esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Use as a Possible Food Additive
D-values decreased uniformly across all tested temperatures: 55 \({}^{\circ}\)C a 91% decrease; 57.5 \({}^{\circ}\)C an 88% decrease; 60 \({}^{\circ}\)C a 90% decrease; 55 \({}^{\circ}\)C 79% decrease) compared to the control (without citral) [17]. Moreover, there is a correlation between citral concentration and its efficacy. Increasing the concentration has favored its bactericidal effect against _S. Typhimurium_ in fish cubes samples. After 4 d at 4 \({}^{\circ}\)C, a 3% concentration exhibited higher activity compared to geraniol [13]. Citral also displays selectivity towards the food matrix as an additive. For example, [PERSON] and [PERSON] [138] evaluated the effect of citrus EOs (orange, lemon, bergamot) and citral alone on bacteria survival (_Campylobacter jejuni_, _E. coli_, _L. monocytogenes_, _B. cereus_, and _S. aureus_) in cabbage and chicken skin. In cabbage, citral showed a higher inhibition capacity, achieving a 6-log reduction for _S. aureus_ and _L. monocytogenes_, while its effect on chicken skin was negligible. A drawback of citral lies in its sensory attributes; it can exhibit a strong odor and characteristic flavor, poising challenges for direct incorporation into food products. Innovative technologies, including modified atmosphere packaging (MAP), antimicrobial active packaging (AAP), and edible coatings, have been introduced to address this issue. For instance, [PERSON] et al. [137] developed an AAP functionalized with citral (7.5% \(w/w\)) and oregano essential oil (7.5% \(w/w\)) within an ethylene-vinyl alcohol copolymer (EVOH) matrix to preserve minimally processed salads. After 8 d, AAP-citral showed a more pronounced reduction in lactic acid bacteria than AAP-oregan essential oil (reductions of 1.19 and 0.44, respectively), with sensory acceptance favoring the salad stored in AAP-citral. In addition, an edible coating based on pectin-citral (2.0% and 0.15%, respectively) effectively extended the shelf life of fresh-cut apples, resulting in less weight loss, improved firmness, and enhanced color retention after 8 d at 4 \({}^{\circ}\)C [14]. However, it is worth noting that higher citral concentrations in edible coatings (\(\geq\)0.5%) can significantly alter the flavor and odor of the product [134]. In the development of edible coatings based on alginate containing a citral-eugenol mixture (0.15 and 0.1%, respectively) and pectin with citral (0.15%), it has been possible to improve the quality of strawberry fruits without compromising their sensory properties (appearance, texture, aroma, and flavor), even after 14 d of storage at 0.5 \({}^{\circ}\)C [134]. Citral's potential enhance food safety and preservation positions it as a valuable additive in the food industry. The current findings in various food matrices strongly suggest that incorporating citral into food production processes can significantly contribute to producing safer and more flavorful food products with extended shelf life, satisfying the industry's and consumers' changing demands.
## 1 Introduction
\[f\big{(}d_{p}\big{)}=\left\{ \begin{array}{c}1-exp\left[-5\left(\frac{d_{p}- 5}{20}\right)^{1.12}\right],\ d\geq 5\\ 0,\ d<5\end{array} \right\} \tag{2}\]  
In the following, the two digital filters used in this study are referred to as \"F1-PN \(>\) 23\" for d50 = 23 nm and d90 = 41 nm and \"F3-PN \(>\) 10\" for d65 = 10 nm and d90 = 15 nm. This naming is in line with the study by [PERSON] et al. in [37] that used these same two filters. Many studies have been performed to understand the relationship between soot and engine running conditions, engine metrics (air-to-fuel ratio--AFR, spark timing, start of injection--SOI, mass of fuel burnt--MFB, exhaust temperature), or fuel type. [PERSON] et al. [41], using a DMS with and without a catalytic stripper (CS), found that the engine emits most particles at fast idling conditions at 0 Nm-1250 rpm, while the lowest amount was at 20 Nm-2250 rpm. In this experiment, it was noticed that a high level of total unburned hydrocarbons (THCs) encourages higher solid particle numbers. [PERSON] et al. [40] investigated 14 different fuels and several engine conditions. Literature [17; 42; 43] shows that fuels' propensity to generate soot grows from alkanes to aromatics, with aromatics being the most prone to cause high PN. There is also evidence that oil characteristics can affect emissions. [PERSON] et al. [44] showed how sulfur content had an impact on nanoparticle formation. [PERSON] et al. and [PERSON] et al. [45; 46] saw that zinc additives also affected overall emissions. Volatility was seen to increase oil consumption due to more evaporated oil in the chamber [47; 48]. Nonetheless, with higher volatility, more emissions were encountered by [PERSON] et al. [45] and [PERSON] et al. [49], while viscosity did not have a large impact in these studies. Ash content should be expected to emit more soot as it decreases the oxidation reactivity of particles [50]. This was observed in [PERSON] et al. [51], where a higher ash content oil slightly increased emissions, and above all, it increased the sub-23 nm particle fraction. This work focuses specifically on sub-23 nm particle emissions from various oils under steady-state conditions, in contrast to the majority of existing literature, which primarily examines oils' effects on total particle number (TPN) during transient driving cycles [45; 46; 51; 52; 53; 54]. This is part of a project investigating emissions from lubricant oils in decarbonised engine platforms (H\({}_{2}\) and NH\({}_{3}\)) with a focus on emitted particles below the 23 nm threshold. The overall aim is to assess the viability of new/decarbonised fuels, selecting the most suitable candidate for zero emissions, including the particulate matter, often neglected in H\({}_{2}\)/NH\({}_{3}\) engines. The next step will then be to test the different oil formulations in the decarbonised platforms to remove the effect of the carbon-based fuel.
# Non-Volatile Memory Forensic Analysis in Windows 10 IoT Core
## 4 Analysis Method
### Methodology  
Even though the conventional forensic process models do not entirely suit the characteristics of the IoT environment, they can be adapted to the context in which this investigation takes place. In this case, the methodology is shaped by taking into account that the goal of the forensic analysis is to determine what information stored in the non-volatile memory of the system could be useful in a real investigation in which an incident has occurred. This translates into a more flexible process in terms of forensic soundness, since the examination is being carried out in a controlled environment that is specifically designed for the analysis, and the conclusions extracted from the analysis are not going to be used in a legal process. Therefore, certain measures such as the chain of custody are not required in this experiment. Obviously, the appropriate actions are carried out to avoid tampering with the data that is acquired and analyzed.  
The process model used as a reference is presented in [35], in which an evaluation of the most relevant models produced from 1984 to 2011 is made, creating a generic one based on the commonly shared processes. The phases into which the methodology is divided are the following:  
* Pre-process: preparation work that is executed before the start of the investigation, such as tool set up or warrant obtention.
* Acquisition and Preservation: refers to the identification, acquisition, collection, transportation, storage and preservation of the data.
* Analysis: study of the acquired data to extract information and draw conclusions.
* Presentation: documentation of the findings obtained and submission of the report to the authorities or the requester of the investigation.
* Post-process: relates to the closing of the investigation. Actions such as the return of the evidence are carried out in this phase.  
In this analysis, the presentation and post-process phases are not necessary since the results of the investigation are not going to be presented in the form of a report, and the evidence acquired does not need to be returned. In addition, in order to adapt the model to the characteristics of the IoT environment, a new phase needs to be included in this analysis: evaluation. This refers to the procedure of grouping all the pieces of information collected from the different devices in the analysis phase and extracting conclusions from them about how they fit into the environment as a whole. In conventional process models, it is normally performed in the analysis phase, but, given the increase in the number of devices to analyze in IoT investigations, the task has become more complex and relevant, hence the creation of a new phase.
# Study on EEMD-Based KICA and Its Application in Fault-Feature Extraction of Rotating Machinery
## 3 The Proposed Method  
In this paper, a fault-feature extraction method of rotating machinery based on EEMD-based KICA is proposed. Its advantage is that it can estimate the number of fault signals of a single-channel vibration signal and can extract fault features clearly. This method only needs to input a single-channel signal, and the outputs are fault signals and number, without artificial selection. The procedure of the proposed method has been summarized in Figure 1. The detailed procedure is described as follows:  
Step 1: The collected single-channel signal \(\mathbf{X}(t)\) is decomposed with EEMD, and all IMFs can be obtained. Then, the multidimensional observation signal can be formed with all IMFs.  
Step 2: The observation signal is decomposed with SVD first. Then, singular values are listed in descending order. Next, the ratio of neighboring singular values is calculated. Finally, the sequence number of the maximum ratio will be the number of fault signals \(M\).  
Step 3: Calculate the correlation coefficient of each IMF to \(\mathbf{X}(t)\) and select the top \(M\) IMFs with the highest correlation to form a new observation signal. Finally, use KICA to extract the fault signals from the new observation signal.
## 3 Experiments and Results
### Motion Pattern Recognition Based on Multilayer Perceptrons
#### 3.1.2 Trend Term of Surface EMG Signal  
The average value of the measured surface electromyographic signal was not zero, because the amplifier will produce a zero drift with temperature changes. The deviation of the external performance in the frequency domain of the sensor and the interference of the surrounding environment of the sensor often cause the distance to change and form a small offset, which is called the trend term. In order to remove the trend items of the data, the least squares method was used to find the trend items of the original data for each time window data, and then the original data was subtracted from the trend item data, and finally, the maximum value and minimum value of the data were normalized.  
The characteristic parameter datasets of the unremoved trend item and the removed trend item were established, respectively, with the sliding window length of 1024 ms and the window interval of 50 ms. Seventy percent of the dataset was used for training, and 30% was used for the testing. In the 70% of the dataset used for training, 90% of the dataset was used for training, and 10% of the dataset was used for verification. The classifier used a three-layer multilayer perceptron. The activation function of the input layer selects the sigmoid function, and the activation function of the output layer selects the softmax function. In the cross-validation method, the number of hidden layer nodes was changed from 4 to 60, and the classifier model was trained separately. The number of hidden layer nodes with the highest accuracy was selected as the final classification model.  
As shown in Figure 9, the accuracy of the model after removing the trend term is 96.56%, and the number of hidden layer nodes is 25. The accuracy of the unremoved trend term is 79.54%, and the number of hidden layer nodes is 23. The confusion matrix corresponding to the data in Figure 9a is in Table 12 of Appendix A; the confusion matrix corresponding to the data in Figure 9b is in Table 13 of Appendix A.
# LaRaWAPS: A Wide-Area Positioning System Based on LoRa Mesh
## 4 System Design
### LoRa Ranging and Localization  
This section introduces how to use the LoRa Mesh network to locate and track the target. Firstly, we introduce the working flow of location, then process the ranging results of anchors and the target, and finally give the algorithm of multi-point location.
# Cooperation Based Proactive Caching in Multi-Tier Cellular Networks
## 4 Collaborative Filtering for Content Prediction
### K-Mean Clustering  
In this subsection, the k-mean algorithm is applied to the past requests received on the BSs in each tier. K-mean clustering is an algorithm that tries to partition data set into K predefined distinct non-overlapping subgroups clusters where each data point belong to only one group. The content popularity matrix gives the frequency of the request on the BS. Hence, those BSs that receive similar requests for the contents, are grouped together to form a cluster. As micro BSs comprise of greater number of users as compared to pico BSs. Therefore, the request of the former will be greater as compared to the later. Also, there will be more pico BSs as compared to micro BSs and, hence, the cluster of pico cells will be more crowded compared with the micro cells. The similarity between the two base stations is based on the Euclidean distance. The algorithm has the following steps:  
(1) First of all, specify the number of the cluster in each tier. Let us have K clusters H1,...,..., Hk;2. Initialize centroids by first shuffling the data set which is the past requests on the base stations and then randomly selecting K data points for the centroids without replacement. Random Selection of K SBSs from the rows of P is carried out, which are represented as \(\text{c}_{1}^{(1)}\),......, \(\text{c}_{K}^{(1)}\);
3. Keep iterating until there is no change of data points in the cluster;
4. Compute the sum of the squared distance between data points and all centroids: \[H_{m}^{t^{\prime}}=\left\{p_{i}:\|p_{i}-c_{m}^{t^{\prime}}\|^{2}\leq\|p_{i}-c_ {m}^{t^{\prime}}\|^{2},\ \forall\text{n}\ 1\ \leq\ \text{n}\ \leq\ \text{k}\ \right\}\] where \(p_{i}\) is the \(i\)-th row of p and is assigned to exactly one \(H^{t^{\prime}}\);
5. Assign each data point to the closest cluster (centroid); and
6. Compute the centroids for the cluster by taking the average of the all data points that belong to each cluster. So the new means to the centroid of the new cluster is \(c_{m}^{(t+1)}=\frac{1}{\left\|H_{m}^{(t)}\right\|}\sum_{p_{i}p_{i}p_{m}^{(t)}p _{i}}\).  
The new centroid is subtracted from the old one and the algorithm repeats until and unless this value becomes less than a threshold. Finally, we cluster K clusters. This process is taking place in each tier. The BS in each cluster has similar interest.
# Visual Tracking Control of Cable-Driven Hyper-Redundant Snake-Like Manipulator
## 3 Kinematics Modeling and Control Methodology
### Control Method  
Based on the kinematics discussed previously, a control scheme of CHSM is shown in Figure 6. For the object tracking task, the end-effector is trying to follow the object's moving trajectory and keep a constant distance from the target. The tracking component is applied to infer the target's position in the working space according to that in the pixel space by multiplying the inverse instinct matrix of the camera and then calculate the desired end-effector's pose \(A_{Nd}(x_{Nd},y_{Nd},z_{Nd})\). The desired cable's length \(l_{di}(i=1,2,\ldots,3N)\) are calculated from Equations (11)-(15) with desired joint angle \([\alpha_{di},\beta_{di}](i=1,2,\ldots,N)\) obtained from the inverse kinematics with \(A_{Nd}\). The linear magnetic encoder mounted on the actuator measures each practical cable length \(l_{ri}(i=1,2,\ldots,3N)\) as inputs for the closed-loop PID controller which is designed as follows:  
\[u=K_{P}(l_{d}-l_{r})+K_{I}\int_{0}^{t}(l_{d}-l_{r})d\tau+K_{D}(l_{d}-l_{r}) \tag{19}\]  
where \(K_{P},K_{I},K_{D}\) are PID parameters and the output has a linear proportional relation with the cable's stretching speed.
## 6 Discussion  
### A Comparative Discussion with the Results of Other Methods  
At present, in the field of LEO SOP positioning, the research on epoch weighting and screening is relatively scarce, mainly focusing on satellite screening based on geometric configuration and weighting based on orbital error. It is not difficult to find that the existing LEO SOP weighting methods all have essential differences from the method proposed in this paper, which are specifically reflected in the following two points:  
* Current research can only screen satellites, while this paper delves into the weighting of each epoch of satellites.
* The current research only optimizes based on one or two aspects of parameters, while this paper comprehensively considers nine parameters at four levels.  
Furthermore, the use of different constellations and the opacity of engineering details makes it extremely difficult to reproduce algorithms in the same field under strictly controlled variables. Based on the above discussion, it is difficult for this paper to make an effective comparison with existing algorithms.  
In this work, the test set covers almost all time periods of the 24 h of the day, with an observation duration of 27 h and a total of 80,764 epochs. Among the 97 independent LEO SOP locations examined during the observation period, the AWR-PF outperformed RLR and standard RLS on average. This indicates that the improvement in positioning accuracy by the AWR-PF is not an accidental phenomenon but a fact verified through a large number of experiments.  
All the positioning comparisons in the paper have undergone strict variable control. That is, the only difference among the three methods lies in the weighting strategies for epoch elements (weighted by the agent, randomly weighted, and with a constant weight of 1). The results obtained from this comparison demonstrate that the weighting strategy of the agent optimizes the positioning results rather than due to other factors. This also supports the core innovation point of this article: an agent capable of weighting the fusion of multiple characteristics of epoch elements.
## 3 Experimental Results
### Experimental Results
#### 3.3.3 Experiments with the Used Local Search Optimizer
\begin{table}
\begin{tabular}{c c c} \hline
**DATASET** & **ADAM** & **LBFGS** & **BFGS** \\ \hline WINE & 11.47\% & 10.06\% & **9.77\%** \\ \hline Z\_F\_S & 12.33\% & 10.92\% & **8.53\%** \\ \hline Z\_O\_N\_F\_S & 41.40\% & 45.77\% & **38.58\%** \\ \hline ZO\_NF\_S & 11.26\% & 9.83\% & **6.84\%** \\ \hline ZONF\_S & 2.64\% & **2.35\%** & 2.52\% \\ \hline ZOO & 8.50\% & 8.70\% & **7.20\%** \\ \hline
**AVERAGE** & **21.03\%** & **20.72\%** & **19.52\%** \\ \hline \end{tabular}
\end{table} 
Table 11: Experimental results with local optimization techniques in the current work with application on the regression datasets. The bold notation marks the method with the lowest test error.
# Defining the Ideal Phenological Stage for Estimating Corn Yield Using Multispectral Images
## 2 Materials and Methods
### Processing of Multispectral Data  
The images from the Planet satellite can be obtained in the following three different processing levels: PlanetScope Basic Scene Product, PlanetScope Ortho Scene Product, and PlanetScene Ortho Tile Product. The variations between the supplied images oscillate between the presence of geometric correction (contained in the last two types of images) and the presence of the image in UTM projection (only the last image). In all cases, the supplied images are atmospherically corrected by the company; that is, the digital numbers of the bands are offered in surface reflectance values.  
The images were obtained along the main phenological stages of corn development in the study area. In this phase, we only prioritized the images that presented significant spectral changes in the visible and near-infrared bands because there was little spectral variability in many of the consecutive stages. Note that each area has a distinct spectral characteristic where the spectral variability depends on abiotic and biotic factors of the culture and the date of cultivation.  
The dates corresponding to the phenological stages of the study area are presented in Figure 3, which are indicated by green, whereas the red lines indicate the intervals at which the images were obtained.  
Figure 3: Corn crop, phenological stage V (vegetative), and reproductive (R) stage in image acquisition in the study area.
# Lightweight CoAP-Based Bootstrapping Service for the Internet of Things
## 3 The Bootstrapping Service: CoAP-EAP
### Additional Considerations
#### 3.6.6 Other Security Considerations  
On the one hand, our bootstrapping service makes use of EAP and AAA and associated key management. In terms of security, the solution does not add anything different than existing deployments of EAP and AAA do. In this sense, it follows the _EAP Key Management Framework_[14] and the _Guidance for Authentication, Authorization, and Accounting (AAA) Key Management_[13].  
On the other hand, the AES-based cryptosuite selected as default is well-known and already deployed nowadays in IoT devices [94]. It is worth noting that the cryptosuite negotiation is for the operation at bootstrapping service level. In other words, the DTLS negotiation can select a different set of algorithms.  
Nevertheless, we should clarify how we avoid any downgrading attack during the cryptosuite selection procedure shown in Section 3.6.5. This attack happens when an attacker removes some of the algorithms to limit the set of valid cryptographic algorithms to those more favorable to the attacker. We assume that AAA infrastructure is trusted; therefore the path between the Smart Object and the Controller is where the attack may happen. Our mechanism to detect this attack is simple. If protection at level application is enabled (AUTH option) the last POST message and corresponding ACK (Figure 4--steps **16** and **17**) are integrity protected so any modification (removal or modification of the algorithms specified in the POST message) will be detected by both entities. On the contrary, if DTLS is used, we can observe in Figure 6 that the last POST message and corresponding ACK are not protected. This is why the Smart object and the Controller must end the DTLS negotiation before considering the bootstrapping complete. In reality, the KDF is the only function which needs to be agreed in this case, since DTLS already performs a cryptosuite negotiation. Thus, if an attacker has modified the KDF algorithm, the DTLS_KEY derived by the Controller and the Smart object will be different and DTLS will fail, so that the attack is detected.
## 5 Conclusions and Future Work
### Theoretical Implications  
Our proposal is a novel approach that considers contextual factors to improve the conversational model. Humans employ contextual information in everyday decision making. This is a sensible idea to mimic human conversation in chatbot systems. We performed multiple experiments to showcase that incorporating both left- and right-contexts is more effective than using either one separately and significantly more advantageous than not utilizing context at all. Based on our experiments, having more context can be beneficial when training a chatbot system. It can help the system better understand the user's intent and provide more accurate and relevant responses.  
Moreover, we also proposed an approach that resolves issues with traditional neural network models in conversation response generation by integrating Transformer-based Seq2 Seq models and RL. The experimental results demonstrate that our proposed model has significantly improved when compared to current relevant studies. From an academic perspective, there is still considerable research space for exploring this direction, which is very worthy of in-depth excavation by interested researchers.