esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Impact of Segmentation Errors on Temporal and Kinematic Features  
Temporal and kinematic features were less affected by incorrect segmentation. This is a critical finding because these measurements are most commonly used in the literature and have consistently shown correlations to writing or drawing difficulties [1; 5; 20; 22; 23; 25; 27]. Since kinematic and temporal features have been correlated to fine motor control abilities across populations, and have been critical for developing machine learning algorithms for automatically detecting various pathological conditions [2; 8; 12].  
The temporal features explored in this study were moderately but less affected by incorrect brushstroke segmentation as compared to spatial features. The propagated error, outside of the minimum in-air time, ranged from 0.0-49.2%. The temporal features explored in this study are commonly used in the literature and have proven discriminatory between different populations [2; 11; 15; 17; 18; 19]. It must be noted that features that measured aggregated time, specifically total in-air time, total stop time, total on-screen time, and total movement time, generally demonstrated the smallest error, between 0.0-0.9%; these features have been associated with graphomotor pathologies in previous studies [2; 6; 7; 11; 18; 19]. Our analysis supports that total time measures provide reliable insights into writing or drawing capabilities.  
Kinematic features proved to be the least sensitive to misidentified brushstrokes. The small, propagated error could be attributed to the fact that these measures rely on instantaneous values. The only kinematic measure that was moderately affected was average acceleration which had a propagated error of 12.7%. This high error rate was due to trials 18 and 22 (validation dataset) with average acceleration errors of 25.9% and 32.2%, respectively, while the other trials (5, 13, and 14) had errors between 0.2-3.0%. The non-robustness in the average explains the sensitivity of these measures to a single outlying observation. The robustness of kinematic features to segmentation errors makes them ideal for assessing fine motor control in different populations.  
Several biomechanical features (e.g., minimum brushstroke length, maximum brushstroke width, minimum on-screen time) exhibited large standard deviations in their average propagated errors. This observation can be traced to trial 22 (validation dataset), where the algorithm erroneously identified one brushstroke as two. However, it is important to note that this trial, where the participant traced the entire image with one brushstroke, was the exception. Of the 52 observed clinical trials, only one participant traced in this manner. Thus, features with large variance in propagated error may still be useful.
## 2 Introduction of the Mechanical Ventilation System of Double Lungs
### Dimensionless Pressure Equation  
\[\frac{dp_{r}^{*}}{dt^{*}}=\frac{T_{0}}{p_{ipap}}\frac{R\theta q_{r}}{V_{r}+C_ {r}p_{r}}=\frac{1}{p_{ipap}}\frac{p_{a}V_{rt}}{R\theta q_{max}}\frac{R\theta q _{r}}{V_{r}+C_{r}p_{r}}=\frac{p_{a}^{*}q_{r}^{*}}{V_{r}^{*}+C_{r}^{*}p_{r}^{*}}, \tag{25}\]  
\[\frac{dp_{l}^{*}}{dt^{*}}=\frac{T_{0}}{p_{ipap}}\frac{R\theta q_{l}}{V_{l}+C_{ l}p_{l}}=\frac{1}{p_{ipap}}\frac{p_{a}V_{lt}}{R\theta q_{max}}\frac{R\theta q _{l}}{V_{l}+C_{l}p_{l}}=\frac{p_{a}^{*}q_{r}^{*}}{V_{r}^{*}+C_{l}^{*}p_{l}^{*}}, \tag{26}\]  
Please note that \(p_{a}^{*}\) is dimensionless atmospheric pressure.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variable** & \multicolumn{2}{c}{**Reference Value**} & \multicolumn{2}{c}{**Dimensionless Variable**} \\ \hline Air mass & \(m_{max}\) & Maximal air mass (\(m_{max}\)) & \(m_{r}^{*}=\frac{m_{r}}{m_{max}^{*}}\) \\  & & of the whole system & \(m_{l}^{*}=\frac{m_{r}}{m_{max}}\) \\ \hline Pressure & \(p_{ipap}\) & IPAP & \(p_{r}^{*}=\frac{p_{r}}{p_{ipap}}\) \\  & & & \(p_{l}^{*}=\frac{p_{l}}{p_{ipap}}\) \\ \hline Air mass flow & \(q_{max}\) & Maximum air mass flow & \(q_{r}^{*}=\frac{q_{r}}{q_{max}}\) \\  & & through the throdtle & \(q_{l}^{*}=\frac{q_{r}}{q_{max}}\) \\ \hline Time & \(T_{0}=\frac{m_{max}}{q_{max}}=\frac{p_{r}V_{r}}{R\theta q_{max}}\) & Time to totally exhaust & \\  & & \(m_{max}\) of air at \(q_{max}\) of air & \(t^{*}=\frac{t}{t_{0}}\) \\  & & mass flow & \\ \hline Volume & \(V_{t}\) & Tidal volume & \(V_{r}^{*}=\frac{V_{r}}{V_{r}}\) \\  & & & \(V_{l}^{*}=\frac{V_{r}}{V_{r}}\) \\ \hline Compliance & \(C_{0}=\frac{V_{r}}{p_{ipap}}\) & Ration of \(V_{t}\) and \(p_{ipap}\) & \(C_{r}^{*}=\frac{C_{r}}{C_{0}}\) \\  & & & \(C_{l}^{*}=\frac{C_{l}}{C_{0}}\) \\ \hline Air resistance & \(R_{0}=\frac{R_{r}R_{r}}{K+R_{l}}\) & Total air resistance of the system & \(R_{r}^{*}=\frac{R_{r}}{R_{0}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The reference values and the dimensionless variables.
## 3 Global Positioning System (GPS) L1 Receiver: 2D Pattern vs. 3D Pattern
### Availability and Geometric Dilution of Precision (GDOP)
The continuity can be expressed in terms of percentage of continuous time intervals with the duration equal to or longer than a time interval T. This is shown in Figure 13 for both assumed patterns, for the full trajectory and the defined portions.
## 4 Simulation Results
### Performance Analysis for Cloudy Days  
On cloudy days, solar power data has greater fluctuation and volatility. Thus, the usage of the WT will remove the noisy data as well as increase the forecasting accuracy of the ANFIS model. This idea is in accordance with the comparison results shown by WT-ANFIS and ANFIS models in Table 6. It is obvious that the values of the MAE, RMSE, LCE and WIA obtained from the WT-ANFIS are better than the ANFIS model and these results have signified the goodness of the WT to be integrated with the ANFIS model during solar power forecasting.  
Additionally, great performance is achieved via the utilization of HFPSO to optimize the premise parameters of the WT-ANFIS. By following the results shown in Table 6, the MAE of 25.62 W and the RMSE of 32.06 W shown by the WT-ANFIS-HFPSO are smaller than the MAE and RMSE values obtained from the WT-ANFIS-FF and WT-ANFIS-PSO. Apart from that, the WT-ANFIS-HFPSO produces the highest enhancement of the LCE and WIA values. These results have further indicated the suitability of the proposed WT-ANFIS-HFPSO for solar power forecasting on cloudy days.  
The average power curve of the WT-ANFIS-HFPSO is shown in Figure 9. As can be seen in Figure 9, there is a slight variation between the forecasted and actual solar power data. The variations are mainly attributed to the higher fluctuations of solar power on cloudy days that make the forecasting process to become unstable. However, the result of the WT-ANFIS-HFPSO on cloudy days is considered satisfying as the forecasted solar power data manages to capture the trends of the actual solar power data.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Forecasting Model** & **MAE (W)** & **RMSE (W)** & **LCE** & **WIA** \\ \hline WT-ANFIS-HFPSO & 25.6249 & 32.0590 & 0.98089 & 0.99531 \\ WT-ANFIS-FF & 34.6194 & 43.3176 & 0.96490 & 0.99121 \\ WT-ANFIS-PSO & 26.0965 & 32.5488 & 0.98021 & 0.99511 \\ WT-ANFIS & 32.6104 & 41.0914 & 0.96757 & 0.99091 \\ ANFIS & 38.9978 & 51.7635 & 0.95663 & 0.98795 \\ \hline \end{tabular}
\end{table}
Table 6: Performance comparison of forecasting models on cloudy days.  
Figure 8: Actual versus forecasted curve of average solar power for WT-ANFIS-HFPSO on partially cloudy days.
# A Review: Image Processing Techniques' Roles towards Energy-Efficient and Secure IoT
## 2 Existing Reviews
### Research Questions  
This article aims to answer the following two questions:  
RQ1. Can image processing techniques help design energy-efficient and secure IoT systems?  
RQ2. What are the most suitable image processing techniques applied to help design such an energy-efficient and secure IoT system?  
This review article provides a discussion of the literature followed by an analysis related to these questions. Besides, a suggested map has been highlighted, mentioning a number of proposed solutions (i.e., image processing techniques) that can be suitable to help design an energy-efficient and secure IoT system.
# Anticipating Future Behavior of an Industrial Press Using LSTM Networks
## 6 Discussion  
Anticipating industrial equipment's future behavior is a goal that has been long sought after, for it allows predictive maintenance to perform the right actions at the right time. Therefore, the application of time series and other artificial intelligence models to forecast the equipment's state is a new and growing area of interest.  
The present research uses a dataset of approximately 2.5 years of data of an industrial paper press. A procedure to clean the data is proposed and different experiments are described to use a deep neural model based on LSTM recurrent networks.  
The method proposed is going to be applied in other industrial presses, aiming to improve predictive maintenance. Based on the state of the art and experiments, this architecture presents a good versatility, depending of course on the quality of data and hyperparameter settings.  
The results show that it is possible to optimize neural models to forecast future values 30 days in advance. The model experimented uses as input a vector consisting of concatenation of a number of samples of all variables. The output is a vector with the predictions of all samples too. The performance of the models is generally better for some variables and worse for others. Those differences will be dealt with in future work.  
An important conclusion is that the downsampling used might have been too aggressive. Experiments were performed using one sample per day and two samples per day. The models trained with two samples per day showed a better performance. Hence, more resolution is better for reducing errors and may allow for better learning. That is achieved at the cost of additional processing power. This is also another research question which will be dealt with in future work.
## 6 Discussion
### Challenges of AI in IAM Systems
#### 6.2.2 False Positives/Negatives  
AI-driven IAM systems, while highly advanced, are not immune to errors such as false positives and false negatives [85]. A false positive occurs when the system incorrectly identifies an authorized user or device as a threat, potentially causing unnecessary disruptions and access denials. Conversely, a false negative happens when the system fails to recognize an actual security threat, allowing unauthorized access. These errors can have significant implications, including reduced operational efficiency, compromised security, and decreased user trust. Addressing these challenges involves continuous system training, incorporating diverse data sets, and refining algorithms to improve accuracy and reliability in identity verification and access control.
## 4 Training and Detection Using Deep Learning
### Comparison with Field Investigation  
In Figure 11, the automatic detection results by using deep learning are compared with the results of the field investigation [4]. As a result of deep learning, it shows that the number and proportion of roof damage are almost consistent with the field investigation.  
In addition, in the field investigation [4], the statistical results of the direction of the damage to the roof are shown in Figure 12a. the south side of the roof suffered the most. The method adopted in field investigation is that the investigators stand in one direction for visual inspection. For example, if a roof is found to have been damaged in four directions, it will be counted as damaged in the south, east, west and north. For further discussion, this paper refines the calculation method of damaged direction from the results of deep learning. The center of gravity of the damaged roof is divided into 16 directions, and the central distribution of the damaged area is counted. The results are shown in Figure 12b.  
Figure 11: Comparison with field investigation in the proportion of roof damage.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Detected Object** & **Accuracy** & **Precision** & **Recall** & **Specificity** & **F Value** \\ \hline Root outline & 0.973 & 0.881 & 0.890 & 0.984 & 0.886 \\ \hline Blue taps & 0.999 & 0.900 & 0.918 & 0.999 & 0.909 \\ \hline Roofs completely destroyed & 0.999 & 0.907 & 0.780 & 0.999 & 0.839 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Evaluation results of each detection target.  
The results were slightly different from the results of the field investigation, and it is found that the southeast suffered the most.  
Finally, the trained deep model was used to detect the roof damage in the south of Chiba prefecture (Figure 3). The performance of the PC was the same as that of previous training. Consequently, it was confirmed that it can be completed within 10 min without improving the hardware. Within the range of aerial photos in this study, the deep training model automatically detected 48,315 roofs. Among them, 4838 roofs were damaged, and the rate was approximately 10%.  
Figure 13 is the fitting curve of the damaged roof detected through the deep learning model. The horizontal axis in the figure is the proportion of the damaged area in the entire roof area, whereas, the black curve is the fitted probability density plot, showing a lognormal distribution. The statistical results show that the roof with a damage ratio of 1-10% accounts for 44.3%. Further, the roof with a damage ratio of 10-30% accounts for 35.4%. Furthermore, the roof with an 80-100% damage ratio is 3.6%. Thus, it was found that most roofs were partially damaged, and the proportion of roofs completely destroyed was very small.  
Figure 12: Comparison with field investigation in the location of roof damage. (**a**) the result of the location of the roof damage in the field investigation. (**b**) the result of the location of the roof damage using deep learning.  
Figure 13: Statistics of detected roof damage.
## 2 State of the Art
### Category Theory
#### 2.3.2 Some Interesting Concepts  
We have defined the basic concepts of category theory; we will now focus on some properties of interest to this work.  
In the same way as functions can be injective, surjective, or both (bijective), there are similar concepts for functors between categories.  
**Definition 4**.: _Full and Faithful functors Let \(F:C\longrightarrow D\) be a functor. We say that \(F\) is Full if for any \(x,y\in Ob(C)\), \(F_{x,y}:Hom_{C}(x,y)\longrightarrow Hom_{D}(F(x),F(y))\) is a surjection. We say that \(F\) is Faithful if for any \(x,y\in Ob(C)\), \(F_{x,y}:Hom_{C}(x,y)\longrightarrow Hom_{D}(F(x),F(y))\) is an injection._  
We can also define the concept of equivalence between two categories.  
Figure 2: Illustration of a natural transformation.  
**Definition 5**.: _Equivalence of categories_  
_Let \(C\) and \(D\) be categories._  
_An equivalence of categories is a functor \(F:C\longrightarrow D\) such that there exists:_  
* _A functor_ \(G:D\longrightarrow C\)_;_
* _Natural transformations_ \(\alpha:Id_{C}\longrightarrow G\circ F\) _and_ \(\alpha^{\prime}:Id_{D}\longrightarrow F\circ G\)_._  
The notion of pullback allows defining an object that is the biggest object that respects a constraint towards two objects. Prerequisites for this notion are cones and limits, so we shall define them first.  
**Definition 6**.: _Cone_  
_Let \(C\) be a category. Let there be a diagram over \(C\). A cone over this diagram is an object \(Z\) with morphisms from \(Z\) to every object of the diagram, such that any newly formed triangle commutes._  
A limit is a universal cone, i.e., a cone such that, for any other cone, a unique path exists from the limit to the other objects in the diagram through the cone.  
**Definition 7**.: _Limit_  
_Let \(C\) be a category. Consider a diagram over \(C\). A limit on this diagram is a cone \(Q\) such that for any other cone \(Z\) we have a unique morphism \(\psi:Z\longrightarrow Q\) such that all triangles including \(\psi\) commute._  
Cones and limits are illustrated in Figure 3. The diagram considered is constituted of the A, B, C, and D objects and morphisms between them. \(Z\) and Q, assuming that compositions of morphisms of the diagram with the dotted arrows commute, are cones. Furthermore, Q is a limit of the diagram if for any cone \(Z\), there is a unique morphism \(\Psi:Q\longrightarrow Z\) that makes everything commute. We say that a limit is a universal cone because whichever cone we choose, there is a unique way to obtain this cone from the limit, and this way is the \(\psi\) morphism.  
**Definition 8**.: _Pullback_  
_Let \(A\), \(B\), and \(C\) be three objects of a category. Let \(f\) and \(g\) be two morphisms from \(A\) and \(B\) to \(C\). A pullback in a category is the limit \(Ax_{C}B\) of the diagram from Figure 4._  
Figure 3: Illustration of the cone and limit.  
The pullback is an object with morphisms towards two objects that respects constraints with a third object. This can be interpreted as the biggest object that respects the constraint.
# Optical Medieval Music Recognition--A Complete Pipeline for Historic Chants
## 8 Discussion
### Syllable Detection  
In this section, similar to the extended evaluation of text recognition, some aspects of syllable assignment are examined more closely. To do this, various experimental setups were prepared that addressed different questions about syllable assignment: (1) How does poor OCR affect syllable assignment compared to when fine-tuned models for text recognition are used? (2) How well does syllable assignment work if symbols are corrected beforehand (GT)? And (3) how good is syllable recognition if both lyrics (text) and symbols are corrected in advance? Additionally, these experiments were carried out turning the database with the chant texts on and off to provide a more comprehensive analysis. Similar to before, actions needed to correct errors in the syllable assignment normalized by the total number of syllables (M1) were counted as a performance metric. There are primarily two sources of error here: the syllable was assigned to the wrong note or the syllable is indeed assigned to a correct note, but the text is incorrect (due to an erroneous OCR/syllable split). Additionally, a second count was made in which errors found at the text level were ignored to check if the correct notes were selected for syllable assignment (M2).  
The results of these experiments can be seen in Table 8. If the algorithm is applied to the results of the previous steps without correcting them beforehand, then between 11.2% and 12.8% of syllables need correction when chant-based lyric post-processing is enabled. Several causes can be found here: either the syllable text is incorrect, the syllable was assigned to the wrong note (because the note is missing, for example), or the word was divided into incorrect syllables. If, on the other hand, one assumes that errors are corrected after each step in the pipeline, i.e., possible errors are corrected in advance (e.g., missing notes), then only 2.3% of all syllables need correction. When errors at the text level are ignored, only 0.1% of all syllables still need to be corrected.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Post-Processing** & **Experiment** & **Line CER** & **WER** & **Chant CER** \\ \hline \multirow{2}{*}{No} & Mixed & 24.1\% & 60.3\% & 23.3\% \\  & Fine-tuned & 14.9\% & 43.9\% & 14.2\% \\ \hline \multirow{2}{*}{Yes} & Mixed & 4.7\% & 12.1\% & 3.4\% \\  & Fine-tuned & 3.8\% & 9.3\% & 2.6\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Experiments of the text detection step. Using the post-processing pipeline, the Line CER drops from 24.1 to 4.7 and from 14.9 to 3.8 on the mixed and fine-tuned model, respectively.
## 3 Enhanced Mathematical Model with Atmospheric and Environmental Effects
### Combined Received Power for 5G-Satellite Links  
The combined received power integrating the FSPL, two-ray, and Rician fading models with atmospheric and rain effects is  
\[P_{r}=\left|\left(\frac{P_{t}\lambda}{4\pi r_{1,t}}\right)^{2}e^{-(A_{\text{ atm}}+\gamma_{r})\cdot\tau_{1,t}}+\Gamma\bigg{(}\frac{P_{t}\lambda}{4\pi r_{2,t}} \bigg{)}^{2}e^{-(A_{\text{atm}}+\gamma_{r})\cdot\tau_{2,t}}\right|^{2}+P_{ \text{scattered}} \tag{15}\]  
where \(P_{\text{scattered}}\) represents non-LOS scattering, calculated by integrating the Rician model.
# Fast 3D Semantic Mapping in Road Scenes +
## 5 Experiments and Results  
We demonstrate the performance of our approach on the KITTI dataset [12], which contains a variety of urban scene sequences involving lots of moving objects in various lighting conditions. It consists of various datasets, such as the semantic dataset, the odometry dataset, and the detection dataset. Thus, it is very challenging for the 3D reconstruction. The KITTI dataset contains a 2D semantic segmentation data of 200 labeled training images and 200 test images ([[http://www.cvlibs.net/datasets/kitti/eval_semseg.php?benchmark=semantics2015](http://www.cvlibs.net/datasets/kitti/eval_semseg.php?benchmark=semantics2015)]([http://www.cvlibs.net/datasets/kitti/eval_semseg.php?benchmark=semantics2015](http://www.cvlibs.net/datasets/kitti/eval_semseg.php?benchmark=semantics2015))). Its data format and metrics conform with the Cityscapes dataset [11]. The Cityscapes dataset involves 19 classes within high quality pixel-level annotations of 5000 images with a resolution of \(2048\times 1024\), including 2975 training images, 500 validation images, and 1525 testing images. In our experiment, we train the model on the Cityscapes and then tune it on the KITTI taking the volume size of dataset into account.  
For the training of 2D semantic segmentation model, various encoder models in the DeepLab-v3+ are evaluate including _ResNet_[41], _Xception_[42], and _MobileNet-v2_[26]. We find that the \"poly\" stochastic gradient descent is better than the \"step\" one on these datasets. The _TensorFlow_ library is employed to do the training and inference on the workstation with 4 Nvidia Titan X GPU cards. The hyper-parameters used in training are set corresponding to the datasets and models as shown in Table 2.  
We benchmark the performance of our semantic mapping system on the KITTI odometry dataset ([[http://www.cvlibs.net/datasets/kitti/eval_odometry.php](http://www.cvlibs.net/datasets/kitti/eval_odometry.php)]([http://www.cvlibs.net/datasets/kitti/eval_odometry.php](http://www.cvlibs.net/datasets/kitti/eval_odometry.php))). There are 22 sequences with the consecutive RGB frames, in which there are 11 sequences with the ground-truth poses for evaluation. These scenes involves serious illumination change, moving objects like persons and vehicles, and some turns as shown in Figure 4. These road-scene frames involves two resolutions \(1242\times 375\) and \(1226\times 370\). Our system runs on an Intel Core i7-5960K CPU and a NVIDIA Titan X GPU for online process.  
Since the KITTI sequences are mostly captured in 10 Hz, it is highly below the normal speed requirements of LSD-SLAM about 60 Hz. In addition, the LSD-SLAM is hard to handle severe turning when the platform moves. Due to the limit of the monocular LSD-SLAM, we choose certain sequences to evaluate.  
In the following sections, we show some qualitative results for our approach in Section 5.1 and the quantitative results of our evaluation are presented in Section 5.2, in which we also make the runtime analysis on our semantic mapping approach.