esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### General Characteristics of the Sample  
A total of 3265 patients who underwent coronary angiography were included, among which 1426 (43.7%) belonged to the normal coronary arteries group, 643 (19.7%) to the non-obstructive coronary artery disease group, and 1196 (36.6%) to the obstructive coronary artery disease group. The clinical and demographic characteristics of the population have been previously reported [7]. The indication for angiography was chest pain/chronic coronary syndrome in 52.7% of patients and acute coronary syndrome in 47.3%, with 35% suffering from non-ST-segment elevation myocardial infarction and 12.3% from ST-segment elevation myocardial infarction. Table 1 displays the population baseline characteristics, classified according to the degree of coronary lesion.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Characteristic** & **Lesion \textless{} 20\%** & **Lesion 20–50\%** & **Lesion \textless{} 70\%** & **\(p\) \({}^{*}\)** \\  & **(n 1426)** & **(n 643)** & **(n 1196)** & \(p\) \({}^{*}\)** \\ \hline Age \({}^{1}\) (years) & 61.3 & 65.9 & 65.4 & \textless{}0.001 \\ \hline Sex \({}^{2}\) (male) (\%) & 40.5 & 56.8 & 73.8 & \textless{}0.001 \\ \hline Hypertension (\%) & 65.8 & 69.9 & 64.1 & 0.352 \\ \hline Diabetes mellitus (\%) & 28.0 & 29.2 & 35.7 & \textless{}0.001 \\ \hline Dyslipidemia (\%) & 47.5 & 51.8 & 50.0 & 0.233 \\ \hline Smoking (\%) & 32.7 & 39.8 & 44.5 & \textless{}0.001 \\ \hline Acute coronary syndrome (\%) & 34.6 & 38.4 & 67.2 & \textless{}0.001 \\ \hline Heart failure (\%) & 4.5 & 3.1 & 5.3 & 0.337 \\ \hline Atrial fibrillation (\%) & 13.5 & 10.3 & 6.0 & \textless{}0.001 \\ \hline Left ventricular ejection fraction & & & & \\ \hline Normal & 90.2 & 89.3 & 71.8 & \textless{}0.001 \\ \hline Mild dysfunction & 5.7 & 5.8 & 16.0 & \textless{}0.001 \\ (EF 40–55\%) (\%) & & & & \\ \hline Severe dysfunction & 2.0 & 2.8 & 10.0 & \textless{}0.001 \\ (EF \textless{} 40\%) (\%) & & & & \\ \hline Kidney failure (\%) & 5.3 & 7.6 & 12.4 & \textless{}0.001 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Baseline-adjusted characteristics of the patient cohort according to the degree of coronary lesion.
# Synthesis of Sources of Common Randomness Based on Keystream Generators with Shared Secret Keys
## 9 Security Analysis and Practical Consideration
#### Limitation of Proposed Systems  
While the proposed architectures aim to achieve practical perfect secrecy in the Shannon sense, several limitations merit discussion.  
1. The systems rely on the availability of trusted, high-quality local randomness sources, whose continuous entropy guarantees and unbiased generation are critical but challenging in practice.  
Figure 16: Dependence of the public channel load measure \(E_{BC}\) on \(|K_{G}|\) in the perfect security operating mode.  
2. The requirement for a pre-shared secret key \(K_{G}\), while refreshed autonomously, necessitates an initial secure distribution step.
3. The models assume an authenticated public channel immune to modification, requiring additional mechanisms and key resources for robust authentication.
4. While the ISAR framework provides theoretical secrecy under unlimited ciphertext observation, practical \(KSG(K_{G})\) implementations may face side-channel or physical security challenges affecting effective secrecy.
5. Finite block lengths introduce deviations from asymptotic secrecy rates, with practical IR errors and compression factors impacting performance.
6. Achieving low BER regimes to maximize secrecy rates may incur latency and throughput trade-offs, particularly for large block operations.
7. While information-theoretic security is computation-independent, practical implementations of \(KSG(K_{G})\), Winnow IR, and universal hashing must be carefully designed to avoid vulnerabilities and inefficiencies.
8. The proposed models focus on message secrecy and do not inherently address traffic analysis resistance, which remains an open area for further development.  
Addressing these limitations represents important directions for advancing autonomous, practically secure key distillation and encryption systems.
# Application of Bayesian Neural Network (BNN) for the Prediction of Blast-Induced Ground Vibration
## 2 Materials and Methods
### Blasting Vibration Monitoring at Mikurahana Quarry Site  
Monitoring equipment for the blasting vibration is necessary to measure the vibration and air blast caused by quarry blasting. Locating the monitoring equipment is the first step of installation. Before doing so, the quarry management team evaluated the quarry size, blasting method, and nearby structures. After deciding on the location, we set up and calibrated the equipment, according to the manufacturer's instructions; we used a variety of vibration-measuring equipment at the Mikurahana quarry site and installed them as we had prepared. The main pieces of equipment we used were a laptop computer, seismographs, geophone, and data recorders. All the equipment is depicted in Figure 4. Seismographs monitor the frequency and amplitude of the ground vibrations induced by blasting and are used to assess whether the blast exceeds the regulation limit. On the other hand, accelerometers determine the direction and length of the vibration, by measuring the acceleration of the ground induced by the explosion. Data loggers are used to gather and store the data acquired by the seismographs and accelerometers, which may subsequently be examined and utilized to modify the blasting process, to reduce the environmental effects. Utilizing the correct technology for the monitoring of blasting vibration is crucial for maintaining the safe and effective operation of quarries.  
Figure 3: Blasting design of Mikurahana quarry site, with its blasting design parameters (i.e., bench height, stemming, face angle, burden, spacing, diameter of the hole, and charge column).
# Underwater Excavation Records Using Underwater Acoustic Survey: A Case Study in South Korea
## 4 Application Case: Poor Underwater Visibility (Nakwoldo Site)
### Experimental Setup  
At the Nakwoldo site, the visibility generally did not exceed 10 m, and the researcher diving to the seafloor had difficulties identifying his position and marking the positions of the relics found. This issue was addressed by marking each meter with insulation tape along the rope and installing a 20 m \(\times\) 20 m grid of survey lines at 1 m intervals along the latitudinal axis. Thus, the researcher could identify his position by moving along the grid with the survey rope in his hand. A probe rod was used to probe the subsurface of the seafloor. In this way, five sections (A-E) were surveyed at the Nakwoldo site, of which section A occupied the largest area (120 m (latitudinal) \(\times\) 220 m (longitudinal)). A small number of shells was mixed with solid silt soil at the site, and the average depth was approximately 10 m. Expanding the survey area southward, the researcher surveyed two 10 m \(\times\) 10 m grids and fifty-nine 20 m \(\times\) 20 m grids.  
In the experiment conducted at the Dangampo site in 2017, the SS had difficulty surveying a large site area due to time constraints. In the second UE of the Dangampo site in 2018, we were able to produce a mosaic image of the entire survey area by combining three SS images, indicating that, time permitting, an image of a large area can be produced using an SS system. During section A, the diving survey at the Nakwoldo site, the SS was submerged to the seafloor from the boat (the Nurian) at each new position, where it recorded the seafloor morphology and the survey progress. The frequency and swath used were 675 kHz and 50 m. Eleven images acquired from individual points were combined into a mosaic image of the entire surveyed seafloor surface of section A (Figure 7). Lastly, we produced an excavation chart by drawing a grid over the mosaic image.  
Figure 6: Mosaic scanning sonar image (frequency: 675 kHz; swath: 25 m) of the sediment removal area of the second underwater excavation at the Dangampo site.
## 4 Results
### Meta Model Selection in Ca Structure  
After selecting the base model for each branch, the Meta-model in the Ca structure was determined using the same approach mentioned in Section 3.3. It is important to note that in this stage, PCA was not applied to the output PMs of each branch. In other words, PMs of all branches were directly fed to the ML model. This was done to better show the improvements caused by PCA technique in the next sections. Figure 5 illustrates the performance of different ML models in the Ca structure in terms of OA and Kappa. The optimal hyperparameters of each model were found using the described approach in Section 3.3. The RF algorithm outperformed other ML techniques as the Meta-model in the Ca structure. It achieved an OA of 92.56% and a Kappa of 0.911. The GBT and SVM classifiers ranked second and third, with OA values of 90.13% and 85.76%, respectively, and Kappa values of 0.893 and 0.819, respectively. The classifier with the lowest performance was the CART algorithm, which attained an OA of 77.32% and a Kappa of 0.726. Therefore, RF was selected as the Meta-model in the proposed ensemble structure of this article. It should be noted that the optimal hyperparameters of RF as the Meta-model were [NT: 300, MN: 1, VPS: 1].  
Figure 4: Performance of ML models in different branches of Pa structure in terms of OA and Kappa using 5-fold cross validation of training data.
# The Big Picture: An Improved Method for Mapping Shipping Activities
## 2 Background
### Spatio-Temporal Data and the Automatic Identification System (AIS)  
A trajectory can be captured as a time-stamped series of location points denoted as \(p_{0}(x_{0},y_{0},t_{0})\), \(p_{1}(x_{1},y_{1},t_{1}),...,p_{n}(x_{n},y_{n},t_{n})\), where \(x_{i},y_{i}\) represents geographic coordinates of the moving object at time \(t_{i}\) and \(n\) is the total number of elements in the series (e.g., Figure 1 demonstrates a trajectory of 16 points). To generate the trajectory, a sensor needs to acquire its coordinates \(x,y\) at time \(t\).  
Notice that the approximated trajectory can also be represented as a series of line segments between the stamped positions (given that there is a unique identifier grouping these positions into the same trajectory, usually the moving object id):  
\[\sum traj_{i}=\underline{p_{0}p_{1}},\underline{p_{1}p_{2}},\underline{p_{2}p_{3 }},\underline{p_{3}p_{4}},\underline{p_{4}p_{5}},\underline{p_{5}p_{6}},..., \underline{p_{10}p_{11}},\underline{p_{11}p_{12}},\underline{p_{12}p_{13}}, \underline{p_{13}p_{14}},\underline{p_{14}p_{15}}\]  
AIS messages are broadcasted periodically and can be received by other vessels equipped with the appropriate transceivers, as well as by on-the-ground or satellite-based sensors [15]. This information is transmitted at regular intervals ranging anywhere from 2 s to 3 min, depending on the vessel's behavior. Since 31 December 2004, AIS must be fitted aboard all vessels of 300 gross tonnage and upwards engaged on international voyages, cargo vessels of 500 gross tonnage and upwards not engaged on international voyages and all passenger vessels, irrespective of size [16].  
Even though the AIS protocol is mainly targeted for larger vessels, a large number of smaller boats are also equipped with transceivers to ensure their safety while travelling. The vessels required to carry AIS are equipped with Class A AIS transponders, whereas other vessels can carry either Class A or Class B AIS transponders. The Class A transponder has a more powerful signal and transmits messages more frequently than the Class B transponders; therefore, Class A transponders typically have a finer spatial and temporal resolution. Vessels which may carry Class B transponders include recreational vessels, fishing vessels or small passenger vessels [17].  
Ground-based (Terrestrial AIS or TER-AIS) and space-based AIS (Satellite AIS or SAT-AIS) datasets have some considerable differences:  
* Terrestrial receivers are land-based stations which receive messages from vessels within their line of sight. Once the message is received, it is relayed via network connection to a computer for storage, processing and visualization. Typically, with an optimal terrestrial receiver setup, messages from up to 40-60 nautical miles away can be received.
* Satellite receivers function similarly to terrestrial receivers by transmitting the received AIS message to a computer for data storage, processing and visualization. Having a large field of view (up to 5000 km), satellite receivers are always in view of the transponders [18].
# A Review on Bioactive Compounds and Pharmacological Activities of _Citrus unshiu_
###### Abstract  
Citrus constitutes a group of fruit crops that include lemons, limes, mandarins, oranges, and grapre fruits. These citrus have a variety of essential nutrients and bioactive compounds that exert several pharmacological properties, including antioxidant, anti-inflammatory, anticancer, anti-diabetic, and anti-obesity. The Satsuma mandarin, also known as _Citrus unshiu_ Marc. (_C. unshiu_), is one of the most popular citrus types. This is mostly due to its seedless nature, early maturity, and highly valued commercial trait in the citrus sector. The pharmacological properties and phytochemicals of the two main citrus fruits--sweet oranges (_C. sinensis_) and lemons (_C. limon_)--are given more attention. Satsuma mandarin has not, however, had its therapeutic properties thoroughly examined and explored. Therefore, the purpose of this review is to use multiple databases to compile the information currently available on the pharmacological properties and bioactive compounds of _C. unshiu_. The findings indicate that _C. unshiu_ bioactives (hesperidin; hesperetin; nobiletin; narirutin; naringin; naringin; tangeretin; 3,5,6,7,8,3\({}^{\prime}\),\({}^{\prime}\)A\({}^{\prime}\)-heptamethoxyflavone; neeponcirin; synephrine; quercetin; quercetin; rutin; \(\beta\)-cryptoxanthin; and pectin) exert in vitro/vivo anticancer, anti-obesity, anti-diabetic, cardioprotective, gastoprotective, neuroprotective, hepatoprotective, skin-protective, nasal airway-protective, lipid-lowering, antioxidant, anti-inflammatory, and anti-microbial activities. Future experimental investigations into the potential health benefits of _C. unshiu_ could contribute to a better understanding of the mechanisms behind its therapeutic activities. Clinical studies are necessary to assess the therapeutic properties of _C. unshiu_ bioactives. The therapeutic potential of _C. unshiu_ bioactives should be determined through preclinical in vivo animal studies before they can be applied in clinical settings.  
satsuma mandarin; _Citrus unshiu_; bioactive compounds; pharmacological properties; experimental studies
# Computational Approaches for Grocery Home Delivery Services
## 4 Algorithms for the VRPTW
### Adaptive Neighborhood Search Heuristic
#### 4.3.5 Algorithm  
Finally, we can concisely describe the details of our ANS for the SOP as follows. We temporarily assign the new customer \(j\) to each time window \(u\in\mathcal{W}\). For each tour \(\mathcal{A}\in\mathcal{S}_{u}\) we apply the steps described below, which try to insert \(j\) into \(\mathcal{A}\) within its assigned time window \(w(j)=u\). If this is possible, \(u\) is added to the set of available time windows \(\mathcal{T}_{j}\subseteq\mathcal{W}\).  
In step 1, we ensure that the current tour \(\mathcal{A}\) fulfills CFEAS\((j,\mathcal{A})\). If \(\sum\limits_{i\in[n]}c(i)+c(j)>\mathcal{C}_{\mathcal{A}}\) holds, then customer \(a\) cannot be feasibly inserted into tour \(\mathcal{A}\). In this case, we have to reduce the overall weight \(\sum\limits_{i\in[n]}c(i)\) of \(\mathcal{A}\). This is achieved by applying local search operations 1 move and 1 swap, while as few operations as possible are used. Therefore, as much weight as possible is moved in each step and the operations stop once there is sufficient spare capacity on \(\mathcal{A}\) to insert \(j\), i.e., \(\sum\limits_{i\in[n]}c(i)+c(j)\leq\mathcal{C}_{\mathcal{A}}\) holds. If we do not succeed in modifying \(\mathcal{A}\) such that CFEAS\((j,\mathcal{A})\) holds, we terminate.  
In step 2, we aim to increase the free time \(\lambda_{u}(\mathcal{A})\) through local search operations within _inside_ until the infeasibility condition (1) does not hold anymore. \(\lambda_{u}(\mathcal{A})\) is increased by applying as few operations as possible. That way, the previously optimized schedule \(\mathcal{S}_{u}\) is not altered more than necessary. If a local optimum is reached, meaning no further improvements can be achieved by local search operations, and (1) is still satisfied, the algorithm stops because the following steps cannot result in a feasible insertion of the new customer.  
Next, step 3 is concerned with reducing the loss time \(\chi_{u}(j,\mathcal{A})\) of time slot \(u\) through local improvement operations within _outside_. The operations are applied until either the new customer order can be inserted into the tour, the loss time is equal to zero \(\chi_{u}(j,\mathcal{A})=0\), or a local optimum is reached.  
Finally, in step 4 we try to further increase the free time through local search operations within _inside_. The free time is increased until either the insertion of customer \(j\) is possible or a local optimum (of the free time objective) is reached and hence, we are not able to insert \(j\) within time slot \(u\).  
Note that we apply local search operations during steps 2-4 only if CFEAS\((j,\mathcal{A})\) still holds. Further, we apply local search operations during step 3 only if the infeasibility condition (1) does not hold for the resulting tour \(\mathcal{A}\).
## 3 Results and Discussion  
### Cubesat  
As reported in Table 1, the sample considered is 0.1 m per side, with a minimum wavelength of 0.025 m and a maximum of 0.017 m that refers to a satellite of 3 m per side at 12 GHz and to one of 4.4 m at 18 GHz. The positioning of the CubeSat and the measures are shown, respectively, in Figures 2 and 3. Figure 2 shows only one antenna, the sender, in order to indicate the direction of the signal; the receiving antenna is not shown in the sketch (the whole system can be visible in Figure 1).  
Figure 1: NRL ARCH setup, with horn antennas and anechoic panels.  
The configuration of the greatest reflection is the \"flat\" one. For the other measurements, it can be seen that the CubeSat 45\({}^{\circ}\) configuration reflects between 12 and 13.8 GHz.  
The results lead to the consideration that scaled objects in the flat position and within the dimensions range aforementioned are visible with a 400 MHz radar system. Moreover, that means that satellites between 3 and 3.8 m can be seen when they are in the CubeSat 45\({}^{\circ}\) attitude configuration. The presence of negative curves (meaning more absorption) testifies that in those configurations, the signal is diverted toward the absorbing plane and therefore does not reach the receiving antenna.
# CoDA: Collaborative Data Aggregation in Emerging Sensor Networks Using Bio-Level Voronoi Diagrams
## 3 System Model
### Structure of Voronoi-Cluster
In the practical applications, it is very difficult to determine the value of \(p\), which is related to the scale of the network and the density of nodes. When the cluster head selection is carried out in the Voronoi cell, the candidate cluster heads are subject to two-dimensional uniform distribution in the rectangular area. Because the selection of cluster head is taken place in a local area, it can ensure that the distribution of cluster heads is relatively uniform. \(\circ\)  
**Definition 3**. : _Suppose \(G=(V,E)\), and \(G\) being called as a simple undirected graph should satisfy the following conditions: (1) \(G\) is a connected undirected graph without a self-circle. (2) There is at least one side between any two nodes in \(G\)._  
Graph \(G\) should be guaranteed with k-coverage and k-connectivity, and the single hop communication mode is adopted for cluster-in communication. In the existing clustering schemes, the nodes in the cluster can choose multi-hop or single-hop mode to transmit their gathering data to the cluster head. In order to balance the energy of the cluster head nodes often need to be replaced regularly, and the members may also change. If using multi-hop communication, each rotation forcluster head selection need rebuild the routing tree, which will result in high overhead and hard to maintain. Figure 2 shows the minimum communication radius for inter-cluster transmission. To determine the minimum radius of inter-cluster transmission, the proposed Algorithm 1 is designed as following:  
```
Input: \(G=(V,E)\), Vertex set of \(V(S)\) Output: minimum communication radius \(R_{\max}\)
1. \(R_{\max}=0\);
2 Create the Voronoi diagram V (S) of the point set;
3 for each node \(i\in S\) do
4 Calculate Convex Hull \(CH(S_{i})\);
5 end for;
6 for each \(v\in V(S)\)
7 if \(v\) is located inside the rectangle, then
8 Calculate the radius of circle with \(v\) as the center;
9 \(R_{\max}=R_{v}\);
10 end for;
11 for each edge \(e\in CH(S)\)
12 Find the point \(x\) of perpendicular and rectangular of \(e\);
13 Calculate the distance \(dist(x,e)\) between \(x\) and the endpoints of \(e\);
14 if \(dist(x,e)>R_{\max}\) then
15 \(R_{\max}=dist(x,e)\);
16 end if;
17 end for;
```  
**Algorithm 1**Determination of the minimum transmission radius for inter cluster communication. Figure 3: Dividing the monitoring area into Voronoi-clusters. Figure 2: The minimum communication radius for inter-cluster transmission.
## 3 Results
### Indoor Benzene Distribution  
The results of generating a set of factual/counterfactuals tables for indoor benzene distribution to estimate their ATEs, show an average ATE of 0.203. This positive value reveals the causation between indoor exposure to benzene and the risk of developing AML in early childhood.  
When analyzing the Uplift modeling results, we look at a variant of the Qini curve, representing the incremental uplift as a function of the proportion of the population target. Incremental Uplift measures whether an event would not have occurred without a specific interaction; hence positive Qinis attest to the goodness-of-fit of the Uplift model. Figure 4 shows a typical Qini curve for the indoor benzene distribution scenario.  
The data is first partitioned into subsets that keep the same distribution of treated versus non-treated and responders versus non-responders values. The training was achieved using the formula _DualUplift_ which fits the data using the two-model estimator or approach (logistic regression model), with splits of the data in 70% for the training and 30% for the validation. The first element of the _DualUplift_ class is the baseline model fitted for nontreated individuals, and the second is the baseline model fitted for treated individuals. Using the two-model estimator, a baseline model is fitted for comparison purposes. Using the validation set, the function _predict_ infers the uplift. Finally, to evaluate the quality of the baseline model, we plot the Qini curve, as shown in Figure 4. The Qini coefficients are single indexes of the Uplift model. The x-axis represents the fraction of targeted individualsand the y-axis represents the incremental number of positive responses relative to the total number of targeted individuals. The straight line between the origin and (100, y-max) in Figure 4 represents a benchmark to compare the model's performance to a strategy that randomly targets subjects, as we explained before. In our case, the Qini coefficient is positive (with a value of 0.14) and outperforms random targeting. The uplift percentages are detailed in Figure 5. This reinforces the goodness-of-fit of the Uplift modeling approach, as the observed uplifts are ordered from highest to lowest.  
Our combined ATE/Uplift modeling framework allows us to conclude that there is a causation of benzene exposure (as an air pollutant) to AML in early childhood, analyzing the counterfactual nature of the OR-based relationship found indoors for the simulated data from [PERSON] et al. (2018) and the benzene exposure distribution considered ([PERSON] et al., 2018).