esa-sceva/satcom-chunk-collection huggingface dataset:
# Understanding Unsupervised Deep Learning for Text Line Segmentation
## 3 Data and Evaluation  
Three different datasets are used in the binary document imagery experiments, VML-AHTE, ICDAR2017, and ICFHR2010 datasets; the three datasets pose different challenges. The VML-AHTE [23] datasets contain documents from handwritten Arabic manuscripts with numerous diacritics and text lines close together, in most cases touching each other. The ICDAR2017 dataset [24] is a collection of three medieval manuscripts \(GB55\), \(CSG18\) and \(CSG863\), with a complex layout structure. On the ICDAR2017 dataset, we run our algorithm on pre-segmented main text regions given by the ground truth. The performance on VML-AHTE and ICDAR2017 datasets are evaluated using text line segmentation evaluation metrics, Line Intersection over Union (LIU) and Pixel Intersection over Union (PIU), as in ICDAR2017 competition on layout analysis [24]. ICFHR2010 [25] is a modern handwriting dataset with heterogeneous document resolutions, text line heights, and skews. The performance on the ICFHR2010 dataset is measured using Detection Rate (DR), Recognition Accuracy (RA), and F-measure (FM) metrics [25]. Colour document image experiments are evaluated on the Pinkas dataset [26]. This dataset contains noisy Hebrew handwritten document images with rounded edges. The performance on colour document images is measured using the baseline detection evaluation metrics, Precision (P), Recall (R), and FM [20]. The characteristics are reported in Table 1.
## 6 Discussion
### Development of Explainable Models in Different Application Domains
#### 6.2.3 Forms of Explanation  
The outcome of an explainable model, i.e., the form of an explanation, was the prime concern of _RQ1.2_. Four basic types of explanations were observed, i.e., numeric, rule-based, visual and textual (Figure 12). In addition to that, some of the articles presented mixed explanations, which combined the four types. Generally, visualisations are mostly used, which humans can more easily interpret than other types of explanations. This type of explanation contains charts, trend-lines etc., and conventionally visual explanation is preferable for image processing tasks. Numeric explanations were deliberately adopted in the developed systems targeted by the experts to show the clarification of the decision of a model with respect to different attributes in terms of feature importance. Understanding the numbers associated with different attributes seems slightly more difficult than the visual or textual representation for a general end-user. For providing numeric explanations, ante hoc methods are very few compared to post hoc methods. Rule-based methods are generally produced from the tree-based or ensemble methods, and most of them are ante hoc methods.  
In this type of explanation, the inference mechanisms of the models were presented in the form of a table containing all the rules and tree-like graphs depicting the decision process in short. Finally, the textual explanations are some statements presented in a human-understandable format, which are less common than the other forms of explanations. This type of explanation can be adopted for the interactive systems where general users are involved but it demands higher computational complexity due to NLP tasks. In summary, textual explanations in the form of natural language should be presented for the general users, rule-based explanations and visualisations are found to be appropriate for advanced users, and numeric explanations are mostly appropriate for experts.
# Signal Detection for Ambient Backscatter Communication with OFDM Carriers
###### Abstract  
Ambient backscatter communication (AmBC) is considered as a promising future emerging technology. Several works on AmBC have been proposed thanks to its convenience and low cost property. This paper focuses on finding the optimal energy detector at the receiver side and estimating the corresponding bit error rate for the communication system utilizing the AmBC. Through theoretical and numerical analyses, we present two important results. First, we improve the existing energy detector by calculating the optimal averaging power orders. Second, we take advantage of the early work on orthogonal frequency division multiplexing (OFDM), where the repeating structure of ambient OFDM signals is exploited to cancel out the direct-link interference by using a cyclic prefix, then provide a test statistic in which optimal detection threshold and optimal power order are derived accordingly. The study reveals the inherent limitation of AmBC energy detectors and provides a guidance for achieving optimal power order for a given significance level.  
ambient backscatter communication; energy detector; OFDM; test statistic +
Footnote †: journal: Sensors
# Polarimetry for Photonic Integrated Circuits
## 5 Conclusions  
We have shown a new way of measuring the polarization characteristics of PICs. To the authors' best knowledge, this is the first demonstration of true polarimetry being applied to PICs. We find that an accurate model of the entire fiber setup can be obtained by taking into account residual ellipticity of the involved elements. Doing so, we achieve a measurement accuracy of better than 3\({}^{\circ}\) over the entire Poincare sphere and 100 nm wavelength span, with a typical accuracy around 1\({}^{\circ}\).  
Conceptualization, M.B.; data curation, M.B. and A.S.; formal analysis, M.B. and A.S.; funding acquisition, M.S.; investigation, M.B. and A.S.; methodology, M.B.; project administration, M.B.; resources, F.M.S.; software, M.B.; supervision, F.M.S. and M.S.; validation, M.B. and A.S.; visualization, M.B. and A.S.; writing--original draft, M.B.; writing--review and editing, M.B., A.S., F.M.S., and M.S.  
This research received no external funding.  
The authors wish to thank their colleagues from the HHI photonic networks department for fruitful discussions. Furthermore, thanks goes to the HHI clean room team that helped create the integrated devices.  
The authors declare no conflict of interest.  
Figure 9: EAM measurements. (**Left**) Chirp parameter according to Equation (10). (**Right**) Depolarization. For some wavelength-bias combinations, the exiting signal power was below the polarimeter sensitivity and therefore some data points are missing.
## 2 Materials and Methods
### Block Bootstrapping  
To evaluate the performance of our system, we utilized various assessment methods. One of these methods involves employing the block bootstrapping technique [44] to construct 95% prediction intervals, which are presented in Section 3 afterwards. This method is applied individually to each signal estimation. Initially, we computed the corresponding residuals by computing the differences between our estimate and the true transmitted signal. Then, we generated multiple blocks of length 81 by striding over the residuals vector with a stride of 1, extracting a block at each stride. These blocks were then randomly  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Neural Network** & **Number of Epochs** & **Learning Rate** & \begin{tabular}{c} **Learning Rate** \\ **Scheduler** \\ \end{tabular} & **Factor** & **Patience** & **Optimizer** \\ \hline CFO & 90 & 0.001 & \begin{tabular}{c} ReduceLR \\ OnPlateau \\ ReduceLR \\ OnPlateau \\ \end{tabular} & 0.5 & 10 & \
\begin{tabular}{c} Adam, \(\beta_{1}=0.5\), \(\beta_{2}=0.999\) \\ \end{tabular} \\ Estimation & 35 & 0.001 &
\begin{tabular}{c} ReduceLR \\ OnPlateau \\ \end{tabular} & 0.5 & 5 & Adam, \(\beta_{1}=0.5\), \(\beta_{2}=0.999\) \\ U-Net-Based & & & & & \\ Autoencoder & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Summary of training hyperparameters.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hardware Component** & **Value** \\ \hline CPU & Intel i7-6700 HQ (8 Cores) 2.6 GHz \\ GPU & GeForce GTX 950M \\ RAM & 16 GB \\ \hline \hline \end{tabular}
\end{table}
Table 5: Summary of training and testing hardware.  
sampled with replacement, concatenated, and trimmed to the length of 512. This process was repeated multiple times, resulting in the construction of 3000 residual vectors in our simulation.  
To obtain the 95% prediction interval for the corresponding estimation, we added each of the residual vectors to our estimation one at a time. This process resulted in the formation of a new dataset. Finally, we computed the 2.5 th and 97.5 th percentiles of this dataset, which served as the lower and upper bounds of the prediction interval, respectively.  
The utilization of the block bootstrapping method is preferred over naive resampling approaches due to the interdependence of contiguous samples in our transmitted signal, and consequently the estimated signal, caused by the pulse shaping filter. By attempting to mimic the underlying process that generated the time series, such as the residuals in our case, the block bootstrapping method aims to capture the temporal dependencies.  
The choice of block length is a critical factor in block bootstrapping, as it directly impacts the ability of the method to capture the desired temporal dependencies. It serves as a tunable parameter, and an inappropriate selection of the block length may hinder the effectiveness of block bootstrapping in capturing the intended dependencies. In our case, we chose a block length of 81 to align with the length of our pulse shaping filter, ensuring that relevant dependencies within the signal were properly captured.
# Autonomous and Earth-Independent Orbit Determination for a Lunar Navigation Satellite System
## 3 Proposed Sensors
### Star Tracker
This bias is addressed by employing multiple optical sensors that may usually measure the horizon, distant object triangulation, or craters as a star tracker, where the hardware remains the same but the software loop is changed. In this case, the misalignment factor between the two sensors can be estimated up to the performance of each sensor acting as a star tracker. The performance of this correction is estimated to be 0.01\({}^{\circ}\)[35; 36], and so this will be used as the angle \(\theta_{mis}\) in this work. It is important to note that this estimate would require both sensors to have a star field in view, and so spacecraft pointing would be required. An alternative methodology to reduce the misalignment error is proposed by [PERSON] in the StarNav III solution [37], where three separate star tracker lenses share the same sensor. This is delivered by a complex series of lenses and mirrors to transfer the observed star field photons to a single camera sensor. A similar approach may be used where celestial observation may also be transferred to a single sensor. Even if the error reduction is not negligible, given the complexity of the proposed system, it is not treated in this work.
## 2 Materials and Methods
### Response Surface Parameters  
In order to generate a general response surface for DLST measurements, the most important parameters are used as input parameters. The input parameters can be divided in parameters declaring the size of the defect in the flat bottom hole plate and parameters describing the measurement setup. The output parameter visualised in the response surface is the temperature difference between the temperature measured on the surface of the flat bottom hole plate above the middle of the defect and a reference temperature next to the defect. The sound area is chosen next to the defect in order to be heated at the same time as the defect. The exact location is chosen at one centimetre from the edge of the sample. Placing the reference too close to the edge of the sample will result in biased information as the side of a sample cools down differently than the region in the middle of the sample. As the diameter of the defects is limited in the creation of the response surface, a minimal distance between the sound area and defective area is always minimal 50 mm. The temperature difference is used as the output parameter as thermographic inspections rely on the temperature contrast between defect and sound regions. Despite the temperature difference being the most important output parameter a response surface is also created from the temperature above the defect. This response surface will serve as a way to prevent the sample from reaching its maximum use temperature.
# A Velocity-Aware Handover Trigger in Two-Tier Heterogeneous Networks
## 5 Discussion and Future Directions  
In this article, we inaugrated a general handover decision-making framework which can be bolstered by future system models. An astute decision-making technique and workflow procedure for the heterogeneous wireless network was presented in this paper. An extensive examination of our approach and other conventional plans is shown in Section 4.  
Our approach is general and can be bolstered by future system structures (for example LTE Pro, 5G, cell Internet of Things (IoT), and so-forth). The objectives of such handover decision-making are to diminish HOFs, lessen the packet loss, meet the QoS prerequisite for the velocity of all UE units, and preserve limited and useful signaling flow. Each of these objectives has its difficulties to be addressed in this new network approach.  
A workflow of the suggested MIH-assisted PMIPv6 for the heterogeneous network system is exhibited in Figure 5. The proposed triggering scheme consists of three levels. The initial level incorporates MIH to empower the UE with numerous interfaces to work within the 3 GPP LTE and 5G WiFi interfaces. MIH grants UEs with bolstered mobility in a heterogeneous network, and makes the services outfitted by the MIHF more appropriate in order to guarantee decent inter-layer communication for consistent handover, as required by mobile UE. The second level involves updating the crucial parameter values, velocity, and lifetime for decision-making in the most relevant situations (not too late, and not too early). Ultimately, the arrangement of triggering is restored and for decent reproduction, our approach introduces two triggering conditions: signal degradation or the lifetime, converging towards the handover time execution. If one of the conditions is legitimate, an official announcement of the handover is made. The MIIS, which allows the UE to collect data on the heterogeneous network existing in a geographical area, has been executed in the core network. The objective of arrangement is to have a total perspective of the system by providing full fundamental data on the access network to make strong decisions for mobility management and to perform different activities.  
Soon, we intend to coordinate our examination towards 5G heterogeneous network systems by adjusting and reformulating the way we deal with traffic models for Cellular IoT, as described in 3 GPP release 13 [23]. It is important to examine new arrangements, for example, location-aware handover, and research how to profit from Network Function Virtualization, Software-Defined Networking, and cloud service technologies for mobility management systems of mobile network services. In future work we will examine new ideas associated with the management of mobility, for example resource virtualization and resource abstraction.  
Figure 9: Handover delay according to velocity. The confidence interval is 95%.
# Probabilistic Forecasting of Electricity Demand Incorporating Mobility Data
###### Abstract  
Due to extreme weather conditions and anomalous events such as the COVID-19 pandemic, utilities and grid operators worldwide face unprecedented challenges. These unanticipated changes in trends introduce new uncertainties in conventional short-term electricity demand forecasting (EDF) since its result depends on recent usage as an input variable. In order to quantify the uncertainty of EDF effectively, this paper proposes a comprehensive probabilistic EFD method based on Gaussian process regression (GPR) and kernel density estimation (KDE). GPR is a non-parametric method based on Bayesian theory, which can handle the uncertainties in EDF using limited data. Mobility data is incorporated to manage uncertainty and pattern changes and increase forecasting model scalability. This study first performs a correlation study for feature selection that comprises weather, renewable and non-renewable energy, and mobility data. Then, different kernel functions of GPR are compared, and the optimal function is recommended for real applications. Finally, real data are used to validate the effectiveness of the proposed model and are elaborated with three scenarios. Comparison results with other conventional adopted methods show that the proposed method can achieve high forecasting accuracy with a minimum quantity of data while addressing forecasting uncertainty, thus improving decision-making.  
probabilistic forecasting; Gaussian process regression; kernel density estimation; electricity demand forecasting; uncertainties +
Footnote †: journal: Applied Sciences
# Hierarchical Resource Management for Mega-LEO Satellite Constellation
## 5 Detailed Elaboration of Each Layer  
This section introduces the roles and functions of the RRSC, SRSC, and the USS. It also presents some procedures for realizing these functions. By understanding the functions of each component and the procedures used to achieve them, readers can gain a better understanding of how the overall system operates.
## 3 Image Processing Technology Based on CT
### Image Denoising  
In order to remove the influence of external environmental factors and equipment on electric images, the process of image denoising is essential. In this study, the enhanced images were denoised by means of a mean filtering method using Image J. First, Gaussian noise (mean = 0, standard deviation = 25) was added to the enhanced CT image. Then, the radius of pixels of 2.0 was determined as the optimum setting for the images.
# Dynamic Identification of Tensile Force in Tie-Rods by Interferometric Radar Measurements
## 5 Estimation of Tensile Force Starting from Vibration Data
### Estimation of the Tensile Force
These estimates can be determined based on in situ measurements on the tie-rod and, eventually, by numerical simulations performed by FEM. The final results in terms of the estimated tensile force are reported in Table 8. In particular, for each (known) value of the applied tensile force indicated in the table as \(N_{a}\), the corresponding value \(N_{e}\) estimated starting from interferometric radar measurements by applying the modified Lagomarsino and Calderini approach is indicated. The percentage error between actual and estimated tensile force is also reported: the error is quite low, with a maximum value of 2.42%, which is a very good result for practical applications. It is worth noting that in the case of \(N_{a}=150\) kN, as recalled above, it was not possible to determine the frequency of the third mode starting from interferometric radar data. However, since interferometric radar estimated frequencies are practically the same as those determined from accelerometric data, the minimization of \(G\) was performed by considering as the frequency of the third mode \(\omega_{3}=151.753\) Hz obtained starting from accelerometric measurements. Figure 8: Plots of the objective function \(G\) as a function of the variables \(\gamma\) and \(\delta\) (for fixed \(N=50\) kN). Dynamic data acquired by radar interferometry for applied tensile force 50 kN. Figure 7: Plots of the objective function \(G\) as a function of the variables \(N\) and \(\gamma\) (for fixed \(\delta=1\)). Dynamic data acquired by radar interferometry for applied tensile force 50 kN.
## 3 Discrete Element Numerical Simulation
### Characteristic Analysis of the Monitoring Points
#### 4.3.2 Displacement Monitoring Curve in Z Direction  
As shown in Figure 17, the \(Z\)-direction displacement trends of the A-2-10 monitoring points exhibit a generally consistent pattern. The maximum displacement of the A-2 monitoring point in the middle and upper sections of the excavation face is approximately 3.5 cm, and the displacements recorded at the A-3 and A-4 monitoring points are approximately 0.5 cm. The curve of the A-5-7 monitoring points in the middle of the excavation face demonstrates that the \(Z\)-direction displacement at the intersection of the cataclastic loose rock mass and \(\mathrm{Q^{edl}}\) sediments is the largest, and the disturbance caused by excavation gradually decreases in the deep part of the slope. The curve of the A-8-10 monitoring points at the middle and lower sections of the excavation face increases abruptly, and then the cumulative displacement continues to increase slowly. This finding may be attributed to the proximity of the A-8-10 monitoring points to the slope toe, the low elevation, and the relatively good rock mass quality at the excavation face.
## 4 Experiment Results and Analysis
### Retrieve the Accurate Process of Flood by Selected CNR  
To mitigate the effect of the lower CNR and the high-frequency random noise, only CNRs larger than 40 dB-Hz or elevation angles larger than 45 deg are used to accurately retrieve the whole flood process. The original and fitted CNRs of DOY 199 and DOY 200, 2021 are presented in Figure 16. In Figure 16, the difference in CNRs between DOY 199 (red line and dots) and DOY 200 (blue line and dots) shows that they are almost the same as each other in the early stage, including for G14, G15, and G23. However, this situation changed on the G31 satellite, in which the CNR on DOY 199 is much larger than that on DOY 200 between 15:00 and 16:00. The accurate time can be clearly observed in the enlarged rectangular view in Figure 16, which is 1  
dots), 2021 are demonstrated in Figure 17. The satellites used in Figure 17 are the same as in Figure 16. From Figure 16, it can be observed that the fitted CNR on DOY 201 is significantly lower than that on DOY 200. This phenomenon indicates that there are floods during the whole day of DOY 201. It should be pointed out that, by comparing the CNRs of the G16 and G07 satellites on DOY 201 and DOY 200, the flood on DOY 201 is more serious than that on the afternoon of DOY 200. However, these results cannot be observed in Figure 14 since the fitted result of all CNRs will be influenced by the lower CNR. Thus, this phenomenon indicates that it is necessary to use the larger CNR to accurately retrieve the flood.  
The results of the original CNR on DOY 201 and DOY 202, 2021 are presented in Figure 18. It can be found that the fitted CNR of G14 and G06 on DOY 201 is larger than on DOY 202, but there is an inversion on G15. This phenomenon can be clearly observed in the enlarged rectangular view in Figure 18. Furthermore, this trend continued for the subsequent satellites, such as G23, G31, and G07. These results indicate that the flood reached a crest, compared with DOY 201, and the time was approximately 8:30 am on DOY 202. In addition, by combining Figures 18 and 19, it can be found that the flood disappeared at 10:00 am on DOY 202. Since Figure 12 demonstrates that there is no flood on DOY 203, when the CNR of DOY 202 performs consistently with DOY 203, it means the flood has disappeared.  
Figure 17: Results of consistency analysis on the original and fitted by selected CNRs on DOY 200 (red line and dots) and DOY 201 (blue line and dots), 2021.  
Figure 18: Results of consistency analysis on original and fitted by selected CNRs on DOY 201 and DOY 202, 2021. The subplot denotes the enlarged view of the critical point.  
It should be pointed out that the critical point at which the CNR will be influenced by flooding has still not been determined, thus the time resolution of this method can only persist for about half an hour. In addition, the flood disappearance retrieved by this method only means that the water on the ground will not influence the CNR, but it does not mean that there is no water on the ground.