obtained with their multi-step _E. coli_ fermentation strategy for the opioid biosynthesis.
## 3 Methodology  
### Analytical Approach  
This study employs a systematic framework to analyze the relationship between air pollutants and meteorological parameters. The methodology integrates data preprocessing, statistical analyses, and machine learning techniques to ensure a comprehensive and reliable evaluation of air quality dynamics. The key steps of the methodology are as follows:  
The analysis focused on key air pollutants (PM\({}_{10}\), NO, NO\({}_{2}\), CO) and meteorological variables (temperature, RH, WS, RainF), as detailed in Section 2.2 (Data Collection), where their relevance to air quality and data sources are thoroughly discussed.  
The methodological approach involved several key steps to ensure a robust evaluation and modeling of air quality in the study region. During data preprocessing, missing values were identified and addressed using appropriate imputation techniques to ensure data completeness, while datasets were standardized and checked for consistency to maintain accuracy and comparability across analyses. Descriptive statistics, including mean, standard deviation (SD), and range, were calculated to understand the central tendencies and variability in pollutant concentrations and meteorological factors. Temporal trends in pollutant levels were examined using the Mann-Kendall test, a robust non-parametric method for detecting monotonic trends in time-series data. Correlation analysis was conducted using the Spearman correlation matrix to evaluate relationships between air pollutants and meteorological parameters, enabling the identification of significant linear and non-linear associations. For predictive modeling, two techniques were applied to predict the influence of meteorological factors on pollutant levels: MLR to assess linear relationships and random forest (RF) to capture complex, non-linear interactions and improve predictive performance. The outputs from statistical analyses and machine learning models were interpreted to elucidate the dynamic interactions between meteorological conditions and air pollutant behavior. This structured and multi-faceted approach, summarized in Figure 2, highlights the interconnected steps from data preprocessing to result interpretation, providing both exploratory insights and predictive accuracy.
## 1 Introduction
By contrast, high storage temperature damages the firmness and color of tomato fruits, while low storage temperature causes some damage, including reduced juice and softening of external shape [29]. Therefore, temperature control is an optimal way to maintain the quality of fresh products during the processes of supply chains [28]. Since temperature plays an important role in reducing fresh tomato production losses after harvest, cold storage has become one of the important post-harvest management practices, and it is widely used to maintain the quality of tomato fruits for a long time. Inaddition, the salinity level of irrigation water is another factor that can have a negative impact on the total yield and quality of tomato fruits [30], by decreasing fresh and dry fruit weights [31]. Storing tomato fruits at a temperature below 10 \({}^{\circ}\)C could negatively affect the external shape and color of tomato fruits; however, storage temperature of about 10 \({}^{\circ}\)C was considered the most appropriate to maintain the tomato quality [32]. In general, the capacity of storage depends on the post-harvest quality of the products and the storage conditions. [PERSON] [33] reported that the shelf life and quality standards of tomato fruits mainly depend on environmental conditions (i.e., the humidity and temperature). Tomato fruits are highly perishable, with a short shelf life when stored at ambient temperature. [PERSON] et al. [34] found the longest shelf lives at 5 \({}^{\circ}\)C, 11 \({}^{\circ}\)C, and 24 \({}^{\circ}\)C were 21, 16, and 8 days, respectively. Many studies have been conducted to evaluate the effect of storage conditions on the fruits of tomato grown in greenhouses, but there is a lack of studies on the effect of storage conditions on tomato fruits produced under hydroponic systems. Therefore, the objective of the study was to evaluate the effect of storage conditions on the quality of tomato fruits produced hydroponically at different levels of water salinity.
# An Approach to Dynamic Sensing Data Fusion
## 2 Dynamic Acquisition and Fusion
### Data Change Decision
In addition, in the dynamic adjustment algorithm of acquisition timeinterval, the sampling interval is adjusted by adding or reducing a fixed time unit. When the deviation between the predicted value and the actual value is large, the increase or decrease of the calculation is linear and frequent. When the deviation between the predicted value and the actual value is small, the increase or decrease of the calculation will occur less. This is an effective adjustment method, which has three advantages: (1) simple and easy to implement; (2) high operating efficiency; (3) high fault tolerance if the time slice value is small. The potential algorithms in this paper include data preprocessing, calculation of acquisition interval, acquisition of data transmission rate, dynamic adjustment of acquisition interval, etc. Their logical relationship is that data preprocessing is carried out first, and periodic cycle is then introduced. In the cycle, data are collected according to the current acquisition interval, and the data transmission rate is detected; the mean and variance are also computed at the same time. According to the transmission rate, adjust the acquisition interval or maintain the status quo so that the cycle continues until the completion of data acquisition. Because this algorithm collects data from adjacent time intervals, the number of acquisitions is small, and the training can be completed in a very short time. In addition, there is a local correlation between the data, which also ensures the validity of the training model. The training model is a linear model. According to the current fitting model, the data of the next moment are predicted, and a new fitting model is then generated at the next moment to predict the data of the next time, i.e., iteration. Acquisition and fusion of sensor data is an organic whole. After the data of the first N frames are collected by several sensors, the average values are fused and compared with the actual values to determine the sampling rate of the next frame.
A Hybrid CNN-LSTM Approach for Muscle Artifact Removal from EEG Using Additional EMG Signal Recording  
[PERSON]  
1 Faculty of Electrical Engineering, Warsaw University of Technology, PI. Politechniki 1, 00-661 Warsaw, Poland; [EMAIL_ADDRESS]  
2  
[PERSON]  
1 Faculty of Electrical Engineering, Warsaw University of Technology, PI. Politechniki 1, 00-661 Warsaw, Poland; [EMAIL_ADDRESS]  
[PERSON]  
1 Faculty of Electrical Engineering, Warsaw University of Technology, PI. Politechniki 1, 00-661 Warsaw, Poland; [EMAIL_ADDRESS]  
[PERSON]  
2  
[PERSON]  
3 Department of Artificial Intelligence, Institute of Information Technology, Warsaw University of Life Sciences, 02-776 Warsaw, Poland; [PERSON]##### Abstract  
Removing artifacts from electroencephalography (EEG) signals is a common technique. Although numerous algorithms have been proposed, most rely solely on EEG data. In this study, we introduce a novel approach utilizing a hybrid convolutional neural network-long short-term memory (CNN-LSTM) architecture alongside simultaneous recording of facial and neck EMG signals. This setup enables the precise elimination of artifacts from the EEG signal. To validate the method, we collected a dataset from 24 participants who were presented with a light-emitting diode (LED) stimulus that elicited steady-state visual evoked potentials (SSVEPs) while they performed strong jaw clenching, an action known to induce significant artifacts. We then assessed the algorithm's ability to remove artifacts while preserving SSVEP responses. The results were compared against other commonly used algorithms, such as independent component analysis and linear regression. The findings demonstrate that the proposed method exhibits excellent performance, effectively removing artifacts while retaining the EEG signal's useful components.  
Keywords:electroencephalography; artifact removal; convolutional neural network; electromyography; muscle artifacts; long short-term memory +
Footnote †: journal: Applied sciences
## 3 Data and Methods
### Methods  
#### 3.2.1 Theory of SBAS-InSAR Analysis  
The SBAS-InSAR method proposed by [PERSON] et al. [58] is a popular time-series InSAR method. It utilizes interferometric pairs of small time-space baselines to obtain ground deformation. The SBAS-InSAR technique can overcome the low coherence of interferograms induced by a single super-image and weaken the influence of the atmosphere and troposphere. With these advantages, the SBAS-InSAR technique is considered as an effective method for monitoring land deformation with sub-centimeter accuracy [39; 50; 59]. In this study, the SBAS-InSAR package of StaMPS was used. The detailed theory and workflows were referred from the literature [56; 60; 61].
## 4 Experimentation and Results
### Experiments and Results
#### 4.2.1 Dataset Organization and Preprocessing  
Our dataset consists of eight attributes, with seven serving as input features from the two ECUs (non-target) and one acting as the label. The non-target features include ECU Serial Number, ECU Type, Sensor Type, Sensor Serial Number, Sensor Connectivity (wired or wireless), Sensor Data Timestamp, and Sensor Measurement/Output (motion detection). The label feature categorizes each record as either benign or malicious. Each dataset record corresponds to an individual observation. We have a total of 1000 records in our dataset with an equal distribution of benign and malicious that is 50/50 split. The dataset preparation follows the steps below.  
Data Collection's Experimental Setup  
Our experimental setup involved using two Arduino Nano 33 BLE Sense devices as ECUs for motion detection. We devised a systematic approach to detect motion and associate it with object detection. After continuously reading values from the Arduino ECU boards function as ECUs within a loop, we compared the values in the feature labeled \"Sensor Measurement/Output (motion detection)\" values of paired sensors at the same time stamp that is stored in the feature labeled \"Sensor Data Timestamp\". These values are compared at the third board, which serves as the vehicle's central module. If the values are identical, we classify this record as benign; otherwise, it is classified as a malicious record. Each data entry was then stored in a text file. We carefully considered the error margin inherent in sensor measurements and accounted for it during the comparison process to ensure accuracy. By adjusting the acceptable range of deviation, we mitigated the impact of minor discrepancies due to sensor noise or timing differences, thereby enhancing the reliability of our classification. Finally, we made an adjustment to the delay time using the delay() function in order to meet the required response time of the system.  
Preprocessing  
We deleted null and negative values from our produced dataset during the preparation stage. We also scaled the data using _Min-Max_ normalization between 0 and 1. The formula applied for this normalizing is displayed here:  
\[X_{new}=\frac{X-X_{min}}{X_{max}-X_{min}}\]  
We employ the above formula in the _Min-Max_ normalizing process to standardize the values. \(X\) here is the original value; \(X_{min}\) and \(X_{max}\) correspondingly show the minimum and maximum values of the dataset. Denoted by \(X_{new}\), the output is the normalized value. Dataset entries including null values were either deleted or controlled using mean, median, or mode imputation. Appropriate transformations were performed to manage negative values in the dataset, such as turning them into absolute numbers or changing them by adding the absolute minimum value, guaranteeing all values were non-negative.
## 2 Methods
### Trajectory Preprocess
#### 2.1.4 Trajectory Partition  
A weak signal or closed AIS transmitter leads to trajectory interruption. Additionally, during a voyage, a ship may anchor, berth, and use different channels to enter and leave a port. In order to classify a ship's behavior in different periods, trajectories need to be partitioned. In this paper, stay points were used to partition trajectories. Stay point stands for a geographic region where a ship stayed for a while. There are two categories of stay points (Figure 4). In one situation, like \(p_{3}\) and \(p_{10}\), a ship remains stationary for a period exceeding a threshold. In most cases, this happens when the ship closes the AIS transmitter, experiences a weak signal, or goes out of the study area. In the other situation, like \(p_{5}\)-\(p_{8}\), a ship wanders around within a certain spatial region for a period. At this moment, several points are involved in the spatial region. Consequently, the mean coordinates of these points are used instead of the stay point. In most cases, this situation occurs when the ship is berthed or anchored.  
In the stay point detection algorithm, two thresholds, distance and time span thresholds, need to be set. If a ship stops, the speed is approximately 0 knots, and the signal update interval is longer. Considering that the signal may be lost and the ship moves under the influence of wind and current, if the trajectory of a ship is not updated for more than 30 min, a stay point is obtained. In addition, if some points of a ship appear within 50 m for more than 30 min, these points are set as an alternative stop point set, and if the nearest distance and time between two alternative stop point sets are within 50 m and 30 min, they are merged into an alternative stop point set. The average position of an alternative stop point set is considered to be a stay point. When a stay point is detected, previous points can form a trajectory, which is then sent to the clustering algorithm.
# Determining Signatures for Energy Mix Produced by Photovoltaic Systems and Wind Turbines
## 3 Results
### Characteristics of the Power Generated by a Wind Turbine System  
The power generated by the wind turbine was calculated based on measurement data from the measuring tower 140 m high. Wind speed measurement data were measured using a wind speed sensor and averaged over a 10-min period. Then, they were sent to the cloud server in CSV format using GSM transmission. It should be mentioned that a Vestas 3.45 MW wind turbine has now been built at the measuring tower location. The power generated by the 3.45 MW wind turbine in May, presented in half-hour periods, is shown in Figure 7. In the case of the power generated by the wind turbine, it is no longer possible to see the cyclicality in energy production at first glance, as was the case with the photovoltaic system. However, an observant observer will notice certain clusters of generated power in individual time periods. One of the clearly visible clusters is the low generated power in the morning hours (between 6:00 and 8:00).  
As in the case of a photovoltaic system, creating a box plot provides valuable information about the characteristics of the power generated by the wind turbine. It is shown in Figure 8. During the night hours, the width of the box is very large with long whiskers extending from the minimum values of 0 to the maximum values of 3450 kW. At night, there is a distribution close to symmetry. The shape of the box plot confirms the noticeable cluster of low power in the morning hours. The box is then much smaller in width and has a skewed distribution. During the day, the box widens again and long upper whiskers are visible. There are also outliers in the upper range then, indicating large short-term gusts of wind. In the evening, a second power minimum is visible, after which the power increases until midnight.  
Figure 6: Histogram of the power generated by a 3 MWp photovoltaic system in May 2024.  
Figure 9 shows a histogram of the power generated by a wind turbine divided into 20 classes. The highest frequency of generated power, also as in the case of a photovoltaic system, falls to values of 0 and close to 0. However, the rest of the graph is not so uniform (flat). The rest of the graph decreases exponentially with the increase in the level of power generated.  
Figure 8: Box plot of the power generated by a turbine with a maximum capacity of 3.45 MW in the month of May.  
Figure 7: Power generated by a 3.45 MW wind turbine during the month of May, presented in half-hour periods.
## 1 Introduction
Examples of anchor-free methods are EmbedMask [33], BlendMask [34], CondInst [35], Polarmask [36], CenterMask [37], SOLO [29], and SOLOv2 [38]. However, these modern solutions required high-end GPUs to achieve very high frame per second (FPS) rates. To reduce the complexity of instance segmentation, a simple approach is imposing an upper limit on the number of instances detected but would inevitably introduce biases and limitations; it would not be scalable, as different datasets would be required to train different versions with different limits. Therefore, this solution was discarded to maintain flexibility, especially for AR rehabilitation platforms that may require interactions involving therapists or additional users besides the patient. The deployed model must run within a real-time application, which imposes a minimum average inference rate to the model of approximately 40 FPS, ideally in regular consumer CPUs. In light of these constraints, we opted for a light-weight solution. The instance-specific information required for the task is relatively minimal, as basic instance segmentation data are sufficient to model interactions between multiple objects, such as in a rehabilitation scenario. Thus, we chose simple, fully convolutional networks (FCNs) like U-Net and ResNet, integrated with a pixel-weighted loss function to enable segmentation of distinct instances into separate blobs, particularly emphasizing boundary pixels between interacting hands. Morphological operations are employed as a processing step to extract individual instances from the segmentation mask, adding minimal overhead. Our approach effectively substitutes complex instance segmentation models with simpler semantic segmentation networks, prioritizing real-time performance over high accuracy. Although the instance information produced by this method may be of lower quality, it is sufficient for the task at hand, and it bypasses the complications typically associated with instance segmentation. In this article, we first introduce the materials and methods employed, including the EgoHands dataset, the required image preprocessing, the two proposed FCN architectures(U-Net and ResNet) along with different loss functions (binary cross entropy, focal loss, and dice loss), activation functions (ReLU and PReLU), and performance metrics used to evaluate the models. Subsequently, we present the results obtained for the two proposed architectures, and upon selecting the optimal architecture, we perform a fine-tuning of model hyperparameters, including dropout rate, loss function, and learning rate scheduler. Finally, we discuss and conclude the results obtained, comparing them with state-of-the-art segmentation results.
## 2 Background
### Code-Based GNSS Inter-User Ranging Techniques
#### 2.1.4 Absolute Position Difference (APD)  
The first technique presented in this paper is called _Absolute Positions Differencing_. This technique was considered with the same nomenclature in [23], under the name of Differential Positioning (DPOS) from single-point processing (DPOS) in [52] and also in [45]. It is based on taking the Euclidean norm of the difference of the SPP estimations of each user individually:  
\[\hat{d}_{ij,k}=||\hat{\mathbf{d}}_{ij,k}||=||\hat{\mathbf{x}}_{ijk}-\hat{ \mathbf{x}}_{i,k}||. \tag{8}\]  
This technique will be named APD in the results presented in Section 4. A technique similar to APD was described in the literature in [23; 45] and was called _Pseudorange Ranging_. This technique works by performing a joint estimation of both users' state vectors within the same equation by superimposing the normal equations of the two. This technique was tested but not reported in the present contribution as it did not show added value with respect to APD.