esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Statistical Analysis of Group Differences  
#### 3.3.1 Normality Test Results  
Kolmogorov-Smirnov (KS) and Shapiro-Wilk (SW) tests were performed to assess the normality of the distribution of the responses for each risk factor. Table 4 shows the results for each risk factor.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Risk Factor**} & \multirow{2}{*}{**Measure**} & \multicolumn{2}{c}{**Kolmogorov–Smirnov***} & \multicolumn{2}{c}{**Shapiro–Wilk**} \\ \cline{3-6}  & & **Statistic** & \(\boldsymbol{p}\) & **Statistic** & \(\boldsymbol{p}\) \\ \hline \multirow{2}{*}{Collision} & Severity & 0.215 & 0.02 & 0.829 & 0.003 \\  & Probability & 0.268 & \textless{}0.001 & 0.838 & 0.004 \\ \hline \multirow{2}{*}{Path deviation} & Severity & 0.24 & 0.005 & 0.91 & 0.074 \\  & Probability & 0.19 & 0.069 & 0.88 & 0.021 \\ \hline \multirow{2}{*}{Battery discharge} & Severity & 0.197 & 0.051 & 0.881 & 0.022 \\  & Probability & 0.26 & 0.002 & 0.879 & 0.021 \\ \hline \multirow{2}{*}{Explosion prevention performance} & Severity & 0.307 & \textless{}0.001 & 0.781 & \textless{}0.001 \\  & Probability & 0.263 & 0.001 & 0.794 & \textless{}0.001 \\ \hline \multirow{2}{*}{Sensor} & Severity & 0.185 & 0.086 & 0.922 & 0.121 \\  & Probability & 0.19 & 0.07 & 0.906 & 0.064 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Kolmogorov–Smirnov and Shapiro–Wilk normality test results.  
The KS test showed that most of the variables had \(p<0.05\), indicating that the assumption of normality was not satisfied. However, the assumption of normality was maintained for the probability of occurrence of 'Path deviation' (\(p=0.069\)), the severity of 'Battery discharge' (\(p=0.051\)), and severity (\(p=0.086\)) and probability of occurrence (\(p=0.070\)) of 'Sensor'.  
Similar results were observed for the SW test. The normality assumption was not met for most variables with \(p<0.05\), but the assumption of normality was maintained for the severity of 'Path deviation' (\(p=0.074\)) and 'Sensor' (\(p=0.121\)) and the probability of occurrence (\(p=0.064\)).  
It is particularly noteworthy that the data for 'Sensor' satisfies the normality assumption in both tests. On the other hand, variables such as 'Collision', 'Explosion prevention performance', 'Unauthorized access', and 'External acceleration' have very low \(p\)-values (\(p<0.01\)) in both tests, indicating strong non-normality.  
These results suggest that most risk factor data do not follow a normal distribution. Therefore, further analyses may require the use of non-parametric methods or additional processing, such as data transformation. This suggests that non-parametric tests that do not require normality assumptions are justified, especially when analyzing differences between groups.
## 3 Packet Delays within a Router  
When considering the packet delay through a domain there are a number of factors that need to be considered. These factors include the route selected by the routing protocol, the bandwidth of the links along the selected route and the internal delays within the equipment [25]. Routing Protocols optimize the route selection using a shortest path algorithm based on cost functions for each path [26]. The delays experienced within equipment e.g., routers and switches are often ignored since the link bandwidth has generally been considered as the dominant factor [27]. However as technology has improved the link speeds have increased and so the equipment delays have become more significant [28].  
Analyzing the delay within a domain will therefore depend on the route selected, which can be expressed as, the summation of delays through the components in the route [29]. The link delays are easily calculated since they are proportional to the bandwidth. However the equipment delays are more difficult to quantify. From a theoretical point of view it is possible to identify the causes of delays within a router since it is basically a specialized computer system. A further complexity is introduced due to the real-time operation of the router OS [30]. A practical approach was used to help identify the variation in delay caused by the nature of the processing used in routers. To this end delays have been measured under laboratory conditions.
# A Novel Integrated Fault Diagnosis Method Based on Digital Twins
## 4 Experiments and Results  
### Twin Dataset  
#### 4.1.1 Theoretical Construction of Twin Data  
Twin data refers to all data obtained in the digital twin system through the connection and integration of different modules. These data typically include measured data, simulation data, and service data.  
Taking a spiral compressor as an example, the physical data of this system should cover the external dimensions of each component, dependencies between modules, and real-time data collected by sensors. These physical data provide detailed information about the equipment's physical characteristics and operational status. Simulation data, on the other hand, includes data generated during simulation and prediction processes such as physical models and dynamic models. Through complex calculation and simulation techniques, these data predict the equipment's performance and response under different operating conditions, providing scientific basis for design optimization and fault prevention. Service data record the operational history of the spiral compressor, equipment maintenance  
Figure 7: Flowchart of computing additive attention.  
information, and other relevant operational data. These data reflect the equipment's usage status, maintenance records, and historical failure information.  
By comparing and fitting measured data, simulation data, and service data, simulation data can more accurately reflect the actual operational conditions of the physical equipment.
## 2 Materials and Methods
### Biological Assay
#### 2.3.3 Antiviral Activity  
_Z. pentandra_ extract was tested against three viral strains, namely NDV (Lasoota strain of Newcastle disease virus), H9 (H9N2 strain of Influenza virus), and IBV (H120 strain of Avian infectious bronchitis virus) through a method reported by [22] with some changes. SPF (specific-pathogen-free) embryonated eggs (7 days old) were used for inoculation of virus and before inoculation all the eggs were washed and disinfected with 70% alcohol. A hole in the eggs was made with the help of a sterile needle and the virus was inserted with the help of a 5 mL syringe. After injection of the virus, the eggs were sealed with melted wax and incubated at 37 \({}^{\circ}\)C for 2 days. Post-incubation periods, Haemagglutination (HA) test, also known as Hemagglutination titration, was performed using a 96-well microtiter plate. HA test was performed by preparing red blood cells in 1% solution. In a test tube, [PERSON]'s solution was mixed with 5 mL fresh chicken blood and centrifuged at 5000 rpm for 10 min. The supernatant was discarded, 1 mL phosphate buffer solution (pH 7.4) was taken in an Eppendorf tube and to it 10 \(\upmu\)L of these packed RBCs were mixed. In each well of the microtiter plate, 50 \(\upmu\)L phosphate buffer solution was added, and 50 \(\upmu\)L of thesample was added to the first column of the microtiter plate. This was mixed properly and 50 \(\mu\)L of this mixture was added to 2 nd well of the microtiter plate to prepare a dilution. This process was continued up to the 11 th well and 12 th well was labeled as a control, only containing phosphate buffer solution. RBC solution (50 \(\mu\)L) prepared earlier was added to each well and incubated at 37 \({}^{\circ}\)C for one hour. Red dots at the bottom indicated negative results, whereas a consistent red color (clumps) represented positive results. RBCs interact with HA protein of the virus and form a lattice, which is dispersed as a clump instead of a red dot. HA titer represents the highest dilution of virus-containing samples at which clumps are observed.
## 3 Results
### Reliability of Derived Metrics  
The reliability analysis of the derived physiological and neurocognitive metrics revealed a wide spectrum of measurement consistency, as assessed by test-retest intraclass correlation coefficients (ICCs, Pearson's r) and the coefficient of variation (CV%). Among the 36 metrics evaluated, three demonstrated excellent reliability, including HR\({}_{\text{last}}\) (ICC = 0.915, CV = 2.00%) and Cognitive-Physical Divergence (ICC = 0.915, CV = \(-2.00\%\)), both of which were classified as high reliability due to very low variability and strong reproducibility (refer to Supplementary Table S2). The Anaerobic Fatigue Index also showed a good ICC (0.786) and a very low CV (2.8%), qualifying as acceptable. A moderate number of metrics, such as O\({}_{2}\) Pulse, the lactate threshold speed, VO\({}_{2}\)/kg, and the VE/VCO\({}_{2}\) Ratio, fell into the borderline or mixed/needs review categories due to moderate ICCs and CVs, indicating fair but inconsistent reproducibility. In contrast, several inflammatory (e.g., PLR\({}_{\text{rest}}\), \(\Delta\)WBC), acid-base (e.g., \(\Delta\)BE, \(\Delta\)HCO\({}_{\text{3 std}}\)), and hematological markers (e.g., \(\Delta\)HGB), alongside certain metabolic indices (e.g., lactate peak, the lactate accumulation index), demonstrated poor ICCs combined with high CVs, and were thus classified as Unreliable. These findings suggest that while some derived variables offer robust reproducibility for use in longitudinal or interventional studies, others should be interpreted with caution or further refined to improve their measurement stability.
## 3 Using ML for Water Activities
### Bibliometric Analysis and Search Method for Water Management  
In this bibliometric study, the initial search for the keyword \"Water Management\" (utilizing title, abstract, and keyword fields) yielded a total of 4173 results. We did not impose any specific time restrictions during the searches as we recognized that older methods continue to hold relevance in the field, as we did previously with the bibliometric analysis of the ML methodologies. While conducting the analysis, we observed that keywords such as \"Water Management\", \"Groundwater\", \"Reservoir\", \"Water Quality\", \"Irrigation\", \"Water Demand Forecasting\", and others were prominent. However, certain terms like \"river\" and \"evaporation\" were less frequently encountered in the top keywords, even though they are equally important in the wider field of water management. Moreover, when focusing solely on methodology-related keywords, terms like \"water resource assessment\", \"efficiency analysis\", and \"management models\" were more prevalent compared to the others.  
A significant number of records was excluded prior to the screening process. To be specific, 1523 records were identified as duplicates, 703 records were flagged as ineligible by automated tools, and 505 records were removed for various reasons. This initial culling left us with 1442 records that were further evaluated, and 627 were subsequently excluded, resulting in 815 reports that were sought for retrieval.  
However, from the pool of legitimate reports, 494 could not be retrieved, leaving us with 319 reports to assess for eligibility. Among these 319 elements, (a) 37 articles were found to be irrelevant to the field of water management, (b) 58 articles contained outdated information, (c) 35 articles were not peer-reviewed, and (d) 15 articles were hindered by language barriers.  
This process ultimately led to the inclusion of 174 articles in our study, with 106 of them representing the actual reports of the included studies. Our findings are depicted in the PRISMA diagram (Figure 3).  
Figure 3: PRISMA analysis flowchart for the bibliometrics of the “Water Management” search.
###### Abstract  
Exploring prehistoric settlement patterns and their relationship to natural environments is a key focus in geological research, particularly in historically significant regions. The middle and lower reaches of the Yuanjiang River in northwestern Hunan Province are central to the development of early civilizations. Unlike previous studies that focused on individual sites, this research analyzed settlement distribution and migration trends across the entire catchment area. Combining field research with geological, geomorphological, archaeological, remote sensing data, and Geographic Information System techniques, the study revealed a clear pattern: prehistoric settlements increased from higher elevations to lower areas in the east, with concentrations along rivers, especially at river junctions. High floodplains and terraces, offering flat terrain, abundant water, and rich vegetation, were key to supporting early communities. The analysis showed that climate change significantly impacted prehistoric occupations, limiting population growth and stability due to natural disasters. A regional comparison within the Yuanjiang basin indicated that ancient cultures in the downstream area were more similar to those of the Lishui River Basin, while the Wushui cultural group in the middle reaches displayed distinct differences. These findings highlight the complex relationship between geography and cultural development, with the lower reaches being more influenced by climatic fluctuations and natural disasters, especially during the Shijiahe and Shang-Zhou periods. Additionally, the middle reaches of the Yangtze River may have originated in the Xixiang Area and shifted southward, leading to the rise of cultural centers in the Lishui River Basin. These results emphasize the significance of the Yuanjiang River catchment in Hunan Province in shaping ancient cultures and offer insights for contemporary sustainable practices.  
prehistoric settlement; distribution pattern; cultural evolution; climate variation; geographical context; sustainability; middle and lower Yuanjiang River Basin
## 3 The Proposed Protocol
### Query Dissemination  
The sink stays in the charge of consumer's query dissemination. A query messages is comprised of _data_type_, _region_address_, _hop_count_ and _time_stamp_. This is disseminated by the sink to the first-hop LDSs. The network avoids utilising traditional broadcast routing approaches (e.g., flooding) to forward the queries. This should result in reduced redundant transmissions and enhanced network resource conservation--mainly bandwidth. For this, NDN routing is used to forward the consumer's query. Each LDS first checks its own routing table to figure out any previous record matching the query's _data_type_ and/or _region_address_. It updates the routing table and discards the query message if there is still a fresh record of the query available. However, the LDS forwards the query message to the next-hop ones if this is a new query and matches FIB strategies. For example, an LDS stops forwarding a query message if this is old and/or expired. _hop_count_ is used to calculate path length. This is incremented by each LDS when the query is successfully received. Path length is used by MAs later to figure out optimal/shortest route during data collection procedure. _time_stamp_ allocates a time value to the query and this is used to support data freshness and/or discard expired ones.  
Sensory devices utilise the Hamming distance technique [29] to avoid transmuting redundant and/or misleading data samples to LDSs. Moreover, they use Hamming distance to recognise data samples if they match the consumer's query [30]. This should result in reduced resource consumption and transmitted network traffic, as redundant, uninteresting and/or misleading data samples are not forwarded. According to Algorithm 1, this system counts flipped bits in a binary vector and shows the results as Hamming distance. Then, a data sample is transmitted if its Hamming distance from an existing record is greater than zero (if it is not a redundant value) and/or falls in the user's allowed distance range (for meaningful data based on the application).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Acronym** & **Definition** & **Acronym** & **Definition** \\ \hline LDS & Local sensory data station & DFS & Depth-First Search \\ Tm & Timer & CT & Clustering Table \\ CH & Cluster-head & CM & Cluster Member \\ SID & Sender’s location address & DI & Data Interest \\ PIT & Pending interest table & FIB & Forwarding Information Base \\ PHC & Path hop count & ETE & Average End-To-End delay \\ DAR & Data robustness ratio & TTR & Total transmitted traffic \\ NDN & Named data networking & MA & Mobile Agent \\ \hline \hline \end{tabular}
\end{table}
Table 1: A glossary for MINDS.  
```
Hamming function HAM(D1 \(\wedge\) D2) /*starts from the first bit until end.*/ while\(D1\)(\(i\)) \(\
eq\)\(\backslash\)\(0^{\prime}\)do /*checks each bit for both data items (D1 and D2).*/ if\(D1\)(\(i\)) \(\
eq\)\(D2\)(\(i\))then /*incremented if the data attributes differ.*/ distance++;  end if /*next bit.*/ i++; end return distance;
```  
**Algorithm 1**Hamming distance algorithm [29].
###### Abstract  
Solid Set Canopy Delivery Systems (SSCDS) are fixed agrochemical delivery systems composed of a network of micro-sprayers/nozzles distributed in perennial crop canopies. A previous SSCDS design composed of a 3-tier configuration using hollow cone sprayer nozzles has been shown to provide excellent coverage and deposition in high-density apple orchards. However, the hollow cone nozzles substantially increases the initial system installation costs. This study evaluated the effect of irrigation micro-emitters replacement on spray deposition, coverage and off-target drift. A micro-emitter used in greenhouse irrigation systems was duly modified to enhance its applicability with SSCDS. After laboratory assessment and optimization of the micro-emitters, a replicated field study was conducted to compare 3-tier SSCDS configured with either of modified irrigation micro-emitters or traditional hollow cone nozzles. Canopy deposition and off target drift were evaluated using a 500 ppm fluorescent tracer solution sprayed by the field installed systems and captured on mylar collectors. Spray coverage was evaluated using water sensitive papers. The overall canopy deposition and coverage for treatment configured with modified irrigation micro-emitters (955.5 \(\pm\) 153.9 [mean \(\pm\) standard error of mean] ng cm\({}^{-2}\) and 22.7 \(\pm\) 2.6%, respectively) were numerically higher than the hollow cone nozzles (746.2 \(\pm\) 104.7 ng cm\({}^{-2}\) and 19.0 \(\pm\) 2.8%, respectively). Moreover, modified irrigation micro-emitter SSCDS had improved spray uniformity in the canopy foliage and on either side of leaf surfaces compared to a hollow cone nozzle. Ground and aerial spray losses, quantified as deposition, were numerically lower for the modified irrigation micro-emitter (121.8 \(\pm\) 43.4 ng cm\({}^{-2}\) and 0.7 \(\pm\) 0.1 ng cm\({}^{-2}\), respectively) compared to the traditional hollow cone nozzle (447.4 \(\pm\) 190.9 ng cm\({}^{-2}\) and 3.2 \(\pm\) 0.4 ng cm\({}^{-2}\), respectively). Overall, the modified irrigation micro-emitter provided similar or superior performance to the traditional hollow cone nozzle with an estimated 12 times reduction in system installation cost.  
Keywords:fixed spray delivery; SSCDS; spray drift; deposition; coverage
## 3 Methodology
### Coordinate Frames  
The transformation of quantities, encompassing parameters such as attitude, velocity, and position, across diverse coordinate frames is an essential requirement for any navigation system. In this research, four specific coordinate frames were utilized: the Earth-centered Earth-fixed (ECEF), local level (LLF), body (B), and Earth-centered inertial (ECI) frames. The coordinate frames utilized in this paper are illustrated in Figure 2[3].
## 3 Results and Discussion
### Model Testing Using Real Fractures
Future research could focus on incorporating real rock fracture datasets into the training process to enhance models' ability to generalise the natural geometry that is not present in synthetic data. Used datasets could include real fracture samples with a broader range of parameters to improve models' performance in predicting physical parameters across various fracture types. Additionally, for synthetic data generation, including variations in amplitude factors and having varying apertures in fractures could better mimic the geometry seen in real fractures. Implementing data augmentation strategies to mimic the variability of real fractures or utilising models that process fully three-dimensional input would allow them to capture more information and the characteristics of fractures. Finally, integrating physics-informed machine learning approaches into models' architectures could improve the accuracy of predicting physical parameters.
# Phase Shift Design in RIS Empowered Wireless Networks: From Optimization to AI-Based Methods
## 4 Learning to Optimize An RIS
### Unsupervised Learning  
The connection between unsupervised learning and problem (5) comes from the observation that (5) can be regarded as an unconstrained optimization problem if the variable is viewed in terms of \(\theta_{m}\) instead of \(\mathbf{e}\). This view has been adopted in the GD method in Section 3. However, in contrast to the GD method for solving (9) with respect to \(\mathbf{\Theta}\), unsupervised learning uses a DNN that accepts a channel realization as input and generates the corresponding \(\mathbf{\Theta}\) as output, where the optimization is with respect to the coefficients of the DNN. In unsupervised learning, the objective is to minimize \(\mathbb{E}[f(\mathbf{x},e^{i\mathbf{\Theta}})]\), where the expectation is with respect to the distribution of input channel state information.  
The training procedure involves first generating a large number of channel realizations and then optimizing \(\mathbf{\Theta}\) and \(\mathbf{x}\) under the BCD framework. When optimizing \(\mathbf{\Theta}\), back propagation is used. On the other hand, when optimizing \(\mathbf{x}\), a conventional optimization technique is used with the expectation tackled via sampling approximation. Different from supervised learning, this approach does not require the labeling of data, which saves a significant amount of time in training data preparation. However, a disadvantage is that the obtained solution does not have any quality guarantee.
## 2 Materials and Methods
### Active Equalization  
EQ can also be designed with the use of active elements as amplifiers to gain improvement. The active EQ usually consists of passive EQ and additionally an amplifier is used to enhance the signal's level in the entire frequency domain, high and low. The main disadvantage of this type of active EQ is the overall cost, as more active and passive circuit components are used, but also the increased area occupied by EQ will result in large dimensions of the system. This method is often done by using a split-path approach [11]. The input is split in two paths: one that has unity gain and another is to be high frequency boost. At the end, the two signals are summed into a new equalized input signal into the lossy line [2].
## 2 System Architecture Framework
### Loop Closure Detection  
The loop closure detection module identifies loops when the device returns to previously observed areas and triggers global optimization. To improve loop detection accuracy, this paper proposes a loop detection method combining point and line features. The detection process first searches for potential loop candidate frames in the keyframe database and verifies the loop's existence through point and line feature matching. Once a loop is detected, the system performs local and global optimizations, adjusting relevant keyframe poses and map point coordinates, thereby ensuring system robustness in large-scale environments.