esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Types** & \multicolumn{2}{c}{**Advantages**} & \multicolumn{2}{c}{**Limitations**} & \multicolumn{2}{c}{**Applicable Field**} \\ \hline Low-strain integrity testing [16; 24] & \(\bullet\) & Rapid and cost-effective & \(\bullet\) & Limited penetration depth & \(\bullet\) & Large-scale screening of \\  & [16; 24] & & \(\bullet\) & Significant soil interference & & shallow pile defects \\ \hline Cross-hole sonic logging (CSL) & \(\bullet\) & High precision for large-diameter piles & \(\bullet\) & Requires pre-embedded & \(\bullet\) & Large-diameter cast-in-place \\  & [25; 26] & \(\bullet\) & Effective in detecting longitudinal defects & \(\bullet\) & Signal attenuation in muddy & \(\bullet\) & piles in bridges, high-rises, \\  & \(\bullet\) & Comprehensive coverage of full pile length via multi-tube arrays & \(\bullet\) & High cost for tube & \(\bullet\) & or nuclear facilities \\ \hline Gamma-gamma (\(\
u\)-\(\gamma\)) logging & \(\bullet\) & No pre-installed tubes & \(\bullet\) & Susceptible to reinforcement interference & \(\bullet\) & Specialized engineering \\  & [27; 28] & \(\bullet\) & Enables localized defect inspection & \(\bullet\) & Radiation safety concerns & & applications \\ \hline TIP [29; 30] & \(\bullet\) & Early-stage detection capability & \(\bullet\) & Requires embedded sensors & \(\bullet\) & Cast-in-place pile \\  & \(\bullet\) & Complete pile coverage & \(\bullet\) & Complex system requirements & & \\  & \(\bullet\) & Excellent noise immunity & & & & \\ \hline Parallel seismic (PS) method & \(\bullet\) & Effective for deep piles with deep-seated defects & \(\bullet\) & Requires borehole drilling & \(\bullet\) & Detection of deep defects in \\  & \(\bullet\) & High accuracy in defect & \(\bullet\) & Skilled interpretation of wave arrival times & \(\bullet\) & long cast-in-place piles \\  & \(\bullet\) & High localization & \(\bullet\) & Not suitable for shallow defects & \(\bullet\) & Verification of pile bottom \\  & \(\bullet\) & Less affected by soil-pile interaction compared to low-strain methods & \(\bullet\) & Not suitable for shallow defects & & conditions in bridges or \\ \hline Electromagnetic waves & \(\bullet\) & Non-contact testing & \(\bullet\) & Limited detection depth due & \(\bullet\) & Shallow defect inspection in \\  & \(\bullet\) & Effective for detecting shallow defects in existing piles & \(\bullet\) & Inference from metal & \(\bullet\) & Supplementary verification \\  & \(\bullet\) & Ripd scanning capability for large pile groups & \(\bullet\) & Retromed accuracy in wet/conductive soil & & distribution or corrosion in \\  & & & conditions & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of PIT methods. both current PIT methods and AI applications in this domain. This review aims to provide valuable guidance for researchers and engineers in selecting appropriate technologies and advancing modernization, automation, and intelligent solutions in PIT.
# Extraction, Characterization, and Applications of Pectins from Plant By-Products
## 5 Techno-Functional Properties and Application of Pectin
### Emulsion
Therefore, according to previous studies, pectin structural features and conformation of the carbohydrate domain are the main factors limiting or preventing emulsion instability [137; 139]. The carbohydrate domain creates the stereo and electrostatic effects stabilizing simple emulsions. RG-I and HG domains contribute to the steric and electrostatic effects, respectively. It has been hypothesized that the RG-I generates thick adsorbed hydrated layers, which prevent droplets from coalescing. Moreover, neutral side chains are possibly related to their interactions with ferulic acid and/or proteins can contribute to emulsion stabilization. The non-esterified GaIA units can form a charged stabilizing layer [6; 125]. The pectin charge, which affects emulsion stability, was strongly dependent on its DM and the pH of the aqueous phase [135; 140] and it may confer a negative charge (pH \(>\)\(\sim\)3.5) in the surrounding areas of oil droplets due to its anionic nature (carboxylic groups), contributing to the electrostatic stability of \(o/w\) emulsions [118]. The emulsifying and emulsion-stabilizing properties of pectins extracted from various sources are shown in Table 4. Among the commercial pectins, those from citrus peel and apple pomace are not considered as effective emulsifying agents, contrary to those from sugar beet [141]. The origin of these different behaviors is mainly attributed to the higher protein and ferulic acid contents in the latter [142]. Yang et al. (2018) showed that the emulsion activities (EAs, 44.97-47.71%) and emulsion stabilities (ESs, 36.54-46.00%) of potato pectins were affected by various acids in extraction and were higher than commercial citrus and apple pectins [39]. The pectins extracted from watermelon produced lower dynamic surface tension than gum arabic, which is well known for its emulsifying properties [6]. The surface area moment mean (Sauter mean diameter--D\({}_{[3,2]}\)) and the volume moment mean (De Brouckere mean diameter--D\({}_{[4,3]}\)) show the sizes of colloidal particles, based on distribution moments. D\({}_{[3,2]}\) is most sensitive to the presence of fine particles in size distribution whereas D\({}_{[4,3]}\) is most sensitive to the presence of large particles. Values of D\({}_{[3,2]}\) and D\({}_{[4,3]}\) obtained for emulsions's prepared with watermelon pectin were similar to those collected for emulsions made with gum arabic [6].
## 4 Experiment and Analysis
### Comparison Experiments of Model Clusters with Different Datasets  
In this study, the performance of the PlantVillage model group was analyzed in terms of training and testing. The recognition accuracy of six models was evaluated using the accuracy curve shown in Figure 8a,b. The findings revealed that the IBSA_Net model outperformed the other models, exhibiting significantly higher recognition accuracy in the early stages of training. As the training progressed, the accuracy curve of IBSA_Net tended to flatten after eth 10 th round, indicating a convergence effect. After 50 rounds of training, the IBSA_Net model achieved a training accuracy of 99.7% and a testing accuracy of 99.4%. While the EfficientNet and MobileNetV3 models showed similar training accuracy to IBSA_Net in eth 50 th round, at 98.4% and 97.6%, respectively, the training and testing accuracy gaps of models other than IBSA_Net were over 2%. These findings suggest that these models suffer from overfitting problems and display weaker generalization ability than the IBSA_Net model.  
In Figure 9a,b, the performance of six models on the PDDA dataset is evaluated. The results demonstrate that the training and testing accuracies of all models have decreased compared to those of PlantVillage. This decline in accuracy may be due to the greater complexity of the backgrounds in the PDDA dataset. However, IBSA_Net remains the best-performing model, indicating its ability to remain less influenced by complex backgrounds and display stronger robustness than the other models.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Models** & **Total Training Accuracy** & **Total Test Accuracy** \\ \hline OriNet & 0.913 & 0.896 \\ OriNet + HS & 0.920 & 0.921 \\ OriNet + IBMax & 0.952 & 0.951 \\ OrliNet + SA & 0.943 & 0.949 \\ OriNet + SA + HS & 0.967 & 0.963 \\ OriNet + IBMax + HS & 0.975 & 0.972 \\ OriNet + IBMax + SA & 0.981 & 0.979 \\ IBSA_Net & 0.997 & 0.994 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Effect of different key modules on model accuracy.  
In Figure 10, the loss curve graph reveals that IBSA_Net has the smallest loss and converges the fastest. After eth 10 th round of training, the loss curve of IBSA_Net begins to flatten, while the training losses of the other five models in the first five rounds are significantly larger than those of IBSA_Net.
# A Survey of Wi-Fi 6: Technologies, Advances, and Challenges
## 4 OFDMA in Wi-Fi 6
### Random Access
#### 4.2.4 Integration of EDCA  
For backward compatibility, a Wi-Fi 6 access point must support both legacy Wi-Fi and Wi-Fi 6 stations. However, legacy Wi-Fi stations support only OFDM access, whereas Wi-Fi 6 transmissions may be multiplexed with OFDMA. Thus, the access point will switch between two modes: (i) transmissions in OFDMA with only Wi-Fi 6 stations and (ii) transmissions in OFDMA with legacy Wi-Fi stations. In particular, the access point must exclude legacy Wi-Fi stations when an OFDMA phase engages. To exclude these stations, the access point can tune the EDCA parameters. Examples are the AIFS Number defining the inter-frame space and the minimum and maximum contention windows.  
Wi-Fi 6 defines two sets of EDCA parameters:  
1. Legacy EDCA parameters are applied for non-Wi-Fi 6 stations and Wi-Fi 6 stations in single-user mode;
2. Specific multi-user EDCA parameters are used when several transmissions are multiplexed over multiple RUs. Stations use these parameters after an uplink multi-user transmission from the access point [11; 36]. Conversely, they re-apply the legacy parameters when the multi-user transmissions are terminated.  
Figure 11 depicts the MU EDCA procedure. When the station receives the TF, it applies the multi-user EDCA parameters for channel access, starts transmission, and sets the timer to listen for upcoming TFs to update the timer. If it does not receive a new TF and the timer expires, it falls back to legacy EDCA mode.
## 4 Discussion
### Forecast vs. Reference  
In this section, a detailed comparison, for the full testing subset, between the forecasted values for all three models and the respective reference values is presented, using a dispersion chart. Figure 25 presents the dispersion chart for the cooling energy demand (ANN_C2, SVM_C5 and SVM+SA_C5) and the heating energy demand (ANN_H4, SVM_H4 and SVM+SA_H4) and the corresponding correlation factors (R\({}^{2}\)).  
For the cooling period (the top three charts), all models have a very high correlation between the forecasted values and the reference values. The dispersion is slightly higher for ANN_C2, while SVM+SA_C5 presents a very high concentration of the points around the trendline with a R\({}^{2}\) of 0.9879.  
For the heating period, the correlation factors are lower than in the cooling period. This may be partly due to the fact that the Christmas season is part of the testing subset and the models are not configured to detect holidays, considering them as normal working  
Figure 24: Heating energy demand load profile for the best performing models, for a full week, from the test subset.  
Figure 23: Cooling energy demand load profile for the best performing models, for a full week, from the test subset.  
days. The highest correlation factor is obtained for the ANN_H4 model, which confirms it as the most effective model in forecasting heating energy demand. Moreover, the trendlines for the three bottom models are considerably below the bisector of the chart, meaning that, in all these models, most of the forecasted values for heating energy demand are lower than the respective reference values.
## 4 Results
### Comparison between DInSAR and Optical Inventories
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{5}{c}{**Amount of Active Rock Glacers (ARG)**} \\ \hline
**Basin** & \multicolumn{1}{c}{**ARG**} & \multicolumn{1}{c}{**DCG-RG**} & \multicolumn{1}{c}{**\% Difference**} \\ \hline  & **DInSAR** & **Optical** & **Optical** & **DInSAR-Optical** \\ \hline Calingasta & 86 & 141 & 11 & \(-\)43.4\% \\ Ansilta & 36 & 133 & 6 & \(-\)74.1\% \\ Blanco & 540 & 884 & 20 & \(-\)40.2\% \\ Los Patos & 339 & 501 & 35 & \(-\)36.7\% \\ Castano & 226 & 380 & 6 & \(-\)41.4\% \\ Cuevas-Vacas & 130 & 189 & 17 & \(-\)36.7\% \\ Tupungato & 167 & 294 & 38 & \(-\)49.7\% \\ Total & 1524 & 2522 & 133 & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Detected active rock glaciers with DInSAR and optical techniques in the different studied basins. The acronym meanings are: ARG: Active Rock Glacier; DCG-RG: Debris-covered Glacier-Rock Glacier.
## 1 Introduction
Two geometry parameters, the mixing chamber inlet and outlet widths were modified. They observed a significant effect of the flow structure and the feedback channel flow rate when modifying the inlet width, negligible effects were observed when modifying the outlet width. The output frequency and amplitude effects whenever the (FC) and the mixing chamber (MC) lengths were modified, was studied using a 2D numerical model by [PERSON] et al. [10], the fluid was considered as incompressible, the Reynolds number employed was 5000. They observed that an increase of the feedback channel length generated no modifications on the output frequency, the same observation was previously obtained by [11], in both cases the flow was defined as incompressible, being the reason why the simulations could not provide the correct information. On the other hand, the increase of the mixing chamber length, generated a clear reduction on the actuator output frequency. They defined the length scale to be employed to properly non-dimensionalize the oscillation frequency. The present paper is presenting a numerical evaluation of the same fluidic oscillator (FO) configuration experimentally evaluated in [1]. The effects on the stagnation pressure, net momentum acting onto the jet, output mass flow and mixing chamber incoming jet inclination angle, among other parameters, are analyzed for four different internal geometry modifications, the (MC) inlet and outlet widths and the (MC) inlet and outlet wall inclination angles. Three different Reynolds numbers are considered. The four geometry modifications chosen have a considerable impact on the flow inside the (MC) and the (FC's). The (MC) inlet width decisively affects the reverse flow in the (FC's) and the Coanda effect in the (MC), the (MC) outlet width drastically modifies the pressure inside the (MC), the (MC) inlet inclination angles, affect the Coanda effect and the bubble volume inside the (MC), finally, the (MC) outlet inclined walls drastically change the stagnation pressure in these particular walls, changing as well the (FC) dynamic pressure and amplitude.
# Malicious File Detection Method Using Machine Learning and Interworking with MITRE ATT&CK Framework
## 6 User Perspective  
MITRE ATT&CK is an abbreviation of Adversarial Tactics, Techniques, and Common Knowledge. After observing actual cyberattack cases, the malicious behaviors used by attackers are analyzed from the viewpoint of attack methods (tactics) and technologies (techniques). These are standard data that classify and list information on the attack methods of the attack group. It is a systematization (patterning) of threatening tactics and technologies to improve the detection of advanced attacks. Originally, ATT&CK was used for hacking attacks used in Windows corporate network environments at MITRE. It started with documenting TTPs such as (procedures), and is a framework that can identify the attacker's behavior by mapping TTPs' information based on the analysis of consistent attack behavior patterns generated by the attacker. Machine learning algorithms are used as a means to enhance and respond, and in conjunction with this, it can develop into an intelligent, advanced security control solution that provides real-time visualization to security personnel.  
\begin{table}
\begin{tabular}{c c c} \hline
**Divison** & **Detection Accuracy Ratio** & **Visualization Suggestions** \\ \hline Proposed Method & GREAT & YES \\ [PERSON] et al. & It is lower than the proposed method. & NO \\ [PERSON] et al. & NO & NO \\ [PERSON] et al. & GREAT & NO \\ [PERSON] et al. & NO & NO \\ [PERSON] et al. & GREAT & NO \\ \hline \end{tabular}
\end{table}
Table 10: Comparison of malicious file detection method using machine learning research cases.  
From the user's point of view (security personnel), it is an excellent security solution and has the advantage of efficiently detecting attacks as it can minimize false positives and false positives for attack detection. In addition, since it provides real-time detected attack patterns and detailed information (techniques, tactics, various attack knowledge and attacker information, etc.), it is an all-round excellent solution for effective countermeasures and follow-up management
# PBR Clutter Suppression Algorithm Based on Dilation Morphology of Non-Uniform Grid
## 3 Separate False Alarm Clutter from Data
### Iteratively Calculation Frame by Frame  
To efficiently operate the algorithm, we further explore its iterative and parallel calculating scheme. Referring to Equation (14), we can derive the reference mark-matrix \(\Gamma_{n+1}\):  
\[\begin{array}{rl}\Gamma_{n+1}&=\mathfrak{Bm}(A_{n+1-k}+\cdots+A_{n}+A_{n+2}+ \cdots+A_{n+1+k})\\ &=\Gamma_{n}+\mathfrak{Bm}(A_{n}+A_{n+1+k}-A_{n+1}-A_{n-k})\\ &=\Gamma_{n}+\mathfrak{Bm}(A_{n}-A_{n-k})+\mathfrak{Bm}(A_{n+1+k}-A_{n+1})\\ \end{array} \tag{17}\]  
Assume  
\[\Psi A_{n}=\begin{cases}\mathfrak{Bm}(A_{n}-A_{n-k})&n>k\\ \mathfrak{Bm}(A_{n})&n\leq k\\ \end{cases} \tag{18}\]  
Thus, Equation (17) will be derived into Equation (19).  
\[\Gamma_{n+1}=\Gamma_{n}+\Psi A_{n}+\Psi A_{n+1+k} \tag{19}\]  
where k is the half number of the reference frames. When \(n=1\), \(\Gamma_{1}=\mathfrak{Bm}(A_{2}+\cdots+A_{1+k})\).  
It can be observed that Equations (11), (12), (16), (18) and (19) are relatively independent of calculation. To promote the efficiency of the algorithm, we split the whole process into two parts for parallel calculation and build up an intermediate database to link them together. One is frame processing part, and the other is interframe processing part. Both are designed to operate in parallel. Based on the above analysis, the clutter separation algorithm is described in below chart, shown in Figure 6.  
The data frame F\({}_{i}\) flown from the CFAR processor is input in this system. \(i\) is the index of the current frame flowing in. When \(i>k\), the interframe processing part starts operating. \(n\) is the index of the current processing frame, and is independent of the index \(i\). In frame processing part, mark the data of F\({}_{i}\) on the grids and calculate \(\Psi A_{i}\). Save \(A_{i}\) and \(\Psi A_{i}\) into the intermediate database called by clutter separation in interframe processing part. Finally, iteratively compute and output the mark matrix M\({}_{n}\) in the interframe processing part.  
Figure 5: The sketch map of the grid projection.
## 2 Materials and Methods
### Raw Materials  
Elderberries (S. _nigma_ L.) from the 3 cultivars ('Sabugueira', 'Sabugueiro' and 'Bastardeira') mainly cultivated in the Varosa Valley (Portugal) were collected at Vila Pouca de Salzedas (41 deg04'11.9'' N 7 deg45'00.8'' W) cultivation field, during the first week of August of 2017 and transported under refrigeration (ca. 4 degC) to the laboratory. It was prepared a composite sample (ca. 10 kg) containing a mixture from the 3 cultivars, in similar proportions, simulating what happens during the collection of the berries to be delivered to suppliers, in which there is no separation of cultivars. Thus, this composite sample is representative of the stalks wastes resultant from the elderberries processing, which was stored at \(-20\) degC until further analysis.  
Before analysis, the berries were detached from stalks, which were then washed with water to remove any remaining juice. Stalks were freeze-dried using VirTis BenchTop K (SP Industries, Warminster, PA, USA) and ground in a mill (Ika A10, Staufen, Germany) prior to extraction. Freeze-drying is based on the dehydration by sublimation of a frozen product. This procedure was selected for drying stalks before analysis, as due to the absence of liquid water and the low temperatures required for the process, it preserves the materials from physico-chemical and microbiological deteriorations [38]. To determine the water content, three samples of biomass were dried at 105 degC until reaching a constant weight. The water content of elderberry stalks was 51.72 \(\pm\) 4.35%.  
From freeze-dried stalks, as shown in the workflow in Figure 1, the mineral content was determined, and extractions were performed to study the lipophilic profile and to evaluate the polar fraction regarding its total phenolic content through the Folin-Ciocalteu method and the antioxidant activity through ABTS and DPPH assays.
# Design of a Planar Cable-Driven Parallel Robot for Non-Contact Tasks
## 2 Design Strategies
### Guidance System  
One of the main advantages of _CDPRs_ is the possibility to locate the actuation units almost anywhere. This is possible thanks to the cable guidance system that conveys the cable, from the actuator, through the prescribed anchor points on the frame (proximal) to the anchor point on the platform (distal). For the standard model to be respected, without introducing geometrical-model errors, the cable anchor points need to be geometrically determined. An extensive review of possible construction solutions is reported in [1], where a distinction is made between the proximal and distal anchor systems.  
The mechanical devices that compose the proximal guidance system should allow for large deflection angles and at the same time determine the anchor point position. Swivel (or panning) pulleys and eyelets are primarily used for this purpose. Eyelets allow for the approximate definition of a point and significant cable orientation, but cause high cable wear [40]. However, they are employed in many _CDPRs_ thanks to their simplicity, and the possibly low geometrical error introduced, if the _CDPR_ workspace is large [16; 38; 41]. Figure 2 shows the use of eyelets as proximal anchor points on the fixed frame of the _CDPR_ prototype presented in [16]. Conversely, swivel pulleys can reduce cable wear and still allow for orientation in a broad direction range, since, unlike classical fixed pulleys (Figure 3a), they can rotate about an axis tangent to the pulley groove, the swivel axis (Figure 3b). This kind of solution is adopted in [39; 42], and an example of exit point layout is shown in Figure 4. In general, the presence of pulleys in the guidance system makes the system reliable, but complicates the geometrical model of the robot. This issue can be faced in two ways: by adopting a more complex model, which includes pulley kinematics for _CDPR_ control [4; 32; 43], or by ignoring the actual geometry of the system, and accepting several modeling errors. Otherwise, particular layout and orientations of the pulley system must be adopted.  
Figure 2: Model of the eyelet exit point of the _CDPR_ laser scanner in [16].  
On the platform, at the distal anchor points, cables can be knotted to a fixture (i.e., an eyelet or a hook) or anchored to a universal joint: both these options allow to define a fixed point with respect to the platform. The knot solution is preferred for its simplicity (Figure 5) [16,44], but it is neither accurate nor reliable for long-term use. On the other hand, the universal joint (Figure 6) is more precise but complex and more expensive. Pulleys can also be employed if the cable needs to be deflected and not fixed at the distal anchor point. In [45], a pulley system is used at the distal anchor point on the platform to compensate for the effect of the proximal swiveling pulley geometrical model.  
Figure 4: Application of swivel pulleys for cable proximal anchor point.  
Figure 3: 3D model of pulley transmissions. (**a**) Fixed pulley. (**b**) Swivel pulley.