esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Permutation Entropy  
As Shannon entropy, permutation entropy quantifies the disorder of a system. It has shown a good ability to measure complexity for large time series and basically this method converts a time series into an ordinal patterns series where the order of relations between the present and a fixed number of equidistant past values at a given time are described [19; 62].  
Following this idea, [PERSON] and [PERSON] [20] proposed a permutation entropy method based on the Shannon entropy measurement with the purpose of visualizing and quantifying changes in the time series. The permutation entropy is calculated for a given time series \(\{\mathrm{x}_{1},\mathrm{x}_{2},...,\mathrm{x}_{n}\}\) as a function of the scale factor \(s\). In order to be able to compute the permutation of a new time vector \(X_{j}\), \(S_{t}=[X_{t},X_{t+1},...,X_{t+m-1}]\) is generated with the embedding dimension \(m\) and then arranged in an increasing order: \([X_{t+j_{1}-1}\leq X_{t+j_{2}-1}\leq\cdots\leq X_{t+j_{n}-1}]\). Given \(m\) different values, there will be \(m!\) possible patterns \(\pi\), also known as permutations. If \(f(\pi)\) denotes its frequency in the time series, its relative frequency is \(p(\pi)=f(\pi)/(L/s-m+1)\). The permutation entropy is then defined as:  
\[PE=-\sum_{i=1}^{m!}p(\pi_{i})\ln p(\pi_{i}) \tag{12}\]  
Summarising, permutation entropy refers to the local order structure of the time series, which can give a quantitative measure of complexity for dynamical time series. This calculation depends on the selection of the m parameter, which is strictly related with the length N of the analysed signal. For example [PERSON] and [PERSON] [20] suggested the use of \(\mathrm{m}=3,...,7\) following always this rule:  
\[m!<N \tag{13}\]  
If m is too small (smaller than 3) the algorithm will work wrongly because it will only have few distinct states for recording. When using long signals, a large value of m is preferable but it would require a larger computational time.  
In our particular case and due to the computational cost derived from analysing signals composed from 5000 samples, the m parameter was fixed at a value of \(\mathrm{m}=4\).
# Advances in the Application of Microcapsules as Carriers of Functional Compounds for Food Products
## 5 Recent Advances in the Stabilization of Bioactive Compounds
The authors concluded that with the results obtained it is possible to guarantee the maintenance of the antioxidant activity of the vanilla extract after microencapsulation, enabling their incorporation into functional foods. Different living cells have been microencapsulated by spray-drying and successfully stabilized, such as _Saccharomyces cerevisiae_[43], _Bifidobacterium_ spp. [45], _Lactobacillus casei_[46], and _Lactobacillus acidophilus_[44]. The feasibility of probiotics as they pass through the gastrointestinal system is an important step in the food field. Except for the last study, the main objective of probiotic microencapsulation was to evaluate the effect of different wall materials on the survival of probiotics during the in vitro simulation of gastrointestinal conditions. It was observed that the microencapsulation of probiotics improves the efficiency of bacterial survival, with the choice of the type of wall material being a decisive factor. Maltodextrin, arabic gum, whey protein concentrate, and gelatin were used as wall materials for _S. cerevisiae_ by [PERSON] et al. [43]. Inulin and oligofructose were used to encapsulate goat's milk _Bifidobacterium_. Trehalose and maltodextrin were used by [PERSON] et al. [46] for skim milk _L. casei_. The authors of each study recommended, respectively, the use of gelatin or arabic gum, goat's milk itself or goat's milk/inulin, and skim milk itself as carriers of probiotic bacteria, providing a survival rate above 85% in the gastrointestinal tract. Moreover, [PERSON] et al. [44] investigated the viability of microencapsulated _L. acidophilus_ after microencapsulation process and during storage at 4 and 23 \({}^{\circ}\)C, under 10% and 97% vacuum. The wall materials used were maltodextrin, arabic gum, and maize starch. The viability of probiotic bacteria after microencapsulation reduced by on average 15% for all wall materials containing a count of approximately 8 log CFU/g. During the 60 days of storage, the combination of the temperature of 4 \({}^{\circ}\)C and 97% vacuum was shown to be more suitable to guarantee the minimum of 6 log CFU/g within the microcapsules, which meets the recommended levels to have therapeutic effects in the human host.
# Efficient Next-Hop Selection in Multi-Hop Routing for IoT Enabled Wireless Sensor Networks
## 4 Proposed Solution
### Proposed Next-Hop Selection Scheme
The \(CH\) will carry the \(k+1\) rank. \(CH\) has the highest level of energy within the cluster. If the condition at step 17 is true, then the next-hop will be set as current hop, i.e., current neighboring node under process. Ifthe condition is false, then the _NhFlag_ is set to false so that another next-hop can be chosen in the next steps. In steps 22 to 24, the algorithm checks whether the RE value is 0, i.e., whether the node battery has expired. If yes, the node status will be set to expired. At the initial state of the network, every node has 100% energy. In step 26, the loop is terminated. In steps 27 to 33, the algorithm checks whether the _NhFlag_ is true, then send the message to next hop. If the _NhFlag_ remained false, then the node with minimum distance could not find a node with the RE larger than the threshold \(\text{TV}_{0}\). In such cases, a node with minimum distance will be taken from the list \(Arr\_D\_N_{Ci}\) that was saved in step 14 for only those nodes whose distance was less, and reset the minimum distance value _mDi_. This node will be selected as next hop to transmit the data. In the final step, the previous hop value is set to current value, which will become the previous hop for the next execution of the steps. By considering \(n\) neighbors, a loop is applied to calculate the RE so that the computational asymptotic cost is O(\(n\)).
# XAI-Based Framework for Protocol Anomaly Classification and Identification to 6G NTNs with Drones
## 4 Interpretable Anomaly Detection Framework for 6G NTN Protocols
### Explanation and Collaboration Demands  
DL-based anomaly detection methods process protocol data and output detection results. The explanatory and collaboration demands of DL-based anomaly detection are analyzed considering both the detection process and the detection outputs of rule detection and baseline detection, with a focus on classifying uncovered protocol attacks and identifying anomalous protocol sequences.  
To classify uncovered protocol attacks, we need to develop a self-evolving rule detection method based on the fingerprint features of uncovered attacks. Despite the lack of fingerprint features for uncovered attacks, these fingerprint features can be reflected by the deviations captured during the baseline detection process and used as the basis for analyzing the detection results. Therefore, the baseline detection results must be explained to output the deviation information, which can be used to extract the fingerprint features of uncovered attacks. To establish a new rule classification method using external fingerprint features in an online environment, we need to make the rule detection process interpretable, focusing on how fingerprint features influence the detection process and results. With this approach, the system can self-evolve, and the focus of the feature can be adapted according to the external fingerprint feature information.  
The collaboration between rule and base detection methods is also analyzed. The detection results for uncovered attacks from rule detection can trigger baseline detection. Baseline detection can abstract and output the weights of fingerprint features to rule detection. Rule detection must be designed to support explanations and adjust the weights of fingerprint features accordingly.  
To identify anomalous protocol sequences online, tracing anomalous protocol sequences on the basis of the detection and/or classification results is essential. An explanation of the detection process focusing on the high-dimensional matching process offers little assistance for this task. The baseline detection results can be used to determine the quantitative contributions of the fingerprint features, as the features with larger contributions may reflect the anomalous protocol sequences caused by attacks. However, extracting these features using irreversible statistical methods or high-dimensional mapping approaches makes it difficult to obtain the original anomalous protocol sequences. To address this challenge, we must integrate XAI capabilities into baseline detection methods. This integration enables the tracking of protocol data and the original information throughout the entire mapping process, allowing us to output the detection results alongside the corresponding anomalous protocol sequences.
###### Abstract  
Partial Discharge (PD) is one of the symptoms of an electrical insulation problem, and its permanence can lead to the complete deterioration of the electrical insulation in high-voltage equipment such as power transformers. The acoustic emission (AE) method is a well-known technique used to detect and localize PD activity inside oil-filled transformers. However, the commercially available monitoring systems based on acoustic sensors still have a high cost. This paper analyses the ability of low-cost piezoelectric sensors to identify PDs within oil-filled power transformers. To this end, two types of low-cost piezoelectric sensors were fully investigated using time-domain, frequency-domain, and time-frequency analysis, separately. Thereafter, the effectiveness of these sensors for PD detection and monitoring was studied. A three-phase distribution transformer filled with oil was examined. PDs were produced inside an oil-immersed transformer by applying a high voltage over two copper electrodes, and the AE sensors were coupled to the housing of the transformer. By extracting typical features from the AE signals, the PD signals were differentiated from on-site noise and interference. The AE signals were analyzed using acoustic signal metrics such as peak value, energy criterion, and other statistical parameters. The obtained results indicated that the used low-cost piezoelectric sensors have the capability of PD monitoring within power transformers.  
Keywords:partial discharge; power transformers; piezoelectric sensors; acoustic signal analysis +
Footnote †: journal: Applied sciences
## 1 Introduction
* The proposed method outperforms all state-of-the-art methods across multiple benchmark datasets, demonstrating a significant performance improvement of around 10% on various task sequences in CIFAR100 and TinyImageNet. **Comparison with Existing Works** In the rapidly evolving field of continual learning, recent research approaches can be broadly categorized into three streams: replay-based methods, regularization techniques, and parameter isolation strategies. Replay-based methods [8; 9; 10; 11] store examples from previous tasks but face scalability challenges with large-scale models like CLIP. Regularization techniques [22; 23] penalize updates to critical parameters but often compromise the model's ability to learn new tasks effectively. Parameter isolation approaches, particularly prompt-based methods [2; 16; 17; 19], have shown promise by introducing task-specific learnable parameters while freezing the pretrained backbone. However, existing prompt-based methods predominantly focus on single-modality knowledge and rely on computationally expensive Softmax classifiers requiring continuous storage of old samples. Our work addresses these fundamental limitations through IT-Prompt, which uniquely leverages both visual and textual modalities of CLIP, and our novel C-NCM classifier that eliminates the need for storing previous task data--innovations that significantly advance the state of the art in multimodal continual learning.
## 4 Discussion  
This study examined the active (i.e., at 20% of MVIC) shear modulus as well as neuromuscular parameters in hannstrings before and after repeated sprinting in professional football players with and without HSI. To the best of our knowledge, this is the first study to examine the acute effects of repeated sprints in the hannstring load sharing on the active shear modulus using SWE in football players with and without previous HSI. The main findings were as follows: no differences in the sprint performance, load sharing, and most neuromuscular parameters were seen between limbs with previous HSI and without previous HSI. Secondly, we verified with different analyses that no differences in load sharing and neuromuscular parameters were seen between BFlh previously injured players and healthy control group (same-side) with the same age and dominance. In addition, no differences in load sharing and neuromuscular parameters were seen between previously injured hannstring limbs and the contralateral limbs. Finally, only a significant difference in RTD 0-50 ms was seen between BFlh previously injured limbs and their contralateral limb. These findings contradict our initial hypothesis since it was expected that limbs with HSI history would have a different load sharing pattern shear modulus with a greater relative contribution of BFlh. Contrary to our initial hypothesis, no differences in sprint performance were seen between players with previous HSI and healthy ones, which is also opposite to what some previous studies have reported. [PERSON] et al. (2014) reported a decrease in acceleration performance at return to play ([PERSON] et al. (2017), with [PERSON] et al. (2017) showing a 16% higher drop in speed during repeated sprint tests ([PERSON] et al. (2014) in players with previous HSI. It is curious that [PERSON] et al. (2017) used an 8 \(\times\) 20 m sprint with the same recovery time (30 s), and since the 16% decrease was calculated between the mean of the first two sprints minus the mean of the last two sprints, it was expected that a higher volume in the present study (10 \(\times\) 30 m) would induce greater fatigue between the previously injured vs. healthy control players. One possible explanation could be the time difference between injury and testing, which is not possible to compare since [PERSON] et al. (2017) did not report the time of this period, which is essential for the comparison. [PERSON] et al. (2014) showed a decrease in acceleration performance at return to play between players with previous HSI and healthy ones. However, it was normalized after two months of follow-up without any specific supplementary or preventive training, indicating that regular football play is sufficient to restore sprint acceleration performance, which is in concordance with our results. In our study, all the players have at least 2 months between sustaining the hannstring injury and performing the RSA protocol. Therefore, it is possible to suggest that even without recovery treatment, a greater time of exposure to load positively im
## 3 Experimental Section
### Volatile Profiling Method  
The volatile profiles of the wines were assessed using an automated headspace Solid Phase Microextraction-Gas Chromatography-Mass Spectrometry (HS-SPME-GC-MS) method, similarly to the methods described in [23,65]. A 2 cm mixed-phase SPME fiber (50/30 \(\upmu\)m DVB/Carboxen/PDMS; Supelco, St. Louis, MO, USA) was employed for extraction and concentration of the volatile compounds. Five milliliters of wine, 2.0 \(\pm\) 0.1 g of sodium chloride (Fisher Scientific), and 10 \(\upmu\)L 2-undecanone [51.3 mg/L] as internal standard (IS) were placed in amber HS glass crimp vials (20 mL, Agilent Technologies, Santa Clara, CA, USA), and closed with a magnetic crimp cap (Supelco). Volatiles were extracted at 40 \({}^{\circ}\)C for 30 min and 250 rpm agitation (5 min incubation prior to extraction with 500 rpm agitation) with an autosampler (MPS2; Gerstel US, Linthicum Heights, MD, USA), and thermally desorbed for 16 min at 270 \({}^{\circ}\)C in the hot inlet equipped with a narrow diameter SPME inlet liner (Supelco). Each wine was prepared and analyzed in triplicate. Six different wines were analyzed per day, with each sample coming from a separate bottle. Replicate samples were spread through the analysis within the analysis day to control for potential sample aging while sitting on the autosampler. An Agilent 7890A GC with a 5972C MS was used to separate and detect the extracted compounds, using an HP-5 msUI column (30 m \(\times\) 0.25 mm \(\times\) 1 \(\upmu\)m; Agilent Technologies), and an oven program as follows: 30 \({}^{\circ}\)C for 2 min, ramped with 5 \({}^{\circ}\)C/min to 180 \({}^{\circ}\)C, followed by a 20 \({}^{\circ}\)C/min ramp to 280 \({}^{\circ}\)C, with a final hold for 15 min. Separation was achieved using a constant Helium carrier gas flow (99.99% purity; Airgas, Sacramento, CA, USA) of 1 mL/min in split mode (20:1). Compounds were detected in the MS (MS source 240 \({}^{\circ}\)C, MS quadrupole 150 \({}^{\circ}\)C, MS transfer line 280 \({}^{\circ}\)C) usingelectron impact ionization (EI), in simultaneous selected ion monitoring (SIM) and Scan mode, providing both untargeted and targeted profiling [65]. In scan mode the MS scanned between 35 and 350 amu with 2.9 scans/sec, while in each of the 37 consecutive SIM windows between two and seven ions were detected with 45 of 50 ms dwell time for each ion. Detected compounds were analyzed and areas were integrated in MSD Chemstation (version E.02.02, Agilent Technologies). Compounds were identified by matching their linear retention indices (RIs), calculated as described in [66] and mass spectra to pure standards, if available, and to mass spectral libraries (NIST/EPA/NIH Mass Spectral Library NIST 05). Identified compounds are listed in Table 7. Relative compound concentrations were calculated assuming a response factor of 1 between the compound and the internal standard (IS).
# A Data-Driven Approach to Improve Customer Churn Prediction Based on Telecom Customer Segmentation
## 6 Discriminant Telecom Customer Loss Model
### Empirical Analysis for the Discriminant Model
#### 6.1.2 Analysis of Discriminant Model
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Function** & **Wilks’ Lambda** & **Chi-Square** & **Value of df** & **Value of Sig.** \\
**Testing** & **Value** & **Value** & **Value** & **Value of Sig.** \\ \hline
1 & 0.971 & 121.638 & 7 & 0 \\ \hline \hline \end{tabular} (3)  
[PERSON]’s linear discriminant function test  
Y1 and Y2 represent the customer churn and customer existence, respectively (Table 19).  
\end{table} 
Table 18: Wilks’ lambda values. \begin{table}
\begin{tabular}{l c c} \hline \hline  & \multicolumn{2}{c}{**The Loss or Retain of Customers**} \\  & **Customer Loss-Y1** & **Customer Retain-Y2** \\ \hline Factor score for F1 & \(-1.518\) & \(-0.1\) \\ Factor score for F2 & \(0.257\) & \(0.176\) \\ Factor score for F3 & \(0.588\) & \(0.135\) \\ Factor score for F4 & \(6.021\) & \(0.291\) \\ Factor score for F5 & \(-0.712\) & \(-0.211\) \\ Factor score for F6 & \(-1.051\) & \(-0.02\) \\ Gender & \(5.963\) & \(6.397\) \\ (The constant) & \(-16.592\) & \(-4.81\) \\ \hline \hline \end{tabular} The Fisher linear discriminant equation  
\end{table} 
Table 19: Classification function coefficients—adapted from Zhang [6]. From the above Table, we can see that the overall prediction accuracy rate was 75%. Among the 50 retained customers, 36 were predicted successfully. The accuracy rate was 72%. Furthermore, among the 50 churn customers, 39 of them were predicted successfully, and the accuracy rate was 78%.
# Development and Evaluation of Nano-Silica Sustainable Concrete
## 2 Materials and Methods  
### Materials  
The materials used to prepare the concrete mixtures are nano-silica (NS), ground granulated blast furnace slag (GGBS), Portland cement (OPC), water, 20 mm aggregate, 10 mm aggregate, 5 mm washed sand, 5 mm crushed sand, red dune sand, and megaflow 1000 polycarbonate superplasticizer. The characteristics of nano-silica are 99+% SiO\({}_{2}\), 20 nm average particle size, spherical morphology of particles, \(\leq\)120 m\({}^{2}\)/g specific surface area, hydrophilic surface performance, amorphous crystallographic structure, 0.03-0.05 g/cm\({}^{3}\) bulk density, white color, 5.5-6.5 pH, \(\leq\)6.0 wt% loss on drying (110 \({}^{\circ}\)C/2 h), and \(\leq\)10.0 wt% loss on calcination (850 \({}^{\circ}\)C/2 h). The chemical analysis of the GGBS is 33.0 SiO\({}_{2}\), 0.28 IR, 14.7 AL\({}_{2}\)O\({}_{3}\), 0.4 Fe\({}_{2}\)O\({}_{3}\), 39.7 CaO, 7.7 MgO, 0.08 SO\({}_{3}\), 0.86 S, 0.42 Na\({}_{2}\)O, 0.29 Mn\({}_{2}\)O\({}_{3}\), 1.9 LOI corrected for sulfide, 0.01 Cl\({}^{-}\), and 98.8 glass content. The Portland cement was manufactured to comply with BS EN 197-1:2000 CEM I, grade 42.5 N and ASTM C-150-2000 Type I. The chemical composition of the Portland cement is 20.5 SiO\({}_{2}\), 0.34 IR, 5.0 Al\({}_{2}\)O\({}_{3}\), 3.9 Fe\({}_{2}\)O\({}_{3}\), 64.2 CaO, 1.5 MgO, 2.1 SO\({}_{3}\), 0.50 Na\({}_{2}\)O, 2.6 LOI, 0.02 Cl\({}^{-}\), and 6.6 C\({}_{3}\)A. Mega flow 1000 polycarbonate superplasticizer is a high range water reducer (HRWR), and it was selected due to the high demand for water that occurs with the addition of nano-silica to the concrete mixture.
## 1 Introduction
### Space-Based Information Networks  
Space-based information networks consist of satellite constellations at different Earth orbits, including: enumerate 1. Earth Sensor Web and other satellites used for investigating planets such as Mars and the Moon in the solar system; 2. Network infrastructure that connects satellites to the backbone, access, and proximity networks [4]. enumerate Earth observation and monitoring systems collect time-sensitive information about the Earth using remote sensing satellites [5; 6]. Constellations of a large number of small satellites are used to collect simultaneous and distributed measurements or observations in monitoring Earth resources, weather, and disaster situations with an increased temporal resolution of collected data [7]. Besides, the National Aeronautics and Space Administration (NASA) is ambitiously developing future space and ground architecture for deep space and planet exploration to meet the final objectives of lunar and Mars exploration, habitation, and colonization [8; 9]. The future space architecture will provide communications, navigation, and inter-networking services for space missions from Earth's orbit to Mars and other planetary exploration missions.
# An Integrated DQN and RF Packet Routing Framework for the V2X Network
## 3 Design of the IDRF System
### Offline Phase
#### 3.1.4. Utilizing Random Forests for the Prediction Unit
Our objective was to use the estimated average vehicle speeds of various road sections over different periods to predict the next intersection \(I_{6}\), the intersection after the next \(I_{7}\), and the corresponding arrival times of vehicles using the RF prediction unit, as depicted in Figure 14. Please note that the corresponding arrival times to the next intersection and the intersection after that are calculated by the SDN CN using the information on real trajectory points belonging to the current and next road sections, respectively. Therefore, the error for the arrival time at the intersection, caused by traffic congestion, the vehicle's moving direction, and speed, is confined to the corresponding road section. Figure 13: The RF model utilized in this study.