esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient Lattice-Based Cryptosystems with Key Dependent Message Security
## 2 Preliminaries
### The RLWE Problem  
This simple version of the RLWE problem comes from [22], and the LWE problem can choose the secret from the noise distribution by the transformation \(T\).  
**Definition 1**.: (RLWE). _For security parameter \(\lambda\), let \(f(x)=x^{d}+1\) where \(d=d(\lambda)\) is a power of \(2\). Let \(q=q(\lambda)\ \geq 2\) be an integer. Let \(R=\mathbb{Z}[x]/\left(f(x)\right)\) and let \(R_{q}=R/qR\). Let \(\chi=\chi(\lambda)\) be a distribution over \(R\). The RLWE\({}_{d,d,\chi}\) problem is to distinguish the following two distributions: In the first distribution, one samples \((a_{i},b_{i})\) uniformly from \(R_{q}^{2}\). In the second distribution, one first draws \(s\gets R_{q}\) uniformly and then samples \((a_{i},b_{i})\in R_{q}^{2}\) by sampling \(a_{i}\leftarrow\mathbb{R}_{q}\) uniformly,\(e_{i}\leftarrow\chi\) and setting \(b_{i}=a_{i}\cdot s+e_{i}\), let this distribution be \(A_{s,\chi}\). The RLWE\({}_{d,q,\chi}\)assumption is that the RLWE\({}_{d,q,\chi}\)problem is infeasible.  
Lemma 4: (see [8]). _Let \(q=p^{e}\) be a prime power. There is a deterministic polynomial-time transformation \(T\) that, for arbitrary \(s\in\ \mathbb{Z}_{q}^{n}\) and error distribution \(\chi\), maps \(A_{s,\chi}\)to \(A_{\pi,\chi}\) where \(\overline{x}\leftarrow\chi^{n}\), and maps \(U\left(\mathbb{Z}_{q}^{n}\times\mathbb{Z}_{q}\right)\) to itself. The transformation also produces an invertible square matrix \(\overline{\mathbf{A}}\in\mathbb{Z}_{q}^{n\times n}\) and \(\overline{\mathbf{b}}\ \in\mathbb{Z}_{q}^{n}\) that, when mapping \(A_{s,\chi}\) to \(A_{\pi,\chi}\), satisfy \(\overline{x}=-\overline{\mathbf{A}}^{\overline{\mathbf{T}}}s+\overline{ \mathbf{b}}\)._  
Theorem 1: (see [23]). _Let \(K\) be the \(m\)th cyclotomic number field having dimension \(n=\varphi(m)\) and \(R=\mathcal{O}_{K}\) be its ring of integers. Let \(\alpha<\sqrt{\log n/n}\), and let \(q=q(n)\geq 2\), \(q=1\) mod \(m\) be a \(poly(n)\) -bounded prime such that \(aq\geq\omega\big{(}\sqrt{\log n}\big{)}\). Then there is a polynomial-time quantum reduction from \(\widetilde{O}\big{(}\sqrt{n}/\alpha\big{)}\) -approximate_ SIVP (or SVP) _on ideal lattices in \(K\) to \(R\)-_DLWE\({}_{q,\chi_{\alpha}}\)_. Alternatively, for any \(\ell>1\), we can replace the target problem by the problem of solving \(R\)-_DLWE\({}_{q,D_{\xi}}\)_given only \(\ell\) samples, where \(\xi=\alpha\left(n\ell/\log(n\ell)\right)^{1/4}\)._
# MANET Routing Protocols' Performance Assessment Under Dynamic Network Conditions
## 3 Routing Protocols in MANET
### Reactive Routing Protocols  
Reactive routing protocols create routes only when needed, which reduces the overhead but can increase the initial route discovery time. Table 2 outlines the reactive routing protocols that create routes only when needed, significantly reducing overhead in stable networks. AODV routing establishes routes to destinations on an as-needed basis using route request and route reply messages, which lowers overhead but can lead to higher initial latency due to the route discovery process. DSR uses source routing and maintains route caches for efficient data transmission, which reduces the need for frequent route discoveries but introduces overhead from maintaining route caches and source routing [15]. AOMDV extends AODV to provide multiple redundant paths, enhancing fault tolerance but increasing complexity and overhead. TORA establishes a directed acyclic graph for routing based on link reversal, offering high adaptability and fault tolerance but with significant overhead to maintain the graph. These protocols adapt well to dynamic environments, balancing route discovery latency and overhead.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Protocol** & **Description** & **Advantages** & **Disadvantages** \\ \hline \multirow{3}{*}{Optimized Link State Routing (OLSR) [13]} & Uses link-state information & Low latency due to & High overhead due to \\  & to establish routes and & up-to-date routing & periodic updates. \\  & periodically disseminates & information. & \\  & topology information. & & \\  & Maintains a consistent, & & \\  & up-to-date routing table in & & \\  & each node using sequence & & \\  & numbers to ensure & & \\  & loop-free routes. & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Proactive Routing Protocols.
# Dynamic Path Planning for Unmanned Surface Vehicles with a Modified Neuronal Genetic Algorithm
## 5 Results and Analysis
### Significance Test
Based on the experimental results, the proposed method produces better performance for convergence speed in both static and dynamic environments. This performance increase is significant based on the statistical tests. For the travel distance parameter, the proposed method is superior in static environments, but not in dynamic environments. However, for both environments, the increase and decrease in the performance of the proposed method is not significant based on statistical tests. For the travel time parameter, the proposed method does not produce better performance for static environments, but it manages to show significantly better performance in dynamic environments (also proven via statistical tests). Based on the experimental results, compared to that on under the baseline method, the proposed method can produce a faster convergent solution for USV path planning with competitive performance for total distance and time traveled in both static and dynamic environments. This is because we applied a partially exponential fitness function, as explained in the Section 4.2. This proves that if we apply the fitness function in exponential space, the slightly different output fitness values will be more visible because there is the influence of the power values \(p\), \(q\), and \(r\). In addition to this, an inverse of the time parameter allows the agent to move in a shorter time to obtain a greater fitness value. Thus, this form of modification can be applied in other genetic algorithm research to produce higher convergence speeds.
## 5 Discussion
### Limitations of STPC  
Although the proposed STPC demonstrates a stable three-phase output under various load conditions, it has several inherent limitations that should be addressed in future work:
## 7 Conclusions  
We have proposed a simple traffic model covering both temporal and spatial traffic variations. The model has been realistically parametrized and used to generate sets of traffic matrices. These traffic matrices have been used as input for running the energy-aware approach EWA, which determines the set of LCs to be put into SM during network operation.  
Our results, obtained from a realistic scenario, confirm the strong influence of temporal traffic variation on energy consumed by LCs in the network. In contrast to this aspect, the pace of traffic variation has a limited impact on energy consumption and no impact on devices lifetime according to our model. Furthermore, our evaluation shows that energy savings are possible when traffic varies over space. Moreover, application of EWA leads in general to a reduction of LCs lifetime, as a consequence of the different power state transitions introduced by this energy-aware approach. However, positive profitability is achieved in most of the cases.  
As future work, we plan to vary network topologies as well as network design methods to avoid routing incompatibilities. Furthermore, we are going to consider alternative heuristics for dynamic network operation to directly target QoS, devices lifetime, and profitability when making decisions  
Figure 11: AF after 14 days vs. the HW parameters with \(|T_{low}|=72\), \(|T_{inc}|=36\).  
Figure 12: Profitability \(R_{total}\) after 14 days vs. the HW parameters with \(|T_{low}|=72\), \(|T_{inc}|=36\).  
about which LCs to put into SM and which ones to keep active. Eventually, variation of different sets of parameters in the proposed traffic model is a step towards reality despite the complexity of its influence on results.  
**Acknowledgments:** The research leading to these results has received funding from the Polish National Science Center (decision DEC-2014/12/S/ST7/00415) and the German Federal Ministry of Education and Research within the SASER project (Safe and Secure European Routing). We would like to thank [PERSON], [PERSON], and [PERSON] for their contributions in the initial phase of this work. We would like to thank also the anonymous reviewers for their valuable feedback.  
**Author Contributions:** This work was led by [PERSON]. Preparation of the manuscript was performed by [PERSON]; [PERSON] prepared the MILP model for the SBN design and provided the SBNs; [PERSON] provided the toy example and contributed to the spatial traffic model; [PERSON] contributed to the parametrization and results analysis (particularly regarding the devices lifetime); Overview of related work, input data and EWA simulation results were provided by [PERSON].  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Mathematical Formulation
### Second Stage: Joint Restoration Scheme  
In the event of a natural disaster that affects the grid, there may be line damages in the distribution network and also a failure in the supply from the transmission substation. Microgrids play a vital role in grid restoration during this crisis. The grid restoration scheme considered in the model uses a microgrid formation that incorporates available DGs and fleet vehicles. The objective function of the joint restoration scheme is to minimize the expected cost of load shedding, shown in (4).  
\[\rho(s)=min\ \sum_{s}\sum_{t}\sum_{i}(VoLL_{i}\times s_{i,t,s}^{d})+\sum_{s} \sum_{t}\sum_{j}(l_{j,t,s}\times\tau_{l}) \tag{4}\]  
In the equation, \(\rho(s)\) represents the objective function, which is a function of scenario \(s\), and minimizes the sum of VOLL multiplied by the load shedding and \(I^{2}r\) losses. \(VoLL_{i}\) represents VOLL at node \(i\), and \(s_{i,t,s}^{d}\) represents the load shedding at node \(i\) in time \(t\) of scenario \(s\). The constraints for the joint restoration scheme are network reconfiguration and microgrid formation, DG, time-space network (TSN), and fleet vehicle constraints.
# Image Representation Using Stacked Colour Histogram
## 2 Related Works
### Multi-Texton Histogram (MTH)  
[PERSON] et al. [27] extended concept on the original work of TCM [23]. They put forward an enhanced version called Multi-Texton Histogram (MTH) [27]. The MTH represents heterogeneous images, unlike TCM, which extracts textural information only. MTH leverages the co-occurrence matrix and colour histogram strength as it contains information about the spatial relationship of colours and orientations of texture. In MTH, two adjacent pixels with the same colour should be in the same direction and vice versa. However, such assumptions are not always applicable. This assumption leads to other types of textons being disregarded in the image.
## 1 Introduction
### Positional Accuracy of Forest Inventory Data  
Numerous research groups have analyzed the performance of different GNSS receivers considering various forest types and conditions [(13; 14; 15; 16)]. They concordantly found that the absolute planar accuracy of field survey plot centers strongly depends on the GNSS receiver used, the GNSS signal quality, measurement duration, and whether or not a differential correction has been applied. The signal quality relies on the satellite geometry during recording, open sky conditions, and forest stand density [(15)]. With increasing stand density, the positional accuracy significantly decreases [(14; 15)].  
[PERSON] et al. [(14)] investigated different mapping-grade GPS receivers and found average absolute horizontal GNSS errors under closed canopy ranging from 1.8 to 2.6 m. The average standard deviation of the horizontal errors was 1.5 m. Using differential GNSS, the average errors decreased to 1.2-2.0 m. In comparison, [PERSON] et al. [(16)] observed average errors ranging from 0.90 to 2.14 m, and from 0.75 to 1.0 m after 10 or 20 min measurement time, respectively. Based on these findings and related previous studies on co-registration [(17; 4)], GNSS errors of up to 20 m can be expected.  
In forest inventories (e.g., in the German National Forest Inventory, BWI), individual tree positions are typically determined by measuring their distance to the plot center (e.g., using measuring tape or an ultrasonic distance meter and laser meter), and the corresponding azimuth angle using a compass. Thus, the accuracy of a tree position depends on the experience and care of the field staff, the accuracy of the measuring tools, and the distance of the tree to the plot center. The relative horizontal accuracy-level of tree positions can be assumed to be in a range between 0.3 m and 1.0 m [(3; 4; 12)].  
Tree heights are typically determined using distance and zenith angle measurements that result in similar error sources. [PERSON] et al. [(18)] investigated the precision of conventional field measurements in boreal forests, and observed a standard deviation of height measurements of 2.9%.
## 3 Results
### Synchronism Phenomenon When Sailing in Regular Beam Waves in a Non-Resistant Environment  
Analysing Equation (6) corresponding to the induced motion during the synchronism phenomenon, it is found that the maximum value \(\pm\theta_{\text{MW}}\) is reached when \(\cos\left[\frac{2\pi}{\text{Td}}\cdot\text{t}\right]=\pm 1\), which is achieved with a periodicity of \(\text{t}=\left(\frac{\text{n}+1}{2}\right)\cdot\text{Td}\). Furthermore, if different values are given for t (time) in Equation (6), the ship's rolling angles as a function of the maximum angle of the wave slope are deduced, as shown in Table 1.  
According to the results of Table 1, it can be concluded that for each half-Td (\(1/2\cdot\text{Td}\)), the ship's rolling angle is increased by \(\
icefrac{{1}}{{2}}\cdot\theta_{\text{MW}}\cdot\pi\). This interesting conclusion can be verified in the representation of the free rolling motion in Figure 3, where a usual ship's loading condition, represented by the natural roll period, is simulated (Td = 10 s). Therefore,  
\begin{table}
\begin{tabular}{c c} \hline \hline \(\text{t}\) & \(\text{\theta}\) \\ \hline \(\frac{1}{2}\cdot\text{Td}\) & \(\theta_{\text{MW}}\cdot\frac{\pi}{2}\) \\ \hline Td & \(-\theta_{\text{MW}}\cdot\pi\) \\ \hline \(\frac{3}{2}\cdot\text{Td}\) & \(\theta_{\text{MW}}\cdot\frac{3\pi}{2}\) \\ \hline \(2\cdot\text{Td}\) & \(-\theta_{\text{MW}}\cdot 2\pi\) \\ \hline \(\frac{1}{2}\cdot\text{Td}+\text{n}\cdot\text{Td}\) & \(\theta_{\text{MW}}\cdot\pi\left(\frac{1}{2}+\text{n}\right)\) \\ \hline \(\text{n}\cdot\text{Td}\) & \(-\theta_{\text{MW}}\cdot\text{n}\cdot\pi\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Moments of reaching the ship’s rolling angles as a function of the wave slope.  
as Td is theoretically a constant value, the angular velocity will be greater each time the ship reaches an upright position.
# Dynamic Response Analysis of Soil around Curve Section Tunnel under Train Vibration Load
## 3 Analysis of Pore Water Pressure Response to Vehicle-Induced Vibration Loads
### Characteristics of Soil Pore Water Pressure under Train Operation Conditions  
According to the effective stress principle proposed by [PERSON], it can be inferred that the effective stress and pore water pressure in a saturated soil always should equal the total stress in the saturated soil. The relationship should satisfy the following equation: \(\sigma=\sigma^{\prime}+\mu\), where \(\sigma\) is the normal total stress in a plane, \(\sigma^{\prime}\) is the effective normal stress in a plane, and \(\mu\) is the pore water pressure. Therefore, in order to increase the effective stress in a saturated soil, it is generally beneficial to improve soil drainage, allowing for faster dissipation of pore water pressure induced by vibrations. Based on the previous analysis of the pore water change curve, it can be inferred that the pore water pressure changes gradually with time and gradually dissipates. Assuming that the total stress \(\sigma\) remains constant, the effective stress \(\sigma^{\prime}\) increases, and the volume of the soil gradually decreases, while the strength also increases. The increase in effective stress causes a new stress balance to be established, and the effective stress increases, resulting in the soil skeleton being compressed and ground settlement occurring.
# Defect Detection on Rolling Element Surface Scans Using Neural Image Segmentation
## 5 Classification
### Inference  
During its application, the system runs on a workstation desktop PC with a Nvidia RTX 2070 Super GPU. The camera captures one \(1395\times 3335\) px image per 0.5 s. During this time, the system preprocess, segments and post-processes the previous image. The preprocessing part of the system is added to the system after training. It consists of converting the images from RGB scans to gray images and normalizing them. This is necessary, since the network was trained on gray images, as was discussed in Section 4. The conversion is done with a preset convolutional layer. This layer has a single filter kernel with a \(1\times 1\) size. Its weights correspond to the channel weights as defined in the CIE 1931 linear luminance and shown in Equation (2). Implementing the conversion as a convolution layer allows us to add it to the neural network's computational graph.  
\[\mathrm{Y}\ =0.2126\cdot\mathrm{R}+0.7152\cdot\mathrm{G}\ +0.0722\cdot\mathrm{B} \tag{2}\]  
The normalization of the images deducts their mean and divides every pixel by the standard deviation of the image. This makes the whole system invariant to differences in brightness as they can arise when the camera's aperture is changed or the LED lighting ages.  
Both preprocessing steps, as well as both post-processing steps described in the previous section were added to the computational graph and run on the GPU. This allows these operations to be accelerated though parallelization. Furthermore, this means the entire system from input image to binary classification is encapsulated in a single graph definition. Updates to the classification system only require a single file to be swapped during production. This further reduces maintenance time.  
Since the segmentation network consists solely of size invariant operations, it can process images of any given size. While the network was trained on windows of size \(128\times 128\) px, the inference is done on the whole image of size \(1395\times 3335\) px. The batch size during inference is one, resulting in memory requirements of similar magnitude. Segmenting the whole image at once removes the necessity to slice up the image for inference. This removes artefacts that would occur at the cutting edges. At the edge of the scan itself, reflection padding further reduces artefacts, creating a clean segmentation. Figure 7 summarizes the inference process.
# RD-SLAM: Real-Time Dense SLAM Using Gaussian Splatting
## 6 Conclusions  
This paper proposes a new dense visual SLAM system, RD-SLAM, which utilizes 3D Gaussian as the underlying map representation while using G-ICP for fast tracking. The proposed dual keyframe selection strategy and scale regularization enable RD-SLAM to reconstruct realistic and dense maps dynamically. The experimental results on the Replica dataset show that the system of the proposed method in this paper runs more than 60 times faster than SplaTAM, which is also a 3 DGS-based SLAM. We demonstrate through extensive experiments that our method achieves SOTA performance in terms of localization, reconstruction, and system speed. Overall, RD-SLAM provides a promising  
Figure 7: Dual keyframe selection strategy and scale regularization for ablation experiments. The second row shows zoomed-in details of the colored squares.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Keyframes** & **Regularization** & **PSNR [dB]**\(\uparrow\) & **SSIM**\(\uparrow\) & **LPIPS**\(\downarrow\) \\ \hline \(\times\) & \(\surd\) & 23.74 & 0.829 & 0.195 \\ \(\surd\) & \(\times\) & 29.06 & 0.840 & 0.245 \\ \(\surd\) & \(\surd\) & **34.55** & **0.956** & **0.057** \\ \hline \hline \end{tabular}
\end{table}
Table 8: Quantitative results of ablation study.  
solution for dense real-time visual SLAM by effectively combining 3 DGS, G-ICP, a dual keyframe selection strategy, and scale regularization loss.  
Conceptualization, [PERSON] ([PERSON]), C.G. ([PERSON]) and X.L.; methodology, C.G. ([PERSON]) and X.L.; software, C.G. ([PERSON]) and Y.B.; validation, C.G. ([PERSON]), C.G. ([PERSON]) and Y.B.; formal analysis, X.L.; investigation, [PERSON] ([PERSON]) and Y.B.; resources, C.G. ([PERSON]) and X.L.; data curation, [PERSON] ([PERSON]); writing--original draft preparation, [PERSON] ([PERSON]) and [PERSON] ([PERSON]); writing--review and editing, [PERSON] ([PERSON]); visualization, [PERSON]; supervision, X.L.; project administration, Y.B. and X.L.; funding acquisition, X.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China under grant No. 2022 YB84701100 and by the National Natural Science Foundation of China under grant No. U1913211.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflicts of interest.
## 1 Introduction
### Related Work  
The German day-ahead market is a blind auction. Hour increments of the next day's electrical energy are traded daily. Market participants send two types of orders to the auction: First, for each delivery period, orders reflecting their willingness to buy or sell for all price ticks between the minimum and maximum price of the auction and a given quantity. Second, block orders link several delivery periods. The Power Exchange creates demand and supply curves based on the buy and sell orders. Both for each hour of the following day. The intersection of both results in the market clearing price (MCP), the day-ahead electricity price [9].  
Many approaches have been attempted to predict Germany's hourly day-ahead electricity price. Many publications deal with workflow, feature engineering, pre-processing, training, validation, and forecasting. The literature review of [PERSON] et al. [10] and [PERSON] et al. [11] gives a comprehensive overview of previous work, primarily focusing on feature engineering and models. Besides the German electricity market, other European countries are also investigated [12], considering couplings within the European electricity markets [13; 14]. The class of deep learning models, especially LSTM and CNN or a combination, dominate the ranking for high-performance model candidates for predicting electricity prices using training and validation data before 2020 [15; 16; 17; 18; 19]. To evaluate the German spot market under lockdown conditions of COVID-19 and the Russo-Ukrainian War with training and validation data after 2020, research on the impact of the reduced electricity demand on the spot price in several countries, including Germany is necessary [2; 3; 4; 5; 6; 7]. Also, the impact of the Russo-Ukrainian War on energy markets in general and electricity processes with possible changes of the market mechanisms as a response to increased gas and electricity prices is already investigated [20; 21]. Here we see an apparent deficit for the German spot price market: the models have been tested on outdated data and are often highly individualized [22; 23]. It is interesting to see how the models deal with the changed volatility and price levels. In this study, we re-evaluate proven model candidates on recent market data. We aim to close the gap to see how the models perform under real-world conditions. No research could be found on modeling electricity prices shaped with trends, up/down peaks and spikes, acyclical day-ahead behavior, and offsets within a forecast horizon of 168 h and evaluated model performance.