esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### The Content of Total Phenolics
It has been observed that methanolic extracts from _A. bisporus_ (white) caps contained approximately 44% more phenolic compounds than those from stems [26]. In this study, however, the difference was significantly lower (\(\sim\)19%), which could be caused by the use of a different solvent. Similarly, [PERSON] et al. [27] noticed that _L. edodes_ caps had \(\sim\)24% more phenolics than stems (in this study the difference amounted to \(\sim\)54%). Interestingly, in contrast to this study, [PERSON] and coworkers [28] observed that stems of _L. edodes_ were substantially richer in phenolics compared to their caps. It is also worth noting that the present study showed that, in the case of _P. nameko_, phenolics were distributed equally between caps and stems. The starkest difference can be observed in _P. ostreatus_, where the content of phenolic compounds in caps was almost three times higher than in stems. The specific distribution of phenolic compounds can vary among different mushroom species and even within the same species. A few factors play a key role here, like the environment in which they are grown, as well as the stage of development. Moreover, the concentration of phenolics can also be also influenced by light exposure, temperature and substrate composition [29]. In order to evaluate which extract accumulated the highest quantity of phenolics, the data on extraction yields were taken into account. The calculated amounts of phenolics per one gram of an extract can be seen in Figure 1b. In general, ethanolic extracts were richer in phenolics compared to mushroom powders. The highest amounts were present in the extracts obtained from caps. The extracts can be ranked in the following order: _P. ostreatus_ > _A. bisporus_ (brown) > _A. bisporus_ (white) > _L. edodes_, _P. nameko_, _P. eryngii_. However, in the cases of _A. bisporus_ (white) and _L. edodes_, the extracts from stems contained phenolics at levels close to that of the extracts from caps (the differences did not exceed 13%). Interestingly, _P. ostreatus_ is an example of a mushroom which produces a low yield of total soluble compounds, however much of these compounds could be classified as phenolics, according to the assay.
# A Survey on Recent Advanced Research of CPS Security
## 3 Reviews
### New Attacks
#### 3.21.1 Power Grid  
[PERSON] et al. [12] proposed coordinated load-changing attacks based on the assumption that an adversary can create power fluctuations to attack a power grid with a botnet of zombie computers. The authors developed three different attacks against the power grid: static load, dynamic load, and inter-zone attacks. Their simulations show that between 2.5 and 9.8 million infected devices are sufficient to strike the European synchronous grid without relying on smart grid features to modulate power consumption.  
HARVEY [13] is a new PLC rootkit whose target is the industrial power grid. It replaces the legitimate control commands from PLC output modules with optimally calculated malicious commands to the physical plant's actuators. HARVEY is implemented with a commercial PLC controller and evaluated on a real-world power grid testbed. The delay caused by attack logic is much less than the normal control logic, so HARVEY is feasible.  
[PERSON] et al. [14] studied the collective effects caused by an IoT botnet of high wattage devices and found that such a botnet could lead to local outages or large-scale blackouts, depending on the scale of the compromised devices. The authors revealed a new type of potential attack on power grids: manipulation of demand via IoT (MadjoT) attacks and divided the MadjoT attacks into five variations. The authors evaluated the attack effectiveness via advanced simulators on real-world power grid models. The results proved that such attacks can also be used to damage a grid while benefiting a few utilities in the electricity market.  
[PERSON] et al. [15] re-evaluated the impact of MadjoT [14] attacks when power transmission grids encounter such attacks. A novel cascading outage analysis tool was leveraged to determine how the protection equipment in the power grid and protection algorithms respond to cascading events that could lead to a power blackout. The authors applied their tool to an extensive North American regional transmission interconnection system, including more than 5000 buses, to study the influence caused by MadjoT attacks. The authors found that embedding protections in the transmission grid's operation can prevent a system blackout.
## 6 Conclusions  
To help retail firms conduct sales forecasting, market analysis, and performance assessment, this research proposes a novel framework, and the top three US retail firms, Walmart, Costco, and Kroger, are used to evaluate the research validity. More importantly, these three critical issues are surrounded by the concept of business analytics from start to finish. In summary, the research contributions are outlined as follows:  
* A statistical regression known as Lasso is used to select the economic indicators for Walmart, Costco, and Kroger, and machine learning methods (MARS, SVR, DNN) are used for sales forecasting,
* The Lotka-Volterra model is applied to conduct competitive analysis between the top three US retail firms, and to estimate stable market equilibriums in order to reveal insights,
* Data envelopment analysis is used to derive operational efficiencies and to indicate the actions required for inefficient firms to improve their input resource variables.  
Experimental results show the identified economic indicators incorporated into machine learning work well in sales forecasting (the average MAPEs are below or around 10%). Besides, the demonstrated interrelationship known as _mutualism_ indicates that the total market is still a growing pie, and thus, each firm can benefit alongside the other. Finally, from 2009 to 2019, and also 2021, Kroger performed the worst in operational efficiency. The required improvements suggested include a decrease of full-time employees, a reduction of COGS, and a reduction of operating expenses.  
Needless to say, this research is not without limitations: (1) due to limited information, only aggregate sales were collected and analyzed for Walmart, Costco, and Kroger. Product sales with respect to detailed categories (perishable food, home supplies, electronic appliances, snacks, etc.) could provide more insights [4]; (2) only onsite retailers with branch stores were analyzed and compared, while the competition arising from online e-commerce platforms, such as Amazon, were omitted. Moving forward, the boundary between onsite stores and online platforms is expected to blur, and hence, their competition deserves to be addressed [2; 31]; and (3) the STP issue, market segmentation, customer targeting, and product positioning should be considered in order to fit consumer preferences for the accomplishment of upselling and cross selling. Furthermore, purchasing transaction records should be linked to customer demographics to develop attractive product strategies and promotion plans.  
Conceptualization, framework, article writing, C.-H.W.; data collection, methodology implementation: Y.-W.G. All authors have read and agreed to the published version of the manuscript.  
This research is under the support of Taiwan Ministry of Science and Technology.  
All data used in this research was extracted from the public lists for US economic indicators and financial reports of US retailers.  
The authors declare no conflict of interest.
## 4 Results
### Baseline Models
Among these, the Linear Kernel yielded the highest accuracy. In the training of the SVM classifier, nine different loss parameters (logistic, hinge, modified huber, squared hinge, perceptron, squared error, huber, insensitive, squared epsilon insensitive) were utilized. Among these, the logistic loss parameter resulted in the highest accuracy. 2. CNN specializes in deep learning models for image and grid data processing [50; 51]. They use convolutional layers to detect features, pooling layers to downsample, and fully connected layers for classification. CNNs excel in tasks such as image recognition and have wide applications in computer vision and beyond. In the dataset, the following parameters yielded the most optimal results. The number of epochs was set to 8, following experimentation in the range of 6 to 100, while the batch size was configured to 64, tested across a range from 32 to 128. An embedding dimension of 300 was used, accompanied by 100 filters and filter sizes of 2, 3, and 4. A dropout rate of 0.5 was applied during training. The optimizer employed was Adam, and the criterion was defined as CrossEntropyLoss, since the task is multiclass classification. The text was tokenized using the spacy.load (\"ko_core_news_sm\") tokenizer supported by space, which is the Python library. The pre-trained model used for the tokenizer is \"ko_core_news_sm\". These parameters were crucial in achieving the desired outcomes, as highlighted in the provided data. 3. BERT is indeed a type of LLM, similar to GPT, but it's a smaller model with only 0.3 billion parameters compared to GPT-3.0's 175 billion parameters [39; 52]. The number of parameters in LLMs is proportional to the size of the training dataset. To investigate whether there is a performance difference in the dataset based on the number of parameters, experiments were conducted using BERT. The experiments were conducted using Python and the Keras TensorFlow package [52; 53]. The training of the BERT model was based on the BERT-Base model available from Google on GitHub. The BERT-Base model supports 104 languages and consists of 12 layers, 768 hidden units per layer, 12 attention heads, and 110 million parameters. For optimization, the RAdam optimizer was chosen, incorporating a weight decay of 0.0025 [54; 55]. Since the task involves multi-class classification, the sparse categorical cross-entropy loss function was employed. Furthermore, the following parameters that produced the best performance were used in this paper: sequence length (128), batch size (16), epochs (8), learning rate (0.00001), optimizer (Adam).
# Watermarking Tiny MLCommons Image Applications Without Extra Deployability Costs
## 3 Related Works
### Watermarking Applied to Data
#### 3.1.2 With ML Methods  
While former studies focused on applying watermarking techniques directly to data, there was an increasing tendency to integrate ML algorithms as part of watermarking methods.  
A review was presented in [21] that introduced a watermarking process exploiting a multi-head cross-attention mechanism. The watermark was generated by resizing the image, which was then binarized using a pixel threshold approach. The process was repeated to create a mixed watermark. The system included four components, which were as follows:  
* An embedder;
* An encoder;
* A decoder;
* An extractor.  
These components worked together to create the WMd image, aiming to take out critical characteristics through the generation of invariant domains and extracting the watermark, which had to be similar to the original. The encoder incorporated convolutional layers and fully connected layers. Another method combined convolutional neural networks (CNNs) with DWT for embedding and extracting watermarks [22]. This method involved two stages to embed the WMd into the image:  
* For the image host;
* For the watermark.  
The DWT was used to split up the colored host image into four levels, one of which was chosen for watermark inclusion. During these processes, CNNs facilitated the watermarking procedure.  
Furthermore, a \"box-free\" multi-bit watermarking method was introduced by [23] to protect IP, which was robust against various attacks. This method incorporated watermarking by introducing a loss factor during the training of GANs, ensuring the presence of an invisible watermark in images that can only be recovered by a pre-trained watermark decoder. This approach was adapted to CNN-based architectures, protecting their robustness by converging to a large and flat minimum of the watermark loss factor.  
Ref. [24] proposed an end-to-end network for watermarking, and a CNN assessed the robustness based on image content. This approach embedded, attached, and extracted gray scale watermarks within a single frame. The method employed a stochastic gradient descent (SGD) optimizer during training to improve robustness.  
Ref. [25] used a watermarking method to protect GAN IP by embedding a watermark in the model. This method involved inserting a pre-trained CNN watermark that decoded a block into the G output and modifying the G loss to include a watermark loss term, which made it easier the watermark extraction from the generated images. The watermark was then incorporated through a fine-tuning procedure.
# Object-Level Semantic Map Construction for Dynamic Scenes
## 2 Related Work
### Dynamic SLAM
[41] presented a stereo-based dense mapping algorithm for large-scale dynamic urban environments that can simultaneously reconstruct the static background, moving objects, and potentially moving but currently stationary objects separately. However, this algorithm cannot operate in real-time and is mainly designed for large indoor scenes. As in our method, [PERSON] and [PERSON] [38] used Mask RCNN predictions to detect object instances. Their objective was to reconstruct and track moving instances densely using an ElasticFusion [3] surfel model for each object, as well as for the background static map. They did not attempt to reconstruct high-quality objects as pose-graph landmarks in room-scale SLAM. [PERSON] et al. [37] proposed a method of reconstructing and tracking each moving object with a separate 3D model, being one of the first real-time methods to perform dense tracking and fusion of multiple objects to handle dynamics explicitly. These approaches have naturally paved the way towards interaction and dynamic object reasoning. In addition, motion detection from still cameras has been studied for several years [42; 43], where foreground subtraction [44] and the optical flow [45] have been exploited. However, these approaches are not directly usable in many applications that involve moving cameras. Some new research on this problem has appeared in recent years [14; 46; 47; 48]. These works have mainly adopted motion segmentation approaches to handle motion detection using a moving camera. For example, [PERSON] et al. [49] projected keyframes onto the current frame for appearance and structure comparison, which could reveal changed regions. [PERSON] et al. [7] used input points with no close model correspondence to seed a region-growing procedure to segment the current image into static and dynamic parts. Subsequently, model points matched with dynamic input points are removed from the reconstruction. This approach can only be utilized once a confident reconstruction of the scene is in place. [PERSON] and [PERSON] [50] proposed edge-based polyhedral tracking to achieve dynamic object judgment. Further, [PERSON] et al. [30] used geometric clustering to divide objects into dynamic and static parts. Nevertheless, in these approaches, no spatial or temporal coherence is enforced among the detected dynamic points between consecutive frames. These methods are more dependent on the shape and color characteristics of the object and do not have sufficient adaptability to the environment.
## 5 Validation of the Proposed Method
### Comparison Experiments  
In the comparison experiments, the proposed model was compared with the original GNG, GWR, GNG-I, SOIIN, and K-Means. The experiments were conducted on a computer equipped with an Intel(R) Core (TM) i7-10700 CPU at 2.90 GHz and 16 GB of RAM. According to the results of the test set, the values of the corresponding evaluation criteria are shown in Table 4. For each dataset, the result of the best-performing method is highlighted in bold, and we can come to the conclusion that BOA-GNG achieves good results on all datasets.  
Given the sequence nature of streaming data, divide the test set into three equal parts in order (part 1, part 2, part 3). To simulate the continuous arrival of streaming data, define that Stream 1 includes part 1, Stream 2 includes part 1 and part 2, and Stream 3 includes part 1, part 2, and part 3. For each dataset, the results of the rolling test of BOA-GNG are shown in Table 5. As the data flow continues to expand, the values of the corresponding evaluation criteria of BOA-GNG remain at a relatively high level.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Datasets** & **Criteria** & **GNG [1]** & **GWR [24]** & **GNG-I [20]** & **SOINN [17]** & **K-Means** & **Proposed Method** \\ \hline \multirow{3}{*}{Shuttle dataset} & \(p\) & 0.9762 & 0.9976 & 0.9988 & 0.9836 & 0.9976 & **0.9988** \\  & \(r\) & 0.9818 & 0.9613 & 0.9601 & 0.9692 & 0.9612 &  
For online abnormal detection scenarios, computational efficiency is important as it is the high accuracy of anomaly detection, which includes learning time and detecting time for incremental learning techniques. As a result, the experiments compare the computational efficiency of BOA-GNG and other GNG-based models. The results are shown in Table 6, and the bold fonts denote the highest computational efficiency of the methods on each dataset. \(v_{1}\), \(v_{2}\), respectively, represent the average velocity of the learning and detecting processes. We show that BOA-GNG outperforms other models on most datasets in terms of learning and velocity detection.
# Synthesis and Characterization of Unsaturated Succin Acid Biobased Polyester Resins
## 2 Materials and Methods
### Synthesis of UPRs and Cross-Linking Procedure  
Typically, succinic acid and maleic anhydride (1 eq), along with EG or PEG200 (1.25 eq), 0.2 wt% FASCAT 4101, and BHT (400 ppm), were charged into a three-necked round-bottomed flask, which was equipped with a mechanical stirrer and a Dean-Stark apparatus. Using toluene as an entrainer, the mixture was heated gradually until homogenization,and then the temperature was set at 180 \({}^{\circ}\)C. The course of the reaction was tracked by the analysis of the acid value (AV). When the AV reached a value of less than 5 mg KOH/g, the toluene was removed under reduced pressure. The polyester was obtained without any further purification as a yellow viscous liquid. According to this procedure six unsaturated polyester resins were prepared and the ratio of monomers used for the synthesis of each resin is given in Table 1.  
The acid value (AV) corresponded to the non-reacted acid groups. It was defined as the number of milligrams of potassium hydroxide required to neutralize 1 g of sample and was determined according to DIN EN ISO 2114 by titrating the remaining carboxylic acid groups of the sample with a solution of potassium hydroxide in methanol (0.3 mol/L).  
To cross-link the synthesized resins, a mixture of the prepared UPR from Table 1 and AA in a mass ratio of 2:1 was mixed. For the preparation of the cross-linked resins, a mixture of UPR and AA in a mass ratio of 2:1 was mixed at room temperature. Curing of the materials was initiated using 3 wt% MEKP or BPO initiator and Co(II) octoate as the accelerator. After adding the initiating system, the mixtures were put in Teflon holds and heated at 100 \({}^{\circ}\)C for 4 h.
## 3 Results and Discussion  
### Effect of Ar and He Addition on Electrical Characteristics  
We compared the discharges without noble gas addition, with Ar addition, and with He addition under the same discharge conditions, with the pulse voltage set to 13 kV, the pulse repetition frequency set to 1200 Hz, and the amount of noble gas added to 40% of the total gas volume. Maintaining the gas ratio of H\({}_{2}\):N\({}_{2}\) = 3, the total gas flow rate was set at 120 mL/min without the addition of noble gas, and at 200 mL/min when noble gas was added. Refer to Table S1 for the various gas flow rate configurations (the impact of gas flow rate will be examined subsequently). Figure 2 shows the voltage-current profiles for the three cases of no noble gas addition, Ar addition, and He addition. The figure illustrates that the current's amplitude escalates with the incorporation of Ar and He. Without any noble gas, the current amplitude stands at 18.49 A, but this figure climbs to 25.18 A with Ar and further to 27.24 A with He. This rise in current amplitude is attributed to the reduction in breakdown voltage caused by the addition of Ar and He [26]. The lower breakdown voltage is responsible for its stable discharge at lower energy input [28]. In contrast to the case without noble gas addition, the waveform of the discharge current becomes smoother in the discharge, indicating that the uniformity of the discharge is improved. Previous research has shown that the most significant impact of adding noble gases to discharge plasma is the facilitation of a more uniform and diffuse plasma generation [31]. This is due to the fact that the metastable energy levels of Ar and He atoms are higher in energy than the metastable energy levels of N atoms. The addition of a noble gas to the feed gas results in a Penning reaction, which causes the ionization coefficient to increase, thereby reducing filamentary discharges and making the plasma more uniform and diffuse [32]. In addition, the increase in voltage amplitude is greater for the case of adding He, which also indicates that the discharged power is higher for the case of adding He. This is related to the thermal conductivity of Ar and He. The thermal conductivity of Ar is 0.018 W/mK, compared to He is 0.15 W/mK. The superior thermal conductivity of He contributes to maintaining discharge stability by facilitating rapid heat transfer. In addition, rapid diffusion during discharge contributes to the uniformity and diffusion of the discharge because the diffusion coefficient of He is much higher than that of Ar [28].
## 3 Material and Methods
### Machine Learning Model Descriptions
#### 3.3.4 XGBoost  
XGBoost, an application of GBM, stands out because of its efficiency and performance, especially when working with complex and high-dimensional data. The structure of the model allows the integration of multiple weak predictors to create a robust final model that can address a variety of predictive modeling challenges. XGBoost uses both linear and tree learning algorithms to improve prediction accuracy [23]. Due to its adaptability and scalability, this technique has been chosen to tackle the complex problem of potential wind energy estimation.  
The XGBoost algorithm is an advanced ensemble tree method that produces predictions using a large number of multiple regression trees. The basic step of the algorithm is to make the first prediction (base score); this is usually accomplished with a default initial value of 0.5. This value provides an iterative convergence towards the result with the improvements made in the next steps of the algorithm. By following the gradient boosting methodology, XGBoost adds a new decision tree to minimize existing errors in each iteration. In this process, each decision tree is constructed to learn the deviations (residuals) from previous predictions. These residuals are calculated as the difference between the actual values and the model's predictions. The power of the algorithm lies in the fact that the sum of these weak learners can form a strong overall model by systematically reducing errors. In XGBoost, the branching of each decision tree is performed by optimization to minimize a customized objective function [34].