esa-sceva/satcom-chunk-collection huggingface dataset:
# Inter-Blockchain Communication Message Relay Time Measurement and Analysis in Cosmos +
## 6 Discussion
### Strategies for Optimizing G to T and R to A Intervals  
Optimizing the G to T (Generation to Transmission) and R to A (Relay to Acknowledgment) periods in inter-chain communication is essential to increase the overall effectiveness and dependability of blockchain networks, such as Cosmos.  
To enhance G to T intervals, consider using parallel processing strategies that enable the Cosmos Hub to execute transactions simultaneously. Critical data transfers can be sped up by classifying transactions according to priority or urgency. To avoid bottlenecks, load-balancing algorithms assist in distributing transaction processing responsibilities equally across nodes. Additionally, investigating more effective consensus methods might hasten transaction validation without sacrificing security.  
Focus on building lightweight acknowledgment protocols that utilize little processing resources to optimize R to A intervals. Message acknowledgment is ensured by network redundancy even when a significant route faces congestion or delays. Predictive analytics can estimate network congestion trends ahead of time and modify acknowledgment intervals accordingly. Using dynamic scaling to allocate resources based on demand, allows quick acknowledgments during peak activity. Intelligent caching strategies keep frequently accessed acknowledgment data closer to nodes, lowering retrieval time. Finally, real-time monitoring and feedback systems give information about acknowledgment timings, enabling automated network adjustments to optimize R to A intervals based on actual network conditions. Continuous monitoring and iterative enhancements are required to ensure efficient inter-chain connectivity within the Cosmos ecosystem while adhering to specific use cases and the expanding blockchain.
## 6 Determinant Factors of Obesity
### Neurohormonal Regulation of Appetite
#### 6.1.1 Hypothalamic Centers  
The hypothalamus acts as the most important relay between the rest of the central nervous system and incoming orexigenic/anorexia stimuli. Integrating the information processed within the hypothalamic nuclei leads to the generation of the sensation of hunger or satiety. The most important nuclei are the arcuate nucleus of the hypothalamus (ARC), the paraventricular nucleus of the hypothalamus (PVH), the ventromedial nucleus of the hypothalamus (VMH), and the lateral hypothalamic areas (LHA). A schematic representation of these areas, the types of neurons found within, and the effect of regulatory peptides involved in modulating appetite, as well as the main neural connections between these centers and other relevant neural structures, is outlined in Figure 12, and described in [153] as well.  
Figure 12: Neurohormonal regulation of appetite [153]. Pro-opiomelanocortin (POMC) neurons in the arcuate nucleus (ARC) suppress appetite by means of secreting a-melanocyte-stimulating hormone (a-MSH) which acts upon melanocortin 4 receptors (MC4R) exhibited by neurons in the paraventricular nucleus (PVH), ventromedial nucleus (VMH), lateral hypothalamic area (LHA), dorsomedial nucleus, and several other non-hypothalamic brain regions. Neuropeptide Y (NPY)/agonist-related peptide (AgRP) neurons have an opposite effect and stimulate hunger by acting upon the NPY receptor and by antagonizing the action of a-MSH on MC4R. In addition, AgRP also acts upon the Kir 7.1 potassium channel and induces the hyperpolarization of MC4R-expressing neurons. By projecting both stimulatory and inhibitory appetite signals in the same areas, the neurons within ARC serve as key modulators in this respect, depending on which particular neuron population is activated predominantly. Furthermore, NPY/AgRP neurons inhibit POMC neurons by interacting with the GABA receptor, thus prioritizing the generation of hunger sensation over suppressing it. Leptin secreted by adipocytes acts upon the arcuate nucleus by passing through the blood–brain barrier, while peptides produced by the gastrointestinal tract use the vagus nerve to interact with the hypothalamus. The effects of these peptides on appetite are further detailed in text (Sections 6.1.2 and 6.1.3). Red lines represent inhibitory pathways, while green ones represent stimulatory ones. Hormones and peptides with blue text have an overall effect of stimulating appetite, while those with orange text have an inhibitory effect in this respect. Genetic mutations presenting with obesity in childhood include congenital leptin deficiency, the hypothalamic receptor for leptin mutations, alteration of POMC-aMSH pathway, and loss of function mutations of MC4R receptors [154, 155]. Further detailed in Section 6.2.2.
# Secure and Privacy-Respecting Documentation for Interactive Manufacturing and Quality Assurance
## 3 Security and Privacy Model
### Protection Goals  
As indicated earlier, the primary protection goal we are concerned with is the _confidentiality of documentation data_ in the interest of the data subject, be it video snippets or workflow protocols. Technical means are supposed to protect the data subject against covert performance monitoring based on documentation data. This requires that access to documentation data must be granted by the data subject. We specify this protection goal even further by requiring that at least one other trusted party besides the data subject must consent to the access to the documentation data so that the data subject cannot be pressurized to grant access.  
Figure 2: Basic system design.  
The data subject as well as the operating organization have an interest in the _integrity and authenticity of documentation data_. Both parties are interested in ensuring that documentation data cannot be manipulated unnoticed. More specifically, this means that no documentation data is changed, deleted or added unnoticed outside the system behavior given by the system specification.  
To meet these protection goals, we must go even further and demand, as derived protection goals, that the _integrity and authenticity of the smart documentation system_ is ensured and that the _authenticity of parties accessing documentation_ data is verified. The former protection goal ensures that the smart documentation system cannot be manipulated or even replaced unnoticed.  
We do not cover protection goals on metadata, which are used to retrieve documentation data and to request authentication of the parties before access, since the approach presented in this paper is not capable of restricting access to metadata and preventing potential leaks of sensitive information that could be derived from metadata. However, we discuss this question as well as envisioned countermeasures in Section 5.
# Radiometric Comparison within the Sentinel-1 SAR Constellation over a Wide Backscatter Range
## 4 Discussion  
The discussion section summarizes the radiometric results derived from different target types w.r.t. their long-term radiometric stability and intercomparability between S-1A and S-1B over a wide backscatter range. By focusing on noise measurements and transponder recordings, the reasons for the different results derived from S-1A and S-1B in particular for low backscatter targets are further investigated and discussed.
# Multi-Source Satellite Imagery and Machine Learning
## 5 Experiments and Results  
We will present the algorithm that obtained the best precision for each parameter, accompanied by its ROC curve and confusion matrix.  
The black dashed line in the ROC curve represents the random classifier or the line of no discrimination. This line has an area under the curve (AUC) of 0.5, meaning it performs no better than random chance.
## 4 FIAE-GAN Digital Watermarking Model
### FIAE-GAN Watermarking Network Model  
A robust watermarking model should ensure that the embedded watermark remains mainly unaffected by various image attacks and that the embedding involves invisible perturbations. To achieve this, the model extracts robust features, integrates them with the watermark, and distributes them over inconspicuous areas to maintain invisibility.  
As shown in Figure 6, FIAE-GAN primarily consists of the encoder \(E_{\alpha}\), the decoder \(D_{\beta}\), the noised layers \(Noise\), and the discriminator \(DIS_{\delta}\), where \(\alpha\), \(\beta\), \(\delta\) represent the trained parameters of the encoder, decoder, and discriminator, respectively. These parameters are continually updated during iterative training to achieve high watermarking invisibility and robustness. Given an original image \(F_{cover}\) of size \(H\times W\times C\) and an original binary watermark \(W_{in}\) of length \(L\), inputting \(F_{cover}\) into \(E_{\alpha}\) generates the encoded image \(F_{en}\).  
The discriminator \(DIS_{\delta}\) evaluates the likelihood that \(F_{en}\) matches the original image by comparing the similarity between \(F_{cover}\) and \(F_{en}\). The feedback from \(DIS_{\delta}\) assists \(E_{\alpha}\) in generating \(F_{en}\). To ensure watermarking robustness, various types of noise are introduced in \(Noise\). Concurrently, adversarial training is conducted where \(D_{\beta}\) is used to extract the decoded watermark \(W_{out}\) from \(F_{noise}\) aiming to make \(W_{out}\) as identical as possible to \(W_{in}\).
# Conserved Plastid Genomes of _Pourthiaea_ Trees: Comparative Analyses and Phylogenetic Relationship
## 2 Materials and Methods
### Sequencing, Assembly, and Annotation  
High-throughput sequencing was performed using the PE150 sequencing method on the Illumina NovaSeq sequencing platform by Novogene (Tianjin, China). Sequencing yielded 3.40-3.68 Gb of raw data per species. The raw Illumina reads were removed adaptors and low-quality reads using fastP (-q 15 -n 10) [33]. For the de novo plastome assembly, GetOrganelle v1.7.2 [34] (-k 21,45,65,85,105 -R 15 -F embplant_pt) was used to assemble the clean reads into circular plastid genomes. The annotation of the four plastomes was conducted using GeSeq [35] and further manually adjusted in Geneious v9.0.2 ([[https://www.geneious.com/](https://www.geneious.com/)]([https://www.geneious.com/](https://www.geneious.com/))) (accessed on 7 December 2023). The gene map of the plastome was generated with the program OrganellarGenomeDRAW (OGDRAW) [36]. Additionally, the mtDNA sequences of the four species were also assembled using GetOrganelle v1.7.2 [34] (-k 35,85,115 -R 10 -F embplant_nr). The four newly obtained complete plastid genomes (PQ197388-PQ197391) and mtDNA sequences (PQ117736-PQ117739) were deposited in GenBank (Tables 1 and S9). The inverted repeat/single-copy (IR/SC) boundaries of the 13 _Pourthiaea_ plastomes (including 10 published plastomes) were compared to visualize the contraction and expansion of IR regions.
# Monitoring of Ionospheric Anomalies Using GNSS Observations to Detect Earthquake Precursors
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} ASHA & Adjusted Spherical Harmonic Analysis \\ CDDIS & Crustal Dynamics Data Information System \\ CODE & Center for Orbit Determination in Europe of the Astronomical Institute of the University of Bern \\ CORS & Continuously Operating Reference Station \\ DCB & Differential Code Bias \\ DIX & Disturbance Ionosphere indeX \\ DOY & Day of Year \\ DTEC & Differential VTEC \\ ECEF & Earth-Centered, Earth-Fixed \\ EPN & European Permanent Network \\ ESA & European Space Agency \\ GIM & Global Ionosphere Map \\ GIX & Gradient Ionosphere indeX \\ GNSS & Global Navigation Satellite System \\ IGS & International GNSS Service \\ IONEX & IONosphere-map Exchange (format) \\ IPP & Ionosphere Pierce Point \\ JPL & Jet Propulsion Laboratory \\ NRT & Near Real-Time \\ PPP & Precise Positioning Processing \\ RINEX & Receiver INdependent Exchange (format) \\ ROT & Rate Of TEC \\ ROTI & Rate Of TEC Index \\ SIDX & Sudden Ionosphere Disturbance indeX \\ SNR & Signal-to-Noise Ratio \\ Sol. & Safety of Life \\ SONEL & Systeme d'Observation du Niveau des Eaux Littorales \\ STEC & Slant TEC \\ TEC & Total Electron Content \\ TIDs & Traveling Ionospheric Disturbances \\ UNAVCO & University NAVSTAR Consortium \\ VTEC & Vertical TEC \\ \end{tabular}
## 4 SDP Solution Method for the FFP-IM
### The SDP Solution Method  
The key contents of the SDP are briefly reviewed below to help readers better understand the solution algorithm based on the SDP method for the FFP-IM index.  
The SDP method is extensively applied in modeling nonlinear stochastic systems and time series analyses, as documented in seminal works [30; 31]. Initially pioneered by [PERSON] et al., the application of SDP to compute variance-based IM with independent variables has markedly enhanced computational efficiency, thereby facilitating the integration of variance-based IM into engineering applications [32; 33]. The efficacy of SDP in this context is grounded in the utilization of first-order terms derived from the high-dimensional model representation (HDMR) within the framework of functional analysis of variance [34]. This approach involves estimating the first-order HDMR terms through a recursive criterion thatresolves the parameters of the input-output relationship, culminating in the determination of the IM index.
## 2 Materials and Methods  
### Vineyard, Vine Cultivars and Sampling  
Plant material was collected from the Vine Cultivar Collection (VCC), School of Agriculture, Aristotle University of Thessaloniki (AUTh). The vineyard is located at the University Farm (N40.53829, E22.99633). The soil is calcareous sandy loam, and the cultivation follows a semi-conventional low-input system. More specifically, synthetic fertilizers are applied after soil fertility evaluation as needed, and plant protective fungicides, sulfurand Bordeaux mix, are applied three times per year. Fungicide (Switch; Syngenta, Greece) is applied against _Botrytis_ once per year.  
The VCC collection hosts more than 150 grapevine varieties of Greek origin along with several of the most important international varieties, including Cabernet Sauvignon,Sauvignon Blanc, Merlot, Syrah, and Riesling, among others. Tissue samples were collected from 36 varieties (names of the varieties are indicated in Tables S1 and S2) and consisted of bark and buds in early spring (March 2019), immature berries at the phenological stages of berry set in the spring season (May-June 2019), and mature berries at harvest (August-September 2019).  
Bark and bud tissue samples (10 g) were collected aseptically from a single cane per vine and 5 independent vines (trees) from each cultivar, pooled together, equally mixed, and stored at \(-80\)\({}^{\circ}\)C for further analyses [17]. For berry sampling, undamaged berries, including their pedicels at the phenological stages of berry set (Stage I, 20 g per sample) and harvest (Stage II, 60 g per sample), were taken from 5 plants from each cultivar. The samples from each phenological stage were placed in 0.5 L sterilized bottles, filledup with TENP buffer (30 mL and 100 mL, respectively), and shaken using a horizontal shaker for 1h at 100 RPM. The supernatants were centrifuged, and subsequently, pellets were collected and stored at \(-80\)\({}^{\circ}\)C for DNA extraction.
# An Enhanced Energy Balanced Data Transmission Protocol for Underwater Acoustic Sensor Networks
## 4 EBET and EEBET: Proposed Techniques
### Throughput Maximization Model  
We use linear programming to design objective function and its constraints for throughput maximization as follows.  
Objective function:  
\[\textit{Maximize}\qquad\sum_{t=1}^{t_{max}}T_{p}(r) \tag{30}\]  
Subject to:  
\[E_{u}\leq E_{o}\qquad\forall\ u\in N \tag{31a}\] \[d_{u,v}\leq d_{opt}\qquad\forall\ u,v\in N\] (31b) \[f_{u,v}\leq f_{max}\qquad\forall\ u,v\in N\] (31c) \[d_{min}\leq d_{u}\leq d_{max}\] (31d) \[P_{l}\geq P_{g}\qquad\forall\ u\in N\] (31e) \[\sum_{u=1}^{n}E(u)\cong\sum_{v=1}^{n}E(v)\] (31f) \[\forall\ n\in N,u=1-S_{r}\textit{ and }v=1-S_{r}\]  
Our objective in Equation (30) is to maximize the total number of successfully received packets at sink \(T_{p}\) time span \(t_{max}\) for which the network is alive. Equation (31a) deals with energy of sensor nodes; each sensor node \(u\) that belongs to the set of nodes \(N\) has limited battery supply with an upper bound \(E_{o}\). Therefore, energy of sensor nodes should be utilized in an efficient way to prolong network lifetime, which in turn increases throughput. Equation (31b) means that the transmission distance between any two communicating nodes \(d_{u,v}\) should be less than or equal to the optimum transmission distance \(d_{opt}\). Failing to implement this constraint increases packet drop rate which means decreased throughput. Equation (31c) describes flow constraint of the physical link; the data flow between node \(u\) and \(v\) must be less than the upper bound \(f_{max}\). Violation of this constraint leads to overflow situation leading to high packet drop rate. Equation (31d) illustrates the transmission distance of any node \(u\) from set \(N\), \(d_{u}\) must be optimum, provided the upper \(d_{max}\) and lower bounds \(d_{min}\). Transmitting data at very long distance by increasing transmission range leads to huge amount of packet drop. While transmitting at very short distance by decreasing transmission range leads to high energy consumption, hence, short network lifetime and decreased throughput. Our balanced energy transmission technique effectively compensated for this trade-off. Equation (31e) [24] suggests that the probability \(P_{l}\) of current link status have to be at least \(P_{g}\); _i.e._, the minimum probability required for successful data delivery. Equation (31f) indicates that the total amount of energy consumed by all nodes in a ring sector needs to be approximately equal to any other ring sector node's energy consumption. Network energy consumption is needed to be balanced in order to get high throughput. As long as the network is energy balanced, network remains alive and throughput increases. Therefore, to get high throughput, energy of all sensor nodes is required to be used in a balanced way.
# Process-Oriented Stream Classification Pipeline: A Literature Review
## 4 Data Processing
### Preprocessing
#### 4.1.1 Feature Selection  
According to [91, 99] dimensionality reduction can be achieved by feature selection. Here, methods are divided into filters (applied prior to the actual learning algorithm), wrappers (built upon an evaluation of features), and embedded methods (where the selection is a part of the learning algorithm itself). For stream scenarios, especially the first category is of interest, since the other two approaches are usually too time-intensive or require multiple iterations over data points. Further, filter methods are robust concerning changes in the data streams. Classical filtering methods are, for example, the evaluation of the features based on the information gain or the \(\chi^{2}\) index [99].  
A well-known filter approach is the DXMiner, which can react to drifts and changes in feature space. The algorithm uses the deviation weight measure to rank features during the classification phase [100]. DXMiner then uses the most discriminative features of the latest labeled instances to classify new, unseen observations. Within their exhaustive benchmarking study, ref. [99] demonstrate that DXMiner is the only algorithm that addresses the problem of concept drifts in stream data. The authors point out that feature selection never improved classification accuracy, but some algorithms yield comparable results with lower model complexity. Again, DXMiner is proposed as the best-performing method here.
## 4 Discussion
### LOS Variation to GPP Increment
The index assumes that changes in growing season length (LOS) have a proportional effect on GPP, which may hold true in temperate or tropical ecosystems as well. However, ecosystem-specific dynamics, such as variations in \(GPP_{max}\) and LOS, may require calibration. A limitation of the current study is that \(GPP_{max}\) and LOS estimates were derived from existing data, meaning our approach cannot be used for GPP prediction unless these variables can be inferred from other indicators. Future research could address this limitation by developing methods to estimate \(GPP_{max}\) and LOS from biotic and abiotic drivers measured at a global scale, a topic currently under extensive investigation. Once these variables can be reliably inferred, Equation (6) could provide a useful tool for estimating and predicting annual GPP across various ecosystems.
## 4 Discussion
### Limitations and Future Directions  
While this scoping review highlights clinically valuable insights, several limitations inherent to the available evidence must be acknowledged. First, several studies featured small sample sizes (\(n<30\)), limiting statistical power and generalizability. Significant heterogeneity exists across included studies regarding sample characteristics (age, sex, graft type, physical activity level), intervention protocols (contraction types, duration, postoperative phase for CE initiation), and outcome measures (e.g., use of isokinetic vs. isotonic testing, different timepoints). This variability along with a relatively low price index (<50%) complicates direct comparisons and hinders standardized clinical recommendations. We advocate for an individualized, multisystem approach that considers prior knowledge and workload (external/internal loads), as these factors critically influence both injury prevention and management [40; 59]. Second, despite observed functional improvements, direct evidence of CE's neuroplastic effects in ACLR populations remains scarce. Only one study evaluated cortical structure and corticospinal excitability, leaving a key gap in understanding the neurophysiological mechanisms behind these adaptations. Most neuroplasticity data derive from healthy subjects or other neurological conditions, limiting generalizability. Further, inconsistent use of standardized measures for strength, neuromuscular activation, and knee function risks methodological bias and confounds result interpretation. Some trials also exhibited moderate bias due to unblinded designs or lack of active controls, potentially compromising internal validity.  
Future research should prioritize the following:  
* Advanced neurophysiological techniques (fMRI, TMS, evoked potentials) in real-world ACLR rehabilitation settings;
* Standardized CE protocols tested across rehabilitation phases, graft types, and fitness levels;
* Larger samples with greater inclusion of women and recreational athletes, plus longitudinal designs to assess long-term effects;
* Investigation of CE combined with adjunct therapies (e.g., neuromuscular stimulation [60], blood flow restriction [61; 62], nutritional interventions [63; 64]).