esa-sceva/satcom-chunk-collection huggingface dataset:
# Coverage and Rate Analysis for Location-Aware Cross-Tier Cooperation in Two-Tier HetNets
## 1 Introduction  
The deployment of multi-tier network architecture, namely heterogeneous networks (HetNets), is a promising approach to handle the exploding data traffic demands of users [1; 2]. A typical HetNet consists of different types of base stations (BSs) such as macro, pico, and femto BSs (MBSs, PBSs and FBSs, respectively), which operate simultaneously to serve users, but differ in terms of their coverage range, spatial density, and most importantly, transmit power [3]. However, the large transmit power disparity of macrocells and small cells will lead to unbalanced user loads among different BSs [4]; that is, most users will prefer to associate with MBSs when the conventional maximum-received-power (MRP) user association scheme is applied. This results in an underutilization of resource utilization at small cells. To cope with this problem, the biased user association, also known as the cell range expansion (CRE) scheme, has been proposed [5], wherein the macro users are proactively offloaded to small cells. Unfortunately, the drawback of CRE is that the offloaded users are liable to experiencing excessive cross-tier interference, which is always the obstacle in HetNets. Naturally, the macro edgeusers (MEUs) also are vulnerable users. In this context, the concept of BS cooperation is expected to tackle such excessive interference [6; 7]. One form of BS cooperation is coordinated multipoint (CoMP) transmission, wherein the users' data is concurrently transmitted by multiple BSs in order to mitigate interference, and thereby improve the overall system performance [8; 9].
# Comparison of ANN and ANFIS Models for AF Diagnosis Using RR Irregularities
## 2 Materials and Methods
### ANN and ANFIS Techniques  
#### 2.5.1 Ann  
One of the classification techniques for signal processing is the artificial neural network (ANN), which computes correlations and finds solutions based on layers of neurons. ANN can find the answer through experience from training datasets [37]. It has 3 layers consisting of input, hidden, and output. The hidden layer responds to neurons from the input layer. Then, the response is delivered by generating the output vector of the output layer. Each layer has a fixed number of neuron components associated with adjustable weights. During the training process, weight values are adjusted and optimized to find the minimum error that is acceptable for use. The ANN is trained by adapting the appropriate algorithm to solve the specific problem according to the characteristic of the input data. Patterns and relationships of data are detected. The feed-forward backpropagation algorithm is commonly used in ANN [38]. The output is computed by multiplying by the weight vector and summing the results, as shown in (5), where \(y\) is the output form ANN, \(F\) is the activation function of \(k\)th neuron, \(n\) is the number of neurons in the consecutive layer, \(w_{k}\) is the weight of the respective connection of the \(k\)th neuron, \(x_{k}\) is the \(k\)th input, and \(b_{k}\) is the bias for the \(k\)th neuron. ANN has been implemented in diagnosis applications, such as computed COVID-19 detection, breast cancer, schizophrenia, and others in cardiac failure. This research diagnosed AF with different hidden layers of ANN (\(n\) = 10, 15, 25, 50, and 100) that are normally used in many works [39; 40].  
\[y=F\biggl{(}\sum_{k=1}^{n}w_{k}x_{k}+b_{k}\biggr{)} \tag{5}\]
## 2 The Elephant in the Room: Great Diversity
### Protocols Involved within the Application Layer: CoAP and REST
#### 2.4.1 CoAP: Constrained Application Protocol  
CoAP is a lightweight protocol designed for machines. It is akin to a compact version of HTTP for devices with limited resources, using a straightforward model for sending and receiving messages. Unlike HTTP, it operates over UDP, making it better suited for the minimal power and processing capabilities of IoT devices. CoAP supports interactions in IoT applications that require minimal bandwidth and energy consumption [37].
# Travel Route Planning with Optimal Coverage in Difficult Wireless Sensor Network Environment
## 5 Performance Evaluation
### Comparison of Energy Consumption  
The energy consumption of the network using different methods is firstly compared. As Figure 10 demonstrates, the energy consumption of the four algorithms increases while SHDGP obtains a better performance compared to the other three algorithms. SHDGP achieves a little improvement in the performance of energy consumption compared to TRP-MC. However, energy consumption in SHDGP increases more rapidly than TRP-MC. When the time is about 10000 s, SHDGP consumes the same energy as TRP-MC, and we can infer that TRP-MC will achieve better performance on energy  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter Name** & **Parameter Value** \\ \hline Number of SPs (\(spn\)) & 15 \\ Number of particles in PSO (\(pn\)) & 50 \\ Inertia coefficient of particles in PSO (\(\lambda\)) & 0.7 \\ Weight coefficients of local update in PSO (\(\alpha\)) & 0.4 \\ Weight coefficients of global update in PSO (\(\beta\)) & 0.6 \\ Number of ants in ACO (\(m\)) & 30 \\ Control factor for pheromone concentration in ACO (\(\alpha\)) & 2 \\ Control factor for inspired factor in ACO (\(\beta\)) & 3 \\ Volatilization rate of pheromone in ACO (\(\eta\)) & 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters for PSO and ACO.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter Name** & **Parameter Value** \\ \hline Length of the sensor field (\(L\)) & \(400\times 400\) m \\ Number of sensors (\(N\)) & 200 \\ Communication range of sensors (\(r\)) & 60 m \\ Primary energy of each sensor (\(E_{0}\)) & 0.05 J \\ Data generation rate of each sensor (\(v_{s}\)) & 1 bit/s \\ Capacity of each sensor (\(C_{s}\)) & 2 MB \\ Moving velocity of the mobile collector (\(v_{m}\)) & 2 m/s \\ Number of SPs (\(\mathrm{nsp}\)) & [12–16] \\ Sojourn time for each SP (\(E_{0}\)) & 5 s \\ Energy consumption of transmission circuit (\(E_{dec}\)) & 50 nJ/bit \\ Amplifier parameter for free-space model (\(\varepsilon_{fs}\)) & 10 pJ/bit/m\({}^{2}\) \\ Amplifier parameter for multi-path model (\(\varepsilon_{mp}\)) & 0.0013 pJ/bit/m\({}^{4}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Network parameters.  
consumption than SHDGP after 10,000 s. LEACH consumes the most energy because it is a multi-hop based routing schema and many cluster heads have to transmit data packages to the fixed sink via long-distance communication. MWR, TRP-MC, and SHDGP all adopt mobile collector to gather the data, therefore, their energy consumption decreases in various degrees compared to LEACH. MWR defines compatible pairs for data forwarding while the communication between data collector and compatible pairs are much smaller than LEACH. TRP-MC and SHDGP are both single-hop based schemas, therefore, they both economize much energy and have similar performance in aspects of energy consumption.
# Beam-Hopping-Based Resource Allocation in Integrated Satellite-Terrestrial Networks
## 5 Resource Allocation Algorithms for MMCDR
### Greedy Suboptimal Algorithm
```
0:\(\{D_{i}^{sat}\}\), \(\{D_{j}^{ler}\}\), \(\{\bar{c}_{i}^{sat}\}\), \(\{c_{j}^{ter}\}\), \(\{d_{i,j}^{st}\}\), \(\{d_{i,k}^{sat}\}\), \(\forall i,k\in\mathcal{N}_{sat},j\in\mathcal{N}_{ter}\)
0:\(\{x_{i,t}\},\{y_{j,t}\},\forall i\in\mathcal{N}_{sat},j\in\mathcal{N}_{ter},t\in\Gamma\)
1:\(x_{i,t}\gets 0,C_{i^{*}}^{sat}\gets 0\), \(\forall i\in\mathcal{N}_{sat},t\in\Gamma\)
2:\(y_{j,t}\gets 0,C_{i^{*}}^{ler}\gets 0\), \(\forall j\in\mathcal{N}_{ter},t\in\Gamma\)
3:for each \(t\in\Gamma\)do
4:\(\Omega_{s}\leftarrow\mathcal{N}_{sat}\), \(\Omega_{t}\leftarrow\mathcal{N}_{ter}\)
5:while\(\Omega_{s}\
eq\emptyset\) and \(\sum_{i=1}^{N_{sat}}x_{i,t}<N_{b}\)do
6:if\(\min_{i\in\Omega_{s}}(C_{i}^{sat}/D_{i}^{sat})\leq\min_{j\in\Omega_{t}}(C_{j} ^{ter}/D_{j}^{ter})\)then
7:\(\Theta_{s}\leftarrow\{i\in\Omega_{s}|C_{i}^{sat}/D_{i}^{sat}=\min_{k\in\Omega_ {s}}(C_{k}^{sat}/D_{k}^{sat})\}\)
8:\(i^{*}\leftarrow\)argmax\({}_{i\in\Theta_{s}}\{D_{i}^{sat}\}\)
9:\(x_{i^{*},t}\gets 1\)
10:\(C_{i^{*}}^{sat}\leftarrow C_{i^{*}}^{sat}+\bar{c}_{i^{*}}^{sat}\)
11:\(\Omega_{s}\leftarrow\{k\in\Omega_{s}|d_{i^{*},k^{sat}}^{sat}\geq d_{th}^{ sat}\}\)
12:\(\Omega_{t}\leftarrow\{j\in\Omega_{t}|d_{i^{*},j}^{st}\geq d_{pro}^{st}\}\)
13:else
14:\(\Theta_{t}\leftarrow\{j\in\Omega_{t}|C_{j}^{ter}/D_{j}^{ter}=\min_{k\in\Omega_ {t}}(C_{k}^{ter}/D_{k}^{ter})\}\)
15:\(j^{*}\leftarrow\)argmax\({}_{j\in\Theta_{t}}\{D_{j}^{ter}\}\)
16:\(y_{j^{*},t}\gets 1\)
17:\(C_{i^{*}}^{ler}\gets C_{j^{*}}^{ter}+c_{j^{*}}^{ter}\)
18:\(\Omega_{t}\leftarrow\Omega_{t}-\{j^{*}\}\)
19:\(\Omega_{s}\leftarrow\{i\in\Omega_{s}|d_{i,j^{*}}^{st}\geq d_{pro}^{st}\}\)
20:endif
21:endwhile
22:for each \(j\in\Omega_{t}\)do
23:\(y_{j,t}\gets 1\)
24:\(C_{j}^{ler}\gets C_{j}^{ter}+c_{j}^{ter}\)
25:endfor
26:endfor
```  
**Algorithm 2** Greedy algorithm for MMCDR. The computational complexity of the greedy algorithm for MMCDR can be analyzed in a similar way as shown in Section 4.2. Before the resource allocation, it also requires \(\mathcal{O}(N_{sat})\) and \(\mathcal{O}(N_{ter})\) operations to calculate the transmission capacity of the satellite terminals and the transmission capacity of the terrestrial systems, respectively. During the resource allocation process, the worst-case complexity is \(\mathcal{O}((N_{ter}+2N_{b})N_{slot})\) since it requires at most \(\mathcal{O}(N_{ter}+2N_{b})\) operations to update the candidate sets when one of the time slots in \(\Gamma\) is allocated.
# IAACaaS: IoT Application-Scoped Access Control as a Service +
## 4 IoT Application-Scoped Access Control as a Service
### The IoT Device  
Requests from the _Device_ may target two components of the architecture: either the _IdP_ or the _PEP_. In the case of the former, the _Device_ will make use of the Implicit OAuth 2.0 grant [16] to obtain an access token. As explained before, IoT devices have special requirements with regard to computational capabilities and network connectivity [26]:  
* Device heterogeneity: IoT is characterized by a large heterogeneity in terms of devices taking part in the system. The use of standard technologies to perform the needed operations benefits the interoperability and the implementation independently of the device type.
* Energy limitations: Minimizing the energy to be spent for communication/computing purposes is a primary constraint. The less operations we perform in the device, the better the performance and durability of the system.
* Network connectivity: In many cases, entities in IoT are provided with short-range wireless communications capabilities. Using light communication protocols also benefits the system performance and stability.  
OAuth 2.0 is a standard protocol that can be supported by a range of technologies and programming languages [27]. This makes it easy to integrate in any device. On the other hand, the use of HTTP or CoAP makes the communication extremely light [12; 13]. Therefore, this approach meets the computational restrictions that the _Device_ imposes. In fact, any entity implementing an OAuth 2.0 client library is eligible for working as a client in this architecture; thus, interoperability with other non-IoT systems is guaranteed.  
The _Device_ will include the access token as a header when making a request to the _Service_. Note that, as can be seen in Figure 1, requests to the _Service_ are actually intercepted by the _PEP_.
## 6 Case Study
### Data Analysis  
An initial visual inspection of the spectral data indicated the presence of a recurring component near 4.2 Hz, later identified as a characteristic frequency related to belt vibration. This frequency was calculated based on the known geometry and speed of the transmission system, and its amplitude was monitored over time as a degradation indicator.  
### Data Analysis  
The first step in monitoring the frequency components of the belt characteristics is to calculate the frequency of the belt (\(f_{b}\)) [32]. In this case, \(f_{b}\) is the characteristic frequency of the belt given by the following:  
\[f_{b}=\frac{\left(D_{motor\_pululby}\cdot\pi\cdot f_{r}\right)}{L_{belt}} \tag{4}\]  
where \(f_{r}\) is the rotation frequency, \(D_{motor\_pulby}\) is the diameter of the motor pulley, and \(L_{belt}\) is the length of the belt. The sideband components of the fundamental are 323 at\(f_{1}\pm f_{b}\).  
To reduce noise and dimensionality, the dataset was filtered to isolate amplitude values corresponding to frequencies between 4.1 and 4.3 Hz. Outliers due to load variation or unrelated mechanical anomalies (e.g., eccentricity near 24.5 Hz) were removed to ensure data consistency. As seen in Figure 5, when the asset is operating with issues, there is significant noise across the spectrum, with the frequency of the belts exhibiting the highest amplitude.  
In Figure 6, it is possible to see a scheme illustrating the data transformation process leading to the obtained curve of the belt failure over time.
# Design and Analysis of Adaptive Hierarchical Low-Power Long-Range Networks
## 5 Real-World Evaluation
### Assessing the Ability to Adapt to Channel Failures  
Channel failures refer to a situation where a device is unable to successfully send most of its outgoing messages due to temporary noise on the wireless communication medium. Such channel failures were emulated by introducing a special device called \"the Jammer\" that continuously broadcasts long messages in order to create collisions, reduce link quality and in general reduce the message delivery rate. The Jammer device has normal communication range, identical to all other devices. The Jammer device is positioned in a way such that it can reach almost 50% of the network.  
The experiments conducted consisted of three stages. Firstly, the sub-network detection and formation modules were allowed to operate for a period of 10 min so that they can both reach a stable state. Then, the Jammer device was activated for 10 min and finally the Jammer device was deactivated and the network was allowed to stabilize once again. Observing the function of the Jammer in Figure 15, it is evident that it heavily disrupts the smooth operation of both modules. During the channel disruption, the sub-network detection module continuously produces events and so does the sub-network formation module. Essentially, it creates the need to send more control messages in order to adapt to the new network state increasing the network traffic. When the disruption is over, the network stabilizes and new events are rare.
## 4 Discussion  
### Applicability of the ANN Model to Algal Blooms in an Altered Estuary  
Since the 1990s, the ANN model has served as a useful tool for understanding and predicting the non-linear relationship between phytoplankton and environmental factors [27; 31]. Nevertheless, there have been few simulations of algal blooms in seawater regions of an altered estuary, such as the YR estuary, which has irregular and wide variations due to the direct influence of freshwater inflow. In fact, the input data used in the present study showed substantial variations in the distribution of size-fractionated phytoplankton biomass: 0.18-35.84 (total), 0.02-21.02 (net-size), and 0.02-29.22 (nano-size) \(\upmu\) L-1 (Table 1). The substantial variations in phytoplankton biomass were attributed to environmental changes such as the presence or absence of freshwater discharge, differences in discharge quantity, and tidal forcing, in addition to seasonal changes [15; 28]. The ANN model in the present study accurately reproduced the observed variations in phytoplankton biomass and size structure in the YR estuary (Figure 4). The statistical validation of  
Figure 6: Influence of input parameters (environmental factors) on the predicted chlorophyll \(a\) values in the ANN model. (**a**) chlorophyll \(a\) of total phytoplankton, (**b**) chlorophyll \(a\) of net-sized phytoplankton, and (**c**) chlorophyll \(a\) of nano-sized phytoplankton (Dis.: discharge; Temp: water temperature; Sal.: salinity; \(\Delta\)T: surface and bottom temperature difference; \(\Delta\)S: surface and bottom salinity difference; Trans.: transparency; PAR: photosynthetically active radiation; DS: duration of sunshine; SR: solar radiation; NO2- + NO3- : nitrite + nitrate; NH4+: ammonium; DSi: dissolved silicate; PO43-: phosphate; and DIN/DIP: dissolved inorganic nitrogen to dissolved inorganic phosphorus ratio).  
the ANN learning outcomes showed that for all predicted values, the SSE (\(\leq\)0.0003) and RMSE (\(\leq\)0.0173) were extremely low. Furthermore, the accuracy of the model predictions was high (\(R^{2}\geq 0.9952\)) when comparing the predicted values with the actual measurement data (Table 2). Based on this, the direction and level of contribution of the intricately related and simultaneous environmental factors could be determined. Thus, the ANN model employed here is likely to be a powerful tool for studying irregular and highly variable phenomena, such as algal blooms in an altered estuary.
## 3 Result and Discussion
### Verification of Predictive Model  
All the experiments were conducted to verify the variations in the values of the antioxidant potentials of the red pitaya peel extract based on the optimum conditions obtained from the target constraint. The experimental values for the DPPH, FRAP, and BCB assays were 75.98%, 7.45 mM/g, and 93.29% respectively. The experimental values for the three responses based on these optimum conditions were found to be very close to the predicted values. This indicates a high fit degree between the experimental values and the predicted values from the regression model. Therefore, response surface modeling is a reliable method which could be applied effectively to predict the optimum extraction conditions to maximize the antioxidant potentials of the red pitaya peel extract. The optimized extract was further analyzed to determine its suitability as an active ingredient for skin whitening purposes.
## 6 Ethical Data Acquisition and Privacy Safeguards  
In controlling research that involves live CCTV camera feeds from seaport environments, we are intensely aware of the ethical and privacy implications that accompany the handling of such data. Recognizing our responsibility, we have instituted a series of protocols and measures aimed at safeguarding the privacy of individuals and ensuring the ethical integrity of our data acquisition process [57].  
1. Data Anonymization: The anonymization of personal information is crucial to our data handling strategy. In this method, this means applying techniques to blur or pixelate faces and license plates, thereby stripping away any details that could  
Figure 4: The number of deaths and injuries at port container terminals is increasing every year. This indicates that safety accidents in the port area are continuously occurring (Figure adapted from The Korea Port Logistics Association’s 2022 Port Unloading Disaster Statistics and Cases report [51]).  
potentially reveal personal identities. Our commitment is to ensure that no individual is identifiable in the dataset that we compile and use. 2. Access Control: We are rigid about access to the data. Access is strictly compartmentalized and reserved for the team members who require it for the execution of their duties within the scope of this project. All access is logged and monitored to provide a clear audit trail of data interactions. 3. Regulatory Compliance: Our procedures are meticulously aligned with current data protection laws and various local data protection statutes [58; 59]. We constantly update our practices in response to the evolving legal frameworks in order to ensure full compliance. 4. Informed Consent: The cooperation and informed consent of the seaport authorities are the cornerstones of our data collection efforts [60]. Through ongoing dialogue, we ensure that our practices are transparent and mutually agreed upon, respecting the operational confines and privacy standards upheld by the seaports. 5. Data Retention Policies: Sensitive data are retained only for as long as they serve a legitimate purpose in the context of our research. We have clear guidelines on the duration for which data can be stored, and mechanisms are in place for the secure deletion of data once they are no longer required.  
Through the implementation of these measures, we seek to navigate the complex waters of ethical data acquisition with due diligence [61]. It is our belief that such conscientious practices are not only a mandate for legal and ethical compliance, but are also critical for maintaining the integrity and credibility of our research within the domain of seaport management.
## 3 Results
### Changing the Setting of Buffer Input Parameters
#### 3.2.3 Change in Desired Order Cycle  
The last part of the one-factor analysis was to evaluate the impact of changing the value of the desired order cycle (DOC) on the efficiency of inventory management and the value of the output parameters. In this set of simulation experiments, DOC = {4, 6, 8, 10. 12 days}  
Figure 11: Simulation outputs (change in LTF).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Variability**} & \multicolumn{3}{c}{**Service Level (\%)**} & \multicolumn{3}{c}{**Average Inventory (pcs)**} & \multicolumn{3}{c}{**Total Inventory Cost (EUR)**} \\ \cline{2-10}  & **Min.** & **Average** & **Max.** & **Min.** & **Average** & **Max.** & **Min.** & **Average** & **Max.** \\ \hline
0.1 & 97.99\% & 99.29\% & 100.00\% & 215.52 & 224.78 & 234.28 & 140,758.0 & 147,108.2 & 154,180.0 \\ \hline
0.3 & 98.21\% & 99.15\% & 100.00\% & 209.38 & 218.83 & 229.04 & 142,852.0 & 145,067.8 & 147,376.0 \\ \hline
0.5 & 97.13\% & 99.55\% & 100.00\% & 213.77 & 222.42 & 234.46 & 138,926.0 & 144,973.6 & 151,498.0 \\ \hline
0.7 & 99.48\% & 99.91\% & 100.00\% & 230.34 & 239.50 & 251.63 & 147,424.0 & 153,015.8 & 158,944.0 \\ \hline
0.9 & 99.27\% & 99.86\% & 100.00\% & 227.82 & 244.66 & 251.47 & 147,868.0 & 155,444.6 & 159,130.0 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Summary results of simulation experiments (change in VF).  
Figure 12: Simulation outputs (change in VF).  
values were sequentially simulated. The simulation results are displayed in Table 6 and Figure 13.  
The results of the simulation confirmed the direct dependence of the average stock on the length of the order cycle. The longer the order cycle, the larger the order quantity must be, and the larger the average stock [44]. Therefore, from the point of view of inventory levels, it would be ideal to use a minimum order cycle. However, shortening the ordering cycle led to an increase in order costs. From the point of view of the minimum total cost, the optimal value seemed to be DOC = 6 days. Not only did it ensure the lowest value of total inventory costs (average total inventory cost = EUR 144,770.8), but at the same time, the simulation experiments showed a very low dispersion of output parameter values at this DOC value.