esa-sceva/satcom-chunk-collection huggingface dataset:
# Visual-Based Localization Using Pictorial Planar Objects in Indoor Environment
## 6 Performance Study
### Robot Localization Accuracy  
As shown in Figure 13, we have built a moving robot to test the localization accuracy. The performance study was conducted in a 3.5 \(\times\) 3.5 m indoor space with 5 pre-trained pictures hanging on the surrounding walls. The top-left window shows the robot-top camera view in which the yellow boxes are the outputs from the SSD object detection and green boxes are produced by PicPose used for the robot's pose calculation from each pictorial object. The top-right window shows the picture matching results for each pictorial object: the left side of each matching pair is the original picture in the library and the detected picture from the robot view is on the right; each line between two picture objects connects the matched feature points. The bottom-left window shows the third-party view where the robot is facing the left wall. The bottom-right window shows the robot location and viewing direction on the 3D map (ceiling view).  
The robot is programmed to cruise on a specific route and report the localization outputs to be compared with the ground truth locations. Comparisons on some cruising tests are shown in Figure 14. The blue dots on the figures show the ground truth locations and the red dots are the locations reported by the robot using PicPose. Only one picture marker is seen by the camera in Figure 14a and its MAE is 0.063 m. In Figure 14b, the robot moves from top-left to bottom-right and can see several picture markers. As a result, the MAE is improved to 0.054 m. In general, with the increasing distance to pictures, the errors become bigger since feature points are more difficult to identify with smaller pictures on the camera view. In Figure 14c, the robot moves in a circular route. Estimating the robot location when the robot is making turns presents a challenge since the camera view is often distorted by the constantly changing angles. In Figure 14c, the reported locations are not as close to the ground truth with an MAE of 0.073. On certain part of the route, no picture marker can be seen so that no location is reported.  
Figure 13: Robot Localization Study.
# Hybrid Cooperative Cache Based on Temporal Convolutional Networks in Vehicular Edge Network
## 3 System Model
### Problem Formulation
Therefore, the optimization goal \(P1\) of cooperative caching is to minimize the average content delivery delay when the RSU cache capacity is limited. The optimization objectives are as follows:  
\[ \begin{split} P1:\min\sum_{m=1}^{M}\sum_{i=1}^{F}p_{m,i,i}d_{r_{ \theta}r_{m},i}\\ \text{s.t.}\ \sum_{i=1}^{F}x_{m,i}\,\mathbf{s}_{i}\leq u_{m}, \forall r_{n}\in R,\\ x_{m,i}\in\{0,1\},\forall r_{n}\in R,\forall e_{i}\in E,\\ x_{0,i}=1,\forall e_{i}\in E.\end{split}  \tag{5}\]  
The goal is to minimize content delivery latency. The first constraint ensures that the total amount of content cached in the RSU does not exceed the capacity of the RSU \(r_{m}\). The last constraint indicates that the central server stores all the content. The content in this question only has the option of caching, and no content can be partially cached.
# Data Fusion Based on Adaptive Interacting Multiple Model for GPS/INS Integrated Navigation System
## 4 Real Ship Experiment and Its Performance Analysis
### Performance Analysis and Comparison of Proposed Algorithm
The IMM filter provides good performance in comparison with EKF, but inadequate knowledge of measurement noise restricts the accuracy. Figure 7 illustrates the velocity and velocity errors from the combination of GPS and INS sensor measurements by using AIMM. It can be concluded that AIMM model obtain centimeter/second level accuracy in north, east, and down components. Nevertheless, it has a maximum error of 0.27 m/s which is owing to the vessel steering. Figure 5: Comparison of position accuracy among different filters. Figure 6: Position RMS error for EKF, IMM, and AIMM filters. Figure 8 plots the velocity RMS errors for different filters. It can be shown the AIMM filter is able to perform well in east and north directions in comparison with EKF filter. Compared to the EKF filter, the proposed AIMM filter ameliorates the velocity solutions in the north and east components by 55% and 47.4%, respectively; despite a slight drop in the down component. Figure 9 describes a performances comparison about attitude and attitude errors by using AIMM filter. From the comparison results we can see that the precision of roll and pitch are obviously higher than the heading precision. Figure 10 also proves that AIMM has better performance in attitude estimation than EKF. Attitude accuracy of IMM has similar performance compared with AIMM. Figure 11 describes the model probability of the AIMM filter. The AIMM use the model probability to check the filter switching capability. The ability of soft-switching allows the filter to switch autonomously between upper and lower limits of \(Q_{k}\); and it tracks the vessel motional trend. Table 2 summarizes the comparisons of three different methods. For the position RMS error, the IMM filter provides better performances in comparison with EKF and IMM. It can be shown that the AIMM filter is able to perform well in east and north directions in comparison with EKF filter and IMM for the velocity RMS error. In terms of attitude RMS error, both AIMM and IMM have better performance in attitude estimation than EKF. Figure 8: Velocity RMS errors for different filters. Figure 7: Velocity and velocity errors based on AIMM filter. Figure 11: Model probability in the AIMM filter. Figure 10: Attitude RMS errors for different filters. Figure 9: Attitude and attitude errors based on AIMM filter.
# Barrel Shifter Physical Unclonable Function Based Encryption
## 3 Block Encryption Protocol  
Encryption must entangle the physical randomness of BS-PUF with the message. Physical randomness is extracted by measuring the delay of message bits along a shift path. An XOR of the message bits and delay accomplishes entanglement; this allows for commutativity and reversibility.  
A BS-PUF uses an \(n\)-bit key as the shift amount. This allows for a \(2^{n}\)-bit BS-PUF challenge (message) resulting in a \(2^{n}\)-bit BS-PUF response. Alternately, one could view (\(n\)-bit key, \(2^{n}\)-bit message) as a challenge. We take the former \(2^{n}\)-bit challenge view in this paper. For a barrel-shifter, practical values for \(n\) are limited to be in the range 7-10 bits leading to a message block size of 128-1024 bits. This means that a method of entanglement/encryption for plaintexts greater than \(2^{n}\) bits is needed.  
Entanglement could occur by serializing the blocks of plaintext at BS-PUF input and concatenating the generated ciphertexts. However, this approach reveals patterns in the plaintext; the same plaintext will always encrypt to the same ciphertext. This leaks information by allowing an adversary to identify plaintext patterns.  
The technique of cipher block chaining (CBC) is typically applied in block ciphers such as AES [11], Like AES, BS-PUF encrypts a fixed number of plaintext bits. Thus, it can be viewed as a block cipher. A practical barrel shifter or permutation network implementation might consist of 128-1024 bit blocks.  
Figure 2 applies CBC to two blocks of plaintext. Before applying BS-PUF, the plaintext \(p_{i}\) is XOR'ed with the previous ciphertext \(c_{i-1}\). The output of BS-PUF using key \(K\), \(BS\)-\(PUF(p_{i},K)\), is the ciphertext, \(c_{i}\). Thus, encryption of the \(i\)th block is \(c_{i}=BS\)-\(PUF(p_{i}\oplus c_{i-1},K)\). The result is a cipher text \(c_{1}||c_{2}||\ldots||c_{m}\) for \(m\) blocks where \(||\) denotes concatenation.  
\(c_{0}\) is an initialization vector (IV). This IV must be updated with each message; otherwise, the same plaintext will encrypt to the same ciphertext. This would again allow an eavesdropper to identify patterns. Unlike traditional CBC algorithms, IV for BS-PUFs based encryption does not need to be public because ciphertext will be sent back to sender for decryption. It could be generated with any PUF, e.g., SRAM PUFs [12].  
Decryption utilizes BS-PUF's inverse. \(p_{i}\) is recovered by the reverse process. Ciphertext \(c_{i}\) is given to the inverse BS-PUF operation. The \(\oplus\) of the output and \(c_{i-1}\) is then taken. Thus, decryption of the \(i\)th block is \(p_{i}=BS\)-\(PUF^{-1}(c_{i},K)\oplus c_{i-1}\).  
Message encryption requires a secret key. The key determines the bit shift path; it is used as the shift amount. The BS-PUF response depends both on the challenge (plaintext) and the key. The key does not change as frequently as the plaintext does.  
Figure 1: Encryption protocol with message encryption based on invertible and commutative PUFs \(f_{Bob}\) and \(f_{Alice}\).
# Gaze Estimation Based on a Multi-Stream Adaptive Feature Fusion Network
## 5 Conclusions  
In this work, we propose MSMI-Net, a novel multi-stream, multi-input gaze estimation model that effectively extracts both high-dimensional and low-dimensional features from eye images and fused eye-face images through two independent streams. To enhance feature fusion, we introduce the JointCBAM mechanism with adaptive weights, improving the quality of eye and face feature integration and thereby enhancing gaze estimation accuracy.  
Through ablation studies, we validate the effectiveness of the dual-stream design and demonstrate the contribution of each module to improving model performance. Furthermore, we conduct extensive evaluations on EyeDiap, MPIIFaceGaze, and Gaze360, showing that MSMI-Net achieves strong robustness in complex scenes and multi-angle tasks. However, our experiments also reveal that the model struggles with large head pose variations, particularly in the Gaze360 dataset, highlighting a limitation in its ability to capture extreme head pose shifts.  
To address this, future work will focus on enhancing head pose estimation capabilities and increasing dataset diversity, enabling better generalization in extreme gaze scenarios. Additionally, we aim to explore more advanced feature fusion mechanisms and leverage self-supervised learning to further improve model robustness and accuracy in real-world applications.  
Conceptualization, C.L.; Methodology, C.L.; Software, E.T.; Validation, E.T.; Formal analysis, K.Z.; Investigation, E.T., K.Z., N.C., Z.L. and Z.P.; Data curation, N.C.; Writing--original draft, E.T.; Writing--review & editing, C.L.; Supervision, C.L.; Project administration, Z.P. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
The data that support the findings of this study are openly available at [[https://gaze360.csail.mit.edu/](https://gaze360.csail.mit.edu/)]([https://gaze360.csail.mit.edu/](https://gaze360.csail.mit.edu/)) (accessed on 28 December 2023), [[https://www.idiap.ch/en/scientific-research/data/eyediap](https://www.idiap.ch/en/scientific-research/data/eyediap)]([https://www.idiap.ch/en/scientific-research/data/eyediap](https://www.idiap.ch/en/scientific-research/data/eyediap)) (accessed on 27 December 2023), and [[https://www.perceptualui.org/research/datasets/MPIIFaceGaze/](https://www.perceptualui.org/research/datasets/MPIIFaceGaze/)]([https://www.perceptualui.org/research/datasets/MPIIFaceGaze/](https://www.perceptualui.org/research/datasets/MPIIFaceGaze/)) (accessed on 19 January 2024).  
The authors declare no conflict of interest.
# Benchmarking Anomaly Detection Methods for Extacardiac Findings in Cardiac MRI
## 3 Materials and Methods
### Benchmark Methods  
Due to the anisotropic nature of the sequences--characterized by a significantly larger interslice distance compared to in-plane resolution and a small number of slices per sequence--and the limited number of sequence samples overall, only 2D anomaly detection methods were explored. This work builds upon the unsupervised AD benchmark conducted by [PERSON] et al. [18], including image reconstruction (VAE [36], r-VAE [14], f-AnoGAN [38], and H-TAE-S [15]), feature modeling (DFR [31], FAE [19], RD [26], CFLOW-AD [28], and PaDiM [25]), attention-based (expVAE [30] and AMCons [20]), and self-supervised approaches (DAE [16], CutPaste [32], and PII [24]). Additionally, further image reconstruction (AE [23] and OS-DDPM [13]) and feature modeling (ReContrast [29]) methods were included. The OS-DDPM, which stands for One-Step DDPM, is a variant of pDDPM [13] that processes the entire image instead of using a patch-based approach. This approach, resembling AnoDDPM [22], predicts the denoised image in a single inference step. The benchmark was further expanded to incorporate a one-class semi-supervised method (DDAD [23]) and two open-set supervised AD methods (DRA [27] and BGAD [33]). To assess the effectiveness of these AD strategies, all methods were compared against two fully supervised baselines: (1) a binary image classifier, henceforth named SupIC, derived from the DRA method [27] and employing a ResNet-18 backbone with two fully connected layers (of dimensions 256 and 1, respectively); (2) a U-Net binary segmentation network [40], henceforth named SupIS, derived from the DAE method [16] and featuring four sets of convolutional layers, a balanced batch sampler (2/3 normal and 1/3 abnormal samples), a cosine decay learning rate scheduler [41], and a loss function combining cross-entropy and soft dice loss. The selection of these networks allows a fair comparison between AD and fully supervised methodologies.
# Continuous-Service M/M/1 Queuing Systems
## 4 The System and Queue Time Distributions  
This section is focused on obtaining the system time and queue time distributions for our continuous-service M/M/1 queueing systems. The average time a real customer spends in the system, denoted by \(W\); and the average time a real customer spends in the queue, denoted by \(W_{q}\), are then determined. The following results are employed to achieve our goal in the subsequent two subsections. Let random variable \(S\) represent the service time of a (real or imaginary) customer, where \(S\sim EXP(\mu)\) with density function \(s(t)=\mu e^{-\mu t}\), where \(t\geq 0\). Then, \(P(S\leq t)=1-e^{-\mu t}\), where \(t\geq 0\). Let \(T=\sum_{i=1}^{n}S_{i}\), where \(n=1,\ 2,\ \ldots\), and \(S_{i}\sim EXP(\mu)\). Then, \(T\) follows a Gamma distribution with a shape parameter \(n\). Equivalently, \(T\) follows an Erlang distribution with a shape parameter \(n\) having the following probability density function (PDF) and cumulative distribution function (CDF), respectively,  
\[f(t)=\frac{\mu^{n}t^{n-1}e^{-\mu t}}{(n-1)!},\ t\geq 0,\ \mu>0\]  
and [26],  
\[F(t)=1-\sum_{i=0}^{n-1}e^{-\mu t}\frac{(\mu t)^{i}}{i!},\ t\geq 0,\ \mu>0\]
# Preventive Maintenance Decision-Making Optimization Method for Airport Runway Composite Pavements
## 2 Methods
### Long-Term Maintenance Decision-Making Optimization Method
#### 2.3.2 State Variable  
The target of multi-objective decision-making optimization is to allocate as few maintenance funds as possible for preventive maintenance of several or all composite pavement regions while maintaining the overall pavement performance in an acceptable state during the whole maintenance period. This study established two state variables: \(s_{k=t,ni}^{1}=PCI_{k=t,ni}\) and \(s_{k=t,ni}^{2}=c_{k=t,ni}\). \(PCI_{k=t,ni}\) represents the current \(PCI\) of region \(n\) at the end of stage \(t\), which is treated by maintenance technology \(i\) at the beginning of stage \(t\). Furthermore, \(c_{k=t,ni}\) represents the cumulative cost of maintenance technology \(i\), which has been implemented in region \(n\) several times until the end of stage \(t\).
## 4 Global Path Planning Based on Network Analysis Method
### Special Road Processing  
When the road feature is a junction in Figure 6(a), and it is turning down the right path in the crossroad, so two special junction feature points are gained. If the unique number of the right point is a in Figure 6(b), and the unique number of the left point is b in Figure 6(a), we can get a unique index c by processing a and b in Figure 6(b); then, we can search the special route database through the unique index c, so a path with some detailed GPS points is planned in Figure 7. Then, this curve path is tracked by controller according to these detailed GPS points. The radius and curvature of each of the corner points are different, so it is brief and effective to utilize the GPS points to replace the fitting curve.
## 4 Experimental Results
### Verification and Analysis of Measurement Results
#### 4.1.1 Precision Analysis of Measurement Values on the Standard Platform  
The precision of the developed measurement system was evaluated using the one-way ANOVA homogeneity test applied to data collected from the standard platform. The system was configured to continuously collect 100 sets of data. Each data set comprised measurements at three points, with four repeated measurements per point, yielding a total of 1200 data points. For this data set, measurement values to the second and third decimal places were divided into four groups on the basis of the four repeated measurements. The homogeneity of variance in the data set was then tested to assess the level of decimal precision the system could reliably achieve. During measurement, the standard platform was placed on the three-jaw chuck table, with the measurement axis aligned perpendicularly to the platform. The axis was lowered until the probe made contact with the platform to collect data.  
The results of the homogeneity of variance test revealed a significant difference between the variance of values to the second versus third decimal place (_Levene_ = 1779.26, \(p<0.001\)). Specifically, the variance for values to the second decimal place was 0.213, whereas that for values to the third decimal place was 7.081. This indicates a substantial difference in the variability of values beyond the second decimal, confirming that the developed system achieves stable precision up to the second decimal place (Table 2).
# A Simple Neural-Network-Based Decoder for Short Binary Linear Block Codes
## 1 Introduction
Authors in [9] analyzed the problem of the binary cross entropy during the training epochs of the DL-based decoder and introduced the negative bit error rate loss function to improve the decoding performance of the DNN-based decoder. This paper aims to develop a generalized NN-based decoding architecture for short linear block codes. The specific contributions of this paper are summarized as follows:  
1. Multi-class NN-based decoding framework is presented, where only the received signal sequence acts as the input and one fully connected layer is required. 2. The presented NN-based decoder outperforms the existing syndrome-based DNN decoder and denoiser [13; 14] in terms of decoding time and error performance. 3. The decoding performance of the proposed decoder is close to the well-known SOD algorithm [12], which requires plenty of candidate codewords for evaluation and is involved in complicated Gaussian elimination. 4. The presented multi-class NN-based decoder is general and applicable to different short linear block codes with no additional HDD required as compared to the Chase-type algorithm [11]. The remainder of this paper is organized as follows. Section 2 provides the preliminary of traditional soft decision decoders. The existing syndrome-based DNN decoder and the framework combined the HDD and DNN-based denoiser are discussed in Section 3. Section 4 presents the multi-class NN-based decoder. Sections 5 and 6 describe the dataset generation and simulation procedure. We make a conclusion in Section 7.
# Performance Comparison of Machine Learning Disruption Predictors at JET
## 4 Discussion and Conclusions
Nevertheless, the use of appropriate diagnostic signals, of a physics-based feature extraction and of automatically detected \(t_{pre-disr-AUT}\), specific for each disruption, allowed us to train the models on a reduced number of discharges, to enable the detection of destabilization phenomena with a larger warning, and to maintain the performance on more recent discharges (up to the 2020 campaign), with very limited aging of the predictors. Several metrics were adopted in evaluating the predictors' performance, from the confusion matrix to the typical metrics adopted in the machine learning community, such as recall, precision, etc. However, among all the proposed metrics, the accumulated fraction of detected disruptions against the warning time, together with the corresponding false alarms rate provided a clear and synthetic overview of the performances of the models, suggesting the use of these metrics in the evaluation of the predictors. Concerning the portability of data-driven models to different devices, even if in the last 30 years many attempts have been made to implement data-driven disruption predictors for single machines, few papers present cross-predictor approaches to this problem [24; 26; 34; 37; 47; 48; 49]. The main result of these investigations is that, for machines dominated by the same disruptive chains of events, there is a comparable ensemble of physics mechanisms leading to the disruptions that can be described similarly in a unified framework of physics-based indicators [37]. Thus, the knowledge that data-driven algorithms learn on existing devices can be re-used to explain the disruptive behaviour on another device. This is an encouraging result in view of more extended studies to validate the transferability of the data-driven predictors. Alternatively, the use of at least some data or simulations from the new machine will be needed. **Author Contributions:** Conceptualization, E.A., B.C., A.F., F.P. and G.S. ; methodology, E.A., B.C, F.P. and G.S. ; software, E.A., B.C. and F.P. ; validation, A.F. and G.S. ; formal analysis, E.A., B.C., A.F., F.P. and G.S. ; investigation, E.A. ; resources, E.A. and G.S. ; data curation, E.A., C.S. ([PERSON]), P.C. and C.S. ([PERSON]); writing--original draft preparation, A.F. ; writing--review and editing, E.A., B.C., A.F., F.P. and G.S. ; visualization, F.P. ; supervision, B.C. ; project administration, A.F. ; funding acquisition, A.F. All authors have read and agreed to the published version of the manuscript. This work has been carried out within the framework of the EUROfusion Consortium, funded by the European Union via the Euratom Research and Training Programme (Grant Agreement No 101052200--EUROfusion). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Commission. Neither the European Union nor the European Commission can be held responsible for them. **Data Availability Statement:** Not applicable. The authors declare no conflict of interest.