esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Principal Component Analysis (PCA)  
PCA could deduct dimensions and observe a primary evaluation of the between-class similarity PCA is a projection method that allows an easy visualization of all the information contained in a data-set. In addition, PCA helps to find out in what respect a sample is different from others and which variables contribute most to this difference. The integral value, wavelet energy value, maximum gradient value, average differential value, relation steady-state response average value and variance value were applied as characteristic parameters to perform a principal component analysis on fish meal at six different storage time levels. Figure 3 showed a principal component analysis diagram of fish meal of six different qualities with different characteristic parameters. The diagram showed that the sum of the first principal component analysis value and the second principal component analysis value was greater than 85% when all characteristic values were employed as parameters. This finding showed that the electronic nose adequately reflected the volatile gases of fish meal at different storage times, and the distribution results were similar but the classification results were not satisfactory. For each feature vector classification result, all fish meal samples were divided into two categories--the sixth and the other--that is, rotten samples were clearly distinguished from fresh samples but the distinction was almost not distinct for the first five categories of samples, which indicated that the odor volatilized from rotten samples and fresh samples distinctly differed while all fresh samples overlapped due to similar volatile odors. Considering all PCA analysis charts, when the relation steady-state response average value was employed as the characteristic parameter of fish meal of different quality grades, the PCA analysis chart had a better discrimination effect, and the discrimination effect using the maximum gradient value and the variance value was the worst. In some cases, the distance between groups was smaller (fresh samples) and the distance within groups was larger (corrupt samples).
## 6 Conclusions  
The current study utilized hydrogeochemistry and environmentally stable isotopes to determine the main recharge sources and geochemical processes affecting groundwater in the Qena basin, located in the Eastern desert of Egypt. This basin comprises three main groundwater aquifers: the Quaternary aquifer, the Post Nubian aquifer (PNA), and the Nubian Sandstone aquifer (NSA), which is mainly controlled by lithological and structural features. Groundwaters in the upstream watershed generally had fresh to brackish water, whereas those downstream were mainly brackish to saline. Isotopic data (\(\delta^{18}\)O and \(\delta^{2}\)H) revealed that Nubian groundwater was relatively depleted, primarily of meteoric origin and a paleo-water recharged in a cooler climate. The NSA had higher groundwater temperatures than the other aquifers. Quaternary groundwater located at the upstream watershed received considerable recharge from recent meteoric water and upward leakage from the NSA. The downstream Quaternary aquifer in the delta of the Qena basin was characterized by mixed groundwater composed of upstream water with recent River Nile water. Isotopic analysis confirmed recharge of the Post-Nubian groundwater aquifer mainly from upward leakage from the NSA under artesian conditions and seepage from the upper unconfined Quaternary aquifer. NETPATH geochemical model results indicated that the evaporation process, water-rock interaction, and mixing are the physical and geochemical processes controlling groundwater quality, where leaching and dissolution processes of terrestrial minerals and silicate weathering prevail. The Nubian groundwater aquifer has a great expanse, considerable thickness, possesses good groundwater quality, and should be well explored and well managed for sustainable groundwater use.  
A.R.: Carried out the fieldwork and chemical analysis and field data interpretation for the geochemical groundwater characteristics. M.E.: Carried out the fieldwork, conceived of the presented idea, and write the part of the methodology, as well as interpretation of geochemical and isotopic data and contribute in writing the whole manuscript with input from all authors. I.E.S.: revise the whole manuscript and propose the research point, E.D.: Carried out measurements for stable environmental isotopes at her laboratory. M.S.: Carried out the interpretation of geochemical analyses for surface and groundwater samples. and S.M.: Shared with writing the part on groundwater geochemistry, in addition, she wrote part of the result and discussion in the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Temporal Subtraction Technique for Thoracic MDCT Based on Residual VoxelMorph
###### Abstract  
The temporal subtraction technique is a useful tool for computer aided diagnosis (CAD) in visual screening. The technique subtracts the previous image set from the current one for the same subject to emphasize temporal changes and/or new abnormalities. However, it is difficult to obtain a clear subtraction image without subtraction image artifacts. VoxelMorph in deep learning is a useful method, as preparing large training datasets is difficult for medical image analysis, and the possibilities of incorrect learning, gradient loss, and overlearning are concerns. To overcome this problem, we propose a new method for generating temporal subtraction images of thoracic multi-detector row computed tomography (MDCT) images based on ResidualVoxelMorph, which introduces a residual block to VoxelMorph to enable flexible positioning at a low computational cost. Its high learning efficiency can be expected even with a limited training set by introducing residual blocks to VoxelMorph. We performed our method on 84 clinical images and evaluated based on three-fold cross-validation. The results showed that the proposed method reduced subtraction image artifacts on root mean square error (RMSE) by 11.3% (\(p<0.01\)), and its effectiveness was verified. That is, the proposed temporal subtraction method for thoracic MDCT improves the observer's performance.  
digital healthcare; medical imaging analytics; computer aided diagnosis; temporal subtraction technique; nonrigid image registration; deep neural network; VoxelMorph; residual blocks +
Footnote †: journal: Medical and Control Engineering
# An Energy Demodulation Based Fiber Optic Sensing System for Landslide Early-Warning
## 4 The FOS-LW Components
### The Alarm System  
A critical component of the alarm system is to set up an appropriate threshold level. FOS-LW used a graded threshold approach. Based on previous experiences, a typical landslide is expected to move slowly in the early stage and the movement will accelerate prior to full collapse. Figure 6 shows the different stages of a collapse with color coded to the corresponding alarm levels, which are described as follows:  
* I--Creep Stage  
The creep stage (green level) is a relatively safe stage, and during which, the sensors in the field should be monitored carefully with periodic inspections and analysis of the resulting data regularly.  
* II--Acceleration Stage  
At the acceleration stage (yellow level), the slope movement is picking up its pace, and there should be an increase in the frequency of data acquisition and review of the data, such that the underlying cause of slope movement can be determined through the data analysis. The most important aspect of this monitoring stage is to determine whether the threshold level should be maintained or lowered to ensure safety.  
* III--Failure Stage  
If the slope on the acceleration stage is out of control, and the slope deformation accelerates, the alarm system should raise the alarm level to red (warning level). At this stage, the engineers will be able to determine the proper courses of action either to minimize or eliminate possible economic and life losses associated with the slope failure.  
It should be noted that the complete movement of a landslide can last for a few days or several years while the change in key factors may be very rapid. Because fiber optic sensors can be distributed and networked for different level conditions, the alarm system can be designed to send out specific alarm message in various ways: The alarm signal can be transmitted through sound and light warnings around the danger area. For distances further from the source of danger, the warning could be transmitted by text messaging via mobile devices. Remote scheduling and guiding work can be communicated to the system via wireless transmitting networks. Through this alarm system, one not only can warn of the danger, but also help to direct rescue efforts efficiently and remotely.  
Figure 5: The signal transmission flow and the relationship between each integrated modules.
# Performance Analysis of Undifferenced NRTK Considering Time-Varying Characteristics of Atmosphere
## 2 Positioning Model of BDS Undifferenced NRTK  
Starting from the BDS UD observation equation, the generation of single-reference-station UD error correction values and multi-reference-station UD error correction values, user error interpolation, and positioning methods are introduced.
## 5 Numerical Results  
### Simulation Setup  
This section presents the system performance derived through computer simulations, applying the proposed matching game approach, a greedy algorithm based on the selection of the EDs at minimum delay (MDG), the random one (RA). Then, we have considered two well-known game theory based frameworks: the Kolkata paise restaurant game (KPRG) [37; 38], the potential game approach (POT) [39; 40], and the method proposed in [41], hereafter referred as REL.  
The REL algorithm act as follows  
* a threshold for the quality condition of the link towards each node is set. The threshold is expressed in terms of link delay;
* every link which satisfies the quality condition is selected as a relay link;
* the connection graph \(\mathcal{G}_{d}\) is updated in accordance with the degree of the nodes selected as relays;
* when any link has a quality greater than the fixed threshold, direct links are established until all the nodes are reached.  
The considered MDG approach consists of the following steps:  
1. the SN \(\mathcal{S}\) establishes direct links with the disconnected EDs, according to the topology of \(\mathcal{G}_{d}\);
2. the SN \(\mathcal{S}\) selects as a relay node the ED at minimum distance;
3. the topology of \(\mathcal{G}_{d}\) is updated;
4. repeat 1-4 until all the EDs are reached by one link.  
Furthermore, the random algorithm has been adapted as follows:  
1. the SN \(\mathcal{S}\) selects the direct link communication mode with the disconnected EDs in \(\mathcal{G}_{d}\);
2. the SN \(\mathcal{S}\) selects with uniform probability whether to establish direct link or to select a relay node;
3. the topology of \(\mathcal{G}_{d}\) is updated according to the previous decision;
4. repeat 1-4 until all the EDs are reached by one link.  
The DLM acts by establishing a direct link between \(\mathcal{S}\) and each ED. In order to apply the KPRG strategy to our problem scenario, we have modified the approach as follows:  
* during each step, \(\mathcal{S}\) establishes one direct link, as far as possible;
* during each step, \(\mathcal{S}^{\prime\prime}\) selects one relay, as far as possible;
* during each step, \(\mathcal{S}\) establishes one direct link towards the most disconnected ED.
* during each step, \(\mathcal{S}^{\prime\prime}\) selects as a relay the ED with maximum degree.  
Finally, we implemented the potential game approach already proposed in [39], but considering its sub-optimal version as in [40], in which the players involved in the game can deviate from the Nash equilibrium up to \(\epsilon\), with \(\epsilon=0.01\), in order to consider an alternative approach with an acceptable computational complexity.
## 3 Proposed Methodology
### Indexing Strategy Based on Quadtree
#### 3.2.3 Complexity Analysis  
**Lemma 1**.: _The time complexity of quadtree building with input size \(n\) is \(O(n\log n)\)._  
Proof.: Due to the linear relationship between the complexity of compression algorithms and input size, analyzing the complexity of quadtree building only requires calculating the sum of the product of the number of nodes in the construction tree and the size of node maintenance data. We denote it as function \(Sum(n)\). Without losing generality, we assume that input size \(n=4^{k}\), and we will obtain  
\[Sum(n)=\sum_{i=0}^{k}4^{k-i}\cdot 4^{i}=k\cdot 4^{k}=O(n\log n) \tag{5}\]  
Therefore, the time complexity of quadtree building with input size \(n\) is \(O(n\log n)\).\(\square\)  
**Lemma 2.** The time complexity of the range query of size n is \(O(n\log n)\)  
Proof.: The complexity of range queries mainly consists of two parts: one is the time complexity involved in decompressing node data, and the other is the time complexity involved in merging decompressed results. \(\square\)  
In the worst-case scenario, the queried nodes will go deep into the bottom layer. According to the feature of the tree structure, the depth of the quadtree is \(O(\log n)\). We denote the sum of the data scale of all queried nodes as \(Sum(n)\). According to Lemma 1, we can draw the following conclusion:  
\[Sum(n)\leq n\log n \tag{6}\]  
So in terms of decompressing node data, at least the time complexity of \(O(n\log n)\) is required.  
In addition, the complexity of the recursive algorithm merging process is linear complexity. According to the master theorem, the time complexity of merging decompressed results is \(O(n\log n)\).  
Taking into account the complexity of both, the total complexity is \(O(n\log n)\).
# Computer Vision System: Measuring Displacement and Bending Angle of Ionic Polymer-Metal Composites
## 2 Computer Vision System
### Contour Extreme Points: Finding Displacement and Bending Angle  
As IPMC displaces under the influence of voltage, the tip of the IPMC will always be at the end--an extreme point of the contour. OpenCV accesses pixels through a Cartesian coordinate system, represented as 2-tuples (x,y), with the origin (0,0) being at the top-left corner of the image [27]. The values in the x and y axes increase positively as you move diagonally across the image. Deducing the tip of the IPMC to always have a maximum y-value, in the code, this point can be located using the _argmax()_ function, specifying it for the y-value of the contour points [28]. This can be used to find the tip point in every frame and calculate the distance between those points to get the displacement values. A linear displacement method with reference to the initial frame is utilised, similar to the method described in Figure 2. It should be noted that the bending is more accurately described by the arc length between two IPMC tips. However, due to the non-uniform curvature and off-axis deformations of IPMCs [14], the bending centre changes. Calculating the arc length  
Figure 11: Sample contour approximation using (**a**) _CHAIN_APPROX_NONE_ and (**b**) _CHAIN_APPROX_SIMPLE_.  
is difficult as the pivot point and radius (effective length of the sample) change. [PERSON] et al. proposed a method to utilise the arc length calculation by detecting several points on the IPMC to find the radius and using the intersection of lines through those points to find the bending centre [11; 12]. However, no details were given as to how those points were detected by the algorithm.  
The IPMC tips are also used to calculate bending angle. One of the many ways to determine the bending angle in this case is by employing the Law of Cosines, a law which relates the lengths of the triangle to the cosine of the angles. Such relations are given in Equations (1)-(3). Relating the triangle to the IPMC sample in Figure 12, points A and B are the tips of the IPMC as displacement occurs. The angle of interest, \(\gamma\), is the bending angle. Point C is a reference point--the topmost point of the IPMC contour. This point can be identified using the _argmin()_ function, which finds the minimum value, and specifying it for the y-value of the contour points. Having detected all three points of the triangle, Equation (1) can be used to find bending angle \(\gamma\). In this application, points B and C are identified in the initial frame and are kept constant as they serve as reference points for measurement. Only point A changes with each subsequent frame. In the code, the _Math_ module was employed to use the arccosine function (_math.acos()_) to find the angle. Note that this function outputs the angle in radians. Conversion from radians to degrees can be done using the _math.degrees()_ function.  
\[c^{2}=a^{2}+b^{2}-2 ab\cos\gamma \tag{1}\]  
\[a^{2}=b^{2}+c^{2}-2 bc\cos\alpha \tag{2}\]  
\[b^{2}=a^{2}+c^{2}-2 ac\cos\beta \tag{3}\]
## 2 Methodology
### Processing Strategies  
With the strategies listed in Table 1, satellite clocks can be estimated using the above four models. In PPP, time-varying SCBs are not estimated because of the difficulty of eliminating rank deficiency. Therefore, if the satellite clocks employed in PPP are estimated using the model extracting biases, code equations will be disturbed. In this study, we reduce this adverse effect by amplifying the noise of code observations.
# A Novel System of Mixed RF/FSO UAV Communication Based on MRR and RIS by Adopting Hybrid Modulation
## 3 Performance Analysis  
### Outage Probability  
The outage probability is a standard performance metric in wireless communication and refers to the probability that the end-to-end SNR \(\gamma\) falls below a specified threshold \(\gamma_{th}\) resulting in an interruption of data transmission. It is expressed as [39; 40]:  
\[P_{out}(\gamma<\gamma_{th})=F_{\gamma}(\gamma_{th}). \tag{13}\]
# Collective Communication Performance Evaluation for Distributed Deep Learning Training
## 6 Experiments in the PyTorch Environment
### Parameter Server Subroutine
#### 6.1.2 Gather  
In Figure 20, the results of the subroutines gather and allgather among the parameter server methods are displayed. When measuring MPI, gather shows lower latency in a single container (singularity, single docker) than in bare metal when using four GPUs. For GLOO_Gather, there is a noteworthy 36% reduction in latency in the cross-docker environment compared to other environments. This difference suggests that cross-container communication generally takes less time than separate-container processes. In the case of NCCL_Allgather, it is evident that latency significantly increases as the number of GPUs used in the cross-docker setup increases. With four GPUs in use, the latency is 54% higher  
Figure 19: PyTorch parameter server broadcast latency.  
compared to the bare metal environment. This indicates that NCCL's performance is particularly impacted by the configuration and scaling of GPUs in a cross-docker environment.
# Predicting the Duration of Forest Fires Using Machine Learning Methods
## 2 Materials and Methods
### The Used Machine Learning Methods
#### 2.2.6 Random Forest  
Random Forest [104; 105] is a popular supervised machine learning algorithm, used to construct decision trees for classification problems. The method of Random Forest has proven its adaptability and effectiveness in a number of difficult problems, such as remote sensing classification [106], ecology issues [107], bioinformatics [108], text categorization [109], network intrusion detection [110], etc. Moreover, Random Forest has been incorporated in forest fire prediction, such as in the work of [PERSON] et al., where Random Forest was applied to predict forest fires in Borneo [111]. Also, [PERSON] et al. proposed the usage of Random Forest for wildfire risk prediction in northern California [112]. Also, [PERSON] et al. performed a forest fire risk prediction [113] in China using a combination of Random Forest and a neural network trained with the back-propagation method [114].
## 2 BESS and Frequency Regulation Service  
### Bess  
BESS includes batteries, battery management systems (BMS), power conditioning systems (PCS), and energy management systems (EMS). Applications of lithium-ion batteries are very common nowadays due to their several advantages, such as high energy and power densities and longer lifetime than that of other technologies [6]. Specifically, the lithium ternary battery (LiNiMnCoO\({}_{2}\) battery, NMC battery) and the lithium iron phosphate battery (LiFePO\({}_{4}\) battery, LFP battery) are in the mainstream of MW-level battery energy storage systems.  
The main applications of BESS can be roughly classified into five categories: large-scale energy services, ancillary services, services for transmission and distribution, client services, and renewable energy integration. Specifically, renewable energy smoothing, frequency regulation services, voltage regulation services, peak-shaving, and load-shifting are some of the most common applications in modern life [7; 8]. This study will focus on frequency regulation services, which is going to be discussed in detail in the following section.
## 3 Methodology
### Utility Score Calculation  
Finally, to calculate the utility scores, we weighted these scores according to their influence on answer quality, with weights ranging from 1 to 5. The most important aspects of a high quality answer were domain knowledge and factual correctness. Therefore, contextual understanding was weighted with 5, and validity was weighted with 4. Credibility, which was tied to the absence of grammatical errors, led to grammar being weighted with 3. Structure and style, which ensured comparability of answers, were weighted with 2. Finally, latency was weighted with 1, since the generation time did not affect the correctness of the answer.  
The calculation of the utility score is summarized in Figure 7. For each experiment, an answer_score was calculated by multiplying the weights with the scores of each category. These scores were then summed across all experiments and averaged by dividing the sum by 4. Finally, the benchmark scores were weighted with the maximum weight of five, as the benchmarks allowed for a quantitative analysis of the model performance in the use case, and then added to the experiment score.  
\begin{table}
\begin{tabular}{l l c} \hline \hline
**Criterion** & **Description and Scoring** & **Weight** \\ \hline \multirow{2}{*}{Latency} & Points based on response time: \textgreater{}5 s (5), \textgreater{}7.5 s (4), \textgreater{}10 s & 1 \\  & (3), \textgreater{}12.5 s (2), \textgreater{}15 s (1), \textless{}15 s (0). & 2 \\ \multirow{2}{*}{Structure and Style} & Evaluates clarity: list format (1), 10 bullets (1), precise & 2 \\  & language (1), simplicity (1), no repetition (1). & 3 \\ \multirow{2}{*}{Grammar} & Assesses errors: no errors (5), 1 error (4), 2 errors (3), 3 errors (2), 4 errors (1), \textgreater{}4 errors (0). & 3 \\ \multirow{2}{*}{Validity} & Relevance to GDPR guidelines: 100\% (5), 90\% (4), 80\% & 4 \\  & (3), 70\% (2), 60\% (1), \textless{}60\% (0). & 4 \\ \multirow{2}{*}{Contextual Understanding} & Measures comprehensiveness: no redundancy (1), specific fields (1), coverage of six key contexts (3). & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Scoring criteria for answer evaluation.  
In the following section, we present the results of the experiment and apply the defined model evaluation criteria.