esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
The present work studied the effect of temperature, reaction time and microalga concentration to assess and optimise the yield and quality of the biocrude produced by HTL from _N. gaditana_. A second-order mathematical model with a high degree of confidence was obtained for the three responses chosen, i.e., biocrude yield and nitrogen and oxygen content. Temperature showed a positive effect and was the most significant factor affecting biocrude yield, where the three binary interactions and the quadratic effect of temperature were also significant factors. On the other hand, for the responses of the content of N and O, the only significant factor was the concentration of biomass, which negatively affected the N content and positively the O content. The experimental design results showed that the maximum production of biocrude (43.08 wt%) was attained at 320 \({}^{\circ}\)C, 10 min and 10% of microalga, yielding a product with an HHV of 35.74 MJ/kg. Considering the quality of the biocrude obtained under these conditions, it had a low content of N (6.12 wt%) and O (10.77 wt%) as well as a low content of trace elements with respect to the starting microalgal biomass. It is essential to highlight the high content of hydrocarbons (30%) in the HTL biocrude, making it suitable as liquid biofuel after further upgrading.  
The following are available online at [[https://www.mdpi.com/article/10.3390/app11104337/s1](https://www.mdpi.com/article/10.3390/app11104337/s1)]([https://www.mdpi.com/article/10.3390/app11104337/s1](https://www.mdpi.com/article/10.3390/app11104337/s1)), Table S1: ANOVA for biocrude response. Table S2: ANOVA for N content response. Table S3: ANOVA for O content response. Table S4: Elemental composition of biocrude at 320 \({}^{\circ}\)C, 10 min and 10 wt% of biomass.  
Conceptualisation, G.V. and L.F.B.; methodology, A.S.-B. and I.M.H.; validation, R.R. and V.M.; formal analysis, A.S.-B.; investigation, A.S.-B.; resources, A.S.-B. and I.M.H.; data curation, R.R. and V.M.; writing--original draft preparation, A.S.-B. and I.M.H.; writing--review and editing, G.V. and L.F.B.; supervision, G.V. and L.F.B. and funding acquisition, G.V. and L.F.B. All authors have read and agreed to the published version of the manuscript.  
This research was financed by the Comunidad de Madrid through the ALGATEC-CM projects (P2018/BAA-4532) and the industrial doctorate with the company AlgaEnergy (IND2017/IND). Additionally, it was partially funded by the Ministry of Economy and Competitiveness of Spain through the BIOHIDROALGA project (ENE2017-83696-R).  
Not applicable.  
The authors acknowledge the work carried out by the laboratory technicians: [PERSON] in the autoclave reactor maintenance work, [PERSON] in the elemental analysis and [PERSON] in the GC-MS analysis.
# Clustering-Based Energy-Efficient Self-Healing Strategy for WSNs under Jamming Attacks
###### Abstract  
The Internet of Things (IoT) is a key technology to interconnect the real and digital worlds, enabling the development of smart cities and services. The timely collection of data is essential for IoT services. In scenarios such as agriculture, industry, transportation, public safety, and health, wireless sensor networks (WSNs) play a fundamental role in fulfilling this task. However, WSNs are commonly deployed in sensitive and remote environments, thus facing the challenge of jamming attacks. Therefore, these networks need to have the ability to detect such attacks and adopt countermeasures to guarantee connectivity and operation. In this work, we propose a novel clustering-based self-healing strategy to overcome jamming attacks, in which we denominate fairness cooperation with power allocation (FCPA). The proposed strategy, aware of the presence of the jammer, clusters the network and designates a cluster head that acts as a sink node to collect information from its cluster. Then, the most convenient routes to overcome the jamming are identified and the transmit power is adjusted to the minimum value required to guarantee the reliability of each link. Finally, through the weighted use of the relays, the lifetime of each subnetwork is extended. To show the impact of each capability of FCPA, we compare it with multiple benchmarks that only partially possess these capabilities. In the proposal evaluation, we consider a WSN composed of 64 static nodes distributed in a square area. Meanwhile, to assess the impact of the jamming attack, we consider seven different locations of the attacker. All experiments started with each node's battery full and stopped after one of these batteries was depleted. In these scenarios, FCPA outperforms all other strategies by more than 50% of the information transmitted, due to the efficient use of relay power, through the weighted balance of cooperative routes. On average, FCPA permits 967,961 kb of information transmitted and 63% of residual energy, as energy efficiency, from all the analyzed scenarios. Additionally, the proposed clustering-based self-healing strategy adapts to the change of jammer location, outperforming the rest of the strategies in terms of information transmitted and energy efficiency in all evaluated scenarios.  
clustering; energy efficiency; Internet of Things; jamming power control; self-healing; security; wireless sensor networks +
Footnote †: journal: Sensors  
0203  
23 June 2023  
Revised: 26 July 2023  
Accepted: 31 July 2023  
Published: 3 August 2023  
Corrected: 19 January 2024
## 4 Experiments
### Quantitative Results on DIOR Datasets
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**Split1**} & \multicolumn{4}{c}{**Split2**} \\ \cline{2-9}  & **3** & **5** & **10** & **20** & **3** & **5** & **10** & **20** \\ \hline RepMet [11] & 5.2 & 5.6 & 5.9 & 6.8 & 5.5 & 5.7 & 7.4 & 7.8 \\ MPSR [30] & 12.9 & 16.2 & 22.7 & 28.8 & 14.7 & 15.9 & 21.0 & 24.7 \\ FsDet [40] & 12.9 & 14.5 & 19.4 & 21.7 & 14.4 & 18.3 & 22.0 & 25.0 \\ P-CNN [39] & 14.5 & 14.9 & 18.9 & 22.8 & 16.5 & 18.8 & 23.3 & 28.8 \\ TFA [31] & 5.4 & 7.6 & 8.6 & 10.7 & 4.3 & 7.6 & 15.3 & 19.6 \\ FSCE [21] & 15.1 & 18.2 & 23.3 & 27.4 & 12.8 & 17.0 & 21.5 & 27.3 \\ G-FSOD [50] & 14.1 & 15.8 & 20.7 & 22.7 & 16.0 & 23.3 & 26.2 & 32.1 \\ MSOCL [22] & 16.8 & 20.6 & 24.9 & 29.2 & 19.6 & 23.4 & 26.0 & 30.2 \\ ST-FSOD [23] & 17.0 & 20.2 & 26.5 & 32.8 & **20.5** & 25.0 & 30.7 & 33.8 \\ \hline
**Ours** & **17.9** & **21.9** & **27.8** & **33.7** & 20.2 & **26.0** & **30.9** & **34.5** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Mean average precision (mAP) at different methods on the DIOR dataset is presented in percentage terms. The reported accuracies of all models are the percentage accuracies of mAP50 for the novel class obtained through five random sampling training sessions.The bold parts in the tables indicate the best results under each setting.
## 1 Introduction
This approach is also called early lumping (first approximate and then design), while the results discussed before belong to the so-called late lumping (first design then approximate) approaches. Early lumping based on the combination of DMDc with state estimation has already been shown in several application scenarios to yield satisfactory performance [3; 17; 18; 19; 20]. In these studies the Kalman Filter [21; 22] has been used on the basis of the obtained finite-dimensional linear discrete-time model equations. The Kalman Filter is known to provide satisfactory state estimation results, as it ensures a minimum estimation error covariance taking into account _a priori_ known knowledge on the perturbation statistics, with the perturbations on the states and measurement considered as white noise. One potential drawback for implementations of the Kalman Filter in real-time applications relies on the fact that it requires \(n^{2}+n\) dynamic variables, where \(n\) is the number of states in the reduced-order model, given that the \(n\times n\) error covariance matrix must be calculated online to provide the optimal filter performance. In comparison to this approach, alternative design methods, such as the reduced-order [1; 2] or the geometric observer [23] do actually require less than \(n\) dynamic variables. Motivated by the above considerations, in the present study the autoencoder-based approximation of finite-dimensional low order basis representations is employed in combination with DMDc to identify a reduced-order model for a linear parabolic diffusion-convection-reaction system for which a reduced order observer and a Kalman Filter are implemented and compared. The training data is generated on the basis of a high-fidelity numerical approximation of the solutions of the pde model and the comparison of the approaches carried out on the basis of solutions obtained for different input signals. Given the early lumping nature of this approach, it somehow represents a complementary counterpart of the late-lumping reduced-order design studies in [6; 7; 8; 9; 10]. To the author's knowledge, this is the first time such a comparison with reduced order observers is carried out, in particular for this class of systems. The result is an inductive step towards the consideration of distributed parameter models involving transport-reaction mechanisms as typically found in chemical engineering applications [7; 24; 25; 26; 27; 28; 29]. The paper is organized as follows. In Section 2 the problem statement is provided. The model-order reduction using autoencoder-based DMDc (ae-dmdc) is discussed in Section 3. In Section 4 the state estimation problem is addressed using the reduced order observer (roo)and the Kalman Filter (kf). The obtained results are discussed in Section 5. Conclusions and an outlook are provided in Section 6.
# Tightly-Coupled GNSS/Vision Using a Sky-Pointing Camera for Vehicle Navigation in Urban Areas
## 3 Vehicle Motion Estimation Using an Upward-Facing Camera
### Feature Detection, Description and Matching
#### 3.2.1 Feature from Accelerated Segment Test Feature Detector  
FAST considers a pixel at the centre of a candidate corner \(p\) and performs a test on a circle of sixteen pixels around \(p\) as shown in Figure 1a. If twelve of those pixels are brighter than the pixel at the centre of the candidate point, then \(p\) is a corner. To speed up the processing, FAST starts by setting a threshold intensity value \(\mathsf{T}\) (e.g., 20% of the intensity of \(p\)). Then, it compares the intensity of the pixels at Indexes 1, 5, 9 and 13, as highlighted in Figure 1b with \(\mathsf{T}\). If at least three out of four of these pixels' intensities are above or below the threshold (threshold test), the test continues with the other sixteen pixels, and at least twelve of these pixels intensities should satisfy the threshold test to declare \(p\) as an interest point. If at the first level there are not at least three (out of four) pixels' intensity satisfying the threshold test, FAST rejects the current candidate corner. Despite its computation efficiency, FAST features do not have an orientation component. The enhanced FAST used in the ORB algorithm addresses this issue by using the intensity centroid [48]. This method uses the vector representing the offset of a corner intensity from its centre. The enhanced FAST uses this vector to impute the orientation information.
# A Highway Pavement Crack Identification Method Based on an Improved U-Net Model
## 5 Experiments and Analysis of Results
### Ablation Experiments  
In this paper, ablation experiments were performed on the CRACK500 dataset to study the effect of multi-scale feature prediction fusion, an improved bottleneck layer, and an improved parallel attention module on the performance of pavement crack segmentation. In this paper, the improved U-Net model was trained by different combinations of multi-scale feature prediction fusion, the improved bottleneck layer and parallel attention module, and the remaining settings were consistent. The original U-Net model is denoted as RU-Net. The U-Net model with multi-scale feature prediction fusion is denoted as RNU-Net. The U-Net model with a modified parallel attention module is called RPU-Net. The U-Net model with improved bottleneck layers and multi-scale feature prediction fusion is recorded as RGU-Net. The U-Net model with multi-scale feature prediction fusion, an improved bottleneck layer, and an improved parallel attention mechanism module is recorded as RNGU-Net. The results of the ablation experiments are shown in Table 5.  
It can be seen from the above table that RNU-Net improved in all four indicators compared to RU-Net. This indicates that the inclusion of multi-scale feature prediction fusion led to an enhancement in segmentation performance. Compared with RU-Net and RNU-Net, RGU-Net improved in four indicators. This proves that the improved bottleneck layer enhanced the segmentation effect. Compared with the other three models, RNGU-Net improved in four indicators. This indicates that the improvement scheme proposed in this paper helped to improve the segmentation results. It can also be seen from the above table that the enhancement of the improved parallel attention module proposed in this paper was greater than the multi-scale feature prediction fusion.
# A Review on Two-Phase Volumetric Expanders and Their Applications
## 8 Discussion
### Considerations about the Literature Analysis  
The piston systems appeared to be the most investigated in the literature because of their different architectures, while the other typologies received roughly the same attention (Figure 18a). In detail, the 32% of piston systems studies were composed of studies about rolling pistons (31%), free and reciprocating (25%), swing (13%) and rotating systems (6%).  
The analysis indicated that most of the studies (70%) focused on the behaviour of two-phase machines for heat pump and cooling applications because of the possibility of replacing the throttling valve and driving the compressor (Figure 18b). Conversely, the studies concerning power production by ORC technology were 24%, thus, represent about one-third of the percentage of the studies on heat pump and cooling applications. The remaining studies (6%) focused on research purposes because they employed a two-phase expander to investigate the flash expansion process in a variable volume chamber.  
The literature indicated that most studies about two-phase volumetric expanders have been published from 1990 to the present day, thus, covering at least three decades. However, the literature survey suggested that piston, screw, vane, and scroll systems were investigated between 1995 and 2017 for heat pump and cooling applications (Figure 19). Conversely, screw devices appeared to be the most investigated for power production applications between 1996 and 2021, despite some studies involving scroll and piston machines. To the best of our knowledge, no study about vane machines for power production was found. However, we found only a review in the literature that dealt with two-phase volumetric devices, whose main topic concerned the potentialities of several numerical models to analyze the flash expansion in variable volume chambers [22].  
Figure 18: Results from the literature analysis, showing an investigation of the expander technologies (**a**) and of their applications (**b**).
# A Review of Convolutional Neural Network Applied to Fruit Image Processing
## 4 CNN-Based Approaches for Fruit Classification Tasks
[59] adapted the AlexNet model to create Fruit-AlexNet aiming to classify tropical fruits. They used the Supermarket Produce dataset [71], which contains 2633 images of 15 categories, including fruits inside a bag. The images were collected at several times and days for the same category. They reduce the AlexNet model complexity. Their proposed network comprises of five convolutional layers joined to max-pooling after the first, second, and fifth layers. Finally, they stacked three fully connected layers with dropout layers after the first and second ones. Experimental results outperform previous works [5] on the same dataset achieving a classification accuracy of 99.56% and of 100% using statistical color and texture descriptors, respectively. [PERSON] [61] used a modified VGG model for fruit and vegetable classification. The images were downloaded from picture websites, and the database has 26 categories. Firstly, they use a bottom-up graph-based visual saliency (GBVS) model to segment the fruit region. Then, a CNN model learn image features to perform the classification task, obtaining an accuracy rate of 95.6%. [PERSON] et al. [62] proposed the VegFru dataset for fine-grained visual categorization (FGVC). VegFru is a domain-specific dataset that covers 25 upper-level categories and 292 subordinate classes of vegetables and fruits, containing more than 160,000 images. Moreover, they presented a framework called HybridNet comprised of two DCNNs for separate classification by exploiting the label hierarchy of the FGVC. They compare HybridNet, with VGGNet and CBP-CNN, for 292, 100, and 200 sub-classes of VegFru Dataset. The accuracy of HybridNet was better in all tests: VGGNet 77.12%-84.46%-72.32%, CBP-CNN 82.21%-87.49%-84.91%, and HybridNet 83.51%-88.84%-85.78%. [PERSON] et al. [63] designed a 5-layer CNN model for fruit classification on the UEC-FOOD100 dataset [72] and a self-established fruit dataset. The UEC-FOOD100 dataset is comprised of about 15,000 from 100 classes of food image and the second dataset has more than 40,000 fruit images. For evaluation purposes, they elaborated two groups of controlled tests: single fruit vs multi-food images and gray vs RGB images. The experimental results concludes that features based on color information are not always useful to improve the classification accuracy. The best achieved accuracy was 80.8% on the fruit dataset and 60.9% on the multi-food dataset.
## 5 Selenium-Containing Polysaccharides
### Se-Glycosides and Selenopyranoses  
As explained in Section 4, in most of the selenosugars and their derivatives, e.g., selenopyranoses, selenofuranoses, selenoglycans, selenonucleosides, and Se-alkylselenides, selenium is divalent. Most of the selenosugars containing divalent selenium are small molecules obtained by chemical synthesis. However, as previously described, four natural selenoglycans, identified as derivatives of galactosamine and glucosamine, have been detected in human urine [167, 168, 169]. The results of research conducted by [PERSON] et al. [168] suggested that the major selenium metabolite in mammals urine is a selenoglycosocide (Figure 5a). Therefore, since mammals are capable of selenium sugars biosynthesis, in which selenium is glycosylated bound, it is highly probable that plants, fungi, and microorganisms may also have such capability. In fact, multiple reports describe the ability of these organisms to accumulate selenium in the structure of polysaccharides.  
The isolation of selenium-containing polysaccharides from fungi [108,246, 247, 248, 249], algae, and microorganisms [250,251] has been widely described.  
In a series of studies on selenium-enriched mycelial cultures of the medicinal mushroom _Lenitinua codes_, [PERSON] et al. proved that mycelium effectively accumulated Se from the cultivation medium enriched in sodium selenite [252,253] and that selenium was also incorporated into the mycelial polysaccharides [171,254]. Further, [PERSON] et al. (2018) [39] isolated a selenium-containing polysaccharide-protein fraction from the Se-enriched mycelial cultures of _L.odes_ composed of glucose and mannose, containing 190 \(\upmu\)g Se/g dry weight. X-ray absorption of fine structure (XAFS) spectra analysis in the near edge region (XANES) confirmed that selenium in the Se-polysaccharide structure is divalent and organically bound. The simulation analysis in the EXAFS (extended X-ray absorption fine structure) suggested that selenium is bound in a \(\beta\)-1,3-, \(\alpha\)-1,4-glycosidic linkage or substitutes oxygen in a pyranosidic ring (Figure 9).  
Figure 9: The hypothetical local structures around Se in Se-polysaccharide [39]: **(a)** 1,3-Se-\(\beta\)-D-glycosidic bond in glucan chain, **(b)** 1,4-Se-\(\alpha\)-D-glycosidic bond in mannan chain, and **(c)** Se in glucopyranose ring.  
According to the calculations performed with Gaussian 03 software, incorporation of selenium cause deformations in the polysaccharide structure, including change in bond lengths and torsion angles, and, as a result, disappearance of hydrogen bonds in the vicinity of the selenium atoms [13]. This modification may affect the interaction with specific polysaccharide receptors and introduce changes in the biological activity [11].
## 5 Experimental Evaluation and Results
### Benefits of Metadata  
The enhanced performance and scalability of ESH can be attributed to its efficient utilization of metadata or headers at the bucket entry, which significantly reduces data access. This advantage is evident when comparing ESH with other state-of-the-art schemes that employ similar hashing schemes across a range of data sizes and varying numbers of threads. The improved throughput observed in Section 5.4 for insertion, search, and delete operations, with different thread configurations, underscores the benefits of using metadata to streamline the search process. By accessing bucket information from the metadata, unnecessary time spent searching records within the bucket is minimized, resulting in reduced PM access and improved response times. The insertion operation leverages the \(OB\) and \(OFRB\) metadata elements to determine the bucket status, while delete operations also consider the \(OC\) metadata before proceeding to access the records within the bucket.
## 2 Materials and Methods
### Experiment Settings
#### 2.2.3 Software Platform  
The features extraction and classification experiments of CNN-AFE are carried out on the same server with 32 G RAM, NVIDIA GeForce GTX Titan GPU (NVIDIA, Santa Clara, CA, USA.) and Linux 64-bit operating system (Canonical, Isle of Man, UK). The model is built with the Pytorch framework (Facebook, Menlo Park, CA, USA), and the programming language is Python.