esa-sceva/satcom-chunk-collection huggingface dataset:
# Retinal Image Analysis for Diabetes-Based Eye Disease Detection Using Deep Learning
## 2 Proposed Methodology
### Segmentation of Regions through Fuzzy K-Means Clustering  
The localized regions are cropped using calculated coordinates to determine the accurate affected region's boundary. We have used the fuzzy k-means (FKM) technique separately for the segmentation of regions of three eye diseases. In the case of DR segmentation, the localized regions are segmented separately. The localized regions are set to foreground and the value of other portion is set to zero means black color. Similarly, the localized macular region is segmented through FKM clustering method. At last, we segmented the glaucoma localized regions i.e., OD and OC separately. The FKM method is described as follows: The method [49] divides the image into \(k\) regions where \(R_{i}\), (\(l\) = 1, 2, 3,..., \(k\)) are related to the cluster centered \(C_{r}\). There is a fuzzy relationship between regions and image data. The FKM method is based on minimizing distortion given by Equation (4):  
\[L=\sum_{j=1}^{k}\sum_{i=1}^{N}b_{i,j}^{f}g_{i,j} \tag{4}\]  
Here \(k\) is the number of clusters while the \(f\) is fuzzifier parameter that pointedly manipulates the data points and resultant clusters. While for a given data point \(X_{i}\) and a cluster \(C_{j}\), \(b_{i,j}\)\(\in\) [0,1] represents the belongings among the centers of the cluster \(C_{j}\) and \(X_{i}\), while \(g_{i,j}\) represents the distance among \(C_{j}\) and \(X_{i}\).  
FKM clustering improves by mapping illustrative vectors and partitioning of data points [50]. It performs the following steps:  
1. Specify the number of clusters \(C_{j}\).
2. Set \(C_{j}(0)\) to the initial clusters.
3. Compute the membership \(b(i,j)\) of all datapoints \(X_{i}\) for each \(C_{j}\) using following equation: \[b(i,j)=((g_{(i,j)}^{(1/m-1)}\sum_{l=1}^{k}{(\frac{1}{g_{il}})}^{\frac{1}{m}-1 }))^{-1}\] (5) where \(m\) is the fuzzification coefficient.
4. Update cluster centers using following equation: \[C_{j}(p)=\frac{\sum_{j=1}^{N}{b_{i,j}}^{m}X_{i}}{\sum_{j=1}^{N}{b_{i,j}}^{m}}\] (6)
5. Repeat from step 3, till the FKM is converged (the centroids updated between two passes is not greater than \(\varepsilon\), the defined sensitivity threshold).  
Finally, we have clustered the regions into affected and non-affected portions.
# Research on Shore-Based River Flow Velocity Inversion Model Using GNSS-R Raw Data
## 3 Shore-Based Velocity Inversion Experiment  
### The Experimental Set-Up  
On 22 April 2021, a shore-based GNSS-R river flow velocity inversion experiment was carried out on the south bank of the Yangtze River basin (31\({}^{\circ}\)57\({}^{\prime}\)43\({}^{\prime\prime}\) N, 118\({}^{\circ}\)38\({}^{\prime}\)27\({}^{\prime\prime}\) E) near Dashengguan, Nanjing, China. The shore-based experiment was performed for almost 24 h, but because of damage to the receiver's raw IF data reception during the experiment, the experimental results analysis only used about 2 h of data, from 9:30 a.m. to 11:30 a.m. (LT). Detection antennas and receiving equipment for receiving GPS/BDS dual-frequency direct/reflected signals were set up on the deck of a stationary cargo ship. The specific experimental set-up and antenna tilt settings are shown in Figure 7.  
The parameters were as follows: The direct antenna points upward to maximize direct signals' contribution from above and to optimally suppress reflections. The height of the antenna is 5.45 m. The reflecting antenna points downwards with a tilt angle of 45.8 degrees, and the vertical height difference between the direct antenna and the reflecting antenna is 0.38 m. The orientation of the erected antenna is 304 degrees northwest (facing the Yangtze River). Table 1 is a summary of the experimental parameters.
# Gathering Big Data in Wireless Sensor Networks by Drone +
## 2 Related Works
However, they focused on reducing the number of data mules to gather data and did not consider the data mule limitation. [PERSON] et al. [11] proposed a mobile sink trajectory algorithm to reduce the delay for data delivery and prolong the network lifetime. It shortens the trajectory length of the mobile sink and balances the load of rendezvous nodes. This algorithm is based on multiobjective particle swarm optimization. To shorten the mobile sink's trajectory length, the authors designed a mechanism to select potential visiting points within communication overlapping ranges of sensor nodes. Additionally, according tothe mobile sink's trajectory characteristics, they designed an effective trajectory encoding method to generate a trajectory containing an unfixed number of visiting points. [PERSON] et al. [19] also proposed a mobile sink trajectory algorithm. However, they focused on reducing the network energy consumption for data gathering. This algorithm is based on LEACH_C to create clusters and on Dijkstra to define the mobile sink trajectory. The authors of both works did not consider the limitation of the mobile collector. Furthermore, they consider sensor nodes with only one data packet to send. [PERSON] et al. [20] proposed an algorithm to define the tour followed by mobile sinks to collect data from WSNs. It also creates a virtual grid to divide the nodes into clusters, each with a cluster head. The proposed algorithm defines where the sink has to go after each data gathering. The author mentioned that one of the major problems is the mobile sink's slow speed, which demands too much time to collect all data. They consider a sink with no trip distance limitation. [PERSON] et al. [21] also considered mobile collectors for data collection in large-scale WSN. They proposed a clustering algorithm to calculate the optimal number of cluster members to minimize energy consumption. However, again they do not care about the limitation of the battery of the mobile collector. Finally, [PERSON] et al. [8] proposed an analytical model to determine sensor nodes' energy consumption in large-scale WSN using mobile collectors. They proposed a model to determine how nodes are divided into clusters to minimize energy consumption. In this work, the WSNs are disconnected, that is, they are subdivided into groups of geographically separated nodes. So, the nodes of a group do not communicate with other groups. They consider neither the mobile collector limitations nor nodes storing a large volume of data.
# Dynamic Performance of Soil-Tunnel System under Transverse Sinusoidal Excitations
###### Abstract  
Input of sinusoidal excitation with specified frequency is an optimal way to capture the mechanism of soil-tunnel interaction. Focusing on the relationship between the frequency of input sinusoidal motions and the dynamic response of a system, this study carried out a series of shaking table tests on both a free-field model and soil-tunnel model in the background of the tunnel in soft ground. To detect the detailed deformation of segmental linings, a refined lining ring of the model tunnel was developed, and the stiffness ratio between the soil and tunnel was verified. Seven sinusoidal excitations were designated to cover the fundamental frequency of the model ground, with the input of transverse direction. Effects of frequency of sinusoidal excitations on soil-tunnel interaction can be evaluated by the detailed responses of segmental linings, such as cross-sectional deformations, extension/closure of longitudinal joints, dynamic normal earth pressures, and dynamic strains of segments. Results shows that the differences of the acceleration responses, on the respects of waveform, phase, and peak, between the upper soil layer and the lower soil layer are obviously increasing with the input frequency increasing. The presence of the tunnel induces a relatively high effect on acceleration responses of the ground within excitation frequency varying from 9 to 17 Hz. The maximum responses of the tunnel are highly influenced by both the fundamental frequency of the model ground and lower frequency of excitations.  
shaking table test; refined lining ring; sinusoidal excitation; soil-tunnel interaction; detailed deformation +
Footnote †: journal: Applied Sciences
# Examining the Efficiency of Electric-Assisted Mountain Biking across Different Types of Terrain
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Uphill**} & \multicolumn{2}{c}{**Downhill**} & \multicolumn{2}{c}{**Flat**} & \multicolumn{2}{c}{**Technical**} \\ \cline{2-9}  & **e-MTB** & **MTB** & **e-MTB** & **MTB** & **e-MTB** & **MTB** & **e-MTB** & **MTB** \\ \hline HR (ppm) & 156 \(\pm\) 10 & 166 \(\pm\) 4.9 & 133 \(\pm\) 18 & 143 \(\pm\) 8.0 & 147 \(\pm\) 14 & 159 \(\pm\) 6.9 & 152 \(\pm\) 18.8 & 164 \(\pm\) 6.1 \\ Rf (br/min) & 46.2 \(\pm\) 4.5 * & 52.4 \(\pm\) 5.1 * & 38.9 \(\pm\) 8.4 & 45.6 \(\pm\) 7.1 & 43.9 \(\pm\) 7.5 * & 54.3 \(\pm\) 6.5 * & 48.3 \(\pm\) 7.4 & 55.7 \(\pm\) 8.9 \\ Ve (L/min) & 117 \(\pm\) 19 * & 138 \(\pm\) 9 * & 80 \(\pm\) 23 & 92 \(\pm\) 23.0 & 98 \(\pm\) 29. & 123 \(\pm\) 15 & 104 \(\pm\) 38 & 132 \(\pm\) 24 \\ VO\({}_{2}\)/kg(mL/min/kg) & 56.4 \(\pm\) 2.8 * & 60.9 \(\pm\) 3.8 * & 31.8 \(\pm\) 6.5 & 33.6 \(\pm\) 9.4 & 48.8 \(\pm\) 8.4 & 50.2 \(\pm\) 6.4 & 48.6 \(\pm\) 11.1 & 54.1 \(\pm\) 5 \\ GE & 16.5 \(\pm\) 1.3 * & 19.2 \(\pm\) 1.2 * & 9.5 \(\pm\) 4.3 * & 16.5 \(\pm\) 5.8 * & 15.0 \(\pm\) 1.4 * & 20.7 \(\pm\) 2.6 * & 13.7 \(\pm\) 3.3 * & 19.1 \(\pm\) 2.7 * \\ \hline \hline \end{tabular}  
* The values presented are means \(\pm\) standard deviations. HR—heart rate frequency; Rf—respiratory rate; Ve— ventilation; VO\({}_{2}\)/kg (mL/min/kg)—relative oxygen uptake; GE—gross efficiency;
* Indicates statistical differences at \(p<0.05\) between the electric-assisted and conventional mountain bikes.  
\end{table} 
Table 3: Comparative analysis of physiological parameters in different types of off-road terrains: electric-assisted (e-MTB) vs. conventional (MTB) mountain bikes. The main objective of this comparison was to evaluate the impact of electric assistance on mountain biking performance requirements on different trail sections. By focusing on the uphill section, this objective was achieved, and the profound impact of electric assist on power demand was highlighted.
## 2 Materials and Methods
### In Situ Experiment  
The experiment commenced in 2010 and constitutes a long-term positioning test covering an area of 84 m \(\times\) 160 m (Figure 2). A plot with adjacent level terrain and relatively homogeneous basic soil fertility was chosen, and five treatments were configured, respectively: (1) continuous soybean cultivation (SC); fertilizer application: diammonium phosphate 120 kg\(\cdot\)ha\({}^{-1}\), potassium oxide 90 kg\(\cdot\)ha\({}^{-1}\); (2) continuous maize cultivation (MC); only compound fertilizer was applied, N-P\({}_{2}\)O\({}_{5}\)-K\({}_{2}\)O (26-10-12), with an application rate of 260 kg\(\cdot\)ha\({}^{-1}\); (3) maize-soybean rotation (MSR); single application of compound fertilizer, N-P\({}_{2}\)O\({}_{5}\)-K\({}_{2}\)O (26-10-12), with an application amount of 260 kg\(\cdot\)ha\({}^{-1}\); (4) Recreational Grass Belt, referred to as grass belt (GB): no crops were planted, no fertilization was conducted, and vegetation grew naturally; (5) Fallow Forest Belt, referred to as forest belt (FB): poplar trees (_Populus_ L.) were planted and grew naturally, without tillage and any fertilizer application. Each treatment was replicated three times. The soybean variety used was Jinong 40, while the maize variety was Xianyu 335. The planting of crops was undertaken in May, with harvesting occurring in October. Prior to the spring planting, both maize and soybean fields required fertilization. All the fertilizers required in this experiment were from Liaoning Feichi Fertilizer Industry Co., Ltd. (Dandong, China). It was essential to fertilize the base of the field trench before sowing at the end of April, after which it was covered with a thin layer of soil. The crops were subsequently planted in May and were maintained through weeding with tools throughout the growing season. Following the harvest in October, all maize and soybean straw was mechanically crushed and returned to the soil. Additionally, grass in the GB treatment and trees in the FB treatment grew naturally without artificial interference or fertilization.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**SOC (g\(\cdot\)kg\({}^{-1}\))** & **Total N (g\(\cdot\)kg\({}^{-1}\))** & **Total P (mg\(\cdot\)kg\({}^{-1}\))** & **Total K (g\(\cdot\)kg\({}^{-1}\))** & **pH** \\ \hline
16.8 & 1.29 & 1.05 & 2.39 & 6.6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic properties of the soil at the beginning of the experiment.  
Figure 1: Study site.
## 4 Density for Clustering Calling Patterns and Detecting Outliers
### Density  
The method that is proposed in this research work is based on the density (D) value that can measure the similarity between a specific data observation and other previous observations. For example, if only two features are considered, each data record can be represented in a two-dimensional space as a point (Figure 2). In this figure, the density at point B is low, while at point A it is high. Thus, B could be a potential outlier and/or be part of a new cluster.  
Figure 2: Representation of two features in a two-dimensional space.  
In this research, the feature vector can be constructed using the calls data (time, duration, and location) as follows:  
\[x_{k}=\ [\ TimeCall_{k},\ DurationCall_{k},\ CellTowerLocation_{k}], \tag{1}\]  
where \(x_{k}\) is the \(k^{th}\) observation/call and \(TimeCall_{k},\ DurationCall_{k},\ CellTowerLocation_{k}\) are the information about the \(k^{th}\) call.  
In addition, using the pre-process dataset _CallsDay1h_, the feature vector should be constructed as follows:  
\[x_{k}=\ [T_{1},\ T_{2},T_{3},T_{4},\ldots,T_{21},\ T_{22},T_{23},T_{24}], \tag{2}\]  
where \(x_{k}\) is the \(k^{th}\) observation/caller and \(T_{h}\) represents the total duration of calls (seconds) made by the caller from \(h-1\) to h hours by the caller during a specific day.  
Using the pre-process dataset _CallsDay6h_, the feature vector should be constructed as follows:  
\[x_{k}=\ [T_{1},\ T_{2},T_{3},T_{4}], \tag{3}\]  
where \(x_{k}\) is the \(k^{th}\) observation/caller and \(T_{h}\) represents the total duration of calls (seconds) made by the caller in the range of six hours (\(h\)) during a specific day.  
Using this vector of features, the similarity measure can be expressed as an inverse of the sum of distances between the current data observation and the previous (\(k\)\(-\)\(1\)) ones, as follows:  
\[D(x_{k})=\frac{1}{1+\ \frac{1}{k}\ \sum_{i=1}^{k}\|x_{k}-x_{i}\|^{2}}, \tag{4}\]  
where \(x_{k}\) is the vector data of the current (\(k^{th}\)) observation (call or caller) whose similarity, to previous observations, is measured; \(k\) denotes the number of observations read, and \(n\) is the number of attributes that are being used to represent a specific mobile call or caller.
## 3 Results
### Determine the Optimal Exposure Time  
The optimal exposure time was determined based on the analysis of samples containing a series of pins and holes. Figures 19 and 20 show samples printed with the shortest and longest analyzed exposure times, as well as with a time of t = 3 s, which was identified as optimal based on the study (as will be presented later in the article). The darker color of the samples with holes is due to backlighting from below to make the holes more visible. The holes were processed in Affinity Photo 2 by filling the dark color with white using the \"Flood Fill Tool\" (Figure 20).  
Based on the measurements of the number of holes and pins, an optimization process was carried out, where the objective function was to maximize the number of holes and pins. It was assumed that holes and pins were considered equally significant in the decision-making process. The dependence of the number of pins and holes on exposure time was modeled using quadratic functions. The prediction profiler for the functions for the number of holes(t) and number of pins(t), along with the desirability function, is presented in Figure 21. As expected, the number of holes decreases with increasing exposure time, primarily due to greater light bleed. On the other hand, a longer exposure time results in a higher number of correctly printed pins, as the resin is more fully cured. The optimal time determined from the objective function was approximately 3 s (2.99 s).  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Term** & **Estimate** & **Std Beta** & **Std Error** & **t Ratio** & **Prob \textgreater{} \textgreater{} \textbackslash{} \textbackslash{} \textbackslash{} \\ \hline Intercept & \(-0.058\) & 0 & 0.016 & \(-3.62\) & 0.0005 \\ sample type [solid] & \(-0.003\) & \(-0.037\) & 0.003 & \(-0.91\) & 0.3635 \\ dimension type [external] & 0.072 & 0.883 & 0.003 & 21.74 & \(<\)0.0001 \\ t, s & 0.007 & 0.055 & 0.005 & 1.47 & 0.1457 \\ dimension type [external] \(\times\) (t \(-3.125\)) & 0.047 & 0.350 & 0.005 & 9.38 & \(<\)0.0001 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Significance of factors influencing the error caused by light bleed.  
Figure 19: Views of the samples with pins printed with the shortest, optimal, and longest exposure times \(t\).
# Optimisation of Box Girder Design of Overhead Cranes
## 3 Application of the Developed Methodology and Parameterisation of the Bridge Crane Cross-Section
### Evaluation and Comparison with Existing Cranes  
After optimisation, we obtained a beam cross-section for which the one-meter length weighed 656 kg.  
The weight of the beam without cross bracings was 20.87 tonnes and with cross bracings it was 25.53 tonnes, with internal dimensions of the enclosure of 840 mm \(\times\) 1900 mm, wall thickness of 12 mm, and flange thickness of 22 mm.  
After performing the analysis using the finite element method in SolidWorks, we calculated the deflection of the existing beam w = 13.3 mm (Figure 13). In this case, the standard allowed a maximum deflection of 45 mm. According to this deflection value, we could conclude that the existing beam was oversized.  
Given the same input data and boundary conditions, the generated optimisation program designed an enclosure with the same wall thickness of 12 mm and flange thickness of 22 mm, but with an internal enclosure dimension of 605 mm \(\times\) 2105 mm. This designed beam had a mass per meter of length of 597 kg, so the mass of the designed beam without cross bracings was 19 tonnes and with cross bracings it was was 24 tonnes.  
After performing the analysis using the finite element method in SolidWorks, we calculated the deflection of the designed optimised beam w\({}_{\mathrm{opt}}\) = 18.2 mm (Figure 14). In this case, the standard allowed a maximum deflection of 45 mm, the same as in the previous case. Hence, the designed beam also complied with the standard, but it was more slender.  
Figure 11: Quantity of suitable cabinet cross-sections.  
Figure 12: Proposed beam before FEA analysis. Red arrow—gravity, purple arrow—wheel pressures from the crane trolley, green arrows—fixation of the crane to the runway.  
By comparing the two beams overall, we could say that the optimised beam was 1.5 tonnes lighter than the existing beam, while meeting all of the conditions set out in the standard. This was a 5.8% weight savings. For a twin girder crane, this was a total material savings of 3 tonnes, which was already a significant value.
## 2 Open Challenges When SS Meets ISAC
### Machine Learning for Spectrum Sensing in ISAC  
Deep learning and reinforcement learning offer promising solutions for dynamic SS, but require extensive training data and real-time adaptability. Developing lightweight AI models for low-latency ISAC applications remains an open problem.
# A Perspective on Information Optimality in a Neural Circuit and Other Biological Systems
## 1 Introduction
### Efficiency of Mathematical Operations  
It is also possible to consider the efficiency of multiplication and division operations in the context of Boolean algebra and logic gates. Hypotheses and models of fly motion detection involve the use of these operations.  
[PERSON] and [PERSON] [21] had reviewed prior work on visual motion detection in the fly. In their review of potential models of motion detection, they discussed a model's dependence on sampling the luminance values at two or more points across a visual space. One mathematical model includes a calculation of velocity, along with a division operation. Another model relies on a computation with a correlation between two spatially separated luminance values. They further discuss that the correlation-based model is the preferred one because it fits better with current knowledge about the information processing in the fly visual system.  
Theory and empirical studies led to a later study, and in support of the latter model, otherwise referred to as a \"delay-and-compare model\" of fly motion detection, where [PERSON] and others [22] showed evidence of a \"multiplication-like nonlinearity\" in a single neuron of this system, a prediction expected by prior theory on information flow [22, 23]. This process was also shown to be dependent on a biological mechanism involving \"pairs of shunting inhibitory and excitatory synapses\" [22, 23]. This merging of  
Figure 1: (**A**). Truth table for the basic Boolean operations AND, OR, and NOT. (**B**). The logic gate symbols represent the operations in (**A**).  
theory with biological evidence has the potential for producing biologically realistic models of neural circuits, with an expectation of applicability to animals in general, given an evolutionary convergence at the level of the neural circuit, and similarity in the molecular mechanisms of neuronal communication [21, 22].  
Even though biologically realistic models are preferred in neurobiology, it is still possible to idealize information flow in making predictions on optimality in a neural system, as pursued in this study. If theory predicts a high complexity in the computation of information, regardless of the type of system, then physical limitations will constrain the rate of computation. Therefore, this study includes simple examples using Boolean models for modeling information optimality, including in the case of multiplication and division, operations not native to Boolean algebra.