esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
The results demonstrate the potential of _Y. lipolytica_ W29 to grow and produce microbial lipids from a mixture of VFAs (acetate, propionate, and butyrate) in two-stage batch cultures--growth phase in glucose or glycerol, followed by the lipogenic phase in VFAs. A single pulse of 6 g\(\cdot\)L\({}^{-1}\) VFAs strategy led to the mobilization of microbial lipids synthesized owing to the complete consumption of VFAs after 48 h of cultivation. However, the addition of a higher amount of VFAs (3 pulses of 6 g\(\cdot\)L\({}^{-1}\) VFAs each or 1 pulse of 18 g\(\cdot\)L\({}^{-1}\) VFAs) avoided the complete depletion of VFAs for the duration of the experiments, increased the C/N ratio, boosted the production of microbial lipids, and prevented the lipid turnover. _Yarrowia lipolytica_ lipids, rich in unsaturated fatty acids (particularly oleic acid) and odd-chain fatty acids (heptadecenoic and margaric acids), are a promising feedstock for the bioenergy industry. The biodiesel produced from these lipids meets the criteria of the EU biodiesel standard EN 14214, demonstrating the potential of _Y. lipolytica_ W29 as a pivot in the production of feedstock for high-quality biofuels from low-cost substrates, according to the biorefinery and bioeconomy circular guidelines. The results described herein open new perspectives on the development of an integrated bioprocess, in which crude glycerol (a by-product of the biodiesel industry) or glucose-rich by-products (e.g., lignocellulosic biomass hydrolysates) could be the substrate for cellular growth, and organic wastes-derived VFAs (obtained by anaerobic fermentation) will be used for lipids synthesis. In this sense, future research will focus on the two-stage batch production of microbial lipids by _Y. lipolytica_ from a VFA-rich medium obtained in the anaerobic fermentation of food waste.  
Conceptualization, I.B. and M.L.; methodology, A.S.P.; validation, I.B. and M.L.; investigation, A.S.P.; resources, I.B.; writing--original draft preparation, A.S.P. and M.L.; writing--review and editing, I.B. and M.L.; supervision, I.B. and M.L. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UIDB/04469/2020 unit and Doctoral grant (SFRH/BD/129592/2017).  
Not applicable.  
The authors declare no conflict of interest.
# Short-Term Power Load Prediction of VMD-LSTM Based on ISSA Optimization
## 5 Experiment and Analysis
### Example 1 Experiment
Quantitative error metrics (Table 7) and visual comparisons collectively demonstrate that the ISSA-optimized GRU and LSTM models outperform non-optimized baselines. Specifically, the ISSA-enhanced architectures achieve a superior fitting performance across all test months, with notable improvements in capturing load peaks and valleys (e.g., July peak load prediction error reduced by 31.2% vs. non-optimized LSTM). Figure 11: Comparison of April forecasts. To further validate the generalizability of the proposed method, other months are used for prediction. The comparisons of the prediction results for April, July, and October are shown in Figures 11-13, respectively, and the comparison of the errors is shown in Table 7. Specifically, the ISSA-enhanced architectures achieve a superior fitting performance across the two datasets. The results are shown in Table 8. The results are shown in Table 9. The results are shown in Table 10. The results are shown in Table 11. The results are shown in Table 6.0 The results are shown in Table 6.1 The results are shown in Table 6.2 The results are shown in Table 6.3 The results are shown in Table 6.4 The results are shown in Table 6.5 The results are shown in Table 1  
all test months, with notable improvements in capturing load peaks and valleys. This indicates that the improved model proposed in this paper has certain advantages. The comparative analysis of the prediction performance demonstrates that applying Variational Mode Decomposition (VMD) significantly enhances the forecasting accuracy for the regional power load in Tetuan City compared to direct prediction methods without decomposition. This improvement primarily stems from VMD's capability to effectively decompose complex load signals into relatively stable intrinsic mode functions (IMFs), thereby extracting essential temporal features while reducing the data complexity through adaptive noise reduction. As illustrated in Figure 14, the forecasting accuracy exhibits notable seasonal variations, with January and October demonstrating superior prediction performances compared to April and July. The observed forecasting discrepancies can be attributed to distinct seasonal characteristics: April represents a transitional period between spring and summer characterized by significant temperature volatility and abrupt increases in cooling demands. This meteorological instability, combined with altered consumption patterns during public holidays (e.g., Tomb-Sweeping Day), results in substantial deviations from typical load profiles. July presents unique forecasting challenges as the peak summer month, where extreme heat events drive cooling loads beyond conventional projections. Concurrently, industrial load patterns become less predictable due to strategic load-shifting measures implemented by energy-intensive enterprises and potential power rationing during grid stress conditions. These operational adaptations create short-term load distribution anomalies that conventional forecasting models struggle to capture effectively.
## 4 Discussion  
Based on the model design, simulation experiment, and trial production of key components, an integrated remote-operation-type dragon-fruit-picking device was designed. Compared with the manual picking of dragon fruit, according to the calculation of 12 h of work per day, manual picking can only pick 1500 dragon fruits per day, but this picking device can pick 7000 fruits per day. The cost of the manual picking of dragon fruits is approximately CNY 1500/mu, but the cost of machine operation is approximately CNY 200/mu. In other words, the efficiency of this picking device is 4.67 times that of manual picking, and the cost saving is approximately CNY 1300/mu. This picking device only needs one person to complete the picking operation of the machine, saving labor while reducing costs. The picking device integrates picking, sorting, and removing fruit, greatly reducing the labor force and labor time, freeing up the hands of fruit farmers. The picking device can complete both the picking of a single fruit and the picking of a plurality of fruits grown at a growing point, so that the device can better complete the picking of dragon fruits with complex growth. The entire control system of the device selects the STM32F4 microcontroller as the main control module, the software part of the control system is programmed in the C language using Keil5 software (5.25 version), and the modification and debugging of the target detection algorithm is carried out in the Python language on PyCharm, through the establishment and debugging of the hardware and software of the control system. The sensing ability of the picker to the external environment was enhanced, and the working efficiency of the mechanism was improved.  
Compared with the domestic DELIXI fruit-picking mechanism, the dragon fruit damage rate of the DELIXI fruit-picking mechanism was 6.0%, the net picking rate was 80.7%, the dragon fruit damage rate of the machine was 2.9%, and the net picking rate was 90.5%. Moreover, the DELIXI fruit-picking mechanism is only able to complete the picking of single fruits; it is not able to pick more than one fruit growing at one growing point. Under identical picking conditions, this machine can guarantee the efficient harvesting of dragon fruit and satisfy the operational demands specific to dragon fruit picking. Vietnam, Thailand, and the Philippines are world-renowned dragon fruit producers, and Vietnam alone  
Figure 16: Response surfaces of interacting factors on the net picking rate (**a**–**c**) and damage rate (**d**–**f**) of dragon fruit.  
accounts for more than 50 per cent of global dragon fruit production; therefore, the dragon-fruit-picking device mentioned in this paper has promising applications in countries such as Vietnam, Thailand, and the Philippines. At present, the picking device still suffers from the disadvantage of a limited picking area, and the shortcomings of the device will be addressed in the future by optimizing the mechanical structure and transmission system.
## 6 Complexity Analysis
### Complexity Analysis of ANN and SVM-Based NLEs  
As mentioned, in contrast to DBP and IVSTF-based equalizers, the complexity of ANN and SVM-based equalizers depends on the parameters of the modulation format. In the particular case of OFDM, it depends on the number of data subcarriers (\(N_{SC}\)) and the number of bits coded in each subcarrier (\(M\)).
## 4 Case Verification and Analysis  
### Running Environment and Parameter Settings  
At present, small mobile devices such as UAV mostly employ CPU processors. In order to simulate the hardware environment of UAV, the case analysis in the paper is carried out on the PC terminal, and the hardware configuration and software development environment are shown in Table 1.  
Figure 4: Data augmentation: (**a**) original image, (**b**) mirroring, (**c**) rotation 180\({}^{\circ}\), (**d**) cropping and scaling, (**e**) adjust contrast, (**f**) adjust brightness.  
The descriptions of the adaptive cosine annealing learning rate (ACLR) and freeze training are given below.  
The learning rate (LR) is a very important hyperparameter at the model training stage, which directly controls the pace of the parameters' updating process. The gradient descent algorithm is usually used to optimize objective function, the learning rate should become smaller so as to make the model as close as possible to the minimum when the loss value is nearing the global minimum. According to this principle, the learning rate can be reduced by the cosine function of the cosine annealing method, and a good effect can be obtained by combining this descent pattern with the learning rate [28]. The adaptive learning rate training method can monitor and adaptively adjust the learning rate according to the change of a certain parameter (batch size). Therefore, on the basis of the cosine annealing learning rate, the model training method of adaptive learning rate is introduced, and the learning rate is adjusted adaptively according to the batch size (batch size/64 \(\times\) LR). The learning rate updates will be adjusted at a high pace when the batch size is large (freeze training), and the learning rate updates will be adjusted at a low pace when the batch size is small (unfreeze training).  
At the model training stage, the input image size is large and the large parameters need to be updated, so the freeze training method is adopted to improve the training speed of the ECA-YOLOX-Tiny model. At the freeze training stage, the first 125 layers of the backbone feature extraction network are frozen, and the weights are unchanged, but the later 142 layers are not frozen and the weights are optimized iteratively. Therefore, the number of parameter variations is small, and the hardware requirements are low at the freeze training stage. At the unfreeze stage, the whole network is fine-tuned and the whole network parameters are changed, so the hardware requirements are high and the training time is long. The freeze training method avoids the too-random phenomenon of network weights initialization in the case of small sample insulator datasets, which significantly improves the training efficiency of the model, and accelerates the model convergence speed and shortens the training time.
# Volatiles in Berries: Biosynthesis, Composition, Bioavailability, and Health Benefits
## 3 The Chemical Composition of Volatile Compounds in Berries
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Compound** & **Strawberry** & **Blueberry** & **Raspberry** & **Blackberry** & **Cranberry** \\ \hline _Alcohols_ & & & & & \\ Cis-3-hexen-1-ol & [65] & & & & \\ (E)-2-hexenol & & [68] & & & \\ (Z)-3-hexenol & & [68] & [76] & & \\ (Z)-3-hexenol & & [74] & & & \\ 2-heptanol & & [74] & & & \\ Phenylethyl alcohol & & [69] & & & \\ 2-ethylhexanol & & [69] & [69] & [69] \\ 1-octanol & & & [76] & [73,80] \\ (Z)-hexenol & & & [79] & & \\ Ethanol & & & & [73,80] \\ 1-hexanol & & & & [73] \\ p-cymen-8-ol & & & & [73] \\ Nopol & & & & [73] \\ 4-methyl-1-pentanol & & & & [69] \\ 4-penten-2-ol & & & & [77] \\ Benzyl alcohol & & & & [77,82,84] \\ \hline _Aldehydes_ & & & & [77,82,84] \\ Hexanal & [65,85] & [68,74,78] & [76,79] & [73] & [75] \\ Trans-2-hexenal & [65] & & & [73,80] & \\ Cis-3-hexenal & [67] & & & [75] \\ (E)-2-hexenal & & [68,74,78] & [76,79] & & [75] \\ Vanillin & & [81] & [69] & & \\ (Z)-3-hexenal & & [74,78] & & & \\ (E,Z)-2,6-nonadienal & & [74] & & & \\ (E,E)-2,4-nonedienal & & [74] & & & \\ Pentanal & & [74] & & & [75] \\ Octanal & & [74] & & & [75] \\ (E,E)-2,4-hexaIenal & & [74] & & & \\ Decanal & & [74] & & & \\ Hexadienal & & [78] & & & \\ Heptanal & & [78] & [76] & & \\ Benzaldehyde & & [76] & & & \\ cuminaldehyde & & [69] & & & \\ Methylbutanal & & & & [80] & \\ Methional & & & & [71] & \\ Trans,cis-2,6-nonadienal & & & & [73] & \\ 3-methylbutanal & & & & [73] & \\ 2-methylbutanal & & & & [75] \\ (E)-2-heptenal & & & & [75] \\ (E)-2-octanal & & & & [75] \\ Trans-2-decanal & & & & [75] \\ 2-octanal & & & & [83] \\ \hline _Surfurs_ & & & & [83] \\ methanethiol & [66] & & & \\ \hline _Norsibgrenoids_ & & & & \\ \(\beta\)-damascenone & & [78] & & \\ Cis-1,5-octaleni-3-one & & & [71] & \\ \hline _Furanones_ & & & & [73,80] \\ Furaneol & & & & [73] \\ Mesifurane & & & & [73] & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._
## 3 Results and Discussion
### Differential Scanning Fluorimetry  
In recent years, the importance of protein preparations in medicine has increased significantly; however, most of the preparations available on the market are in liquid form, which requires storage at low temperatures. This is associated with difficulties with high transport costs, among others. Moreover, during long-term storage of proteins in solutions, the activity of proteases shortens their activity time and also causes reactions such as oxidation, leading to protein unfolding that may also occur [49; 50]. One method to overcome these obstacles is to store proteins in lyophilized form. In order to determine the usefulness of the PelA\({}_{\text{h}}\) and PsIG\({}_{\text{h}}\) enzymes in freeze-dried and soluble form, their stability was tested after 30 days using the DSF method, which is a biophysical technique widely used to study the thermal stability of a protein [51]. Figure 4 shows the plot of the  
Figure 3: SEM images of the surface morphology the BC (**a**) and Ar_BC after 480 s LPArP treatment (**b**).  
first derivative of the fluorescence melting curves of PeAl\({}_{\text{h}}\) and PsIsG\({}_{\text{h}}\) in freeze-dried and soluble form at the beginning and after 30 days of storage at 4 \({}^{\circ}\)C. The T\({}_{\text{m}}\) values of the PeAl\({}_{\text{h}}\) in the soluble form were 52.7 \({}^{\circ}\)C and 52.8 \({}^{\circ}\)C at days 0 and 30 of storage, respectively, while upshifting of the freeze-dried form by -1.3 \({}^{\circ}\)C was observed after 30 days, suggesting the acquisition of a slight thermal structural stability. In the case of the soluble form of PsIsG\({}_{\text{h}}\), there was no significant difference in the T\({}_{\text{m}}\) value during the storage conditions (T\({}_{\text{m}}\) equal to -53.1 \({}^{\circ}\)C), and the same observations were made with the freeze-dried form (T\({}_{\text{m}}\) equal to -53.7 \({}^{\circ}\)C).
# Markov Chain Model-Based Optimal Cluster Heads Selection for Wireless Sensor Networks
## 2 Related Work and Background
### Relay Node Selection  
In large WSNs, the CHs cannot forward the gathered data directly to the BS, it employs some other type of nodes to convey its data towards the BS. The other type of nodes is known as relay nodes. This type of node receives its assistant's data, discloses this data and then conveys it in the direction of the destination (BS). An easy way to select a relay node is that CH also works as a relay node [29; 37; 38]. The CHs cannot be selected as relay nodes because the CHs are fulfilling two responsibilities at the same time, leading to depletion of the power resources earlier. In a randomly installed network, every node senses the data and then performs data fusion, although the normal node has limited data fusion capability. For this purpose, the location of every node is obtained via Global Positioning System (GPS) to forward the data. However, there are two issues, firstly, the bits disclosed by the normal nodes are not secure. Secondly, the inter-user carrier error propagation is a big issue. To deal with the first mention problem, secure and appropriate data aggregation is discussed in [31], which is a very challenging issue for large scale WSNs.
# An Automatic Embedded Device Registration Procedure Based on the OGC SensorThings API
## 3 Methodology
### Device Description Data Model and Encoding
#### 3.1.2 Extended SensorThings API  
As mentioned earlier, the first part of the SensorThings API is only designed for the sensing capability but did not consider the tasking capability. To address this issue, [PERSON] and [PERSON] [4] proposed the Extended SensorThings API (Figure 3) as a potential solution to support tasking capabilities in the SensorThings API ecosystem. Firstly, the data model is extended, where each Thing can have zero to many TaskingCapability entities. Each TaskingCapability is executed by an Actuator. When a client can create Tasks based on applicable Parameters of a TaskingCapability, an Extended SensorThings API service will apply the HTTPProtocol of the TaskingCapability to translate Tasks into requests following the device protocol. To be specific, [PERSON] and [PERSON] [4] proposed the keyword replacement solution to perform automatic translation between the device protocol and the SensorThings API protocol. Based on this solution, no human intervention is required as long as devices can describe their tasking capabilities based on the defined data model.  
Comparing with the original SensorThings API standard, the Extended SensorThings API provides an extended data model to describe tasking capabilities of SW-IoT devices. Based on the data model, users can remotely control devices through Extended SensorThings API services. As OGC is still defining the second part of the SensorThings API, this research applies the Extended SensorThings API [4] as the Service Layer to support both sensing and tasking capabilities.
## 5 Modeling Results
### Influence of Variable Section Thickness of Sleeve Arch on Reinforcement Performance
#### 5.2.1 Reinforcement Performance of the Medium Crack Lining  
As shown in Figure 15, the stress at Point A in a thin sleeve arch of varying thicknesses is directly proportional to the load and inversely proportional to the thickness. In the initial loading stages, the stress at Point A in thin arches of different thicknesses is approximately uniform; however, as the load increases, the stress also gradually increases. Thin sleeve arches with thicknesses of 50 mm, 60 mm, 75 mm, 85 mm, and 100 mm exhibit compressive stresses at Point A of \(-\)7.942 N/mm\({}^{2}\), \(-\)7.815 N/mm\({}^{2}\), \(-\)6.707 N/mm\({}^{2}\), \(-\)6.403 N/mm\({}^{2}\), and \(-\)4.903 N/mm\({}^{2}\), respectively, when subjected to a load of 1000 KN.  
As depicted in Figure 16a, during the initial stages of loading, the thin arch at Point B is predominantly subjected to compression. Upon reaching a specific load, the compressive stress diminishes, and the arch, along with the original lining, begins to share the load. As the load increases, the concrete at Point B primarily experiences tension, and the tensile stress gradually escalates. At a load of 1000 KN, the thin arches at Point B with thicknesses of 50 mm, 60 mm, 75 mm, 85 mm, and 100 mm exhibited tensile stresses of 0.503 N/mm\({}^{2}\), 0.445 N/mm\({}^{2}\), 0.382 N/mm\({}^{2}\), 0.369 N/mm\({}^{2}\), and 0.293 N/mm\({}^{2}\), respectively.  
Figure 16: Relationship curve between load and stress: (**a**) Point B; (**b**) Point D.  
Figure 15: Relationship curve between load and stress at Point A.  
As depicted in Figure 16b, during the initial stages of loading, the concrete in thin arches of varying thicknesses is primarily compressed at the composite surface at Point D of the arch and lining. Upon reaching a specified load, the compressive stress diminishes. As the load increases, the concrete at the composite surface primarily experiences tension. At a load of 1000 KN, thin arches at Point D with thicknesses of 50 mm, 60 mm, 75 mm, 85 mm, and 100 mm exhibited tensile stresses of 0.094 N/mm\({}^{2}\), 0.087 N/mm\({}^{2}\), 0.080 N/mm\({}^{2}\), 0.077 N/mm\({}^{2}\), and 0.072 N/mm\({}^{2}\), respectively.
## 3 Proposed Anonymization Method, Iterative _k_-Anonymous Clustering Method (I\(k\)-ACM)
### Bottom-Up Hierarchical Clustering Process
The anonymized output of I\(k\)-ACM is actually the representative vectors gathered at the end of \(n^{th}\) steps. The formation of output is given in fifth and sixth phases of main function given in Algorithm 1. **Algorithm 1. **I\(k\)-ACM Algorithm. **Function** Cluster Combination  
**Input :** parameter, \(k\), distance matrix, \(D^{h}\), key, \(e^{l}_{step\_no-1}\), anonymization step, step_no  
**Output:** New cluster, \(L^{h+1}_{u}\), updated distance matrix, \(D^{h+1}\)  
1. Find clusters, \(L^{h}_{s}\), \(L^{h}_{t}\), having minimum distance in distance matrix \(D^{h}\);  
create a new cluster \(L^{h+1}_{u}\)  
2. \(V^{h+1}_{u}\gets V^{h}_{s}\bigcup V^{h}_{t}\)  
3. \(|V^{h+1}_{u}|=|V^{h}_{s}|+|V^{h}_{t}|\)  
4. If \(step\_no==1\)  
For each \(z^{th}\) bit string of representative vector, \(R^{h+1}_{u}[z]=R^{h}_{s}[z]\) OR \(R^{h}_{t}[z]\)  
else  
\(R^{h+1}_{u}\leftarrow\) Function Form_Encrypted_Representative_Vector \((L^{h}_{s},L^{h}_{t},e_{step\_no-1})\)  
(Function is given in Algorithm 1.) 5. Remove clusters, \(L^{h}_{s}\), \(L^{h}_{t}\)  
6. Find the distance of \(L^{h+1}_{u}\) to other clusters, update \(D^{h+1}\)  
**Main Function**I\(k\)-ACM  
**Input :** Table, \(T\), number of records, \(r\), number of attributes, \(m\), number of sinks, \(n\),  
anonymization parameters \(k_{1},k_{2},...,k_{n}\), index of group head sensor, \(l\)  
**Output:** Anonymized table, I\(k\)-ACM(\(T\))  
**Initialization**  
1. \(h=1\)  
2. for all \(i\) where \(\{i:0<i<r\}\)  
2.1. Create cluster array, \(\{L^{1}_{i}\}\)  
2.2. Add record, \(T_{i}\) to \(V^{1}_{i}\)  
2.3. Set initial size of cluster, \(|V^{1}_{i}|=1\)  
2.4. Initialize the representative vector, \(R^{1}_{i}\gets T_{i}\)  
2.5. Initialize the distance matrix \(D^{1}\) by using Equation 4  
**Iterative steps for multilevel \(k\)-anonymization**  
3. \(step\_no=1\)  
4. while \(step\_no\
eq n\)  
4.1. while not for each cluster \(|V^{h}_{i}|\geq k_{step\_no}\)  
4.1.1 Call Function ClusterCombination (\(k\), \(D^{h}\),\(e^{l}_{step\_no-1}\),\(step\_no\))  
4.1.2 h=h+1  
4.2. step_no=step_no+1  
**Form the output of I\(k\)-ACM**  
5. I\(k\)-ACM (T) is initialized to empty set  
6. for each cluster, \(L^{h}_{s}\) in \(L^{h}\) where \(\{s:0<s<|L^{h}|\}\)  
6.1. Append \(R^{h}_{s}\) and \(|V^{h}_{s}|\) to I\(k\)-ACM(\(T\))  
**Function** Form_Encrypted_Representative_Vector  
**Input :** Representative vectors, representative vector, \(R_{h}^{s}\), representative vector, \(R_{h}^{t}\), key \(e_{step\_no-1}^{l}\)  
**Output:**Representative vector of new cluster, \(R_{h+1}^{u}\)  
For each attribute \(m\)  
if \(R_{h}^{s}[m]=R_{h}^{t}[m]\) then  
\(R_{h+1}^{u}[m]=R_{h}^{t}[m]\)  
else  
\(R_{h+1}^{u}[m]=E_{e_{step\_no-1}^{l}}(R_{h}^{s}[m]||R_{h}^{t}[m])\)  
**Algorithm 2. ** Forming of representative vector.