esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Discussion of Results  
When the load demand increase was implemented, a significant improvement in the voltage stability of the electrical power grid system was revealed after integrating the large-scale wind power plant (LSWPP) into this study. This finding is not new. However, the approach to reaching it is new in the following manner. The existing studies, like [19; 20; 21] rarely consider a step-by-step approach to obtaining to this finding. They did not consider  
Figure 31: Increased load demand simulations for the modern power grid: Reactive power demand by DLoad1 and reactive power at Bus2.  
Figure 32: Increased load demand simulations for the modern power grid: Bus2 voltage.  
evaluating the renewable energy grid code specifications before integrating the developed systems into the electrical power grid. Unlike the existing studies, this study aligned with the design and operational requirements of WPPs mentioned in [22; 23; 24]. This is due to its approach of first considering the simulations of the developed LSWPP alone to ensure its operation meets the REGC specifications before integrating it into the considered power grid. Furthermore, some studies on wind power plant (WPP) integration use a reasonable megawatt capacity of a single WTGU and treat it as a LSWPP, for instance, in reference [17]. Considering the models used in [17], and other models in [18; 19; 20; 21], the computation time was saved. The challenge is that the models developed in these studies may not provide adequate validation to align the REGCs, whose significance has been mentioned in the work published in the references [1; 3; 4; 14; 26] for LSWPPs in the grid-connected mode of operation.  
Lastly, using the Real-Time Digital Simulator (RTDS) devices with their software Real-time Simulation Computer-Aided Design (RSCAD) in this study enabled the simulation of load disturbance while monitoring them in real time. This has made the overall simulations in the study realistic compared to other studies.
## 2 Materials and Methods
### Spectrophotometry: Total Phenols (TP), Color Intensity (CI) and Color Components (%Ye, %Re and %Bl)  
All assays comprising the utilization of spectrophotometry techniques were realized at _Comissao Vitivinicola da Regiao do Dao_'s official laboratory. Previous to all analytical assessments, all of the samples were centrifugated at 5000 rpm for 10 min. All of the assays were performed using spectrophotometry (U-2900 Spectrophotometer, Hitachi, Tokyo, Japan).  
Total phenol (TP) content was assessed by an internal protocol (MI22--Revisao 4, 2020) where, after pretreatment, the absorbance of the sample was read at 280 nm in quartz cuvettes with a 10 mm optical path length.  
The determination of the color parameters was carried out through the application of the Glories method, presented at the _Compendium of International Methods of Analysis_ by OIV (OIV-MA-AS2-07B:R2009). Thus, the absorbances of the samples were determined at 420, 520 and 620 nm, resorting to a quartz cell with optical path \(b\) of 1 cm. According to Glories [36; 37], color intensity (CI) can be calculated as  
\[CI=\frac{A420+A520+A620}{b} \tag{7}\]  
Furthermore, it is also possible to assess the contribution of compounds of the color yellow (%Ye), red (%Re) and blue (%Bl) from  
\[Ye=\frac{A420}{CI}\times 100\ Re=\frac{A520}{CI}\times 100\ Bl=\frac{A620}{CI} \times 100 \tag{8}\]  
The absorbance of wine at A420 is utilized by several authors as a simple index for the determination of browning of the wines to relate with the oxidation of the product [38; 39]. In our case, we will use the %Ye color compound as an indicator of browning.
# Animal Movement Prediction Based on Predictive Recurrent Neural Network
###### Abstract  
Observing animal movements enables us to understand animal behavior changes, such as migration, interaction, foraging, and nesting. Based on spatiotemporal changes in weather and season, animals instinctively change their position for foraging, nesting, or breeding. It is known that moving patterns are closely related to their traits. Analyzing and predicting animals' movement patterns according to spatiotemporal change offers an opportunity to understand their unique traits and acquire ecological insights into animals. Hence, in this paper, we propose an animal movement prediction scheme using a predictive recurrent neural network architecture. To do that, we first collect and investigate geo records of animals and conduct pattern refinement by using random forest interpolation. Then, we generate animal movement patterns using the kernel density estimation and build a predictive recurrent neural network model to consider the spatiotemporal changes. In the experiment, we perform various predictions using 14 K long-billed curlew locations that contain their five-year movements of the breeding, non-breeding, pre-breeding, and post-breeding seasons. The experimental results confirm that our predictive model based on recurrent neural networks can be effectively used to predict animal movement.  
animal movement; movement prediction; pattern prediction; predictive recurrent neural networks; kernel density image +
Footnote †: journal: Sensors
## 5 Discussion  
### Discussion regarding Errors  
The experiments in both Toronto and Beijing illustrate that buildings and impermeable ground are easily confused, especially in road regions. In the Toronto scene, in Table 3, the precision of buildings increased from 72.5% to 78.2% when roads were removed. In the Beijing scene, as shown the blue box in Figure 10d, we found that although the buildings were discovered, parts of their surroundings were also incorrectly regarded as buildings when the surroundings were combined into the same object. At the same time, the green box in Figure 10d shows that parts of buildings in low brightness caused by shadows cannot be recognized. The same thing also occurs in Toronto building detection. In Figure 10e, we can see that emissions stem from low-height buildings in shadows (circled in red boxes), next to trees (circled in green boxes), on the edges of images (circled in orange boxes), with low gray value (circled in blue boxes), and with missing shadows (pink color outside the boxes). The false alarm, caused by buses or other ground objects that creates shadows, was very limited and occurred in small sizes. Erroneous height judgments occur when shadows are cut by an object like buildings, trees, misclassified bright roads, or the edge of an image. Connected buildings at different heights might be labeled as the highest height level, which means buildings might be classified into the incorrect height level.  
The shadow information affected by trees and neighboring buildings is not very reliable. The main problem is this regard is the low-level buildings detected with a relatively low accuracy. A method used in Ref. [42] that detects areas with the low and dense buildings could alleviate this problem to some extent.
# Cumulative Energy Demand and Carbon Footprint of the Greenhouse Cultivation System
## 3 Results and Discussion
### Greenhouse Energy Performance
This means a decrease in final energy demand by 14.1% compared to scenario 1. The structure of monthly final energy consumption for the scenario with biomass boilers is presented below (Figure 4). The final energy directly corresponds to the amount of fuel needed to generate heat. In its current state, the GH uses 186.6 Mg\(\cdot\)y\({}^{-1}\) of coal per year, corresponding to a value of 149.2 kg\(\cdot\)m\({}^{-2}\)\(\cdot\)y\({}^{-1}\). In scenario 2, the demand for natural gas will amount to 91,817 m\({}^{3}\)\(\cdot\)y\({}^{-1}\), which corresponds to the characteristic value of 73.5 m\({}^{3}\)\(\cdot\)m\({}^{-2}\)\(\cdot\)y\({}^{-1}\). In the case of burning renewable fuels, the annual demand for wood pellets (scenario 3) is 223 Mg\(\cdot\)y\({}^{-1}\), which translates into a value of 178.4 kg\(\cdot\)m\({}^{-2}\)\(\cdot\)y\({}^{-1}\), and in scenario 4 with burning wood chips, the fuel demand is 299.9 Mg\(\cdot\)y\({}^{-1}\), which converts to the characteristic value of 239.9 kg\(\cdot\)m\({}^{-2}\)\(\cdot\)y\({}^{-1}\). Figure 3: Monthly sums of final energy demand used for heating and auxiliary electricity; fuel—natural gas (scenario 2). The results shown in Figures 1-3 provide an overall analysis as well as the determination of energy consumption and the environmental footprint, also for options such as a temporary shutdown of the facility. The total amount of non-renewable primary energy (Equation (3)) consumed by the GH for heating in the reference scenario is 1553.31 MWh-y\({}^{-1}\), and the non-renewable primary energy index for the tested GH is 1242.6 kWh-m\({}^{-2}\)-y\({}^{-1}\). In scenario 2 with natural gas combustion, the corresponding values are 1170.57 MWh-y\({}^{-1}\) and 936.5 kWh-m\({}^{-2}\)-y\({}^{-1}\), respectively (Figure 5). In the biomass combustion scenarios, the total amount of non-renewable primary energy used for heating is 259.59 MWh-y\({}^{-1}\) and the specific value is 207.67 kWh-m\({}^{-2}\)-y\({}^{-1}\). The key information on energy consumption presented (Table 5) shows the importance of gas or biomass intake compared to the coal-fired heat source (reference scenario). The non-renewable primary energy factor may be of particular importance, for its value suggests a reduction in the environmental footprint. In scenario 2 for natural gas combustion, the  
Figure 4: Monthly sums of final energy demand used for heating and auxiliary electricity; fuel—biomass (scenarios 3 and 4). Figure 5: Monthly primary energy sums used for the purpose of GH heating. environmental footprint was reduced by 24.6%, and in scenarios 3 and 4 for biomass combustion--by as much as 83.3%. In the case of natural gas combustion, the reduction in non-renewable primary energy demand is mainly due to the higher total efficiency of the heating system and less need for auxiliary energy compared to scenario 1. In turn, according to the methodology described in [31], biomass as a renewable fuel is not considered in calculating non-renewable primary energy demand.
# Phenolic Profiling of Five Different Australian Grown Apples
## 3 Results and Discussion
### Phenolic Compounds Profile by LC-MS/MS Analysis
#### 3.4.1. Phenolic Acids  
In our research, 27 phenolic acids including hydroxyphenylacetic acids (2), hydroxycinamic acids (18), hydroxybenzoic acids (5), and hydroxyphenylpropanoic acids (2) were identified and characterised in five varieties of apples.  
Compound **1** was tentatively characterised as protocatechuic acid 4-O-glucose present in negative mode of ionisation and identified in Royal Gala, Red Delicious and Fuji apples. The compound had precursor ion at \(m/z\) 315.0718 and on further MS/MS analysis showed product ions at \(m/z\) 125 (loss of CO\({}_{2}\), 44 Da) and \(m/z\) 169 (loss of hexosyl moiety, 162 Da) [50]. In previous study of [PERSON] et al. [21] reported tentatively characterised protocatechuic acid 4-O-glucose from fresh apples. Compound **12** (([M-H]\({}^{-}\) m/z at 325.0925) was tentatively characterised as \(p\)-Coumaric acid 4-O-glucose based on the product ions at \(m/z\) 163, due to the loss of hexosyl moiety (162 Da) from the precursor ions [50]. Identified in Pink Lady, Royal Gala and Fuji apples.  
Compound 7 was tentatively characterised as caffe acid in Smitten variety based on the precursor ion at [M+H]\({}^{-}\) at \(m/z\) 181.0494 and confirmed based on the MS\({}^{2}\) fragmentation with product ions at \(m/z\) 143 (loss of two water molecules, 36 Da) and \(m/z\) 133 (loss of HCOOH, 46 Da) [51]. Compound 15 was observed in Smitten, Pink Lady and Fuji and tentatively characterised as ferulic acid based on the precursor ion at [[M-H]\({}^{-}\) at \(m/z\) 193.0505. Upon further MS/MS analysis, the product ions at \(m/z\) 178 (loss of CH\({}_{3}\), 15 Da), \(m/z\) 149 (loss of CO\({}_{2}\), 44 Da) and \(m/z\) 134 (loss of CH\({}_{3}\)-CO\({}_{2}\), 59 Da) confirmed the compound [52]. Compounds **19** ([[M-H]\({}^{-}\) m/z at 223.0603) identified in Fuji, Pink Lady and Smitten apples. MS/MS analysis confirmed the compound as sinapic acid by fragments at \(m/z\) 205 and \(m/z\) 163 due to the consecutive loss of H\({}_{2}\)O and 2 CHO from the precursor ion respectively [53]. Previously, [PERSON] et al. [54] reported the presence of caffe acid, ferulic acid and sinapic acid in apples. Caffe acid abundantly present in both pulp and peel [54]. Other phenolic compounds to our best knowledge were first time detected in Australian grown apples.
# Study About the Performance of Ascon in Arduino Devices
## 2 The Ascon Cipher Suite
### Ascon Hash Functionality  
In addition to the encryption/decryption algorithms, the Ascon suite includes two versions of a hash function, Ascon-HASH and Ascon-HASHa, which use a sponge construction [13], i.e., a type of function similar to those used in SHA-3 and SHAKE. The first version of the hash function produces a fixed 256-bit digest, whereas the second one also has two versions, Ascon-XOF and Ascon-XOFa, which provide both digests with a variable length output, \(l\). The number of rounds for the hash versions is represented by four values: the number of rounds during initialization, \(i\), the message absorption, \(a\), the compression of the first block, \(c\), and the compression of the remaining blocks, \(b\).  
Moreover, all hash functions in the Ascon suite make use of an extendable output function, denoted as \(\mathcal{X}_{h,r,a}\), which takes as input the message of arbitrary length, \(M\), and returns a hash, \(H\), of arbitrary but specified length, \(l\leq h\), where \(h\) is the maximal output length bits, 32 bits (with \(h=l=256\) for Ascon-HASH and Ascon-HASHa and \(h=0\) for unlimited output in Ascon-XOF and Ascon-XOFa), such that:  
\[\mathcal{X}_{h,r,a}(M,l)=H. \tag{3}\]  
Table 2 shows the different sets of parameters for each version of Ascon variants approved by NIST as hash functions, Ascon-HASH and Ascon-XOF.  
Ascon-HASH and Ascon-HASHa provide 128-bit security against collision attacks and second pre-image attacks, while, if the output size of \(l\) bits is considered, the versions Ascon-XOF and Ascon-XOFa provide at least \(\min(128,l/2)\) bits of security against collision attacks and at least \(\min(128,l)\) bits of security against second pre-image attacks.
# Research on a Multi-Type Barcode Defect Detection Model Based on Machine Vision
## 3 Model Construction
### Feature Representation Submodule-ResNet50  
After completing barcode instance-level object detection, ROI cropping, and size normalization, this paper uses a parallel two-branch method to detect barcode defects. In the ResNet50 branch, convolutional feature extraction is performed on each 224 \(\times\) 224 barcode subimage. ResNet50 belongs to the Deep Residual Network (DRN) family, and its core idea is to establish a residual connection between the convolutional mapping \(\backslash\)mathcal(F)(x)and the identity mapping X, thus alleviating the gradient degradation problem that arises with increasing depth. For any residual unit, the output can be formalized as:  
\[y\;=\mathcal{F}(x;\Theta)+\mathrm{X} \tag{6}\]  
where \(x,y\in\mathbb{R}^{C\times H\times W}\) are the input and output feature tensors, \(\mathcal{F}(\cdot)\) represents the combination of two Conv-BN-ReLU convolutional mappings, and \(\Theta\) are the learnable weights. When the input and output dimensions do not match, a \(1\times 1\) convolution is introduced to align the dimensions.  
ResNet50 consists of a Stem (Conv-BN-ReLU-Maxpool) and four residual stages \(\Big{\{}Conv_{x}^{2}\Big{\}}_{x\;=\;2}^{5}\), where each stage consists of a Conv Block and several Identity Blocks, resulting in a total of 49 convolutional layers and one fully connected layer, as shown in Figure 5. Through convolution and max-pooling, the spatial resolution of the feature maps is progressively compressed to 1/32 of the original size, while the channel dimension is expanded to 2048. Subsequently, the global average pooling (GAP) layer aggregates the spatial information into a one-dimensional vector.  
\[f_{res}\in\mathbb{R}^{2048} \tag{7}\]To align with the feature dimension of the parallel Vision Transformer branch, the original classification layer is removed, and a linear projection is added:  
\[z_{res}\ =\ W_{res}f_{res}\ +\ b_{res},\ W_{res}\in\mathbb{R}^{1024\times 2048} \tag{8}\]  
This produces the unified dimensional representation vector \(z_{res}\in\mathbb{R}^{d}\). After concatenating with the output \(z_{vit}\) from the Vision Transformer, both are involved in the subsequent hierarchical attention fusion.  
In implementation, ResNet50 is initialized with ImageNet pre-trained weights. To balance convergence speed and fine-tuning ability, the backbone convolution layers can be either \"frozen\" or \"unfrozen\" depending on experimental needs, while the linear projection W\({}_{res}\) always participates in gradient updates.  
By utilizing the deep residual structure to perform multi-scale encoding of the barcode ROI's texture, edges, and local defects, the output semantic vector retains the advantages of convolutional networks in local pattern recognition while providing high-dimensional, distinguishable input features for the cross-modal attention mechanism.
## 5 Evaluation  
In the evaluation, we only focus on the user's annotation to the location and transportation means, namely, the labels generated as answers to the questions in the second column in Table 5. The evaluation consists of two parts: proving (1) that annotation mistakes from non-expert users are a quantitatively major problem (Section 5.1), and proving that (2) SkeL is able to detect and mitigate the effect of such mistakes (Section 5.2). This evaluation is based on the following assumptions:  
* The SkeL algorithm is evaluated asynchronously, at the end of each day;
* The SkeL algorithm uses, in any moment, all of the data previously collected, without an explicit \"forget\" mechanism for old data. This is because removing old data helps in adapting to users' changes in behavior and patterns. However, since the data were generated from an experiment lasting 2 weeks, this was not needed;
* The knowledge of the world SK available to the SkeL algorithm was a simplified sub-set of the available one, with elements related to only the use-case (see Figure 2). Moreover, we considered it to be static for the whole duration of the experiment, and not evolving over time.
## 4 Head Node Checkpointing
### System Design  
We propose a checkpointing scheme for the head node in clustering routing protocols to minimize the recovery cost and latency. The head node election phase aims to select a stable node as a cluster head and give an equal opportunity to every node to evenly distribute the node's energy consumption. This phase involves heavy message exchanges among member nodes such as [18]. Each member node broadcasts its weight to others [\(\left(N-I\right)^{2}\)] and the head node, which has the highest weight, sends their ID to member nodes (\(N-I\)). During the head node election step our scheme elects additional backup nodes by using the fact that head nodes notify their IDs in the end of head node election step, to checkpoint the information of a head node. The state of backup nodes is similar to the head node in terms of the residual energy or their ability to operation. All collected information sent by normal nodes to the head node is also saved in backup nodes. The backup nodes periodically detect the state of the head node. If the head node has a transient problem, one of the backup nodes that is the closest node to the head node replaces the failed head node and serves as a new head node.  
Figure 6 shows an overview of our scheme as the checkpointing mechanism is applied to the head node. When the head node operates properly (clusters A and C), backup nodes save only the checkpoint information and monitor the state of the head node. In the case of cluster B, the head node cannot carry out its tasks when it encounters either a software or hardware problem. A backup node then operates as a head node based on the checkpointing information. Through this checkpointing scheme, we can prevent information loss caused by failure in the head node, and we can reduce the recovery latency caused by the frequent reelection of a head node.
# DARI-Mark: Deep Learning and Attention Network for Robust Image Watermarking
###### Abstract
However, the application of attention mechanism to image watermarking is still in its infancy and needs further research. In this paper, a deep learning and attention network for robust image watermarking (DARI-Mark) is proposed. The main novelties and contributions are as follows:  
1. We introduce the attention network, which makes watermark embedding focus on the edge and other areas with complex texture, and the watermark extraction pays more attention to watermarked features and suppresses noise signals. This greatly improves the imperceptibility and robustness of DARI-Mark. 2. We add the residual module to avoid gradient explosion or disappearance problems in deep neural networks. 3. We establish an end-to-end network framework and add the attack layer between watermark embedding and extraction networks to improve the robustness against multiple attacks (JPEG compression, sharpening, cropping, Gaussian noise, salt-and-pepper noise, scaling) through iterative training. The rest of this paper is organized as follows. Section 2 introduces related work. Section 3 presents the proposed watermarking framework and training details. Experimental results and the analysis of the proposed framework are presented in Section 4. Finally, Section 5 presents the conclusions and possible further work.
## 4 Simulation Results
### Simulation Tests Conducted
#### 4.3.1 Test of Quasi-Velocity Based Controller
The observations regarding the larger values of the applied force components when using a complex trajectory instead of a sinus trajectory were confirmed. However, the RMS value was lower for this trajectory; therefore, it is somewhat more accurately tracked. On the other hand, the kinetic energy consumption was indeed higher for the complex trajectory (the observations from the graphs were confirmed). However, it is also interesting to know which quasi-variable is the reason for this energy consumption. For the sine trajectory, it was calculated that \(K_{1}=0.4321,K_{2}=0.1265,K_{3}=0.1469\) J, and for the complex trajectory \(K_{1}=0.6854,K_{2}=0.2661,K_{3}=0.2278\) J. As can be seen from the calculations, the most kinetic energy is consumed by the variable \(\Delta\zeta_{1}\) which corresponds to the movement in the longitudinal direction. **Robustness issue**. In addition, the robustness of the proposed algorithm to changes in the hovercraft parameters was examined. The inaccuracy of the model parameters was assumed as W = 0.5 (50%). It turned out that the controller was equally effective in this case (only small index values were obtained), except for kinetic energy, which had higher values (Figure 4) due to the fact that the vehicle had a greater mass and moment of inertia, or a greater displacement of the center of mass. The increase in the value of the kinetic energy is proportional to the increase in the value of the dynamic parameters. For the sine trajectory, it was calculated that \(K_{m}=1.0511,K_{1}=0.6599,K_{2}=0.11784,K_{3}=0.2128\) J, and for the complex trajectory \(K_{m}=1.7586,K_{1}=1.0355,K_{2}=0.3873,K_{3}=0.3357\) J.