esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
However, in spite of their good CDI performance, carbon-based materials often suffer from some shortcomings, such as co-ion expulsion effect, poor wettability, oxidation of the anode, and limited salt adsorption capacity, which heavily impact on the desalination capacity and stability of electrodes, as well as complexity and high cost of the fabrication process [26]. Therefore, the development of new highly performing advanced materials is crucial for the commercial application of the CDI technology. Recently, high-entropy oxides (HEOs), a new type of oxides that combine five or more metals in an equiatomic or nearly equiatomic ratio, have attracted great interest due to their unique structure and their novel, and often unexpected, functional properties. They are based on the concept of high configurational entropy (\(\geq\)1.5 \(R\), where \(R\) is the universal gas constant) that stabilizes their structure into a single-crystal phase. HEOs exhibit extraordinary stability and electrochemical behavior which distinguish them from ordinary low-entropy metal oxides [27; 28]. HEOs have been extensively evaluated as materials for environmental and energy applications, particularly in the fields of catalysis and energy conversion and storage [29; 30]. This work reports the preparation of HEO and carbon/HEO composites and their characterization, focusing, for the first time, on the evaluation of their specific capacitance to explore the possibility of using them as CDI electrode materials since, generally, materials possessing superior capacitive performance exhibit better electro-sorption properties [18; 22]. Spinel-structured CrMnFeCoNi-HEO NPs and NFs are investigated to clarify the effect of the HEO size and morphology. To study the influence of the oxide phase and the HEO-hosting C matrix, C/CrMnFeCoNi-HEO and C/MgCoNiCuZn-HEO composite NFs are also investigated. The electrode materials are prepared following two synthesis pathways (sol-gel or electrospinning, followed by heating treatment), which have already been proven to allow the formation of single-phase HEOs at temperatures that limit both the effects of sintering on the size of the HEO NPs and the environmental impact of the production process [29; 31] with respect to the solid-state reaction most frequently utilised to prepare the HEOs [32; 33; 34; 35].
## 9 Conclusions  
We designed, implemented, and validated the CHP2 system to simultaneously enable communications, synchronization, and ranging from a single radio platform. In this manuscript, we described (a) the architecture of this proposed system; (b) the estimation techniques that enable it; (c) our choice of consumer-grade hardware on which to implement it; (d) a comprehensive summary of the hardware processing architecture; and (e) some simple experimental validation results. Using this initial COTS hardware prototype, we demonstrated a ranging precision below 10 cm in over-the-air flight tests and as low as 3 mm in a controlled laboratory environment. Because these ranges are generated using a time-of-flight estimation algorithm, the ranging subtask intrinsically enables time synchronization. In this early iteration, the communications protocol operates at a fixed data rate, which has already been extended to adaptive-rate protocols in parallel investigations. Using this initial hardware implementation and concept validation, we are already exploring the following developments: (a) formulating and solving a manifold optimization problem to trade performance between the ranging, synchronization, and communications subtasks; (b) formulating more sophisticated time-of-flight estimation algorithms that generate higher ranging and synchronization precision using a variety of optimal one-shot estimators and adaptive Kalman filtering techniques; (c) implementing adaptive-rate protocols to enable better, on-the-fly flexibility in the communications data rate without modifying the firmware; (d) enabling modern distributed MIMO applications such as distributed beamforming and IoT using networks of multi-antenna CHP2 platforms; and (e) implementing traditional and reinforcement learning resource management techniques to enable and manage network applications using distributed CHP2 platforms. Conceptualization, D.W.B. ; Data curation, H.Y. and A.H.; Formal analysis, H.Y. and A.H.; Funding acquisition, D.W.B. ; Investigation, H.Y., A.H., S.W., S.S., Y.L., H.L., C.C. and D.W.B. ; Methodology, H.Y., A.H., S.W., S.S., Y.L., H.L., C.C. and D.W.B. ; Project administration, D.W.B. ; Resources, N.S., L.S. and K.R. ; Software, H.Y., A.H., S.W., S.S., Y.L. and H.L. ; Supervision, C.C. and D.W.B. ; Validation, H.Y. and A. H.; Visualization, H.Y. and A.H.; Writing--original draft, H.Y. and A.H.; Writing--review and editing, H.Y., A.H., S.W., S.S., Y.L., N.S., L.S., K.R., H.L., C.C. and D.W.B. All authors have read and agreed to the published version of the manuscript. **Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the policies of Arizona State University regarding pending patents and publications. **Conflicts of Interest:** The authors declare no conflict of interest.
# End-to-End Online Video Stitching and Stabilization Method Based on Unsupervised Deep Learning
## 3 Experimental Results and Analysis
### Quantitative Results and Analysis
Upon inclusion of StabStitch++ in the comparative evaluation, it is observed that despite enhancements such as adaptive motion confidence weighting and refined smoothing filters, StabStitch++ continues to depend on absolute trajectory estimations during its stabilization phase. As a result, residual drift persists in extended video sequences. Measured PSNR and SSIM values for StabStitch++ in Low-Light and Fast-Moving scenarios remain inferior to those achieved by our framework. Moreover, the iterative post hoc adjustment of stitching parameters in StabStitch++ cannot rectify alignment errors introduced earlier in the processing pipeline, whereas the end-to-end joint training mechanism of the proposed method inherently corrects misalignments at each stage of the network. In terms of video stability assessment, the proposed method was evaluated based on the stability of target frames. The results indicate that the proposed framework surpasses StabStitch in stability rate, owing to the utilization of historical smoothed trajectories as input. Furthermore, relative coordinate computation facilitates smoother transitions between newly generated output frames and previously stabilized frames, thereby markedly reducing cumulative errors. With respect to distortion rate, the proposed method exhibits clear advantages in most scenes, with only a marginal difference observed in the Low-Texture scenario. In summary, the proposed method not only preserves the authenticity of the video content but also attains superior stability and alignment accuracy through end-to-end joint training, rendering it a competitive solution for real-time video enhancement. When comparing video stability metrics with StabStitch++, it becomes evident that the incremental improvements introduced by StabStitch++'s smoothing algorithm do not surmount the inherent limitation of decoupling stitching and stabilization. Despite StabStitch++'s enhanced motion consistency checks, residual wobble remains evident in prolonged sequences, particularly under rapid viewpoint changes. In contrast, the relative coordinate-based Markov smoothing strategy employed by the proposed framework inherently maintains temporal coherence over arbitrary sequence lengths. Consequently, although StabStitch++ demonstrates slight gains over StabStitch in stability rate, its performance remains significantly inferior to that of the proposed framework.
## 2 Literature Review
Explainability is a critical aspect that ensures that clinicians can trust and interpret the predictions of artificial intelligence (AI) models. Various studies have proposed innovative explainability techniques to make AI-based brain MRI segmentation and classification models more interpretable and reliable. [PERSON] et al. [22] explored explainability in diagnosing Alzheimer's disease using MRI and PET image fusion. The study employed a heuristic early feature fusion framework combined with a modified ResNet18 model to extract descriptive features from multimodal data. An explainable artificial intelligence (XAI) model was implemented to interpret the predictions, making the results transparent and enhancing confidence in predictions. [PERSON] et al. [23] proposed a disease and spatial attention module (DaSAM)-based model for brain tumor detection. The disease attention module (DAM) identifies disease and non-disease regions, while the spatial attention module (SAM) highlights critical image features. By emphasizing key regions and features, these modules enhance model interpretability and provide clinicians with feature maps that explain the decision-making process. [PERSON] et al. [24] proposed a framework integrating DeepLabV3+ and custom neural networks for brain tumor segmentation and classification. The explainability component utilized local interpretable model-agnostic explanations (LIME) to interpret the model's outputs. LIME generates human-readable visual explanations by identifying features that significantly contribute to the predictions, offering insights into how the model localized and classified brain tumor regions. However, these methods face several limitations. Attention mechanisms, while helpful in focusing on critical regions, often lack transparency in their own workings and may highlight regions without clear medical relevance, leading to potential misinterpretation. Post hoc techniques like LIME and SHAP provide local explanations but are computationally intensive and can be inconsistent when applied to complex models, raising concerns about reliability. Multimodal fusion approaches struggle with the heterogeneity of data sources like MRI and PET, leading to challenges in generating cohesive and interpretable outputs. These challenges underscore the need for more transparent, domain-specific explainability frameworks tailored to the nuances of medical imaging.
# Interconnecting Haptic Interfaces with High Update Rates through the Internet +
## 4 Simulation Scenarioof Existing Protocols
### Static Network Bandwidth, Delay and Internet Traffic  
The square nodes in Figure 1 are routers that are connected with each other through the Internet.  
The connection speed between those routers is set to 1, 5, 10, 15, and 20 Mbps for each simulation and is stable for the whole simulation period. The Internet bandwidth of 1 Mbps has been chosen so that a fully congested network can be represented. The Internet bandwidth of 20 Mbps corresponds to  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Connected Cities** & **Avg. Delay (ms)** &
\begin{tabular}{c} **Standard Delay** \\ **Deviation (ms)** \\ \end{tabular} & **Packet Loss (\%)** & **No. Hops** \\ \hline Grevena—thessalonikh & 19.12 & 1.70 & 0 & 5 \\ through grnet [50] & 19.12 & 1.70 & 0 & 5 \\ \hline Grevena—thessalonikh & 53.19 & 5.31 & 0.11 & 8 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Internet status for communication between cities.  
a network with no congestion. The Internet bandwidth of 5 Mbps corresponds to a network with low congestion. The connection between the server nodes 6 and 7 is regarded to be the Internet bottleneck of the simulation.  
The end to end delay in the connection between nodes 6 and 7 was set to 5, 10, 20, 30, 40, 50, and 60 ms for each simulation and was stable for the whole simulation period. The 5 ms delay is a very small delay that rarely occurs in Internet connections. On the other hand, 50 ms is regarded to be the upper tolerable limit of delay, based on Table 1, that's why the 60 ms is set as the maximum delay of the simulations. Of course, the end to end delay is changing dynamically in the real world connections. The authors deliberately kept the end to end delay constant throughout each simulation so as to study the behavior of each protocol at the specific delays. This would help the researchers decide which protocols are preferable when the characteristics of the network delay are known. Most of the diagrams depicted in this paper are for an Internet delay of 40 ms which is an acceptable delay, based on Table 1, and commonly encountered in the network. The simulations tests undertaken were 5 (scales for the Internet speed) \(\times\) 7 (scales for the delay) = for a total of 35 tests.  
The connection speed between the nodes 0-5 and server 6, as well as nodes 8-13 and server 7, is 100 Mbps as they are considered to be in the same local area network. The delay in those connections was 1 ms.  
The simulation time for each simulation was 20 s. At time 0.5 s the FTP application which was attached at node 0 started to send data. At time 2 s all the other Constant Bit Rate (CBR) applications which were attached to nodes 1-5 will start to send packets with a rate of 1000 packets per second. The packet size varied from node to node depending on the header of the transport protocol.
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Variables Set**} & \multirow{2}{*}{
\begin{tabular}{c} **Response** \\ **Variable** \\ \end{tabular} } & \multirow{2}{*}{**Predictor Variables**} & \multirow{2}{*}{**B**} & \multirow{2}{*}{**SE B**} & \multirow{2}{*}{\(\beta\)} & \multirow{2}{*}{\(t\)} & \multirow{2}{*}{\(p\)-Value} & \multirow{2}{*}{**R2** adj.} & \multirow{2}{*}{**F**} \\ \cline{3-3} \cline{5-10}  & & (constant) & & & & & & & \\ \hline \multirow{3}{*}{Phenotypic traits} & \multirow{3}{*}{P 2018} & (constant) & 35.25 & 6.55 & & 5.38 & 0.001 & \multirow{3}{*}{0.422} & \multirow{3}{*}{0.995} \\  & & HC & 0.11 & 0.05 & 0.32 & 2.34 & 0.024 & & \\  & & CWSI & \(-\)80.10 & 28.56 & \(-\)0.38 & \(-\)2.80 & 0.008 & & \\ \hline \multirow{3}{*}{Thermal and humidity indices} & \multirow{3}{*}{P 2018} & (constant) & 44.11 & 3.32 & & 13.26 & 0.001 & \multirow{3}{*}{0.465} & \multirow{3}{*}{19.652} \\  & & T\({}_{\text{IQR}}\) difference & \(-\)4.30 & 1.46 & \(-\)0.33 & \(-\)2.95 & 0.005 & & \\  & & H\({}_{\text{IQR}}\) difference & \(-\)23.33 & 4.97 & \(-\)0.53 & \(-\)4.69 & 0.001 & & \\ \hline \multirow{3}{*}{Combined model} & \multirow{3}{*}{P 2018} & (constant) & 30.87 & 4.96 & & 6.22 & 0.001 & \multirow{3}{*}{0.586} & \multirow{3}{*}{13.164} \\  & & T\({}_{\text{IQR}}\) difference & \(-\)28.76 & 8.86 & \(-\)0.66 & \(-\)3.24 & 0.002 & & \\ \cline{1-1}  & & T\({}_{\text{IQR}}\) canopy & \(-\)9.22 & 3.54 & \(-\)0.36 & \(-\)2.60 & 0.013 & & \\ \cline{1-1}  & & T\({}_{\text{IQR}}\) Side A & 36.97 & 9.78 & 1.06 & 3.77 & 0.001 & & \\ \cline{1-1}  & & T\({}_{\text{IQR}}\) Side A & \(-\)6.43 & 1.66 & \(-\)0.75 & \(-\)3.86 & 0.001 & & \\ \cline{1-1}  & & HC & 0.086 & 0.04 & 0.23 & 2.06 & 0.046 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Final models derived from the multiple linear regression analyses for predicting 2018 productivity.
# A Node Placement Algorithm Utilizing Mobile Nodes in WSN and IoT Networks +
## 4 The Node Placement Algorithm
### Direct Node Placement Algorithm
#### 4.2.2 Creation of a Path Consisting of Mobile Nodes  
In order to reach the sink node, it is needed to create a disjoint path. Each new mobile node placement is calculated from the algorithm so that it is in the range of the previously placed mobile node. The calculation of this specific point is performed as follows: The intersection points of the virtual circles created by the transmitting range of the initially placed mobile node and the virtual straight line between this node to the sink is calculated. The point that is closer to the sink is the one kept. This is illustrated in Figure 2c. The process continues until the mobile node is in the sink.
# Radiation Hardened Read-Stability and Speed Enhanced SRAM for Space Applications
## 2 Proposed RHRSE-20T SRAM
### Double Node Upset Recovery Analysis  
To prevent DNU, the layout is configured to separate node S1 from the other five nodes. Additionally, since S2 is an insensitive node, scenarios where S2 is struck by radiation are not considered. In Figure 5, the recovery process of all possible cases of DNU is presented.
## 3 DoS Attacks on the AC/DC Multi-Area LFC System with Virtual Inertia
### Stability of the Test LFC System under DoS Attacks  
In the following, we show how the DoS attacks can affect the stability of the closed-loop LFC system in the hybrid AC/DC grid with emulated inertia. We have modeled the LFC under DoS attacks as a switched linear system in the proceeding. The stability issue of a switched system has been extensively investigated; one look in [21; 40] for a detailed analysis. From the viewpoint of an attacker, the whole system may be made unstable by choosing a proper switching strategy.  
To study the stability of the test system in (26), let us consider a scenario where the loads keep constant, namely, \(\mathbf{d}[k]=0\) for all \(k\in\mathbb{N}\). The following Lemma 1 indicates that there exist possible switching strategies that the attacker can exploit to launch DoS attacks to make the underlying two-area LFC system unstable.  
**Lemma 1**.: _We introduce a constant \(0\leq\lambda\leq 1\). Then, the switched linear system of (26), where \(\Phi_{i}\in\{\Phi_{1},\,\Phi_{2}\}\), is unstable, if there exists \(\lambda\) such that the equivalent system with system matrix \(\Phi_{1}^{\lambda}\Phi_{2}^{(1-\lambda)}\) has an eigenvalue with magnitude outside the unity circle._  
Proof of Lemma 1.: Let us introduce a time interval \([T_{0},\,T_{d})\) and \(n_{T}=T_{d}-T_{0}\). Similar to the work in [16] (Theorem 2), we can assume that the test system operates normally from \(T_{0}\), i.e., the switched linear system of (26) with \(\Phi_{i}\in\{\Phi_{1},\,\Phi_{2}\}\) stays at \(\Phi_{1}\) for a time period of \(\lambda n_{T}\). Then, afterwards,the test system is attacked by DoS and (26) stays at \(\Phi_{2}\) for a time period of \((1-\lambda)n_{T}\). In the end, the state of the test LFC system at \(T_{d}\) would become  
\[\mathbf{z}[T_{d}]=\Phi_{1}^{\lambda n_{T}}\Phi_{2}^{(1-\lambda)n_{T}}\mathbf{z}[T_{0}]\,. \tag{28}\]  
Let us define \(\Phi(\lambda):=\Phi_{1}^{\lambda}\Phi_{2}^{(1-\lambda)}\). We will have \(\mathbf{z}[T_{d}]=(\Phi(\lambda))^{n_{T}}\mathbf{z}[T_{0}]\). Thus the switched linear system of (26) would be unstable, if its \"equivalent\" system matrix \(\Phi(\lambda)\) has eigenvalues with magnitude outside the unity circle.  
Based on Lemma 1, we can see that if an advanced attacker can choose a proper constant \(\lambda\), it may make the closed-loop LFC system for the AC/HVDC interconnected power system with emulated inertia unstable. To be noted, here we mainly consider the optimal DoS attack strategy that can corrupt the system stability, and thus the attacker is assumed to be with extensive attack resources to corrupt multiple wide-area measurements and also full knowledge of the underlying system (e.g., the parameters of the test system in (22)). Besides, the mitigation and detection schemes that the power systems are usually equipped with are not included in the framework of this article; we leave the possible complex \"interactions\" between DoS attacks and mitigation/detection schemes in the LFC system for the future work.
# Using Compressed JPEG and JPEG2000 Medical Images in Deep Learning: A Review
## 2 JPEG and JPEG2000 Compression Effects on Medical Images
### JPEG and JPEG2000 Compression Techniques  
Irreversible image compression techniques are designed to reduce the size of digital images by discarding information deemed less critical for visual perception. A key aspect of this process is the removal of high-frequency components from an image. High-frequency  
Figure 1: Hounsfield units and corresponding pixel intensity grayscale shades (approximate) associated with tissue type [16].  
components correspond to rapid changes in intensity, fine and detailed textures, and random noise in an image [17].  
The term \"JPEG\", originating from the Joint Photographic Experts Group, refers to a lossy compression format standardized in 1993 [18; 19]. Known for its effectiveness, JPEG is one of the most widely used compression formats. JPEG compression processes an image by handling each 8 \(\times\) 8 pixel block independently. Each 8 \(\times\) 8 pixel block is transformed into the frequency domain with the use of the Discrete Cosine Transform (DCT), converting the original pixels into frequency coefficients. Quality loss is managed using a quality factor ranging from 1 to 100. This factor, along with the corresponding quantization table, scales each frequency coefficient within the 8 \(\times\) 8 blocks. Quantization tables are designed to preserve the low-frequency coefficients while approximating the high-frequency coefficients, often setting the highest ones to zero for lower quality factors. The quantized coefficients are then rounded and reordered in a zigzag pattern, often resulting in many zeros at higher frequencies. Run-length encoding (RLE) efficiently compresses these zeros, while entropy coding further reduces repetitive values to enhance compression and processing speed. The compressed output file contains the encoded sequence along with the necessary headers for decoding. Decompression reverses these steps to reconstruct the image.  
JPEG2000, considered a more efficient compression format compared to JPEG, is a newer algorithm employing multiresolution capabilities based on the Discrete Wavelet Transform (DWT) [20]. A pyramidal structure is formed from sub-band wavelet decomposition, containing image information at several resolutions, including the original image resolution. A significant limitation to consider is that JPEG2000 is not widely supported by most browsers, owing to the complexity associated with its encoding and decoding processes. JPEG, although considered worse in performance than JPEG2000 [21], remains the simplest irreversible compression format for medical images supported in DICOM. The DICOM standard supports grayscale irreversible compression for JPEG2000 for 8-, 12-, and 16-bit images and JPEG for 8- and 12-bit images [8].
# Inter-Annual Variability of Peatland Vegetation Captured Using Phenocam- and UAV Imagery
## 3 Results
### Monitoring Vegetation Phenology
#### 3.2.1 Phenocam Data  
The phenocam data provide a near-continuous time-series of vegetation phenology from mid-2018 until the end of 2021. Although the \(G_{cc}\) is a simple index and not spectrally calibrated, it was able to capture a clear seasonal trend in greenness at the site (see Figure 4). Four phenological phases were identified: (1) green-up, characterised by a steep rise in greenness during spring as the vegetation canopy begins to develop; (2) a peak growing season, where greenness values plateau during the summer once the vegetation canopy is fully developed; (3) green-down, characterised by a rapid reduction in greenness as the canopy starts to senesce; and (4) a dormant phase, represented by relatively stable, low greenness values either side of the growing season. Day-to-day variability was observed in the time-series, particularly when seasonal changes in greenness were low (e.g., during the shoulder seasons). Here, noise in the dataset was observed to exceed temporal trends in greenness.