esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy-Aware Forwarding Strategies for Delay Tolerant Network Routing Protocols
## 4 Evaluation and Analysis
### Parameter Variation  
The use of the pigeon (feeding during energy harvesting) and flying fox (feeding at night, harvesting during the day) models shows the resilience and applicability of the method for two different scenarios. Some other variations of the system parameters were also tested to demonstrate that the method works for different scenarios.  
In Figure 8, the number of mobile nodes is changed from 20 to 10, and it is seen that the performance is largely the same as in Figures 5 and 6 (except only half as many total packets).  
Similarly, the fraction of roosting camps with base stations is changed from two to three (out of five), with the original 20 nodes. Figure 9 shows these results, and the new algorithms still work well for both (a) pigeon and (b) flying fox scenarios.  
Figure 8: Packets delivered for ten nodes for (**a**) pigeon and (**b**) flying fox scenarios.
# A Three-Dimensional Electrochemical Process for the Removal of Carbamazepine
## 3 Results and Discussion
### 3.3D Experiments
#### 3.3.2 Effect of the Particulate Electrode on CBZ Removal  
In a 3D particulate electrode system, the material of the particulate electrode is one of the most important factors influencing the efficiency and cost of the process [52]. In this study, the polymer-based activated carbon from SARATECH(r) and the two biochars from TN vineyard pruning residues were used as the particulate electrode.  
The results of the effect of the particulate electrode on the CBZ removal efficiency are presented in Figure 12. According to the results, all the tested adsorbents led to an increase in the removal compared to the 2D process. The use of the particulate electrode allowed for an increase in CBZ removal (45 min) of 8, 14, and 15%, respectively, for biochars TN \(<\) 75 \(\upmu\)m, TN 1-2 mm, and SARATECH(r) activated carbon when compared with the 2D process. Other studies also reported removal efficiencies that were 10 to 50% higher for 3D processes than for 2D processes [35]. The removal rate of organic pollutants, such as CBZ, improves by using the 3D electrochemical process because, besides the particulate electrode polarization, the large specific surface areas of these particles (adsorbents) can provide reactive sites for pollutant adsorption, or even catalytic reactions [51].  
Figure 11: Schematic representations of the 3D electrochemical cell for the two tested interelectrode distances and effect on the particulate electrode distribution (biochar 1–2 mm, 1 mg/mL solution).  
Figure 10: Effect of interelectrode distance on the CBZ removal from aqueous solutions (operational conditions: j = 6.67 mA/cm2; NaCl = 0.1 M; CBZi = 10 mg/L; pH = 7; reaction time = 60 min; particulate electrode = biochar TN 1–2 mm, 1 mg/mL solution; cathode = STS; anode = MMO).  
The best results were obtained for the activated carbon supplied by SARATECH(r), although there were no significant differences between the three particulate electrodes at 30 and 45 min (\(p<0.05\)). Activated carbon may be polarized within an electric field [53], increasing the overall active electrode area [54], which can explain the results obtained for the tested activated carbon. Nonetheless, the biochars can be a more sustainable and economic alternative to the activated carbon. Surprisingly, the biochar TN 1-2 mm, which showed a much lower adsorption capacity in the adsorption experiments, presented a removal efficiency that was very similar to the activated carbon and slightly better than the one for the biochar TN \(<75\)\(\upmu\)m. It is known that the polarizability of a particulate electrode is of higher importance than its adsorption capacity [53]. In this study, it was also observed that the adsorbent with lower adsorption capacity to CBZ, when used in a 3D process, and thus after polarization, showed a completely different behavior, and very promising application perspectives.
# Dynamic Data Filtering of Long-Range Doppler LiDAR Wind Speed Measurements
## 5 Conclusions
The conducted error analysis has shown that the frequency distributions of errors do not show a normal distribution and are very distinct from each other. In the valuation of filtering results of scanned measurements in full-scale experiments with two different LiDAR devices, it was shown on basis of temporal means that certain error structures in the flow field and the CNR-mapping were filtered by [PERSON] et al. and the dynamic data filter approach in a good manner. Due to the behaviour of the dynamic data filter approach within the here presented test cases, we conclude the assumption of self-similarity to identify valid data points as very reasonable. An accompanying limitation within this approach is the need of a certain amount of valid data to form dense clusters for the calculation of the data density. At the same time, this limitation can be seen as an advantage, since large quantities of data can be processed at once and thereby the proportion of valid data can be increased. Because of the applicability of scanned as well as stored measurement setups we see the dynamic filter approach as a promising tool for different types of LiDAR measurement setups. The results shown here are a further step in the development of filter techniques for explicit LiDAR applications and prove that self-similarity can be used as a criterion for LiDAR data filtering. Regarding the reproducibility of the comparison results, further investigations of the behaviour and limitations of this approach should be performed with a plurality of different measurement situations that could not be part of this study. We thank eno energy systems GmbH and the RAVE (Research at alpha ventus) consortium for the support during the measurement campaigns. We are also grateful to the reviewers and [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] at the University of Oldenburg for the valueable discussions. The presented work has been funded by the Federal German Ministry for Economic Affairs and Energy of Germany (BMWi) in the framework of projects CompactWind (contract No. 0325492B) and GW Wakes (contract No. 0325397A). [PERSON] wrote this paper and conducted related research. Intensive reviews were done by [PERSON]. The authors declare no conflict of interest.
## 3 Variable Block Insertion Heuristic
### Block Insertion Move Procedure  
\(bIM\left(\pi,\ bS,\ mS_{max}\right)\) procedure is a core function in the VBIH algorithm. It takes the sequence \(\pi\), the block size, \(bS\) and the maximum insertion move size, \(mS_{max}\) as parameters. The procedure randomly removes a block of jobs with size, \(bS\) from the current sequence \(\pi\), which is denoted as \(\pi^{b}\). Then, the partial sequence after removal will be denoted as \(\pi^{p}=J-\left\{\pi^{b}\right\}\). Then, the procedure carries out a number \(mS_{max}\) of block insertion moves with the block size, \(bS\). In other words, the block \(\pi^{b}\) is inserted in the partial sequence \(\pi^{p}\) randomly in which the maximum size of the insertion moves is \(mS_{max}\). Finally, it chooses the best one amongst a number \(mS_{max}\) of block insertion moves. The \(bIM\left(\pi,\ bS,mS_{max}\right)\) procedure is given in Figure 5.  
In order to ease the understanding of the block insertion move procedure, we give the following example. Suppose that we have a current sequence \(\pi=\left\{7,4,1,8,2,6,5,3\right\}\). In addition, suppose that the block size is \(bS=3\) and maximum insertion move size is \(mS_{max}=3\). Suppose that we randomly choose a block \(\pi^{b}=\left\{8,2,6\right\}\). Then, the partial sequence will be \(\pi^{p}=\left\{7,4,1,5,3\right\}\). Without considering the total flowtime of the sequence \(\pi\), we randomly choose three positions, for instance, 1, 2, 5, and insert the block in these positions. Hence we have three sequences as \(\pi_{1}=\left\{8,2,6,7,4,1,5,3\right\}\), \(\pi_{2}=\left\{7,8,2,6,4,1,5,3\right\}\), and \(\pi_{3}=\left\{7,4,1,5,8,2,6,3\right\}\). Amongst these three sequences we take the one with the lowest total flowtime criterion. Note that the same speed up methods explained before can be used to accelerate the insertion procedure. For example, after removing the block \(\pi^{b}=\left\{8,2,6\right\}\), \(D_{jk}\) matrix of partial sequence \(\pi^{p}\) is once calculated, and then the fast fitness calculation procedure is used to accelerate the insertion procedure.  
Figure 4: tPF_NEH(x) heuristic.
## 3 System Model
### Network Configuration  
Figure 2 shows the scenario where the primary user (PU) and the cognitive radio sensor network (CRSN) coexist in the same area. We assume that the transmission power of the primary user is very high when it is in the static or mobile condition. Sensors are very little mobile in the range of a cluster. The sensor and PU transmission should not cause any interference. This can be achieved by power control of the sensors, which is beyond the scope of this paper. Also, the sensor should immediately stop the transmission data when PU signals are detected. An _ad hoc_ CRSN has two disadvantages. First of all, it is energy constrained, thereby limiting the transmission range of the sensor. Second, it plays an essential role in the network-wide common control channel of a general CRSN. However low power consumption and a large environment area are not feasible. As such, we propose a multi-channel cognitive radio sensor network structure. The structure includes a cluster head (CH) and a set of cluster members (CMs). The CH is an high energy source with a high performance sensor. CMs are regular low-power sensors. The CH performs spectrum sensing and allocation. To make the spectrum allocation decisions, the parameters of the PU are estimated based on the sample sequence. Then, the CH makes a decision on the packet size and channel assignment based on the estimation results [33, 34, 35].  
Figure 1: (**a**) Wireless transmission system; (**b**) Logical Reliable Channel.  
Figure 2: A cognitive radio sensor networks with the primary user.
# Exposure Bracketing Techniques for Camera Document Image Enhancement
## 3 HDR Document Image Generation
### Exposure Fusion
#### 3.3.1. [PERSON] Exposure Fusion Method  
[PERSON] exposure method [14] was selected because this method has been incorporated in the latest OPENCV library, which indicates its influence. For any exposure fusion method, there are two questions to answer: the first is how to select good weights for each LDR image pixel and the second is how to blend the weighted pixels into the final result.  
For the first problem, in [PERSON] exposure fusion method, three measures are used to select and weight the pixels: (a) Exposedness \(E(p)\): this measure is applied to each color channel separately, then the weights are multiplied. Considering that the pixel values range from 0 to 1, it preserves the pixels that are not too close to the boundaries. A Gaussian curve centered at 0.5 is suggested:  
\[E(p)=\exp\left(-\frac{(p-0.5)^{2}}{2\sigma^{2}}\right) \tag{6}\]  
(b) Contrast D(p): the contrast is closely related to details in local regions. A filter is applied on the grayscale version of the image to enhance details, and D(p) measures the absolute value of the filter response. The filter can be a 3 by 3 Laplacian filter. The basic idea is that over- or under-exposed parts of the image contain few details, while most of the details are in well-exposed regions. (c) Saturation S(p): the saturation is computed as a standard deviation from the R,G,B channels at each pixel. The reasoning behind this is that saturated colors look better in the well-exposed regions.  
The final weight for each pixel in the LDR image is a combination of these three measures using multiplication. Finally, the weighting is normalized by making the sum of the weights equal to one. However, after the weights are calculated, simply linearly blending LDR images does not yield the desired result because the intensities vary wildly among the different images and may contain very sharp transitions. Therefore, a multi-resolution blending based on image pyramid decomposition is used. According to this scheme, a range of Laplacian pyramids are built for input LDR images and their corresponding weight maps are decomposed as Gaussian pyramids. For each pyramid level, regular blending is performed, generating a new Laplacian pyramid for the final result. Finally, the newly generated Laplacian pyramid is collapsed to obtain the output image.
# Blind Detection for Serial Relays in Free Space Optical Communication Systems
## 2 System Model  
An FSO system with IM/DD and OOK modulation is considered. The received signal of hop \(i\), \(r_{i}[k]\) in any discrete time \(k\) is  
\[r_{i}[k]=h\,s_{i}[k]+n_{i}[k] \tag{1}\]  
where \(s_{i}[k]\in\{0,\,1\}\), is the transmitted OOK symbol and \(n_{i}[k]\) is the signal-independent additive white Gaussian noise with zero mean and variance \(\sigma_{n}^{2}=N_{0}/2\). The channel coefficient can be formulated as \(h=h_{l}h_{a}\), where fading coefficient \(h_{a}\), is the time-varying channel state due to atmospheric turbulence and is considered to be constant over a large number of transmitted bits. \(h_{l}\) is the channel attenuation.  
The average electrical signal to noise ratio (SNR) is defined as \(E[\left|s[k]h\right|^{2}]/N_{0}\), where \(E[.]\) denotes the statistical expectation. The proposed system uses a single decode and a forward relay. The relay would help to shorten the communication link as shown in Figure 1. The impact of the decrease of the link length would significantly reduce the turbulence effect and path loss.
# Trustworthiness of Situational Awareness: Significance and Quantification
## 4 Quantifying Trustworthiness of SA
### Required Trustworthiness Measure (RTM)  
To assure the trustworthiness of the COP and the SA perceived by the commander, the information sources making up the COP can have a required trustworthiness measure (RTM). If an information source's trustworthiness falls below RTM, that is, \(\mathcal{T}_{S_{i}}<\) RTM, the information source can be omitted from the COP to preserve the trustworthiness of the COP. Mathematically, this condition can be expressed as  
\[\text{COP}_{\mathcal{T}}=\bigcup_{i=1}^{n}S_{i}\ \ \forall\ \ \mathcal{T}_{S_{i}}\geq\text{RTM}. \tag{5}\]  
The equation given above (Equation (5)) means that, in a trustworthiness-aware COP, denoted as \(\text{COP}_{\mathcal{T}}\), making up the \(\text{COP}_{\mathcal{T}}\) are only those sources \(S_{i}\) whose trustworthiness values are greater than or equal to RTM, that is, \(\mathcal{T}_{s_{i}}\geq\text{RTM}\). Since there are multiple sources reporting a particular area of interest, so even if the data from a particular source are missing due to low RTM, the commander can still always trust the COP. The RTM can also be adaptive, that is, the commander can adjust the RTM value in real-time depending on the SA needs. For example, the RTM value can be increased to ensure only highly trusted and authentic sources are displayed on the COP, or it can be lowered to show more information, with the associated risk that the additional information will have low trustworthiness. The concept of adaptive RTM is analogous to the constant false alarm rate (CFAR) used by radar operators where the CFAR can be lowered to only show definite targets from background noise, interference, and clutter, or the CFAR can be set to a higher value to identify more targets with the associated risk that some of those targets could not be real targets but noise, signals bouncing off clutter, waves at sea, etc. As the trustworthiness of information feeds changes, the commander can adjust the RTM accordingly to maintain the best possible COP and SA of the operation.
# Three-Dimensional Localization of Underwater Nodes Using Airborne Visible Light Beams
## 2 Related Work
While these methods enable underwater signal transmission, they suffer from high power consumption and complex deployment requirements, making them less suitable for mobile or energy-constrained underwater networks. [PERSON] et al. utilized polarization and deep learning techniques for underwater geolocation [38]; however, their approach required a vast amount of data collection. Fusion of aerial image and acoustic image using neural networks has also been explored [39], but it still relies on vast data collection and processing. Despite all the advancements, existing solutions often rely on surface nodes, pressure sensors, or passive uplinks, limiting their feasibility for real-world deployment. To the best of our knowledge, no prior study has demonstrated a fully airborne-enabled underwater localization system that transmits GPS-encoded positioning information directly to underwater nodes without relying on anchored reference points, depth sensors, or pre-collected data.
# Application of Absolute Nodal Coordinate Formulation in Calculation of Space Elevator System
## 3 Numerical Simulation
### System Parameters  
To verify the validity and precision of the methods proposed in this section, the anchor point is set on the equator. The detailed system parameters are shown in Table 1.  
The element numbers of each method are shown in Table 2.
## 3 Graph Embedding Methods
**NetMF**[29]: it shows that graph embedding methods utilizing the Skip-gram model (e.g., DeepWalk, Line, and node2 vec) with negative sampling perform _implicit matrix factorization_ with a _closed_ form. NetMF draws a theoretical connection between DeepWalk's  
Figure 2: Zachary’s Karate graph and its two-dimensional representation. implicit matrix and graph Laplacians leading to constructing a low-rank connectivity matrix for DeepWalk, which is explicitly factorized by the singular value decomposition (SVD) technique [43] to obtain the representation vectors. **ATP**[24]: it is a matrix-factorization-based embedding method that tries to preserve the asymmetric transitivity property in community question answering (CQA) graphs (i.e., if question \(q_{1}\) is easier than \(q_{2}\) and \(q_{2}\) is easier than \(q_{3}\), then \(q_{1}\) is easier than \(q_{3}\)). ATP constructs graph \(G^{\prime}\) by removing cycle links from \(G\), and incorporates _both_ the graph reachability (i.e., transitive closure of \(G^{\prime}\)[24]) and hierarchy (i.e., the rank of nodes in \(G^{\prime}\)) into a single matrix, which is factorized by a non-negative matrix factorization (NMF) technique [44] to obtain the representation vectors; each node has two corresponding vectors since its role in the graph is regarded as both a source and a target. **BoostNE**[30]: it is a matrix-factorization-based embedding method that does _not_ regard the low-rank assumption on the DeepWalk's connectivity matrix \(M\); applying a single NMF on \(M\) may lead to obtain such representations, which are _insufficient_ to encode the connectivity patterns among nodes. Inspired by ensemble learning methods [45], BoostNE performs multiple levels of NMF on \(M\) to construct the representation vectors. **DWNS**[22]: it applies the adversarial training method [46] to DeepWalk in order to improve the _robustness_ and _generalization ability_ of the learning process; it forces the learned classifier to be robust to adversarial examples (i.e., fabricated samples) generated from real ones through small perturbations. The training process is a two-player game where the adversarial samples are generated to maximize the model loss while the embedding vectors are optimized against them by utilizing stochastic gradient descent (SGD) [47]. **NERD**[28]: it mainly considers two different roles, a source and a target, for any nodes in the graph and maintains separate embedding spaces for the two distinct roles; based on this consideration, it exploits two _distinct_ neighborhoods for each node and tries to maximize the likelihood of preserving both neighborhoods in their corresponding embedding spaces at the learning process.
# OpenCare5G: O-RAN in Private Network for Digital Health Applications
## 3 Proposed Solution
### 5G NR/NGC Protocol Framework Stack
It is prioritized and optimized in different ways for the channels during the DL and the UL [41; 42]. There are three types of specific channels [40]:  
* Logical channels that define the type of data to be transferred with the traffic (user data), such as \"paging messages\", dedicated control information;
* Transport channels that define the information to be transported to the physical layer and the characteristics of the data, such as error protection, channel coding, the cyclic redundancy check (CRC) and the size of the data packet;
* Physical channels characterized by their time and access protocols, data rates as traffic channels [40].
# A Review on Automated Detection and Identification Algorithms for Highway Pavement Distress
## 5 Discussion
### Strategic Roadmap for Next-Generation Systems  
To overcome these limitations, this review suggests key research directions for future work:  
1. Environmental Adaptability: Develop comprehensive multi-condition pavement image datasets incorporating robust sensors (e.g., millimeter-wave radar [86]) while enhancing algorithmic robustness to environmental noise and variations.
2. Dataset Optimization: Leverage advanced self-supervised learning techniques [87] to effectively utilize unlabeled pavement images, significantly reducing annotation requirements and improving model generalization.
3. Edge Deployment: Following [PERSON] et al.'s [88] demonstration of YOLOv5s on Jetson TX2 (achieving 90.5% accuracy at 30.7 ms), future work should focus on integrating pixel-level segmentation models into specialized inspection vehicles for end-to-end automated road quality assessment.
4. Predictive Maintenance: Combine high-precision segmentation models with historical inspection data, traffic load patterns, and environmental factors to develop reliable 3-5 year performance prediction models for proactive maintenance planning.  
These advancements will transform pavement management from reactive repairs to proactive maintenance, supporting intelligent transportation infrastructure development.  
**Funding:** This research was funded by Jiangsu Science and Technology Department Projects (grant number 7705008081) and the APC was funded by ongoing research projects of JSTI Group (grant number 8505009479).  
**Conflicts of Interest:** Author [PERSON] was employed by the company JSTI Group. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.