esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Numerical Results
### Optimized Cache Deployment vs. Baseline Strategies  
After having cross-validated our analytical and simulative approaches, we evaluate the advantage of having an optimized cache deployment, referred to as _k-optimized_, through comparing it to two baseline strategies (_Only MCEN_ and _Only AMENs_) in terms of percentage of requests served from each network level and overall RO.  
Table 4 shows the percentage of requests served from the AMEN caches, the MCEN cache and the data center (DC) for the three cases in Table 2 under the three cache deployments; namely, the _Only MCEN_, _Only AMENs_ and _k-optimized_. In all cases, for _Only MCEN_ cache deployment, all the content is pulled from the DC and stored in the MCEN, as the available storage capacity enables storing and serving all of the content from the MCEN cache. This cache deployment, however, results in the highest overall network _RO_. This shows that not utilizing the AMEN caches has a drastic effect on the overall network _RO_. For the _Only AMENs_, all the storage capacity is distributed uniformly among the AMEN caches, storing the most popular items. This cache deployment enables serving a high percentage of the requests (ranging between 58% and 78%) from locations in proximity to end-users; however, it does not utilize the MCEN cache, leaving a significant percentage of VoD content, and therefore, a significant percentage of requests (ranging between 22% and 42%), to be served from the DC, consequently increasing the overall network _RO_. This confirms that deploying all storage capacity in cache locations near end-users is not an optimal way to perform VoD content caching, especially when the amount of storage capacity is limited. Conversely, for the _k-optimized_ cache deployment, we see that it reveals a lower percentage of requests served from the AMEN caches with respect to that of the _Only AMENs_ cache deployment, but a much higher percentage of requests served from the MCEN cache than for Case #1. Consequently, only a low percentage of requests are served from the DC, resulting in the minimal overall network _RO_.  
Figure 4: \(RO_{avg}/req\) with respect to \(k\), the number of items stored in the access-metro edge node (AMEN) caches for each of the three case studies.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Case Study \#** & \(k_{model}\) & \(k_{sim}\) & Error \\ \hline
1 & 266.475 & 267 & \(1.9\cdot 10^{-3}\) \\
2 & 614.415 & 615 & \(9.5\cdot 10^{-4}\) \\
3 & 376.228 & 377 & \(2.05\cdot 10^{-3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Values of \(k_{model}\) and \(k_{sim}\) for each case study.
## 3 Methods
### Economic Analysis
#### 3.3.2 Economic Performance Indicators  
It is indispensable to explore the economic risks and rewards of the proposed system by establishing economic criterion. The calculation formulas for the economic evaluation indicators are given in Equations (4)-(13), and all formulas refer to published studies with a high relevance to this study. Table 4 reveals the key parameters required for the economics calculations, and the data are based on recent official sources, such as the International Monetary Fund. The initial investment cost \(Z_{\rm{IIC}}\) (USD) includes the total equipment investment cost \(Z_{\rm{Eq}}\) (USD) and materials investment cost \(Z_{\rm{Mat}}\) (USD) [14],  
\[Z_{\rm{IIC}}=Z_{\rm{Eq}}+Z_{\rm{Mat}} \tag{5}\]  
\[Z_{\rm{Mat}}=Z_{\rm{oil}}+Z_{\rm{met}}+Z_{\rm{pro}} \tag{6}\]  
where \(Z_{\rm{oil}}\), \(Z_{\rm{met}}\), \(Z_{\rm{pro}}\) is the investment cost of the thermal oil, methanol, and propane (USD). The purchase cost of the LNG per year \(Z_{\text{LNG}}\) (USD) is [36],  
\[Z_{\text{LNG}}=397(t_{\text{dis}}\times m_{27}\times 365/1000) \tag{7}\]  
where \(m_{27}\) is the mass flow rate of the LNG fuel (kg\(\cdot\)h\({}^{-1}\)). The detailed expression of the total annual cost \(AC_{\text{t}}\) (USD) is [14],  
\[AC_{\text{t}}=\alpha\times Z_{\text{IIC}}+P_{\text{in}}\;\times t_{\text{ch}} \times 365\times Z_{\text{E,eff-peak}}+Z_{\text{LNG}} \tag{8}\]  
where \(\alpha\) is the annual operation and maintenance factor, \(Z_{\text{IIC}}\) is the initial investment cost (USD), \(P_{\text{in}}\) is the power input during the charging process (kW), and \(Z_{\text{E,eff-peak}}\) is the unit price of the off-peak electricity (USD-kWh\({}^{-1}\)). The total annual revenue \(AR_{\text{t}}\) (USD) is expressed as [14],  
\[AR_{\text{t}}=P_{\text{out}}\;\times t_{\text{dis}}\times 365\times Z_{\text{E, on-peak}} \tag{9}\]  
where \(P_{\text{out}}\) is the power output during the discharging process (kW), and \(Z_{\text{E,peak}}\) is the unit price of the on-peak electricity (USD\(\cdot\)kWh\({}^{-1}\)). Then, the total annual profit \(AP_{\text{t}}\) (USD) can be determined as follows,  
\[AP_{\text{t}}=AR_{\text{t}}-AC_{\text{t}} \tag{10}\]  
Net present value, NPV, can assess the long-term profitability of a system with the cumulative sum of the total annual profits over the entire lifetime, deducting the initial investment cost. Future profits are discounted at an appropriate discount rate [14],  
\[\text{NPV}=\sum_{n=1}^{\text{SL}}\frac{AP_{\text{t}}}{(1+i)^{n}}-Z_{\text{IIC}} \tag{11}\]
## 4 Movement and Navigation Control Subsystem
### Control of Robotic Platform Movement via the Remote Control and Software Development  
For control of the robotic platform in its early testing stages, a remote-control system was chosen due to its low cost, long range, and ease of implementation. The remote control module used was the Fly Sky FS-i6 (Adafruit Industries, New York, NY, USA), complemented by a six-channel radio frequency receiver, the FS-iA6. The FS-iA6 receiver was connected to a microcontroller (Arduino Mega 2560 Pro model), allowing the user to send signals from the remote-control module to the microprocessor (Figure 14) (see Appendix A for the software code details). Through the code developed for the microcontroller, the appropriate control signals were then applied to the wheels. Additionally, the programming platform used the Arduino IDE 2.3.2 software. The code was developed for implementation on the Arduino Mega 2560 Pro microprocessor, using interrupts to increase the available  
Figure 12: (**left**) Original control electronics for the BLDC wheel motor. (**right**) The motor’s internal winding and the hall sensors for positioning.  
Figure 13: Electrical connection of the motor and wheel controller.  
RAM memory and reduce the program cycle time. The interrupts that were used and their associated pins were INT0 (pin D2), INT1 (pin D3), INT2 (pin D21), and INT3 (pin D20), programmed to activate on a rising edge, as shown in Figure 14. The remote-control module was placed on top of the platform, in order to prevent potential risks due to flooding or waterlogging of the ground.  
To convert the signal read by the microcontroller (from the remote-control receiver) into the output signal of the microcontroller (to the BLDC motor controller) in the form of PWM, the map function was used. This adjusted the PWM range of the remote-control controller's receiver to the value that the microprocessor needed to output for the correct operation of the motors using a PWM signal, taking into account the offset errors from the remote-control module.  
Finally, to achieve rotation of the robotic platform, the technique used by tracked vehicles was chosen. This involves the platform rotating around its own central axis by spinning the wheels on one side while keeping the wheels on the opposite side stationary. Then, pins D25 and D27 from the Arduino board were connected to VR pins from the BLDC controller to control the speed of the motors on the left and right sides, working independently. Pin D26 was used to decide the side over which the wheels would rotate. This provides the platform with enhanced manoeuvrability, allowing it to move over rough terrain with sharp curves and limited turning space, while eliminating the need for a servomotor to steer the driving wheels.
# Applying Machine Learning to DEEC Protocol: Improved Cluster Formation in Wireless Sensor Networks
### Distributed Energy-Efficient Clustering (DEEC)  
Wireless Sensor Networks (WSNs) have emerged as a pivotal technology for various applications, including environmental monitoring, industrial automation, healthcare, and smart cities. One of the most popular challenges in WSNs is the energy consumption of sensor nodes, as they are typically battery-powered and often deployed in inaccessible environments. Clustering is a technique used in WSNs to enhance energy efficiency. Clustering is a method of partitioning a network into clusters (groups of nodes) and selecting a cluster head (CH) for each cluster [1; 2; 3; 4; 5]. A CH gathers and aggregates the data from other nodes in the cluster and transmits it to the base station (BS), which reduces the number of nodes directly communicating the data to the BS and thus saves energy. The Distributed Energy-Efficient Clustering (DEEC) protocol is proposed for heterogeneous WSNs because, in DEEC, the residual energy of nodes is considered in the CH selection. Nodes with higher energy are given more chances of becoming CHs; therefore, this results in an efficient distribution of energy consumption in the network. DEEC is adaptive, according to a network's energy variation, and it is suitable for WSNs with heterogeneous energy capacities [26].
## 3 Taxonomy
### Non-Al-Based Methods
#### 3.2.1 Energy Efficiency  
This sub-section discusses the optimization methods or framework of energy efficiency consumption using IoT devices, and this sub-section includes two (\(n=2\)) articles. In the first article, an approach was developed to the allocation of resources in an IoT sensor network for optimal and sustainable use of resources to monitor the quality of water by developing a successive wireless power sensor network system embedded with a scheduling algorithm and operating as a non-orthogonal multiple access system [34]. In the other study, different energy-efficient solutions are presented for wireless sensor systems to assist in reducing the energy consumption and energy scarcity of real-time water quality monitoring systems [18].
# An Efficient SS-MAC Protocol for IEEE 802.15.4-Based WSNs of Cluster Tree Topology
## 4 Modeling Analysis
### Energy Consumption Analysis  
The energy consumption of SS-MAC (\(E_{tot}\)) consists of two parts: energy consumption during SP (\(E_{setup}\)) and energy consumption during SSP (\(E_{setup}\)). The former is equal to the sum of energy consumption of four sub-periods of SP, which are defined as \(E_{RC}\), \(E_{CP}\), \(E_{CAP}\) and \(E_{DCP}\), respectively. The latter is equal to the sum of energy consumption during SSP caused by data collection between CHs in a session (\(E_{free}^{s}\)), caused by data collection within cluster (\(E_{sl}^{s}\)), and caused by broadcast Session_ST (\(E_{SS}^{g}\)). So, the total energy consumption of SS-MAC is:  
Figure 9: Packet arrives in non-CP.  
\[E_{tot}=E_{setup}+E_{steady}=E_{RC}+E_{CP}+E_{CAP}+E_{DCP}+E_{SS}^{B}+N_{r}(E_{ tree}^{s}+E_{cl}^{s}) \tag{7}\]  
The energy consumption of each period, that is, the right side of expression (7), is calculated as follows:  
\[E_{RC} =\frac{L_{TR}^{B}}{R_{c}}(P_{tx}\sum_{i=0}^{L-2}N_{t}^{i}+P_{rx} \sum_{i=1}^{L-1}N_{t}^{i})+\frac{L_{CHD}^{B}+L_{CSA}^{B}}{R_{c}}(P_{tx}(N_{ch}- 1)+P_{rx}N_{node})\] \[+\frac{L_{IR}^{B}N_{node}}{R_{c}}(P_{tx}+P_{rx})\] \[E_{CP} =\frac{L_{DSR}^{B}N_{node}}{R_{c}}(P_{tx}+P_{rx})\] \[E_{CAP} =P_{idle}T_{cap}(N_{ch}+N_{node}-1)\] \[E_{DCP} =\frac{L_{DSA}^{B}+P_{ack}}{R_{c}}(P_{tx}(N_{ch}-1)+P_{rx}N_{node })+T_{slot}N_{node}(P_{tx}+P_{rx})\] \[E_{SS}^{B} =\frac{L_{SS}^{B}}{R_{c}}(P_{tx}\sum_{i=0}^{L-2}N_{t}^{i}+P_{rx} \sum_{i=1}^{L-1}N_{t}^{i})\] \[E_{tree}^{s} =(P_{tx}+P_{rx})\sum_{i=1}^{L-1}SS^{RX}(i)N_{t}^{i-1}\] \[E_{cl}^{s} =\frac{L_{CSA}^{B}+L_{DSA}^{B}+P_{ack}}{R_{c}}(P_{tx}(N_{ch}-1)+P _{rx}N_{node})+E_{CAP}\] \[+N_{node}(\frac{L_{DSR}^{B}}{R_{c}}+T_{slot}Int[W_{s}\lambda_{max }]^{+})(P_{tx}+P_{rx})\]
# Research on Optimization of Target Positioning Error Based on Unmanned Aerial Vehicle Platform
## 2 Definition of a Coordinate System
### Space Rectangular Coordinate System \((O-X_{e}Y_{e}Z_{e})\)  
The space rectangular coordinate system has the same axes as the geodetic coordinate type, and the coordinates are projected on axes, as shown in Figure 1. The coordinates of a point can be expressed as \((X_{e},Y_{e},Z_{e})\).
## 4 Results and Discussions
### Rbnn  
In this section, the training results with different _spread_ constant (sc) and error goal (eg) values are shown in Table 2. The best result was obtained with sc:10 and eg:10 values. When looking at Figure 7a, the test results are far from what was expected (Figure 2). The structure of the network is seen in Figure 7b,c. Figure 7b is the general network structure of RBNN. While the transfer function used in the neurons in the 'Hidden layer' is called Radbas(n), the transfer function used in the neurons in the 'Output Layer' layer is called Purelin(n). While the input data should be 3069, the same as the number of rows in the 'Hidden layer' of the network, 3032 hidden neurons were produced because the neuron was generated automatically.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & **sc: 0.01** & **sc: 0.1** & **sc: 0.1** & **sc: 1** & **sc: 10** & **sc: 0.02** \\  & **eg: 1.10\({}^{-11}\)** & **eg: 1.10\({}^{-11}\)** & **eg: 0.1** & **eg: 1** & **eg: 10** & **eg: 0.01** \\ \hline Training R\({}^{2}\) & 1 & 1 & 1 & 0.9999 & 0.9999 & 1 \\ Testing R\({}^{2}\) & \(4.94\times 10^{-30}\) & \(4.94\times 10^{-30}\) & \(2.03\times 10^{-31}\) & \(8.69\times 10^{-31}\) & \(3.12\times 10^{-32}\) & \(4.94\times 10^{-30}\) \\ Training RSE & \(1.37\times 10^{-9}\) & \(1.37\times 10^{-9}\) & \(2.8867\) & \(6.1533\) & - & \(1.37\times 10^{-9}\) \\ Testing RSE & \(3349.34\) & \(3349.34\) & \(3348.67\) & \(3349.85\) & \(3339.81\) & \(3349.34\) \\ Training MAE & \(3.96\times 10^{-13}\) & \(3.96\times 10^{-13}\) & \(0.00027\) & \(0.00099\) & \(0.00452\) & \(3.96\times 10^{-13}\) \\ Testing MAE & \(1\) & 1 & 0.99906 & \(1.00071\) & \(0.98652\) & 1 \\ \hline \end{tabular}
\end{table}
Table 2: Performance comparisons of spread and error goal values for the RBNN method.  
Figure 6: GRNN test results and network structure. (**a**) Spread = 0.1 test results. (**b**) GRNN structure. (**c**) GRNN representation in MATLAB.
## 4 Fuzzy Logic Model  
Instead of classical linear controllers or mathematical models, we introduce a fuzzy logic-based controller in this article due to its simplicity, clarity and suitability with WSN applications. Fuzzy systems are very useful in situations involving a highly complex system whose behaviors are not well understood and in situations where an approximate, but fast, solution is warranted [24]. Since guaranteed lifetime and real-time communication in wireless sensor networks are dealing with both kinds of situations, therefore a fuzzy system can be an ideal possible solution. To deploy it in the real world, the proposed fuzzy controller algorithm must be installed on both sink and sensor nodes in the network. The fuzzy controller in the sink node accepts input descriptors, energy and active time, for active/sleep scheduling. Similarly, fuzzy controllers in sensor nodes use controllable inputs of energy and velocity for the selection of the next hop relay node. The proposed fuzzy system consists of three parts; fuzzification, inference engine and defuzzification, as shown in Figure 1, along with fuzzy system factors. Through the proposed fuzzy logic, fuzzy logic determines a node role for the next round, either active or sleep, by taking the active time, velocity and energy on the node. Upon identifying the possible active nodes, a node is designated as either ETR or NTR. Therefore, each node's rule for next round is determined by the proposed fuzzy logic consequently.
## 3 Results and Discussion
### Applications  
Toroid transformers based on soft ferrites allow magnetoinductive designs to be realised with low non-nearest-neighbour coupling. However, core materials must have low loss and high linearity. Some applications also require high power handling. In HF RFID, a modulated AC magnetic field delivers power and data to tags, which couple to the field using inductive loops. For the ISO/IEC 14443 type A standard, the carrier frequency is \(f_{c}=13.56\) MHz, while tags use an internally generated subcarrier at \(f_{m}=\frac{f_{c}}{16}\) to return data, generating sideband spectra with central frequencies, \(f_{c}\pm f_{m}\). Since the carrier power can be orders of magnitude greater than that of the sidebands, receiver protection is required. The single-notch filter presented here can be integrated into existing MI systems via direct connection to the waveguides, has high rejection at resonance, and may be used to transfer residual carrier to a load. However, the filter must be effective at the representative carrier powers, despite potential nonlinearities introduced by ferrite cores. To emulate generic HF RFID signals, we modulated a high-power carrier at \(f_{c}\) using a simple cosine message waveform at \(f_{m}\). The waveform of the input signal \(s(t)\) is:  
\[s(t)=[A_{c}+A_{m}\cos(2\pi f_{m}t)]\cos(2\pi f_{c}t). \tag{26}\]  
Here \(A_{c}\) and \(A_{m}\) are the carrier and message amplitudes. The signal was derived from a first signal generator (Agilent E4433B) modulated by a second signal modulator (Agilent N5181A) with amplification by a 27 dB power amplifier (Minicircuits ZFDWHA-1-20+). \(A_{c}\) was adjusted to deliver a carrier power, \(P_{c}\), into a 50 \(\Omega\) load, while the modulation index \(A_{m}/A_{c}\) was chosen to generate typically small sidebands. Detection was performed using a digital storage oscilloscope (Keysight InfiniVision DSOX3024T, Santa Rosa, CA, USA) using a 30 dB attenuator for unfiltered signals. Figure 9a shows the power spectral density (PSD) of the signal input to port 1 of the single-notch filter for \(P_{c}=1\)\(W\). Amplifier nonlinearities have resulted in minor harmonics of the AM spectrum, which have been partially suppressed at high frequencies with a low-pass filter. Figure 9b shows the PSD at port 2, the stopband channel. The output shows a carrier attenuation of 73 dB and a sideband attenuation of 1.2 dB, implying an output signal with almost intact message and negligible carrier. No significant additional harmonics are introduced. Figure 9c shows the attenuation of the two sidebands and the carrier over a power range between 0.1 and 1 W. Attenuation is almost independent of \(P_{c}\), implying effective power handling despite the use of ferrites.
## 3 Results  
### Selection Process  
The electronic and journal-based searches were conducted in accordance with the PRISMA principles [16] and yielded 314 and 15 additional reports, respectively, for a total of 329 records. During abstract evaluation, 283 references were excluded, and 46 articles were investigated in their full-text form. Furthermore, 27 articles were removed during the full-text investigation, while 19 articles from 13 prospective human clinical trials were included. Four trials [22; 23; 24; 25] reported data from the same pool of patients in more than one publication and data were evaluated in order to keep from counting the same population twice. The analysis was therefore finalized on 13 prospective clinical trials (Figure 1). Risk of bias of included studies was assessed according to the Cochrane Collaboration tool and reported in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & & & \multicolumn{4}{c}{**Risk of Bias**} & \multicolumn{4}{c}{**Risk of Bias (Other Sources)**} \\ \hline
**Study Reference** & **Random** & **Random** & **Illaion** & **Binding of** & **Binding of** & **Incomplete** & **Scheltive** & **Group** & **Sample** & **Contifict of** & **Radigographic** & **Biological** \\
**Low** & **Sequence** & **Coercination** & **Concealment** & **Patients and** & **Outcome** & **Outcome** & **Data** & **Reporting** & **Imbalance** & **Size** & **Contifict of** & **Radigographic** & **Biological** \\
**N/A** & & & & & & & & & & & & & \\ \hline [PERSON] et al. 2018 & N/A & N/A & N/A & N/A & low & N/A & N/A & high & low & low & low \\ \hline [PERSON] et al. 2018 & low & low & high & low & high & low & low & low & high & low & low \\ \hline [PERSON] et al. 2018 & N/A & N/A & N/A & N/A & low & N/A & N/A & low & low & high & low \\ \hline [PERSON] et al. 2017 & N/A & N/A & N/A & N/A & low & N/A & N/A & high & low & low & low \\ \hline [PERSON] et al. 2017 & low & low & high & low & low & low & low & low & low & low & low \\ \hline [PERSON] et al. 2015 & N/A & N/A & N/A & N/A & low & N/A & N/A & N/A & low & low & high \\ \hline [PERSON] et al. 2015 & N/A & N/A & N/A & N/A & low & N/A & N/A & high & low & high & high \\ \hline [PERSON] et al. 2014 & high & high & high & low & low & low & high & high & low & low & low \\ \hline [PERSON] et al. 2009 & low & low & high & low & low & low & low & high & high & high & low \\ \hline [PERSON] et al. 2007 & N/A & N/A & N/A & N/A & high & N/A & N/A & high & low & low & low \\ \hline [PERSON] et al. 2005/2007 & low & low & high & high & low & low & high & high & low & low & low \\ \hline [PERSON] et al. 2003 & N/A & N/A & N/A & N/A & low & N/A & N/A & high & high & high & high \\ \hline [PERSON] and [PERSON] 2001 & high & high & high & high & low & low & high & high & low & low \\ \hline \hline \end{tabular}
\end{table}
Table 1: Quality assessment of the included studies according to the Cochrane risk of bias tool. Characteristic, quality, and heterogeneity of enrolled studies were reported.
## 3 Results
### Backscatter Coefficient  
The raw A-Scan data are shown in Figure 6. The time-domain reference fontwall echo and the backscatter region of D1 and D3 are shown in Figure 6a-c. The frequency-domain response of the signals in Figure 6a-c are shown in Figure 6d-f. Since the attenuation coefficient was used to calculate the backscatter coefficient, we limit the bandwidth to 2-7 MHz as well. Similar to attenuation, the bandwidth outside our analysis is shown in the light red region and is not considered in the work. An example of the backscatter region  
Figure 4: The raw A-Scan data used for calculating the attenuation parameter (\(\alpha\)) are shown. The time domain response of (**a**) reference front wall (FW) echo, (**b**) backwall1 (BW1) along D1 and (**c**) BW1 along D3. The corresponding spectral response is shown for (**d**) FW, (**e**) BW1 along D1 and (**f**) BW1 along D3. D3 is the build direction.  
Figure 5: Attenuation parameter (\(\alpha\)) of the DED sample with waves propagating along (**a**) DI, (**b**) D2 and (**c**) D3 directions. D3 is the build direction.  
in time domain along D1 and D3 are shown in Figure 6c,d. The backscattered signal has a higher amplitude along D3 compared to D1. The backscatter spatial RMS magnitude (\(\Gamma_{rms}\)) is shown in Figure 6e,f. The periodic grain noise along D3 manifests as a strong peak in \(\Gamma_{rms}\). The calculated FOM values are shown in Figure 7. Between 4-5 MHz, the FOM is 0.0025 cm\({}^{-0.5}\), whereas, between 5-6 MHz, it is \(\approx\)0.0225 cm\({}^{-0.5}\) for D3 and \(\approx\)0.01 cm\({}^{-0.5}\) for D2. The FOM values reported in literature for conventional IN718 at 5 MHz is between 0.003-0.005 cm\({}^{-0.5}\)[34]. It is interesting to note that below 5 MHz for the DED samples, the grain noise is similar to conventional structures; however, the strong periodic noise results in a FOM that is an order of magnitude larger.