esa-sceva/satcom-chunk-collection huggingface dataset:
# PLC-Controlled Intelligent Conveyor System with AI-Enhanced Vision for Efficient Waste Sorting
## 4 Performance and Evaluation Results
### Mean Average Precision (mAP50 and mAP50-95)  
The mAP score is a crucial metric to evaluate the overall performance of an object detection model, as it measures how well the model detects objects by calculating the Intersection over Union (IoU), which assesses the overlap between the predicted and ground-truth bounding boxes. The mAP50 metric evaluates a model's ability to correctly detect objects at a more lenient overlap criterion (0.5 IoU), meaning it prioritizes detecting objects but does not necessarily require high localization precision. Figure 15 illustrates the mAP50 score, using a 50% overlap threshold, showing that the model reached approximately 86%. This score is considered adequate for object identification, even when the boundaries are not precisely matched. This high mAP50 score signifies the robust object identification capability of the model, despite the imperfect alignment of the boundaries.  
The mAP50-95 metric is widely used for evaluating object detection models, providing a more comprehensive evaluation by calculating the IoU over a broad range, specifically from 0.5 to 0.95 in increments of 0.05. This method provides a comprehensive examination of the model's ability to precisely detect objects with different degrees of overlap, thus allowing for a more rigorous evaluation of detection and localization accuracy. By assessing multiple IoU thresholds, the metric gives a holistic view of the robustness of the model under various detection conditions. In this case, the model achieved an mAP50-95 score of approximately 70%, indicating strong detection performance across the evaluated IoU thresholds. The equation for calculating the mAP is as follows:  
\[mAP=\frac{1}{k}\sum_{i=1}^{k}AP_{i} \tag{1}\]  
where:* \(mAP\): Mean Average Precision, a single scalar value that summarizes the model's detection precision across classes and IoU thresholds.
* \(k\): The total number of IoU thresholds (or classes) over which the mAP is averaged. For mAP50-95, \(k\) represents multiple thresholds from 0.5 to 0.95.
* \(AP_{i}\): Average Precision for the \(i\)-th threshold, which is calculated as the area under the Precision-Recall (PR) curve for that specific IoU threshold.  
The equation computes mAP by averaging precision scores across specified IoU thresholds, providing a comprehensive measure of object detection performance and reflecting both high-confidence detections and adaptability to varying object overlaps.
## 3 Results and Discussion
### Shelf Life Analysis of Produced Cookies
#### 3.4.5 Sensory Analysis
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Control Cookie**} & \multicolumn{4}{c}{**4\% SCG-Added Cookie**} & \multicolumn{2}{c}{**7\% SCG-Added Cookie**} \\ \cline{2-9}
**Storage** & \multicolumn{2}{c}{**Sensory**} & \multicolumn{2}{c}{**Deterioration**} & \multicolumn{2}{c}{**Shelf Life**} & \multicolumn{2}{c}{**Sensory**} & \multicolumn{2}{c}{**Shelf Life**} & \multicolumn{2}{c}{**Sensory**} & \multicolumn{2}{c}{**Blef Life**} & \multicolumn{2}{c}{**Sensory**} & \multicolumn{2}{c}{**Blef Life**} & \multicolumn{2}{c}{**Shelf Life**} \\
**Tem. ** (**C**) & **Rate** & **(**d**)** **\(\mathbf{\ast}\)** & **(**d**)** **\(\mathbf{\ast}\)** & **(**d**)** **\(\mathbf{\ast}\)** & **(**d**)** **\(\mathbf{\ast}\)** & **Rate** & **(**d**)** **\(\mathbf{\ast}\)** & **Rate** & **(**d**)** **\(\mathbf{\ast}\)** \\ \hline
25 & 0.010 \(  
Based on the results, it was possible to determine the shelf life (Table 6). The shelf life of 0%, 4% SCG-added, and 7% SCG-added cookies at 25 \({}^{\circ}\)C was calculated as 359, 435, and 471 days, respectively. The shelf life of 4% and 7% SCG-added cookies at 45 \({}^{\circ}\)C was calculated as 144 and 104 days, respectively. According to lipid oxidation results, the 4% and 7% SCG-added cookies were not acceptable at 45 \({}^{\circ}\)C for storage times beyond 120 and 75 days, respectively (which exceeded the PV acceptance limit) (Section 3.4.3). This means that, in this case, the sensory evaluation overestimated the shelf life.
# Steering Mirror System with Closed-Loop Feedback for Free-Space Optical Communication Terminals
## 7 Conclusions  
In summary, a novel method of adding closed-loop control to a consumer-off-the-shelf piezo actuator is described. Using low-cost electronics, the actuator can be positioned in a closed loop with a feedback error repeatability of \(1.0\,\mathrm{\SIUnitSymbolMicro m}\) at \(1\,\mathrm{\sigma}\) and an accuracy in line with the angular resolution of the encoder, defined by the period of the lines on the screw knob.  
Two actuators were combined into a custom kinematic mount, producing a large-aperture steering mirror with repeatable angular positioning of less than 45 \(\mathrm{\SIUnitSymbolMicro m}\). Due to the self locking nature of the actuators, the mirror does not require power to maintain a pointing direction, making it ideal for low-power applications. Using the mirror to actively center an incoming beam following a random jitter pattern, the mirror provided over a 5-fold concentration of angular extent on the detector. The actuator assembly has also been  
Figure 9: Image of encoder/actuator test set-up inside vacuum chamber.  
successfully tested at low temperatures under vacuum, potentially enabling the assembly to be used in space applications.  
Conceptualization, C.G., D.B., O.Y., A.R., R.B., E.S., R.T.W. and C.B.; methodology, C.G., D.B. and C.B.; software, C.G.; validation, C.G., D.B. and C.B.; formal analysis, C.G. and C.B.; investigation, C.G., D.B. and C.B.; resources, C.B.; data curation, C.G. and C.B.; writing--original draft preparation, C.G.; writing--review and editing, all; visualization, C.G. and C.B.; supervision, C.B.; project administration: E.S., R.T.W., R.B. and C.B.; funding acquisition, E.S., R.T.W., R.B. and C.B. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the UK Space Agency grant number UKSAG22_0042.  
The Python code that generates the random profile can be found at [[https://github.com/CyrilBourgenot/FreeSpaceOpticalCommunication.git](https://github.com/CyrilBourgenot/FreeSpaceOpticalCommunication.git)]([https://github.com/CyrilBourgenot/FreeSpaceOpticalCommunication.git](https://github.com/CyrilBourgenot/FreeSpaceOpticalCommunication.git)) (accessed on 17 April 2024).  
The authors would like to thank e2E Services Ltd./Telespazio-UK, SMS Electronics Ltd. and Smart Sentry Ltd., partners in the NSIP Phase 3 Project \"ALIGN Laser Optical Communications for CubeSats\", for their input and support.  
The authors declare no conflicts of interest.
## 4 Discussion
The novel GNSS corrections offer upgrades to the ones tested in this study regarding the global availability and no need for a second GNSS receiver other than the rover [42]. The potential of Trimble RTX [6] and Precise Point Positioning (PPP) [4] was recognized in previous studies for GNSS positioning and navigation in farming. Therefore, their implementation is planned to be evaluated using the proposed method in future studies. The positioning accuracy for tested GNSS corrections declared by its suppliers was determined underconditions that do not correspond to the ones in the field during agricultural operations [43]. Among several factors, this refers to the observation of multiple epochs using the stationary GNSS receiver and observation under uniform conditions. Since the GNSS positioning accuracy under common field conditions in farming is subjected to other secondary effects, such as the availability of mobile Internet [44], or distance to the base station [45], every farmer should at times test the performance of GNSS receivers mounted on agricultural machinery. The distance from the rover to the base station exceeding the one recommended by producers is a particularly frequent case in eastern Croatia, as was the case in this study. While CROPOS solves this issue by calculating the virtual reference stations, the commercial solutions provided by the local agricultural suppliers often rely on a simpler approach, which does not negate the effects of relatively large base-rover distances [46]. The GNSS constellation also affected GNSS positioning accuracy in this study, as observations using CROPOS and base station in the first repetition produced higher positioning accuracy with lower GDOP and TEC compared to the second repetition. The GNSS positioning accuracy results from all four GNSS variations indicated that the farmers could not expect either a positioning accuracy as declared by its suppliers nor uniform and constant values. Therefore, to ensure reliability for agrotechnical operations requiring higher positioning accuracy, such as sowing and planting, high-precision corrections are recommended [4], such as using CROPOS and base station.
## 3 Research Methodology
### Decision Trees (DTs)
The amount of information gained is measured in bits, and the mathematical formula used to calculate the Shannon function is as follows:  
\[I(P(u_{1}),\dots,\ P(u_{n}))=\sum_{i=1}^{n}-P(u_{i})\ log_{2}P(u_{i}) \tag{1}\]  
where \(I(P(u_{1}),\dots,\ P(u_{n}))\) represents the amount of information gained by splitting the dataset based on attributes, \(P(u_{1}),\dots,\ P(u_{n})\), and \(P(u)\) represents the proportion of the dataset belonging to each class after the split, and \(P(u_{i})\) is the probability of the possible answer (\(u_{i}\)) [23; 24]. Information gain (IG) is a measure used in DTs to decide which feature to split the data on at each step. The goal is to maximize the information gained from each split, thereby reducing uncertainty or entropy. \[\text{Information }Gain\left(A\right)=I\bigg{(}\ \ \frac{p_{i}}{p_{i}+n_{i}},\ \frac{n_{i}}{p_{i}+n_{i}}\bigg{)}-Remainder\left(A\right) \tag{2}\]  
where \(I\bigg{(}\ \frac{p_{i}}{p_{i}+n_{i}},\ \frac{n_{i}}{p_{i}+n_{i}}\bigg{)}\) is the initial entropy. Entropy denotes the uncertainty before the split, calculated based on the proportion of positive \(p_{i}\) and negative \(n_{i}\) instances. The entropy assesses the level of disorder in a set of data. A dataset with high entropy is highly disordered, denoting a number of different classes included. The DTs aim to decrease entropy to the highest possible by splitting the data into the smallest number of possible classes (Figure 4). Information gain: It quantifies the reduction in entropy achieved by splitting the data on a particular attribute. The attribute with the highest information gain is selected for the split because it best decreases the entropy of the target variable. The remainder (A) represents the weighted average uncertainty after the data is split on attribute A. After the splitting on an attribute, the remainder demonstrates the sum of  
Figure 4: Pruned and unpruned decision trees (DTs). the uncertainty of the subsets, weighted by their sizes. Hence, the information gain shows the difference between the original entropy and the remainder. According to the context of this study, the information gain is used to select attributes in the building of a DT. By increasing the information gain, the DT becomes more effective as it classifies the data, causing increased performance in prediction. Information gain ensures that each split is as informative as possible, leading to more accurate and interpretable highly accurate DTs that can efficiently classify and predict results [22; 23; 24; 25].
## 3 Mathematical Formulation and Methodology
### Small-Scale Fading
#### 3.3.2 Doppler Frequency  
The LCR function is written as a function of the Doppler frequency. Thus, with both theoretical and empirical the values of LCR, it is possible to estimate the Doppler frequency \(f_{d}\)[46] as  
\[\hat{f}_{d}=\frac{LCR_{Empirical}}{LCR_{Thooretical\_normalized}}, \tag{7}\]  
where \(LCR_{Thooretical\_normalized}\) can be obtained from Equations (3)-(6) with \(f_{d}=1\).  
Then, the relative speed between the transmitter and receiver can be estimated as \(\hat{\vartheta}=\lambda\cdot\hat{f}_{d}/\ \cos(\theta)\), where \(\lambda\) is the wavelength of the carrier, and \(\theta\) refers to the angle of the horizontal axis of the antenna to the BS. It is important to make it clear that these equations refer to the calculation of second-order statistics for a given power level. Thus, for each calculated level, there will be a different value for \(\hat{f}_{d}\).
# Survey of Dense Video Captioning: Techniques, Resources, and Future Perspectives
## 3 Techniques for Dense Video Captioning
### Attention-Based Approaches
#### 3.5.4 Multi-Modal Attention Models  
To generate more comprehensive and accurate captions, multi-modal attention models aim to fuse information from various sources, such as visual, auditory, and textual data. [PERSON] et al. [20] introduced an attention-based multimodal fusion model that incorporates visual, motion, and audio features for descriptive video content generation. Similarly, [PERSON] et al. [47] proposed an Attention-based Dual Learning Approach for Video Captioning, which includes a caption generation and a video reconstruction module to bridge the semantic gap between videos and captions by jointly learning both modalities.  
[PERSON] et al. [48] presented a multi-modal feature fusion method with feature attention specifically for the VATEX captioning challenge. This method extracts motion, appearance, semantic, and audio features, utilizing a feature attention module to focus on different features during the decoding process. However, these multi-modal attention models often have higher computational complexity due to the integration of multiple modalities. Furthermore, acquiring high-quality multi-modal features, particularly for audio, can be challenging and may require specialized pre-processing and feature extraction techniques.
## 4 Parametric Study
### Effect of the Diameter of the Rebar  
In this section, the impact of the rebar diameter on the pull-out load and the bond stress of the model is studied. In total, four diameters are considered: 10 mm, 12 mm, 14 mm, and 16 mm. The evolution of the maximum pull-out load for each simulation for the four diameters at the same corrosion degrees is plotted in Figure 11.  
We can see that the greater the diameter of the rebar is, the greater the pull-out load is. The impact of the diameter on the adherence between the rebar and the concrete has been studied, and the results in the literature are scattered. It has been reported in [28] that a greater diameter of the rebar improves the adherence. This can be explained by the fact that when we increase the diameter of the rebar, we also increase the surface in contact with concrete, meaning that the bond stress increases as we increase the diameter of the rebar, for the same corrosion degrees. This can be observed in Figure 12, where we plotted the bond stress as a function of the rebar diameter for the three diameters studied.  
Figure 10: Evolution of the maximum pull-out load as a function of the corrosion degree for different embedment depths.  
We can also see in Figure 12 that the bond stress is greater for larger rebar for low degrees of corrosion and when the drop of the bond stress happens, this trend is reversed. We note that the corrosion degree where this drop of the bond stress occurs is smaller as we increase the diameter of the rebar. This is because the corrosion degree of a rebar depends mainly on its volume and mass. This means that for the same corrosion degree, the volume expansion of rebar of a diameter of 14 mm damages the interface more significantly than a  
Figure 11: Evolution of the maximum pull-out load as a function of the corrosion degree for different rebar diameters.  
Figure 12: Evolution of the bond stress as a function of the corrosion degree for different rebar diameters.  
rebar of a diameter of 10 mm. We can conclude that the diameter of the rebar impacts not only the maximum pulling load of the corroded rebar, but also the bond stress.
# A Review of Early Fault Diagnosis Approaches and Their Applications in Rotating Machinery
## 2 Review of Early Fault Diagnosis Approaches
### Variational Mode Decomposition  
Recently, [PERSON] et al. (2013) proposed a novel variational method, called variational mode decomposition (VMD), to decompose a multi-component signal into an ensemble of band-limited intrinsic mode functions (IMFs). It was demonstrated that the VMD performed well in tone detection, tone separation, and noise removal. Therefore, much attention has been focused on VMD applications.  
VMD is a non-recursive decomposition method, and it extracts the constituent AM-FM components of a complicated multi-component signal adaptively and concurrently. It defines IMFs as explicit AM-FM models, and relates the parameters of AM-FM models to the bandwidth of IMFs. According to the narrow-band property of IMFs, the AM-FM parameters can be found by minimizing the bandwidth, thus obtaining IMFs. This algorithm has good merits over other available mode decomposition methods, such as theoretical rationale and robustness to noise and sampling.  
In essence, IMFs are AM-FM signals, and they have a limited bandwidth. VMD decomposes a signal \(x(t)\) into an ensemble of IMFs \(c_{k}(t)\) that are band-limited about their respective center frequency \(w_{k}\), while reconstructing the signal optimally. It iteratively updates each IMF \(c_{k}(t)\) in the frequency domain, and then estimates the center frequency \(w_{k}\) as the center of gravity of the IMF power spectrum.
## 4 Test Results: Analysis and Interpretation
### Signal Waveform Analysis after Noise Reduction  
Following the outlined procedures, time domain diagrams of ME and EMR signals are obtained through signal processing for the four experimental groups, as depicted in Figure 5. Analyzing the ME and EMR waveforms resulting from rock damage in the specimens under static load conditions reveals that the first arrival time difference between the monitored ME signal and the EMR signal is less than 0.01 MS. This aligns with the established understanding that the propagation speed of EMR is faster than ME in the same medium. In terms of the signal-to-noise ratio, ME signals exhibit higher ratios and smoother waveforms, and both undergo rapid fluctuation rise followed by a gradual decline.  
Moreover, the waveform rise time difference between the ME and EMR signals is minimal, and later stages exhibit sustained vibrations with a notable decrease in amplitude. To anticipate rock fractures, the ME and EMR signals of each specimen group undergo  
Figure 4: Wavelet decomposition and reconstruction of the EMR signal of the J-3 specimen before noise reduction. ((a5) is the fifth layer decomposition low-frequency coefficient; (d5) is the fifth layer decomposition high-frequency coefficient; (d4) is the fourth layer decomposition high-frequency coefficient; (d3) is the third layer decomposition high-frequency coefficient; (d2) is the second layer decomposition high-frequency coefficient; (d1) is the first layer decomposition high-frequency coefficient.  
Figure 5: EMR waveform and ME waveform of the four groups of specimens. (**a**) J-1; (**b**) J-2; (**c**) J-3; (**d**) J-4.  
analysis. This involves determining the mutation position in the EMR signals to further explore the correlation between the EMR and ME signals in both the time domain and frequency domain [27].
# An Investigation into the Utilisation of CNN with LSTM for Video Deepfake Detection
## 3 Research Methodology
### Inclusion and Exclusion Criteria  
Inclusion and exclusion criteria have been developed to ensure appropriate research papers are selected for answering the research questions.  
The inclusion criteria include the following:  
* Peer-reviewed journals and conference articles to ensure high-quality and credible sources.
* Relevant to the specific research questions.
* Topic mainly on video deepfake detection using CNN and LSTM.
* Full and available articles to allow for a comprehensive review of the content.
* English-language articles to maintain consistency in analysis.  
The exclusion criteria were as follows:  
* Articles concerning all other aspects of combining CNN and LSTM apart from video deepfake detection.
* Articles focused on video deepfake detection that do not discuss CNN and LSTM.
* Unpublished articles, non-peer-reviewed articles, and editorial articles to ensure credibility.
* Articles that are not fully available.
* Non-English articles to avoid translation issues and maintain analysis consistency.
* Duplicates of already included articles to avoid redundancy.
## 3 Results
### IMOSs Provide a Fitness Advantage to B. subtilis CUII for Bile Tolerance  
Bile tolerance is a criterion used to select probiotic strains. The bile tolerance of _B. subtilis_ CU1 have been previously observed (personal data), but its viability after 24 h of incubation at 37 \({}^{\circ}\)C in presence of bile salts (0.5%) is lower than those of the _Bacillus clausii_ strains from the Enterogermina(r) product [30]. In this section, we first investigated the influence of the carbon source on the bacteria growth in presence or absence of 0.5% bile by the measurement of the OD at 595 nm (Figure 4A). In the absence of bile, we observed the same growth pattern as reported in Section 3.3. In glucose medium, bile increases the lag-time and reduces the growth rate, but the growth profiles are quite similar compared to the results obtained in absence of bile salts (Figure 4A). However, with IMOSs, the exponential growth phase of _B. subtilis_ started earlier when compared to glucose (Figure 4A). Lag-time due to bile seemed to be reduced in the presence of IMOS as a unique carbon source, and the cell yield after 24 h was higher compared to growth with glucose. _Bacillus_ probiotics are formulated as spore preparations. In this context, it is important to study the impact of IMOSs on spore germination in the presence of bile salts. We regarded the ability of _B. subtilis_ spores to germinate and grow in the presence of 0.5% bile salts with glucose or IMOS as unique carbon sources during 50 h by measuring the OD at 595 nm. The results presented here for spore germination in IMOSs were obtained with spores prepared in IMOSs, while germination in glucose was assessed with spores prepared in glucose. The lag-time before the beginning of the growth phase was reduced by 8 h with IMOS as a carbon source (Figure 4B), indicating an improvement in the germination time in the presence of bile. Moreover, growth seemed to continue for a longer period of time with IMOS (Figure 4B), and the levels of CFU/mL were increased by about 1 log in IMOS cultures after 48 h when compared to glucose (Figure 4C). Taken together, these results show that IMOSs can enhance the bile resistance of _B. subtilis_ CU1 vegetative cells and spores, and favor the germination of _B. subtilis_ CU1 spores. However, we have not observed any effects on germination in the absence of bile (data not shown). To understand the mechanisms underlying this effect, we followed the carbon source consumption during the growth in the presence of 0.5% of bile salts. Figure 4D shows that glucose (DP1) is not the first metabolized carbon source in comparison to our previous results without bile salts. DP1 and DP2 were consumed by the bacteria at the same time, and higher levels of DP2 were rapidly produced in the culture medium, which implies \(\alpha\)-glucosidase release. In the presence of bile salts, the strain seemed to inhibit the repression of IMOS utilization, despite the availability of glucose in the culture medium.