esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Sensory Results  
Concerning the sensory outcomes, the ANOVA output showed that the ageing modalities had a major effect on nine sensory attributes. The results for each ageing modality and control sample (without ageing) are shown at Table 3, combined with the equivalent spider graphics in order to better illustrate the differences between modalities. Other sensory attributes (fruity, wood, rancid, caramel, toasted, dried fruits, smoke, coffee, sweet, green, tails, glue and coautchouc, sweetness, smooth, astringency, roughness, bitter, and persistence) were not significantly affected by the ageing modality. Also, the overall quality of the aged wine spirits was significantly affected by the ageing modality.  
The intensities of vanilla, spicy, unctuous, flavor evolution, flavor complexity, and retronasal aroma increased with ageing process in accordance with previous research [5] and were significantly higher in the wine spirits from CO60 modality, while the lowest values were detected in samples proceeding from nitrogen modality and/or from CO30 modality. The intermediate intensities were found in the samples from the CB and CO15 modalities. Since these sensory attributes are positively correlated with the overall quality [5], the samples from CO60 and CO15 modalities were classified as having the highest overall quality, while the samples from CO30 and CN modalities were rated as having the lowest overall quality. The overall quality of the aged wine spirit samples from the barrels presented intermediate values.
## 2 Related Work
Some works focus on other languages as well, such as Arabic [22; 26], Dutch [27; 28; 29], Greek [30; 31], and Portuguese [32; 33]. Nonetheless, very few works have been done on Indian languages, especially in Bangla; therefore, there is a pressing need for the development of an automatic author identification system in Bangla. Mostly, researchers have used syntactic, semantic, and stylistic-based features but have not explored various other aspects of the Bangla literary articles from different authors having similar writing styles. Thus, this work uses visual features obtained from the text-based as well as statistical-based features that are fed to a CNN for identifying the author of an article. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Reference** & **Approach** & **Accuracy (\%)** \\ \hline [PERSON] et al. [4] & Deep learning & 89.20 \\ [PERSON] et al. [5] & Deep learning & 95.12 \\ [PERSON] et al. [6] & Semantic relationship & 95.30 \\ [PERSON] et al. [7] & Word embeddings and MLP & 95.83 \\ [PERSON] et al. [9] & LDA model with n-grams & 93.17 \\  & and cosine similarity & \\ [PERSON] et al. [10] & Content and stylometric features & 72.00 Confidence \\ [PERSON] et al. [12] & Unigram and SVM & 88.00 \\ [PERSON] et al. [13] & Bag-of-words model with SVM & 80.80 \\ [PERSON] and [PERSON] [16] & Stylometric features & 94.03 \\ [PERSON] and [PERSON] [17] & Graph-based algorithm & 94.98 \\ [PERSON] and [PERSON] [18] & SMO with J48 algorithm & 80.00 \\ [PERSON] et al. [19] & Stylistic feature and SVM & 92.30 \\ [PERSON] and [PERSON] [20] & n-gram and NB & 95.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The brief analysis of the literature study.
## 2 Materials and Methods
### Assessing Coordination between ES Supply and Demand  
In this study, we employed the CCDM to investigate the interactive coordination relationship between ESS and ESD. The CCDM highlights the interdependence of ecological and social systems and aims to understand how they interact and mutually influence each other. By adopting this model, we can develop a holistic understanding of the relationship between ecosystems and human societies.  
The CCDM recognizes the interconnectedness of ecological and social systems and emphasizes the importance of studying them together. This enables us to analyze the developmental pattern of these systems or indicators, progressing from disorder to order [68]. This representation provides insights into the overall effectiveness and synergistic impact between systems [69].  
Mathematically, the CCDM is expressed as follows:  
\[\mathrm{CCD_{i}}=\sqrt{\mathrm{C_{i}\cdot T_{i}}} \tag{4}\]  
\[\mathrm{C_{i}}=\left\{\left(\mathrm{S_{ESSCI_{i}}\cdot D_{ESSDI_{i}}}\right)/ \left[\left(\mathrm{S_{ESSCI_{i}}+D_{ESSDI_{i}}}\right)/2\right]^{2}\right\} ^{1/2} \tag{5}\]  
\[\mathrm{T_{i}}=\mathrm{\alpha\cdot S_{ESSCI_{i}}+\mathrm{\beta\cdot D_{ESSDI_{i}}}} \tag{6}\]  
where \(\mathrm{CCD_{i}}\) represents the coupling coordination degree of county i (\(0\leq\mathrm{CCD_{i}}\leq 1\)) between ESS and the ESSD; \(\mathrm{C_{i}}\) refers to the coupling degree between ESS and the ESSD; \(\mathrm{T_{i}}\) is the comprehensive development index of ESS and the ESSD; and \(\mathrm{S_{ESSI}}\) and \(\mathrm{D_{ESDI}}\) are the values of standardized ESSI and ESDI (\(0\leq\mathrm{SESSI_{i}}\leq 1\), \(0\leq\mathrm{DESDI_{i}}\leq 1\)). \(\mathrm{\alpha}\) and \(\mathrm{\beta}\) are the weights to be determined; due to the equal importance of ESS and the ESSD in the coordination, \(\mathrm{\alpha}\) and \(\mathrm{\beta}\) are given the same weight, that is, \(\mathrm{\alpha}=\mathrm{\beta}=0.5\). Referring to previous research [32], we divide the CCD into five levels: When \(0\leq\mathrm{CCD_{i}}\leq 0.20\), the ESS and ESSD are in extreme incoordination; when \(0.20<\mathrm{CCD_{i}}\leq 0.35\), they are in moderate incoordination; when \(0.35<\mathrm{CCD_{i}}\leq 0.55\), they are in reluctant coordination; when \(0.55<\mathrm{CCD_{i}}\leq 0.70\), they are in moderate coordination; and when \(0.70<\mathrm{CCD_{i}}\leq 1\), they are in superior coordination.
# ICENET: An Information Centric Protocol for Big Data Wireless Sensor Networks
## 3 Fundamental Concepts
### RPL Basics  
RPL presents an energy-efficient solution to construct and maintain a tree topology with redundant paths, which reacts quickly to connectivity changes, called a Destination Oriented Directed Acyclic Graph (DODAG) [29]. The network topology may present multiple DODAGs, rooted at different gateway nodes. By default, each sensor node has a preferred parent node to forward data packets toward the root, and multiple alternative parents to be used as backup routes [14]. A DODAG can support three traffic patterns: (i) Upstream traffic from nodes toward the gateway (multipoint-to-point); (ii) downstream traffic from the gateway toward nodes (point-to-multipoint); and (iii) traffic between nodes (point-to-point).  
There are three RPL messages: DIO (DODAG Information Object), DIS (DODAG Information Solicitation), and DAO (Destination Advertisement Object). Figure 2 shows DODAG construction, which starts when the root sends a multicast DIO message, containing configuration parameters and routing metrics (rank). This information is used by nodes to evaluate the path cost and select the preferred and alternative parents. Receiver nodes compute their own rank and then send the  
Figure 1: Message processing and forwarding in Content-Centric Networking (CCN).  
DIO. Nodes send DIS messages to request DIOs from the neighborhood, in order to join DODAG or update routing information. DAO messages are used to establish downward routes. In addition, RPL supports two modes of downward traffic: Storing or non-storing. In the storing mode, nodes store source-routing table entries for destinations learned from DAOs. When a data packet is received, the node examines the routing table to decide the next hop. Conversely, in the non-storing mode, only the root stores a downward routing table and data packets are source routed to the destination.  
Even after DODAG construction, nodes keep transmitting DIO messages in order to maintain the routing topology. The Trickle Algorithm [46] is run by RPL nodes to determine the rate for sending DIOs. RPL is highly efficient, in terms of energy and message overhead, for maintaining a tree-based wireless topology. Additionally, it can rapidly adapt to changes in channel conditions and network topology. Given these features, RPL is assumed as the underlying protocol for our proposed information-centric architecture.
## 6 Experimental OMPS Radiance Assimilation
A positive value of \(\Delta O_{3}\) here means the absolute value of the second term on the right of Equation (10) is smaller (i.e., an improvement), which implies that \(O_{3}(OMPS\ radiance)\) is better than \(O_{3}(6h\ forecast)\), while negative values indicate that the absolute error of the assimilated analysis was larger than that of the 6-h forecast. Figure 9 is the difference of the two absolute differences defined by Equation (10). We can see more positive values than negative values on the map. The assimilated OMPS radiance data are within \(\pm 3\) h of the analysis time at 06Z 2 March 2021. Since we did not have aerosol and detailed cloud information for this study, a climatological aerosol model profile was used. The ECMWF forecast cloud liquid water and ice cloud water content are used and modeled as non-precipitating clouds with a constant effective radius. Quality control fully depends on the convergence of the minimization. Since the 6-h background is good and close to the analysis, our convergence criteria were simple: simply check whether the cost function has decreased during the iteration. The non-convergent cases are likely due to high amounts of aerosols and clouds. We also analyzed the OMPS NM radiance assimilation at 12Z on the same day. The outcome was very similar to the result  
Figure 8: ECMWF total ozone amount (DU) analysis at 06Z 2 March 2021.  
at 06Z, with a small improvement seen using the OMPS NM radiance assimilation. The bias and standard deviation between the ECMWF analysis and our OMPS NM radiance assimilation at 12Z 2 March 2021 are 0.725 and 4.601 DU, respectively, while the bias and standard deviation between the ECMWF analysis and ECMWF 6-h forecast for those radiance assimilated data points are 0.777 and 4.605 DU, respectively. The geolocation of the OMPS orbital data around 06Z are different from that around 12Z.
## 5 Result and Discussion
### Protocol Comparison: Packet Delivery Metric  
Information correctly transmitted via a network is referred to as packet delivery. Packet Delivery is a sampling measure of the number of IP packets successfully received on a specific node, expressed as a percentage ratio. The graph represented in Figure 7 shows the comparison of existing OLSR [16] with the proposed OLSR technique and AODV [26] the results show that Packet Delivery is maximum in the proposed technique as compared to the existing techniques considered. The obtained result values are shown in Table 4 and a graph is depicted in Figure 7.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Packet Delivery** & \\ \hline Standard OLSR & Proposed OLSR & AODV \\ \hline
85.2 & 94.5 & 83.7 \\ \hline \hline \end{tabular}
\end{table}
Table 4: OLSR vs. Proposed OLSR vs. AODV—Average Packet Delivery.  
Figure 5: Comparison of Standard OLSR vs. Proposed OLSR vs. AODV Protocol: Delay.
# The Optimal Deployment Strategy of Mega-Constellation Based on Markov Decision Process
## 5 Conclusions  
Mega-constellation projects have already been proposed and deployed by many countries and commercial organizations. They can more easily access space due to their advanced launch vehicles and rapid manufacture based on satellite platform. The LEO resources are more intense and congestion problems occurring are the direct consequence of more frequent human space activities. In this article, the space orbital flux model was built to calculate the space collision probability. When the constellation system encounters the collision problems, the performance is degraded or even malfunctions, so it is necessary to launch additional satellites to maintain the normal operations. The optimal deployment strategy was obtained through the Markov decision process. By launching moderate satellites, the system could continue providing services for customers and the net profit could be maximizd. The result could be the basis for decision making and the method shows the engineering application prospect to a certain degree.  
Conceptualization, X.W. and S.Z.; methodology, X.W.; software, X.W.; validation, X.W. and H.Z.; formal analysis, X.W.; investigation, X.W. and H.Z.; resources, X.W. and H.Z.; data curation, X.W.; writing--original draft preparation, X.W.; writing--review and editing, X.W. and H.Z.; visualization, X.W.; supervision, S.Z.; project administration, X.W. and H.Z.; funding acquisition, S.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
All data generated or analyzed during this study are included in this published article.  
The authors declare no conflict of interest.
## 3 Simulative Experiment and Discussion
### The Relationship between Flight Height and Field Angle  
Before calculating the relationship between the attitude error and the maximum central angle, we needed to select the appropriate camera to obtain the field angle range required for the experiment. We selected the camera field of view range when the central angles  
Figure 6: Relationship between central angle and the positioning error.  
were 0.06 degrees, 0.18 degrees, 0.3 degrees, 0.5 degrees, and 0.6 degrees, with a flight height ranging from 30 km to 50 km. The experimental results are shown in Figure 7.  
Figure 7a illustrates the relationship between the field of view angle and flight height for three central angles: 0.06, 0.18, and 0.30 degrees. Flight height and field of view angle can be deduced from each other. With knowledge of the field of view angle, the corresponding flight height can be inferred. Conversely, when the height is known, the corresponding field of view angle can be determined. Similarly, Figure 7b depicts the relationship between the field of view angle and flight height for three central angles: 0.4, 0.5, and 0.6 degrees.
# Period Cycle Optimization of Integrated Energy Systems with Long-Term Scheduling Consideration
## 3 Results and Discussion
### Energy Conservation and Emission Reduction  
Figure 4 shows the energy saving and emission reduction data under different optimization cycles. Table 5 presents the proportion of energy saving and emission reduction during each cycle phase. Under the overall optimization period, the emission reductions of CO, CO\({}_{2}\), SO\({}_{2}\), NOx, and PM\({}_{2.5}\) are 226.6, 830.5, 25.0, 12.5, and 8.3 tons. These reductions can be equivalently translated into the planting of 833,010 trees. The data indicate that the system, driven by electricity and natural gas as energy sources, can effectively achieve energy saving and emission reduction through multiple cycle optimizations, with significant initial optimization results.  
Figure 4: Energy saving and emission reduction curve in each optimization period.  
Figure 3: Energy ratio diagram under three different operating cycles.
## 2 Materials and Methods  
### Database Collection  
The original papers were identified by a literature search between October and December 2019 of all of the principal accessible journals and databases (PubMed, Web of Science, and Scopus) concerning the theme and using the following keywords: \"low-frequency noise\"; \"low-frequency noise and its effects on health\"; \"noise pollution and health\". A database was constructed with some variables, e.g., sample results and main noise sources. A total of 142 articles published between 2016 and 2019 was found (Supplementary Materials). Only studies were included in which it was mentioned in the title or abstract that the association between the low-frequency noise and effects concerning health or well-being was studied.  
The 142 papers selected for the period from January 2016 to December 2019 were grouped into 9 categories: reviews; health effects due to noise and noise pollution; low-frequency sound/infrasound; health LFN case studies (small population); health LFN case studies (large population); LFN case studies (animals); laboratories studies, simulation studies, and computational case studies; and not relevant. A total of 39 articles published between 2016 and 2019 and grouped in the categories \"health LFN case studies (small population)\", \"health LFN case studies (large population)\", and, finally, \"LFN case studies (animals)\" were selected for in-depth evaluation. The evaluation carried out focused on the impacts on health, highlighting the incidence of studies aimed at human health and others aimed at carrying out tests on animals that may lead to a future study on humans. Additionally, the 39 articles evaluated used similar techniques (e.g., questionnaires; data previously collected in other studies; cognitive, physiological, and psychological tests based on medical and auditory equipment; noise measurements and audiometric assessments; and experimental tests based on noise exposure). The 39 articles evaluated were carried out mostly in Asian and European countries and were based on small samples.
## 2 Satellite Sensor Data
### Ers/envisat-Atsr  
The Along-Track Scanning Radiometer, flying on-board ESA's ERS and ENVISAT satellites, is an advanced imaging radiometer, operating in the thermal and reflected infrared wavelengths (3.7, 11, 12 and 1.6 \(\upmu\)m). The instrument views the Earth's surface over a swath-width of about 500 km and with a spatial resolution of 1 km. It has been designed for exceptional sensitivity and stability of calibration to enable the accurate measurement of sea surface temperature using a dual view (nadir and 56 degrees off to nadir) design to estimate and correct for atmospheric effects.  
The sea-surface temperature products have been implemented at the Rutherford Appleton Laboratory (UK) and they are computed for regional and global studies which require the highest levels of accuracy. To check our registration methodology we used the gridded sea-surface temperature product (GSST) containing 512x512 pixels and where nadir and forward-view pixels are collocated, and have been mapped onto a 1 km grid. The GSST product, as described by [19], provides temperature images using both nadir-only and dual view retrieval algorithms and, optionally, includes pixel latitude/longitude positions, X/Y offsets (sub-pixel across-track/along-track) co-ordinates, and the results of cloud-clearing/land flagging. Particularly, to identify the clear pixels in any given ATSR image, a cloud screening process [20] is applied to all the channels and it is mainly based on the brightness tests originally developed by [21] but optimised for use over the ocean.
## 7 Conclusions  
DDoS attacks still form a critical challenge for network systems, especially IoT devices that usually exhibit insufficient protection mechanisms. This research evaluated the effectiveness of multiple ML and DL detection approaches. The RF model demonstrated exceptional performance, with a 99.99% accuracy rate and a remarkable detection time of 0.584 s, indicating that it is appropriate for real-time defense. Although the KNN model attained about 99.1% accuracy, its slower detection times (36 to 39 s) limit its applicability in situations requiring quick reactions. CNN achieved an accuracy of 99.80%, and LSTM achieved about 99.25%. However, both needed a lot of training time, more than 1600 s for CNN and about 4986 s for LSTM. Therefore, environments with strong computational resources are better suited for these DL approaches. The SVM showed a remarkable accuracy of 99.26%; however, its considerable training (85.32 s) and detection (93.09 s) duration make it less suited for fast detection uses. Because of the similarly structured network traffic patterns, all of the examined techniques encountered difficulties distinguishing between certain attack types, such as XSS attacks and DDoS_HTTP, and SQL injection and uploading attacks. The findings suggest that cybersecurity teams should carefully balance the needs for accuracy and processing power when selecting algorithms to defend against DDoS attacks. For most cases, RF remains the most sensible choice even if DL methods can be helpful in instances involving many resources and complex attacks.  
This research was funded by the Public Authority for Applied Education and Training in Kuwait, grant number BS-23-01.  
The data presented in this study are openly available in Kaggle-Edge-ItoTset dataset, [[https://www.kaggle.com/datasets/mohamedamineferrag/edgeiiotset-cybersecurity-dataset-of-lot-iiot](https://www.kaggle.com/datasets/mohamedamineferrag/edgeiiotset-cybersecurity-dataset-of-lot-iiot)]([https://www.kaggle.com/datasets/mohamedamineferrag/edgeiiotset-cybersecurity-dataset-of-lot-iiot](https://www.kaggle.com/datasets/mohamedamineferrag/edgeiiotset-cybersecurity-dataset-of-lot-iiot)), accessed on 12 March 2025.  
The author acknowledges the support provided by the Public Authority for Applied Education and Training.  
The author declares no conflicts of interest.
## 2 Materials and Methods
### Data Collection from Field Trials  
Along with the collected data from the installed sensors, we collected data from each treatment as follows:  
Figure 3: Evolution of (**a**) the air temperature; (**b**) the air relative humidity; (**c**) the illumination; (**d**) the air CO\({}_{2}\) concentration; (**e**) the air VPD condition inside the greenhouse during the field trial.  
* Growth parameters: From the transplant of the crops with a biweekly frequency, four characterizations of the following growth parameters of the crops were carried out: phenological index (BBCH index), crop height, number of expanded leaves developed per plant, and number of tomato fruits produced per plant. These characterizations were carried out on each of the plants of the treatments and at five different moments of the tomato crop cycle 9, 23, 36, 49, and 64 days after transplanting the crops in the greenhouse. The growth parameters were measured only up to the moment of 64 days during the vegetative growth phase.
* Yield: The production of tomato fruits was characterized in every harvesting episode developed during the trial. A total of 19 harvesting episodes were developed during the field trial. The fresh weight and the number of harvested fruits were characterized as commercial and non-commercial fruits. The commercial tomato fruits were classified into commercial categories based on their diameter.
* Qualitative characteristics: A composite sample consisting of six mature tomato fruits was taken from each of the experimental treatments. The six fruits of each composite sample were used to prepare a homogeneous sample in which the following quality parameters were characterized: fresh weight, diameter, hardness, and sugar content. The sugar content in fruits was measured by refractometry with the use of a portable refractometer (model PAL-1, Atago, Tokyo, Japan). The hardness was measured using a portable durometer (model 53215 TP, Turoni, Forli, Italy). The color was measured using a portable colorimeter (model CR-400, Konica Minolta, Japan) to measure the three dimensions of the color space by means of the coordinates L*, a*, and b*.