esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Techno-Economic Analysis at 2060
### Materials and Methods
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & **Baseline 2030** & CC 2060 & CC + TEC(C) 2060 & CC + TEC(C) 2060 & TEC(C) 2060 & CC + TEC(C) 2060 \\ \hline Water depth (m) & \(-\)443 & \(-\)443 & \(-\)443 & \(-\)443 & \(-\)443 & \(-\)443 \\ Distance from shore (Km) & 40.5 & 40.5 & 40.5 & 40.5 & 40.5 & 40.5 \\ Distance from grid (Km) & 49.4 & 49.4 & 49.4 & 49.4 & 49.4 & 49.4 \\
**Mean wind speed at hight (m/s)** & **8.92** & **8.67** & **8.67** & **9.01** & **9.26** & **9.01** \\ Wind farm capacity (MW) & 1000 & 1000 & 1000 & 2000 & 2000 & 2000 \\ Number of turbines & 100 & 100 & 100 & 100 & 100 & 100 \\ Turbine rate (MW) & 10 & 10 & 10 & 20 & 20 & 20 \\ Hub height (m) & 119 & 119 & 119 & 168 & 168 & 168 \\ Terrestrial cable cost (M/Km) & 0.25 & 0.25 & 0.25 & 0.25 & 0.25 & 0.25 \\ Offshore cable cost (M/MW) & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 \\ Total grid-connection costs (M) & 42.73 & 42.73 & 42.73 & 42.73 & 42.73 & 42.73 \\ CAPEX (excluding cables costs) (M/MW) & 2.30 & 2.30 & 2.07 & 2.07 & 2.07 & 1.50 \\
**Total CAPEX (MM/MW)** & **2.34** & **2.34** & **2.11** & **2.09** & **2.09** & **1.52** \\
**OPEX (MM/MW)** & **0.125** & **0.125** & **0.119** & **0.119** & **0.119** & **0.063** \\
**WACC (s)** & **7.00** & **7.00** & **5.00** & **3.75** & **3.75** & **2.50** \\ Farm lifecycle (year) & 30 & 30 & 30 & 30 & 30 & 30 \\ Total production efficiency (\%) & 83.3 & 83.3 & 83.3 & 83.3 & 83.3 & 83.3 \\ Gross annual production (M/Wh) & 4,044,000 & 3,916,000 & 3,916,000 & 8,178,000 & 8,422,000 & 8,178,000 \\
**Net annual production (MW)** & 3,367,481 & 3,260,894 & 3,260,894 & 6,809,906 & 7,013,088 & 6,809,906 \\ Specific productivity (M/Wh/MW) & 3367 & 3261 & 3261 & 3405 & 3807 & 3405 \\ Capacity Factor (\%) & 38.4 & 37.2 & 37.2 & 38.9 & 40.0 & 38.9 \\
**LCE (M/Wh)** & **93.2** & **96.2** & **78.6** & **69.3** & **67.3** & **39.6** \\
**LCE variation referring to baseline (s)** & / & **+3.3\%** & **–15.7\%** & **–25.6\%** & **–27.8\%** & **–57.5\%** \\ \hline \end{tabular}
\end{table} 
Table 8: Case study 3 (Sardinia); net wind speed variation: \(-\)0.25 m/s. (Variable parameters in bold).
## 2 System Overview  
A functional overview of the system is depicted in Figure 1. The goal of the system is to provide a way for effective identification of the intention of the user based on multimodal user commands for enhanced quantification of uncertain terms related to distances (e.g., \"little\" and \"far\") in motional navigation commands such as \"move far forward\". The voice recognition and understanding section converts voice into text and parses the commands with aid of the language memory. The voice response generation section is a text-to-speech converter that can be used in order to generate the voice responses of the robot. The overall interaction between the robot and the user is managed by the interaction management module (IMM). These modules have been implemented similar to the system explained in [23]. The gesture evaluation module is deployed for identifying the non-verbal instructions accompanied with voice instructions by analyzing the skeleton of the user returned by the Kinect motion sensor attached to the robot. The analyzed body postures of the user are then fed into the intention identifier module (IIM) in order to identify the intention of the user related to the given voice instruction. Based on the intention of the user, the required actions for fulfilling a command may be switched by the motion intention switcher (MIS). Subsequently, the parameters required for the quantification of the uncertain information by the uncertain information understanding module (UIUM) [20] will be modified by MIS, if alterations are required.  
The robot experience model (REM) [23] is a hierarchical structure that holds the knowledge about the environment, actions and context in a way that the knowledge can be used by the robot for fulfilling the actions in the robot's domain. The parameters required for interpretation of uncertain information by the UIUM is also retrieved from the REM. The low-level navigation controlling functionalities such as localization within a given navigation map are managed by the navigation controller. The required navigation maps can be created using the Mapper3 application. The information from the low-level sensors of the robot such as sonar sensors is retrieved by the sensory input handling module (SIHM). The spatial information extraction module (SIEM) extracts the information about the environment from the navigation maps and the SIHM. The extracted information is fed into the REM.
# Information-Centric Network-Based Vehicular Communications: Overview and Research Opportunities
## 3 ICN-Based VANET Issues
ICN In-network caching can be achieved using three principles, i.e., democratic, uniform, and pervasive [16]. In the democratic principle, all network nodes have equal rights to publish contents if they have cached them already. In the uniform principle, a routing protocol may be used for all contents or all network nodes, if required. Pervasive principle demands that a cached content should be available/provided to all nodes in the network.
# Impact of Texture Information on Crop Classification with Machine Learning and UAV Images
## Materials and Methods
### Classification Methods and Feature Extraction
#### 2.3.3 Texture Information  
To reduce the noise effects of isolated pixels in classification results, texture information is considered as an auxiliary feature for classification. Image texture analysis methods can be divided into four categories: statistical, geometric, model-based, and signal processing [43]. GLCM, developed by [PERSON] et al. [20], is a widely applied statistical method for remote sensing data processing such as vegetation structure modeling [44] and land-cover classification [45]. The original image is first converted to gray-scale. Then, the spatial features of the gray-scale image are extracted using the relationship of the brightness values between the center pixel and its neighborhood within the predefined kernel. The relationship of the brightness values is represented by a matrix which consists of the occurrence frequency of sequential pairs of pixel values existing simultaneously along the defined direction. By using this relationship, the GLCM can generate different texture information according to gray-scale level, kernel size, and direction. Fourteen texture features defined by [PERSON] et al. [20] are correlated, indicating that using all possible texture features provides redundant spatial contextual information which is not useful for classification. In this study, six texture features [46] were considered: (1) mean (ME), (2) standard deviation (STD), (3) homogeneity (HOM), (4) dissimilarity (DIS), (5) entropy (ENT), and (6) angular second moment (ASM), presented in Equations (1) to (6):  
\[\text{ME}=\sum_{i=0}^{N-1}\sum_{j=0}^{N-1}i\times P(i,j), \tag{1}\]  
\[\text{STD}=\sqrt{\sum_{i=0}^{N-1}\sum_{j=0}^{N-1}P(i,j)\times(i-\text{ME})^{2}}, \tag{2}\]  
\[\text{HOM}=\sum_{i=0}^{N-1}\sum_{j=0}^{N-1}\frac{P(i,j)}{1+(i-j)^{2}}, \tag{3}\]  
\[\text{DIS}=\sum_{i=0}^{N-1}\sum_{j=0}^{N-1}P(i,j)\times|i-j|, \tag{4}\]  
\[\text{ENT}=\sum_{i=0}^{N-1}\sum_{j=0}^{N-1}-P(i,j)\times\ln(p(i,j)), \tag{5}\]\[\text{ASM}=\sum_{i=0}^{N-1}\sum_{j=0}^{N-1}P(i,j)^{2}, \tag{6}\]  
where \(N\) denotes gray-scale level, while \(P(i,j)\) is the normalized gray-scale value at positions \(i\) and \(j\) within the kernel, and its sum is 1.  
The above texture features were generated from omnidirectional 64-shade gray-scale images. To test the impacts of the kernel size, we used three different kernel sizes: 3 \(\times\) 3 (GK3), 15 \(\times\) 15 (GK15), and 31 \(\times\) 31 (GK31).
# From Lean Production to Lean 4.0: A Systematic Literature Review with a Historical Perspective
## 3 Results
### Answering RQ3. Historical Evolution of the Term \"Lean\"
#### 3.2.1. Lean Production (1988)  
Introduced in 1988 by [PERSON] as the \"lean production system\" [23], this expression was used as an alternative to the \"Toyota production system\". It was fully adopted after publication of the seminal book _The Machine that Changed the World_[21], which described lean production (LP) as an alternative to mass production [2], whereby the original holistic approach of TPS was partially lost.  
Interest in LP grew between 1992 and 1996 (see Figure 6), then diminished from 1997 to 2007. Since 2008, it has once again become of academic interest, along with lean manufacturing.  
In 2007, [PERSON] [19] outlined a detailed historical evolution of the term, which was highly influenced by MIT research. The same year, [PERSON] et al. [6] analyzed the historical context and reported for the first time the semantic confusion surrounding the term \"lean production\" while also reinforcing the conception of lean \"as a system\", for which he identified 10 dimensions useful to researchers.  
In 2013, [PERSON] et al. [47] identified six research areas in the field and provided another warning about the system conception becoming fragmented and dissociated. In 2015, [PERSON] et al. [1] concluded that LP continues to have a high impact on academia, practitioners, and consultants. They further propose a holistic rather than \"bits-and-pieces\" approach [1] (p. 16).  
In 2015, _The Analysis of Industry 4.0 and Lean Production_[48] presented the first comparative study between LP and I4.0. With 17 papers published in the last 5 years, the interrelationship between LP and I4.0 has clearly awakened new interest in this research field [49; 50].
# Electromagnetic Field-Aware Radio Resource Management for 5G and Beyond: A Survey
## 5 Challenges and Open Research Topics  
This section discusses key challenges and open research areas related to EMF exposure in 5G networks, including health effects, measurement complexities, resource allocation, and the impact of emerging technologies like RISs.
## 4 Experimental Results Analysis and Discussion
### Experimental Details  
All experiments were implemented using Python 3.9.19 and the PyTorch 1.11 framework. Training was conducted on a workstation equipped with an NVIDIA GeForce RTX 4090 GPU. For the self-supervised pretraining stage, we trained the model for 200 epochs using the LARS optimizer, with a batch size of 256 and a learning rate set to \(5\times 10^{-2}\). The temperature parameter \(\tau\) in the contrastive loss is set to 0.1. During the fine-tuning stage, we trained for 100 epochs using a smaller batch size of 32, and adopted the Adam optimizer with a learning rate of \(5\times 10^{-4}\) for stable convergence. The test batch size was also set to 32 to match the training configuration during evaluation. The VAT weight (\(\lambda\)) is set to 0.3, and the perturbation magnitude (\(\epsilon\)) is set to 4.0. These values are selected to balance the trade-off between task loss and adversarial smoothing. The selection principle of VAT  
Figure 4: The antenna for observing and receiving satellite signals.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Pretraining** & **Fine-Tuning** \\ \hline Number of classes & 10 (0–9) & 10 (0–9) \\ Dimension of samples & \(1\times 4000\) & \(1\times 4000\) \\ Number sample of per class & 800 & \{10,15,20,25,30,35,40} \\ Train/Valid ratio & - & (0.8,0.2) \\ Test set & - & \(1500\times 4000\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Details of BDS signal datasets for pretraining and fine-tuning.  
parameters will be discussed in detail in Section 4.6. The details and simulation parameters are shown in Table 2.  
We use four widely used evaluation metrics for SEI, including Accuracy (_ACC_), Recall (\(R\)), Precision (\(P\)), and F1-score (\(F\)). Among them, Accuracy, Recall, Precision, and F1-score range from 0 to 1. It is worth noting that a higher value of all metrics indicates better performance of the model. These evaluation metrics are defined as follows:  
Accuracy (_ACC_): Accuracy measures the proportion of correct predictions out of the total number of predictions. It is defined as:  
\[ACC=\frac{TP+TN}{TP+TN+FP+FN} \tag{20}\]  
where \(TP\) represents the number of true positives, \(TN\) represents the number of true negatives, \(FP\) represents the number of false positives, and \(FN\) represents the number of false negatives.  
Recall (\(R\)): Recall measures the proportion of actual positives that are correctly identified:  
\[R=\frac{TP}{TP+FN} \tag{21}\]  
Precision (\(P\)): Precision measures the proportion of positive predictions that are actually correct:  
\[P=\frac{TP}{TP+FP} \tag{22}\]  
F1-score (\(F\)): The F1-score is the harmonic mean of precision and recall. It provides a balance between the two metrics:  
\[F=\frac{2\times P\times R}{P+R} \tag{23}\]
# Optimizing DNA Sequence Classification via a Deep Learning Hybrid of LSTM and CNN Architecture
## 5 Performance Evaluations
### Ablation Study
The recall of 0.559 demonstrates that the model can detect about 55.9% of the true positive instances in the data. A harmonic meaning of recall and precision is the F1 score of 0.655. This is the trade-off between recall and precision, and thus performance, and higher values indicate better performance. Overall, these are indicative that the model performed very well on the test set. The accuracy of the 5 machine learning models vs. the CNN+ LSTM model in Figure 11 depicts the classification accuracy performance outcomes of 5 deep learning models against the proposed Hybrid LSTM+CNN model. Table 5 compares the accuracy, sensitivity, and specificity of six models for predicting DNA sequences, which are LSTM + CNN, logistic regression, naive Bayes, random forest, KNN, and XGBoost. The maximum accuracy was for the LSTM + CNN model with 100%, second highest for XGBoost with 81%, KNN with 70.77%, random forest with 69.89, naive Bayes with 17.80, and logistic regression with 45.31%. \begin{table}
\begin{tabular}{l c} \hline \hline
**Models** & **Accuracy (\%)** \\ \hline
**1. Logistic Regression** & **45.31** \\ \hline
**2. Naive Bayes** & **17.80** \\ \hline
**3. Random Forest** & **69.89** \\ \hline
**4. K-Nearest Neighbor** & **70.77** \\ \hline
**5. XGBoost** & **81.50** \\ \hline
**6. LSTM + CNN** & **100.00** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Accuracy scores. Figure 11: Accuracy of 5 machine learning models vs. the CNN+ LSTM model. The results in Figure 12 present the performance of six models on the classification of DNA sequences, using accuracy as the metric. The Hybrid LSTM + CNN, from among the models being compared, recorded the best accuracy of 100%, which manifested its superiority in the classification task and proved its ability to learn both spatial and sequential patterns of DNA data. This was followed closely by the Deepsea model, with a good accuracy of 76.59%, hence showing how appropriate this task is for convolutional neural networks. Dimensionality reduction scored an accuracy of 68.15%, outscoring Deep Variant, which scored 67.00%. Among the models evaluated in Table 6 are the transformation model that yielded a relatively low accuracy of 43.75%, followed by the graph neural network at 30.71%, indicating room for improvement in addressing this classification problem. These results certainly indicate the role of hybrid architecture and advanced feature extraction techniques in performing DNA sequence analysis. \begin{table}
\begin{tabular}{|l c|} \hline
**Model Accuracy (\%)** & **Model Accuracy (\%)** \\ \hline Hybrid LSTM + CNN & 100.00 \\ \hline Deepsea & 76.59 \\ \hline Dimensionality Reduction & 68.15 \\ \hline Deep Variant & 67.00 \\ \hline Transformation Model & 43.75 \\ \hline Graph Neural Network & 30.71 \\ \hline \end{tabular}
\end{table} 
Table 6: Accuracy score. Figure 12: Accuracy score of 5 deep learning models vs. the CNN + LSTM model.
###### Abstract  
The Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) provided twenty years of data on Earth's time-varying gravity field. Due to their design, GRACE and GRACE-FO are inherently limited in their spatiotemporal coverage, limiting their resolution to a few hundred kilometers and temporally to roughly monthly solutions. To increase the global spatiotemporal resolution and allow for the determination of submonthly time-varying gravity field signals, a constellation of GRACE-type satellite pairs is a possible path forward. Advances in small form factor instrumentation for small satellites have become progressively inexpensive, reliable, and of higher quality. This leads us to consider that a constellation of GRACE-type small satellites could be part of future gravimetric satellite missions. In this work, we investigate the viability and limitations of a genetic-algorithm-based optimization and its objective function to generate satellite constellations to recover daily Earth system mass changes. The developed approach is used to create satellite constellations that are optimally designed to recover gravity variations of sufficient resolution at a range of temporal frequencies (i.e., daily to monthly). We analyze a constellation's performance using a combination of observability in space, accounting for directionality, and homogeneity in time. This allows us to navigate through a vast search space in a relatively short period of time and estimate the relative performance of constellations to each other. Using evolutionary theory, we converge towards a set of optimally selected orbits. The characteristics of the designed constellations have been validated using high-fidelity numerical simulations. We summarize these results and discuss their implications for possible future constellations of small GRACE-like satellite pairs. The resulting constellations have an inherently improved spatiotemporal performance, which reduces temporal aliasing errors and allows the characterization of daily mass-change effects.  
Keywords:constellation design; genetic algorithm; optimization; temporal aliasing; mass change observations; gravity recovery and climate experiment; GRACE; GRACE-follow on +
Footnote †: journal:  
_remote sensing_
## 4 Industrial Applications of Terpenoids
### Food Industry  
Terpenoids are widely used in the food industry as natural preservatives, flavoring agents, sweetening agents, and functional food ingredients [171]. Regarding natural food preservatives, terpenoids are significant phytochemicals with a wide spectrum of antioxidant and antimicrobial activities that are found in a wide variety of fruits and vegetables. The bioactivity of these phytochemicals as antioxidants allows them to scavenge reactive oxygen and nitrogen species (e.g., superoxide, hydroxyl, and peroxyl radicals) from meat and other products, which helps to avoid the oxidation of lipids in food products (e.g., meat, fish, fruits, and vegetables). This makes them extremely helpful in food preservation [172]. In this sense, monoterough from EOs from _Lamiucea_ plants, which demonstrate a high antioxidant activity, have been investigated in recent years as natural alternatives to prevent the oxidation of food-related products [173]. Research conducted by [PERSON] et al. [174] investigated the influence of oregano EO (composed of 47.4% carvacrol, 26.4% \(p\)-cymene, and 3.02% thymol) applied within pectin coatings on the inhibition of _Alternaria alternata_ growth, sensorial acceptability, and antioxidant content of tomatoes. The findings demonstrated that the antifungal and antioxidant activities of pectin-oregano EO coatings were enhanced without having a detrimental impact on the sensory acceptability of tomatoes.  
Terpenoids like linalool, \(\alpha\)-pinene, limonene, and geraniol have been designated as \"generally recognized as safe\" (GRAS) by the United States Food and Drug Administration (FDA) and are widely used as natural flavoring agents in the food industry. Geraniol, one of the most in-demand monoterependoids, with an amusing rose-like odor, has been used as a flavoring additive in chewing gum, candy, ice cream, beverages, and many other products [171]. Nerolidol is a classic sesquiterpene, formally allowed as a food flavoring additive by the US FDA, giving a wood- and fresh-bark-like scent [175]. With a market volume of roughly 10,000 kg annually, valencene is another sesquiterpenoid utilized commercially as an addition to give or reinforce a fruity and woody flavor in foods and beverages. Additionally, a few different terpenes with distinct flavors, discovered in a variety of plants, have been explored not only by the food industry but also for their role in consumer products like pesticides and insecticides, among others.
# Improved Performance of Li-ion Polymer Batteries Through Improved Pulse Charging Algorithm
## 1 Introduction
### Pulse Charging Algorithm  
Pulse charging algorithms, when properly designed and implemented, can result in significant benefits in terms of improved battery charge and energy efficiencies, longer battery cycle life, and decreased battery charge time. Pulse charging involves the use of charge current pulses, which have carefully controlled duty cycle, frequency, and peak amplitude to charge the LiPo battery. Figure 2 shows a pictorial representation of the waveforms of a pulse charger under operation. These carefully controlled charge current pulses ensure that the lithium ions that are electrochemically produced during each pulse from the cathode migrates to the anode and successfully intercalate before the next group of ions arrive at that electrode. This prevents accumulation of Li\({}^{+}\) ions on the surface of the electrode and hence mitigates dendrite formation, which is detrimental to the battery cycle life and inherently can be a safety risk [34]. In the CC-CV charging algorithm, the decreasing current used in the CV stage is needed to prevent the undesirable effects of dendrite formation, such as the rupturing of the battery separator, which can lead to thermal runaway.  
Numerous researches have been conducted in the area of pulse charging. [PERSON] [27] proposed a pulse charging strategy whereby the duty cycle was constantly changing to ensure the battery was charged efficiently. [PERSON] et al. [32] developed a technique to dynamically change the frequency of the applied pulses used for charging to minimize battery impedance and improve charge time. [PERSON] [26] also proposed a variable frequency approach for the pulse charger to enhance battery performance. The key to the successful implementation of the pulse charging algorithm is the careful selection of the pulse charger parameters, which include peak charge current amplitude, frequency, and duty cycle. This paper therefore summarizes the combined impact of these parameters, in addition to the ambient temperature at which the battery is charged, on charge time, battery charge, and energy efficiencies, and battery cycle life. This paper is divided into five sections. Section 2 will summarize the key findings on the impact of frequency, duty cycle, peak amplitude, and ambient temperature at which selected LiPo batteries were charged on battery charge time, and battery charge and energy efficiencies. Section 3 will review key findings on the impact of these pulse charging parameters, discussed in Section 2, on battery cycle life and battery impedance characteristics. Section 4 discusses various  
Figure 2: Waveforms of a pulse charger under operation.  
opportunities in the area of battery charging and where the future of battery charging algorithms is headed. Conclusions are then drawn in Section 5.