esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Effect of Design Variables on the Objective Function
### Effect of the Initial Height between the Mobile and Fixed Platform on the Objective Function  
The effect of the initial height between the mobile and fixed platform on the objective function is studied in ADAMS software. The variation curve of the objective function with the initial height \(H\) between the mobile and fixed platform is shown in Figure 10.  
As can be seen from Figure 10, the objective functions \(f_{1}(x)\) and \(f_{2}(x)\) are negatively correlated with the design variables, and the objective function \(f_{3}(x)\) is positively correlated with the design variables. As the initial height increases, the performance of the objective functions \(f_{1}(x)\) and \(f_{3}(x)\) is gradually enhanced, and the performance of the objective function \(f_{2}(x)\) is gradually reduced.
# Image Copy-Move Forgery Detection Based on Fused Features and Density Clustering
## 2 Related Works
This method utilizes scale-invariant feature transform (SIFT) [22; 23] to extract feature points and applies the random sample consensus (RANSAC) algorithm [24] to remove mismatched pairs through affine transformations. However, the time complexity of this algorithm is relatively high, and it cannot detect multiple types of tampering operations. Subsequently, researchers conducted studies on various tampering operations. However, the location accuracy of this algorithm on pixel-level datasets is still slightly insufficient. Currently, feature-point-based methods are not effective at detecting tampering in small or smooth areas. To address this issue, [PERSON] et al. [25] proposed a detection method based on k-means clustering. This method divides the image into smooth and complex regions and uses SIFT and sector mask features to detect tampering in each region. However, the accuracy of this algorithm needs to be improved. When compared to methods based on blocks, methods based on feature points have numerous advantages [26]. For instance, the most representative key points in the target region can be retrieved using the feature point method, enabling more precise detection and positioning of the target region. Feature point extraction methods typically have a certain robustness and can withstand post-processing operations such as rotation and scaling [27]. Compared to block-matching methods, the time required for feature point extraction and matching is usually shorter, resulting in lower computational complexity. However, there are still some issues with feature point-based methods. For example, matching time increases accordingly when the extracted key points are dense, and tampering that occurs in smooth areas is difficult to detect accurately [28].
## 5 Simulation
### Simulation Experiment with Different Target Points  
The simulations are operated with the same initial and final states and different no-fly zones. The simulated results of different target points are analyzed in this section. The target points and simulation results are shown in Table 2. The setting of the no-fly zones is the same as that in Case 3 in Section 5.3. The simulation effect is shown in Figure 9. It can be seen from Figure 9 that for different target points, this method can realize lateral and longitudinal guidance with a high accuracy. When effective avoidance is achieved, reasonable control angles are adopted. Among them, a trajectory with a long range and requiring the avoidance of no-fly zones has more iterations, relatively poor accuracy and more frequent changes in control angles.
# Addressing the Non-Stationarity and Complexity of Time Series Data for Long-Term Forecasts
## 2 Methodology
### Problem Formulation  
LSTF can be categorized into two main problem settings: multivariate TSF and univariate TSF. In the multivariate setting, we are given \(N\) different time series \(X^{1:N}\) = [\(X^{1}\), \(X^{2}\), \(X^{3}\),......., \(X^{N}\)], which are interdependent. If the current time is denoted as \(t\), the values of the \(N\) time series at time \(t\) are represented as \(X^{1:N}_{t}\) = [\(X^{1}_{t}\), \(X^{2}_{t}\), \(X^{3}_{t}\),......., \(X^{N}_{t}\)]. Given a look-back window of length \(T\) aiming to forecast future data points up to a horizon length of \(T^{\prime}\), the input time series can be expressed as \(X^{1:N}_{t-T+1:t}\) = [\(X_{t-T+1}\), \(X_{t-T+2}\), \(X_{t-T+3}\),..., \(X_{t}\)]\({}^{1:N}\), and the forecasts and actual values can be expressed as \(X^{1:N}_{t+1:t+T^{\prime}}\) = [\(X^{1}_{t+1}\), \(X^{2}_{t+2}\), \(X^{\prime}_{t+3}\),..., \(X^{\prime}_{t+T^{\prime}}\)]\({}^{1:N}\) and \(X^{N}_{t+1:t+T^{\prime}}\) = [\(X_{t+1}\), \(X_{t+2}\), \(X_{t+3}\),..., \(X_{t+T^{\prime}}\)]\({}^{1:N}\), respectively.  
The univariate setting is a case where the number of dependent time series, \(N\), is 1. In this case, the input time series can be denoted as \(X_{t-T+1:t}\) = [\(X_{t-T+1}\), \(X_{t-T+2}\), \(X_{t-T+3}\),..., \(X_{t}\)] and the forecasts and actual future values can be represented as \(X^{\prime}_{t+1:t+T^{\prime}}\) = [\(X^{\prime}_{t+1}\), \(X^{\prime}_{t+2}\), \(X^{\prime}_{t+3}\),..., \(X^{\prime}_{t+T^{\prime}}\)] and \(X_{t+1:t+T^{\prime}}\) = [\(X_{t+1}\), \(X_{t+2}\), \(X_{t+3}\),..., \(X_{t+T^{\prime}}\)], respectively.
# LIDAR Point Cloud Registration for Sensing and Reconstruction of Unstructured Terrain
## 4 Experimental Results and Analysis
### Validity Validation of our Improved ICP Algorithm  
To verify the performance of our improved ICP algorithm, the point clouds of two consecutive frames of unstructured terrain shown in Figure 3b are used for registration experiments. Figure 4 shows the registration process. More specifically, Figure 4a is the point cloud after initial registration and filtering. Since the two point clouds still have a certain dislocation, the local features in the point cloud are not obvious. Figure 4b shows the key point extraction result of Figure 4a. It can be seen that the key points are concentrated in the area where the neighborhood undulates greatly. This clearly shows that our key point extraction method not only greatly reduces the number of points but also fully preserves the local features of unstructured terrain point cloud.  
Figure 4c shows the links between the key points. Due to the existence of feature-similar points, there is a case where one point is linked to multiple points. However, as all the linked lines are parallel to each other, this proves that the overall accuracy of the linked point search is high. Figure 4d shows the results of the registration. Compared with Figure 4a, the gully feature is obvious, and the bulges and pits are aligned, which verifies the good performance of our ICP algorithm.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Equipment** & **Specifications** \\ \hline Laser scanner (vlp-16) & Measurement range: up to 100 m, Accuracy: \(\pm 3\) cm (typical), \\  & Angular resolution(horizontal): 0.1–0.4\({}^{\circ}\), 100 \(\times\) 65 mm, 0.83 kg \\ IMU (Xsens MTi-700) & Latency: \(<\)2 ms, Gyroscopes: 450\({}^{\circ}\)/s, Accelerometers: \\  & 200 m/s\({}^{2}\), 57 \(\times\) 42 \(\times\) 23 mm, 0.055 kg \\ Encoder (E6B2-CWZ6C) & Incremental, Resolution: 1000 P/R, Maximum speed: \\  & 6000 r/min, \(\phi\)40 mm, 0.085 kg \\ \hline \hline \end{tabular}
\end{table}
Table 1: Information of Main Sensors.  
Figure 3: System hardware and main experiment object. (**a**) System hardware; (**b**) Main experiment object. IMU: Inertial Measurement Unit.
# Holographic Mixed Reality System for Air Traffic Control and Management
## 4 Proposed Mixed Reality System
### Detail View  
A detailed 3D scenery file of the airfield was included in the detail view. The scenery file held the 3D information of each object on the airfield including the airport's buildings, taxiways, runways, airport apron and major surroundings. The air traffic controller could place the 3D model of the airfield on the real world's plane surface and view the information from different angles using a head-mounted mixed reality display. Instead of depending on the outside views from the control tower, the controller could monitor the airfield using a 4D holographic view of the airfield through the head-mounted mixed reality display. The controller could place the holographic view of the airfield on any horizontal surface inside the control tower. This view presented every single ongoing activity of the airplanes in the airport. It included 4D information about the following types of air traffic:  
* Preparing to take off.
* Traffic on hold in the air and preparing to land.
* Traffic on the taxiways after landing or before takeoff.
* Traffic in the airport's apron.  
Moreover, the necessary information related to an air flight was displayed in a 3D text box attached to the respective airplane. The information consisted of the flight name, speed, altitude, transponder etc. The detail view gave a close interpretation of all the activities of the airplanes going on the ground and around the airport's close vicinity in the air. Figure 2(a),b present a detailed holographic view of the airfield.
# Short-Term Wind Speed Prediction Based on Principal Component Analysis and LSTM
###### Abstract  
An accurate prediction of wind speed is crucial for the economic and resilient operation of power systems with a high penetration level of wind power. Meteorological information such as temperature, humidity, air pressure, and wind level has a significant influence on wind speed, which makes it difficult to predict wind speed accurately. This paper proposes a wind speed prediction method through an effective combination of principal component analysis (PCA) and long short-term memory (LSTM) network. Firstly, PCA is employed to reduce the dimensions of the original multidimensional meteorological data which affect the wind speed. Further, differential evolution (DE) algorithm is presented to optimize the learning rate, number of hidden layer nodes, and batch size of the LSTM network. Finally, the reduced feature data from PCA and the wind speed data are merged together as an input to the LSTM network for wind speed prediction. In order to show the merits of the proposed method, several prevailing prediction methods, such as Gaussian process regression (GPR), support vector regression (SVR), recurrent neural network (RNN), and other forecasting techniques, are introduced for comparative purposes. Numerical results show that the proposed method performs best in prediction accuracy.  
wind speed prediction; PCA; LSTM +
Footnote †: journal: Applied Sciences
## 6 Simulation Setup  
In order to assess the performance of the proposed technique, Matlab simulations have been conducted to compare the error performance as well as bandwidth efficiency of conventional SIM OFDM and DS-PIM-SIM OFDM.  
Figure 7 shows the simulation setup used to obtain SER vs CNR curve.  
At the transmitter end, two-bit streams are generated. One is modulated using a DS-PIM modulator, while the other is modulated through a QAM modulator. IFFT is then performed on the QAM-modulated bit stream. The resulting QAM symbols are assigned specific indices within the subgroup, corresponding to the indices of 1's in the DPIM modulated symbol. The signal is then passed through an AWGN noise source.  
At the receiver, DPIM symbol detection is achieved by comparing the magnitude of the received signal with a threshold level. DPIM demodulation is then performed to extract the original stream (B1). For the second bit stream (B2), FFT is performed on the symbol at the detected indices and the resulting stream is later QAM-demodulated.  
Table 5 contains some of the simulation parameters for the experiment.
# Enhancing Cache Robustness in Information-Centric Networks: Per-Face Popularity Approaches
## 2 Related Work
### Approaches to Secure against Cache Pollution Attacks  
One of the best-known examples in the literature in terms of attempting to fight cache pollution is CacheShield, developed by [PERSON] et al. [12]. This approach records the interest frequency of items that are not cached. As the recorded frequency t increases, the probability that a content object will be cached increases as well due to the shielding function. However, a major weakness of this approach is that an attacker need only determine the threshold of t values and request content objects at a frequency beyond the threshold of t. Then, their own content objects will be cached.  
A cache pollution detection approach using randomness checks was proposed by [PERSON] et al. [19]. The randomness check approach utilizes the fact that attackers launching a locality disruption attack request contents in a nearly uniform manner across the content universe. The authors reason that it is less likely that attackers will be able to know what contents are popular and that this popularity changes with time. Therefore, they consider false locality attacks far less likely than locality disruption attacks, which do not require detailed knowledge of the popularity of contents. The approach performs matrix operations, during which popular contents are more likely to be removed from the matrices due to the Zipf-like distributions, whereas attacker requests will remain. This causes the system to rank content requests as malicious when they remain with a given rank value.  
As an attempt to improve upon CacheShield [16], the authors of [17] assume a slightly more intelligent attack scenario. They enhance their threat model such that the attackers only focus on a small subset of the content space, namely the items at the tail end of the Zipf distribution. This approach uses a machine-learning-based approach. However, it still suffers from weaknesses, including the assumption that all normal behavior is Zipf-like and attackers are always uniform in their distribution of requests.  
A neural network (with fuzzy systems) approach was proposed by [PERSON] et al. in [18]. This approach uses techniques that are used in linguistics to determine the degree to which different inputs and outputs are related. Although this approach is reasonably effective at detecting cache pollution attacks (both false locality and locality disruption), the overhead,both in terms of memory and computational complexity, is significant. For these reasons, this approach is not considered to be very scalable [14].
## 4 Model Optimization Strategies and Result Analysis
### Result Analysis
#### 4.2.3 Impact of the Attention Mechanism on the Model  
To investigate the impact of the attention mechanism on the model training results, under the conditions where the transfer learning method and the learning rate remain consistent, several control experiments were designed, namely Experiments 1, 3, 5, and 7 and Experiments 2, 4, 6, and 8. The comparison results of Experiments 1 and 2 and Experiments 5 and 6 are shown in Figure 10. From the figure, it can be seen that the accuracy of Experiment 1 and Experiment 2 are 75.87% and 79.87%, respectively, with F1 scores of 0.7581 and 0.7894, respectively. The accuracy of Experiment 5 and Experiment 6 are 90.27% and 91.20%, respectively, with F1 scores of 0.9015 and 0.9087, respectively. The introduction of the attention mechanism has improved the model's accuracy and F1 values. The following can be concluded: 1 Introducing the attention mechanism module can weight the importance of winter wheat to enhance the model's focus on its features and improve the model's ability to perceive the importance of different features. In deep learning models, the quality and importance of feature representations have a significant impact on the performance of the task. By introducing the attention mechanism, the model can dynamically adjust the weights and contributions of the features, allowing the model to focus more on key information and enhance the expression of the features. In contrast, models that do not incorporate an attention mechanism may fail to accurately distinguish the importance of different features, leading to suboptimal performance. 2 Introducing the attention mechanism can enhance the model's perception of the importance of different time steps or spatial positions. In deep learning models, for sequential problems or tasks with spatiotemporal structures, the information from different time steps or spatial positions has varying importance. By using the attention mechanism, the model can dynamically adjust the importance weights of different time steps or spatial positions, better capturing the features of sequences or spatiotemporal patterns. Compared to models that do not use attention mechanisms, models that incorporate attention can better leverage temporal or spatial information, enhancing the performance of deep learning models.  
Figure 10: _Cont._
# An Ordered Universal Accumulator Based on a Hash Chain
## 3 Hash Chain Accumulator Scheme
### Scheme Properties  
#### 3.2.1 Self-Certification  
Neither the RSA-based nor bilinear-based accumulator requires storing the entire model. Instead, they only need to store the final accumulated value and generate the corresponding membership or non-membership witness for \(x\in M\) based on the current accumulator value and auxiliary information. The difference between the scheme proposed in this paper and others is the addition of a stored model \(m\), allowing participants other than the accumulator administrator to verify the correctness of the evidence for the generated elements of the accumulator model. To verify the integrity and correctness of the entire hash chain, the participants only need to store the key nodes (\(x_{i\to d}\)) in the witness instead of the whole model.  
**Proposition 1**.: _Let \(H\) be a collision-resistant hash function. The hash chain accumulator HC with the security parameter k is deterministic. For a set X = {x1,..,xn}\(\subseteq M\), the accumulator value \(\mathfrak{Acc}\) is uniquely determined as follows: \(\mathfrak{Acc}\)=H(\(H\)(\(\cdots\)\(H\)(\(H\(\mathfrak{Acc}_{0}\|x_{1}\))\(\|x_{2}\))\(\cdots\)\(\|x_{n}\))), where \(Acc_{0}\) is the initial accumulator value for the empty set._  
**Proof.** Assume \(H\) is collision-resistant. The accumulator is constructed iteratively. \(\mathfrak{Acc}_{0}\) is initialized. For each \(xi\)\(\in\)\(X\), \(\mathfrak{Acc}_{i}=H(\mathfrak{Acc}_{i-1}\|x_{i})\) is updated. Suppose an adversary A outputs a forged accumulator \(\mathfrak{Acc}\)* for a distinct set \(X^{\prime}\
eq X\). Since \(\mathfrak{Acc}^{*}\) must equal \(H\)(\(\cdots\)\(H\)(\(\mathfrak{Acc}_{0}\|x_{1}\))\(\cdots\)\(\|x_{m^{\prime}}\)) for \(X^{\prime}=\{x_{1}^{\prime}\ldots,x_{m^{\prime}}\}\), there must exist a step \(j\) where \(H\)(\(\mathfrak{Acc}_{j-1}\|x_{j}\)) = \(H\)(\(\mathfrak{Acc}^{*}\)\({}_{j-1}\|x_{j}^{\prime}\)), but (\(\mathfrak{Acc}_{j-1}\),\(x_{j}\)) \(\
eq\) (\(\mathfrak{Acc}*_{j-1}\),\(x_{j}^{\prime}\)). This directly violates the collision resistance of \(H\). Thus, no probabilistic polynomial-time adversary can fore a valid accumulator for \(X^{\prime}\
eq X\), and HC is deterministic. \(\square\)
# An Efficient Document Retrieval for Korean Open-Domain Question Answering Based on ColBERT
## 5 Experimental Setup and Results
### Ablation Study  
In our experiment, we tokenized the queries and the entire document set using the Kobert tokenizer, then extracted 1000 documents per query via the BM25 algorithm to evaluate the re-ranking performance of ColBERT. However, unlike English, Korean is an agglutinative language, where grammatical functions are determined by appending various suffixes or particles to words or phrases. Due to this characteristic, it can be more effective to process words by first separating them into morphemes, the smallest units bearing meaning, rather than using the words as they are. Hence, we utilized the Korean morpheme analyzer Mecab [43] to tokenize the queries and the entire document set at the morpheme level, then re-applied the BM25 to extract 1000 documents per query, conducting an additional experiment.  
Table 6 compares the performance of re-ranking when using documents extracted by the BM25 after tokenizing with the Kobert tokenizer and when using documents extracted by the BM25 after tokenizing at the morpheme level. The experimental results demonstrate that re-ranking using documents extracted by the BM25 after tokenizing at the morpheme level yielded a higher performance across all metrics. This highlights the significance of morphological analysis when dealing with agglutinative languages like Korean.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **MRR@10** & **MRR@100** & **Recall@50** & **Recall@200** & **Latency (ms)** \\ \hline \multicolumn{6}{c}{**Re-ranking 1000 docs extracted by BM25 (KoBERT tokenization)**} \\ \hline ColBERT & \multirow{2}{*}{62.55} & \multirow{2}{*}{62.98} & \multirow{2}{*}{86.29} & \multirow{2}{*}{89.99} & \multirow{2}{*}{1578} \\ w/KoBERT & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of re-ranking performance for different tokenization methods on top 1000 documents extracted by BM25.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **MRR@10** & **MRR@100** & **Recall@50** & **Recall@200** & **Latency (ms)** \\ \hline ColBERT & \multirow{2}{*}{67.51} & \multirow{2}{*}{67.85} & \multirow{2}{*}{**87.29**} & \multirow{2}{*}{**90.66**} & \multirow{2}{*}{1618} \\ w/KoSBERT- & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of re-ranking performance by KoSBERT for different tasks.
# A Design of a Lightweight In-Vehicle Edge Gateway for the Self-Diagnosis of an Autonomous Vehicle
###### Abstract  
This paper proposes a Lightweight In-Vehicle Edge Gateway (LI-VEG) for the self-diagnosis of an autonomous vehicle, which supports a rapid and accurate communication between in-vehicle sensors and a self-diagnosis module and between in-vehicle protocols. A paper on the self-diagnosis module has been published previously, thus this paper only covers the LI-VEG, not the self-diagnosis. The LI-VEG consists of an In-Vehicle Sending and Receiving Layer (InV-SRL), an InV-Management Layer (InV-ML) and an InV-Data Translator Layer (InV-DTL). First, the InV-SRL receives the messages from FlexRay, Control Area Network (CAN), Media Oriented Systems Transport (MOST), and Ethernet and transfers the received messages to the InV-ML. Second, the InV-ML manages the message transmission and reception of FlexRay, CAN, MOST, and Ethernet and an Address Mapping Table. Third, the InV-DTL decomposes the message of FlexRay, CAN, MOST, and Ethernet and recomposes the decomposed messages to the frame suitable for a destination protocol. The performance analysis of the LI-VEG shows that the transmission delay time about message translation and transmission is reduced by an average of 10.83% and the transmission delay time caused by traffic overhead is improved by an average of 0.95%. Therefore, the LI-VEG has higher compatibility and is more cost effective because it applies a software gateway to the OBD, compared to a hardware gateway. In addition, it can reduce the transmission error and overhead caused by message decomposition because of a lightweight message header.  
Address Mapping Table; In-Vehicle Edge Gateway; self-diagnosis Article