esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Uncertain-Aware Computation Offloading  
The above research scenarios assume that controllers have access to accurate computing resources and channel information, either statically or dynamically. Nevertheless, in application environments, MECNs are transient, which can considerably impact resource utilization and user QoE. In [4,7], the authors investigated robust task offloading that is tolerant to failures in offloading scenarios and can effectively overcome this. In [8], the offloading of robust tasks that can tolerate server failures was considered to achieve improved application responsiveness. To counter the inaccuracy of channel measurements, the authors of [9] designed a robust offloading strategy for channel information estimation errors. Facing the connection instability between MTDs and small clouds, the authors of [7] proposed a robust computation offloading strategy with failure recovery.  
In contrast to the mentioned robust optimization approach, which trades off significant device performance to ensure robustness, stochastic optimization (SO) is employed. SO studies in mobile edge computing (MEC) take into account the probability distributions of channel resources and the random variations of computational resources in edge computing nodes (ECNs). The authors of [6] proposed a two-stage SO to tackle the challenge of uncertain dynamic environments.
## 5 Data Analysis and Results
### Results Using Impulse Excitation  
The identified coefficients and their deviations from five experiments using impulse excitation are summarized in Table 4. The model identified by LSPL provides identical sparseness with polynomial orders 3 and 5. Except for the fifth experiment, the coefficients \(\xi_{2}\), \(\xi_{3}\) and \(\xi_{6}\) are non-zero values, i.e., the factors of the candidate functions \(x_{rel}\), \(\dot{\vec{x}}_{rel}\), and \(\dot{\vec{x}}_{rel}^{2}\) respectively. In the fifth experiment, the model was even more compact because the candidate function \(\dot{\vec{x}}_{rel}^{2}\) was excluded. The magnitude of the excitations has three levels, so the maximum excitation forces are about 3.4 N, 6.7 N, and 13.1 N.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{\(\mathbf{F}_{max}\) **in N**} & \multicolumn{4}{c}{**Coefficient**} & \multicolumn{4}{c}{**Relative Error**} \\  & & \(\mathbf{\xi_{2}}\) & \(\mathbf{\xi_{3}}\) & \(\mathbf{\xi_{6}}\) & \(|\frac{-\xi_{2}-\omega_{6}^{2}}{\omega_{6}^{2}}|\) & \(|\frac{-\xi_{3}-2D\omega_{0}}{2D\omega_{0}}|\) & \(\mathbf{R}^{2}\) \\ \hline
1 & 3.39 & \(-\)47,360 & \(-\)21.6 & 1498.5 & 8.  
The coefficients \(\xi_{2}\) and \(\xi_{3}\) are consistent in the five experiments. Converted form Table 4, Table 5 shows coefficients of variation of 0.07% and 2% for \(\xi_{2}\) and \(\xi_{3}\), respectively. In comparison with the theoretical values, the identified coefficients \(\xi_{2}\) and \(\xi_{3}\) have consistent offsets by approximately 8.3% and 28.3%, respectively, in Table 4.  
The coefficient \(\xi_{6}\) varies by 84%. It can be seen that the identified coefficient \(\xi_{6}\) decreases to zero as the excitation force increases, as shown in Figure 17. The coefficient \(\xi_{6}\) thus depends strongly on the setup of the experiment.  
Figure 16: The selected intervals from measurement with (**a**) impulse excitation and (**b**) sweep signal excitation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Coefficient** & **Mean \(\xi_{i}\)** & \
\begin{tabular}{c} **Standard Deviation** \\ \(s(\xi_{i})\) \\ \end{tabular} &
\begin{tabular}{c} **Coefficient of** \\ **Variation \(s(\xi_{i})/\xi_{i}\)** \\ \end{tabular} \\ \hline \(\xi_{2}\) & \(-47\),312 & 33.3 & 0.07\% \\ \(\xi_{3}\) & \(-21.4\) & 0.43 & 2\% \\ \(\xi_{6}\) & 1072.5 & 903.66 & 84\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Mean value, standard deviation, and coefficient of variation of the identified coefficients by using impulse excitation.
## 3 Results and Discussion
### Data Preprocessing
The corrocef function in the Python library numpy is used here to calculate the correlation coefficient, with the calculation formula shown in Equation (1). As shown in Figure 4, parameters such as \(T_{1}\), \(p_{1}\), \(T_{d}\), \(p_{4}\), \(T_{f}\), IGV angles, and \(m_{f}\) exhibit high correlations with power \(P_{w}\), confirming their significant influence on prediction accuracy. In contrast, \(p_{f}\) and speed \(n\) exhibit low correlations (\(P\) is less than 0.3 [31]) with power \(P_{w}\). This may be attributed to the fact that \(P_{f}\) is controlled at a constant value during operation to regulate fuel flow, while fuel pressure has a minor impact on the energy content of the fuel, thus resulting in a weak correlation. Additionally, speed n remains essentially constant at 3000 after the gas turbine is connected to the grid, which also minimally affects power. Consequently, these two input variables will be excluded from subsequent modeling.
## 8 Multi-Objective Optimization Integrated with Neural Networks ANN
### Integration with IoT and Big Data for Smart Food Systems  
The rise of the Internet of Things (IoT) and big data analytics in the food industry has amplified the potential of MOO-ANN systems. IoT devices generate vast amounts of real-time data from production lines, storage facilities, and distribution networks, which MOO-ANN models can process to identify optimal solutions [80].  
For example, integrating IoT sensors with MOO-ANN in a fruit storage facility optimized temperature and humidity settings, reducing spoilage rates by 15% and extending shelf life by 20%. Similar applications in meat processing plants have achieved a 10% reduction in energy costs by dynamically adjusting refrigeration settings based on predictive ANN model solutions [80].
## 2 Materials and Methods
### RNA Interference  
#### 2.3.1 Production of dsRNA  
dsRNA constructs for _Scg-Met_ (_dsScg-Met_) and _Scg-Tai_ (_dsScg-Tai_) were prepared using the MEGAscript(r) RNAi Kit (ThermoFisher Scientific, Waltham, MA, USA), designed for the production of dsRNAs longer than 200 bp, according to the manufacturer's instructions. The procedure is based on the high-yield in vitro transcription reaction catalyzed by T7 RNA Polymerase starting from a user-provided linear template DNA flanked by T7 promoter sites. The dsRNA is obtained by annealing the resulting sense and antisense RNA strands. Primer sequences used for obtaining the linear templates are given in Table S1. A PCR with REDTaq(r) DNA polymerase (Sigma-Aldrich, Saint Louis, MO, USA) was performed and the length of the amplified products was checked by 1% agarose gel electrophoresis. These bands were further cloned and sequenced (TOPO(r) TA cloning kit for sequencing, Invitrogen) to confirm the amplicon sequence (Sanger sequencing, LGC Genomics, Berlin, Germany). For the production of GFP dsRNA (_dsGFP_), a PCR fragment flanked by a T7 promoter site was cloned in both sense and antisense direction in a TOPO 4.1 sequencing vector (Life Technologies, Carlsbad, CA, USA) and subsequently used as template for in vitro transcription. The final concentration of the produced dsRNA was estimated using a NanoDrop ND-1000 UV-VIS Spectrophotometer, and 1% agarose gel electrophoresis was performed to assess the integrity of the dsRNA.
## 3 Experimental Investigations
### Measurement of Full Wavefield of Lamb Waves  
An experimental set-up was prepared to automatically measure the full wavefield of propagating Lamb waves every 1 h for 29 h in total. Its diagram is presented in Figure 9. Scanning laser vibrometer, namely PSV-400, was used for out-of-plane vibration registration. A piezoelectric round disc (10 mm of diameter) attached to the specimen's back surface was connected to arbitrary waveform generator through a signal amplifier for excitation purposes.  
Measurements were made on a regular grid of \(291\times 151\) points (1 mm spatial resolution), covering the entire front surface of the specimen. A five-cycle burst sine signal at 75 kHz central frequency was used to excite Lamb wave packages. To increase the signal-to-noise ratio, the specimen was covered with retroreflective tape and 3 times averaging was used during measurements. Wave packages were excited every 4 ms, which was enough for previously excited signals to completely attenuate. Bandpass filtering with cutoff frequencies of 40 and 110 kHz was also applied to registered time responses. At every point, 512 time samples were registered with 1.28 MHz sampling frequency.  
To make sure that the energy of propagating waves did not change due to environmental changes (temperature), PZT aging, glue under PZT degradation and other factors, under ongoing experiments, the reference area energy plots for the 8 J impact tests are given. The reference area was chosen to be in the same distance from the excitation as BVID, and it is graphically presented for both damage scenarios in Figure 10.  
The full wavefields of propagating waves in the specimen with BVID induced at arbitrarily chosen time frames are presented in Figure 11a,b after 1 and 29 h, respectively.  
Figure 9: Experimental set-up diagram.
## 5 Practical Issues on Haptic Representation
### Alignment Method between the Physical and the Virtual World  
For the target applications, AR content must be precisely aligned with the haptic feedback. The alignment problem is a constant when combining physical elements and AR content. In short, it consists of establishing a coordinate system common to the digital and the real world. However, the solutions proposed here are tailored to Ultrahaptics and HoloLens but can be adopted too for instrumental systems based on physical vertical feedback.  
Two strategies to achieve the alignment are proposed:  
* Manual: We have equipped the Ultrahaptics board with three stickers on three of its corners, as Figure 7 shows. In order to perform the alignment, the first task of the user is to move three digital cube anchors using gestures and to place them on top of those stickers. Once this is done, the system is able to calculate a local coordinate system centered on the device. This coordinate system is then used to locate the digital representation of the Ultrahaptics itself on top of which the digital elements will be attached. From this moment on, the haptic representation and the AR content should share the same space. The main problem with this method is that the user may make mistakes when positioning the anchors. Figure 7 illustrates this problem, since the user has positioned some elements on top of the device and some inside it. The more accurate the alignment is, the more precisely the haptic feedback will match the AR content.
* Automatic: In this strategy, the application establishes the coordinate system using mark detection. A QR code or an image is placed next to the Ultrahaptics in a specific position (i.e., known distance and orientation). The device will always be situated in the same position with respect to the mark. By knowing the logical relation between the mark and the device, transformations between both of them are trivial. The framework used to perform the mark detection has been the Vuforia SDK for Unity [49]. Once the system has detected the marker, it shows a transparent red-colored plane over it as will be shown later in Section 6.1. Under certain circumstances (e.g., low illumination or lack of space near the physical device), mark detection may not work as well as expected.
# Deep-Learning-Based Approach to Detect ICMPv6 Flooding DDoS Attacks on IPv6 Networks
## 2 Background
### ICMPv6 Flooding DDoS Attack  
The ICMPv6 flooding DDoS attack is one of the most disruptive security threats in IPv6 networks. An adversary resorts to these attacks to exhaust the victims' network bandwidth and network resources by flooding them with packets within a specific time interval. The arrival of massive traffic forces the victim nodes to process them as quickly as possible, which eventually overwhelms the victims and becomes unavailable. The adversary usuallyuses spoofed ICMPv6 messages to evade detection by network administrators or intrusion detection systems. Additionally, attackers could also send massive ICMPv6 error messages against hosts and routers to propagate their functionality. Similarly, the adversary could achieve the same result with ICMPv6 informative messages, such as neighbor solicitation (NS), echo request and echo reply, and router advertisement (RA) messages [18].  
The outcome of any DDoS attack depends on the utilized packet type and the intended victim. Some attacks impact the victim node's availability, while others affect the victim and the network. For instance, if the destination is the host and the packet is an NS packet sent from different addresses, then the victim host would typically respond to all packets with a neighbor advertisement (NA) packet, resulting in DoS attacks, also known as \"storm NS\", which consume its resources. Overall, attackers misuse the ICMPv6 messages to attempt DoS or DDoS attacks on the IPv6 network to cut off legitimate users from essential services. The router advertisement (RA) message, for example, is an ICMPv6 information message with a type field value of 134. It helps routers provide link-local nodes with network information, including the network prefix and network default gateway, and permits routers to announce their presence on an IPv6 network. However, attackers could exploit the RA messages to flood the network with spoofed messages, forcing legitimate users to constantly update their neighbors' cache tables until their resources are depleted [12; 15].
###### Abstract  
In recent years, taking advantage of renewable energy sources (RESs) has increased considerably due to their unique capabilities, such as a flexible nature and sustainable energy production. Prosumers, who are defined as proactive users of RESs and energy storage systems (ESSs), are deploying economic opportunities related to RESs in the electricity market. The prosumers are contracted to provide specific power for consumers in a neighborhood during daytime. This study presents optimal scheduling and operation of a prosumer owns RESs and two different types of ESSs, namely stationary battery (SB) and plugged-in electric vehicle (PHEV). Due to the intermittent nature of RESs and their dependency on weather conditions, this study introduces a weather prediction module in the energy management system (EMS) by the use of a feed-forward artificial neural network (FF-ANN). Linear regression results for predicted and real weather data have achieved 0.96, 0.988, and 0.230 for solar irradiance, temperature, and wind speed, respectively. Besides, this study considers the depreciation cost of ESSs in an objective function based on the depth of charge (DOD) reduction. To investigate the effectiveness of the proposed strategy, predicted output and the real power of RESs are deployed, and a mixed-integer linear programming (MILP) model is used to solve the presented day-ahead optimization problem. Based on the obtained results, the predicted output of RESs yields a desirable operation cost with a minor difference (US$0.031) compared to the operation cost of the system using real weather data, which shows the effectiveness of the proposed EMS in this study. Furthermore, optimum scheduling with regard to ESSs depreciation term has resulted in the reduction of operation cost of the prosumer and depreciation cost of ESS in the objective function has improved the daily operation cost of the prosumer by $0.8647.  
prosumer; energy management system (EMS); energy storage system (ESS); plug-in hybrid electric vehicle (PHEV); day-ahead optimization; battery depreciation; feed-forward artificial neural network (FF-ANN); weather prediction +
Footnote †: journal: Applied Sciences
# Text-Mining-Based Non-Face-to-Face Counseling Data Classification and Management System
## 3 Proposed Scheme
### Dataset
A combination of TF-IDF and Word Embedding techniques was employed to enable a nuanced analysis tailored to the unique characteristics of counseling data. TF-IDF reduces the weight of frequently occurring words while emphasizing relatively meaningful ones, whereas Word Embedding learns and vectorizes semantic similarities. By integrating these techniques, we achieved a fine-grained analysis that incorporates both the importance of keywords and the semantic relationships among them. For example, when \"friend\" and \"conflict\" appear together, Word Embedding with TF-IDF weighting enhances the importance of \"conflict\", effectively highlighting the core topic of the session. In cases where the relationship between words like \"trust\" and \"friend\" is significant, this approach not only increases the weight of \"trust\" but also preserves its contextual connection to \"friend\", maintaining the emotional context. This combined method plays a crucial role in enhancing semantic accuracy in counseling data analysis. \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Category** & **Friend** & **Personality** & **Appearance** & **Family** & **Other** & **Total** \\ \hline Data Count & 185 & 177 & 176 & 116 & 107 & 761 \\ \hline Data Proportion & 24.3\% & 23.3\% & 23.1\% & 15.2\% & 14.1\% & 100\% \\ \hline \multirow{4}{*}{Key Topics} & Friendship, & Self-esteem, & Body, & Nagging, & & \\  & Conflict, & Confidence, & Face, & Conversation, & & & \\  & Communication, & Stress, & Complex, & Relationship, & & \\  &... & Self-doub, & Voice, & Brothers and Sisters, &... & \\ \hline Avg. & & & & & & \\ Conversation Length & 180 & 170 & 160 & 155 & 150 & 165 \\ (words) & & & & & & \\ \hline Max & & & & & & \\ Conversation Length & 400 & 390 & 370 & 360 & 350 & 400 \\ (words) & & & & & & \\ \hline Min & & & & & & \\ Conversation Length & 60 & 55 & 53 & 52 & 50 & 50 \\ (words) & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of key characteristics of the counseling dataset. The analysis of counseling data revealed that applying Word Embedding with TF-IDF weighting achieved higher accuracy compared to using TF-IDF alone. When TF-IDF was used independently, the clustering and topic modeling evaluation score was 0.7009. However, when TF-IDF weights were applied to the Word Embeddings, the score increased significantly to 0.9852. This indicates that applying TF-IDF weights to Word Embeddings is more suitable for capturing the semantic similarity of words related to emotional or personal expressions. The resulting Word Embeddings were clustered using the KMeans algorithm, with the number of clusters set to six. The top 10 words from each cluster were output, and their TF-IDF importance was represented textually to highlight the relative significance of each word. Table 3 presents the words by cluster and their corresponding TF-IDF importance.
## 4 Results
### Extracted Time-Series Plots  
The resultant time-series plot of the mean greenness value over all useful pixels is illustrated in Figure 5 for each camera. Table 2 provides the root-mean-square (RMS) error on fit and the percentage of time-points present in time-series plots for each camera. It is evident that the plots are,  
Figure 5: Calculated greenness levels using Equation (1) for each camera shown in Figure 1 over the period of analysis. The crosses show individual data points and the continuous curves show the smoothed data used for date extraction.  
unsurprisingly, inherently noisy, particularly those from cameras 8, 9, 11, 17 and 18, because of the uncontrolled conditions in which the images were taken. Furthermore there are missing data from some of the plots as a result of a step movement in the camera (for example from Camera 4 during the autumn of 2011). Despite this in each case there were enough data points available to parameterize a simple sigmoid-shaped model and extract the underlying signal. The missing data does however pose a challenge when visually extracting phenological dates and as such are excluded from further analyses (as per Section 4.3). The phenological signal is clearly well-defined for each camera. In most cases the classic trapezoid phenology profile is displayed and for both 2011 and 2012 there was a great similarity in the curves. In general terms, the greenness values are seen to increase rapidly from March/April, corresponding to a period of leafing-up mainly in the deciduous trees and increased photosynthetic activity in other vegetation types that had a strong enough phenological signal to be included in the area of interest (e.g., coniferous trees and some shrubs which maintain a minimum greenness during the winter months). The curve is seen to stabilize during June, followed by a rapid decrease in greenness from the end of August, marking the onset of the autumnal senescence. The curve reaches a minimum during the months of December to February. Since the signal extracted is an aggregate greenness value of all pixels in the area of interest, those cameras capturing scenes in which there is a higher proportion of deciduous vegetation (for example camera numbers 8, 9, 11 and 18) display a more pronounced seasonal curve due to large range in net primary productivity attained by this vegetation type. Where there are coniferous species in the scene, these will temper the range of greenness captured by the camera with a higher proportion of these since these species will not exhibit as large a seasonal variation in their photosynthetic biomass [42]. This concurs with other studies that have used camera-based indices for characterising canopy phenology [43].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**No.**} & **Vegetation Coverage** & **RMS Error on** & **Valid Time-Points Present** \\  & **on Image (\%)** & **Fit to Time Series (\(\times\) 10\({}^{-3}\))** & **(\% of Whole Sequence)** \\ \hline  
\hline \hline \end{tabular}
\end{table}
Table 2: Quality indicators for the processed cameras.