esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimizing the Quality of Service of Mobile Broadband Networks for a Dense Urban Environment
## 3 Experimental Design
### Relevant 4G LTE Performance Metrics
#### 3.2.4 Signal-to-Noise plus Interference Ratio  
The received signal comes with unwanted parts, which are not very useful to determine the user's experience, nor do they paint an accurate picture of the signal sent by the MNO's equipment. The signal-to-noise plus interference ratio (SINR) is a metric that captures this unwanted noise in the received signal and can be computed by the user's equipment. Since the resource block (RB) is the property of the user's device that is used to measure the SINR, the SINR becomes very important in predicting a modulation code scheme (MCS) that will guarantee a good throughput for the user to transmit data. A poor SINR measurement value on a user's device translates into low throughput resulting from the lower-order MCS being applied to the signal. In general, low SINR values are associated with fewer bits per modulation symbol, resulting in low QoS for an MBB user. The SINR is captured mathematically in Equation (4):  
\[\text{SINR}=\frac{P_{0}}{\sum_{1}^{n}(P_{I}+N_{b})} \tag{4}\]  
where  
\[\text{SINR }=\text{ ratio of signal to interference plus noise }(\text{dB}).\] \(P_{r}=\text{ resource block reading on UE for received signal power}.\)  
\[P_{I}=\text{ neighboring cell interference power on an average}.\] \(N_{b}=\text{ background noise power}.\)
## 2 Materials and Methods
### Data Acquisition
#### 2.2.2 UAV Data Image Acquisition  
Data Acquisition at Different Flight Approximation Distances  
The flight proximity of the UAV was set to 15 m, 20 m, 25 m, or 30 m in this study in accordance with the characteristics of the study area. UAV image data were collected with nap-of-the-object photogrammetry, and another flight proximity of 10 m was utilized to create an experimental control group, as shown in Figure 3. To ensure consistent elevation data throughout the entire monitoring process, we set up the most available route plan [30]. A uniform arrangement was adopted around the study area based on the optimal image control point layout, and a small number of internal control points was implemented [28; 29], with 9 GCPs being positioned in the study area as shown in Figure 4.  
Figure 2: (**a**) Ground checkpoint deployment map; (**b**) Trimble R2 GPS Survey Target ground checkpoints.  
Control-Free-Image-Based Nap-of-the-Object Photogrammetry Data Acquisition  
Two nap-of-the-object photogrammetry techniques, one based on image control and one based on control-free images, were used to gather imagery for the study area. The flight altitude was set to 40 m; the overlap rate of the heading image, to 80%; and the overlap rate of the side image, to 70%. The lenses were all tilted at a 45\({}^{\circ}\) angle with respect to the route for data acquisition. The oblique photography point cloud model collected by nap-of-the-object photogrammetry was used to plan the flight path in the study area, as shown in Figure 5. The flight parameters were set using DPGO 22.03.01 software. The course image overlap rate was 80%, and the side image overlap rate was 70% [31; 32; 33]. The optimal flight proximity was determined as shown in Figure 6.  
Figure 3: Distance course scenarios. (**a**) 10 m (control); (**b**) 15 m; (**c**) 20 m; (**d**) 25 m; (**e**) 30 m.  
Figure 4: Ground control point (GCP) distribution map.  
Figure 5: UAV flight routes.  
Figure 6: The best technical route for flight close distance acquisition.  
Image data for the study area were gathered on 22 June 2022 and on 15 July 2022. The images were taken in the morning under clear weather conditions with minimal wind. Notably, rainfall occurred between these two image collection periods, with a 23-day interval between them. An RG3-M (Cape Cod, MA, USA) rainfall recorder was installed to confirm the presence of substantial rainfall in the study area on both image collection days. The DJI Phantom 4 RTK UAV (Shenzhen, China) was selected, equipped with an autofocus FC6310R camera and a 1-inch 2000-megapixel image sensor. The remote control included a built-in GS RTK App for intelligent route planning to control the UAV data collection process. The parameters of the UAV and camera are described in Table 1.
# Inverted F Type Antenna Design for the Cattle Activity and
## 4 Conclusions  
An inverted F type antenna (IFA) for ZigBee communication of a sensor RF module has been designed and fabricated. The optimized IFA antenna has been measured and installed on the RF TRx module of the sensor board worn by livestock. The performance of the IFA antenna has been analyzed with and without the cover of the board and optimized. The designed IFA antenna has a return loss of \(-\)19 dB and a gain of 1.6 dB at 2.45 GHz. The signal strengths of RSIs with a chip antenna and the proposed IFA antenna have been compared. It is about 20 dB enhancement with the IFA antenna compare to the chip antenna on the module. The proposed IFA antenna can replace the chip antenna on the RF module, and 20 dB RSI can be enhanced on the communication system. This proposed antenna can be applied to an RF module and sensor module, which can be applied to detect the behavior of livestock in general as well as cattle.  
This research was supported by Daegu University Research Grant.  
**Institutional Review Board Statement:** Not Applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** \"MDPI Research Data Policies\" at [[https://www.mdpi.com/ethics](https://www.mdpi.com/ethics)]([https://www.mdpi.com/ethics](https://www.mdpi.com/ethics)).  
**Conflicts of Interest:** The author declares no conflict of interest.
# A New Scene Sensing Model Based on Multi-Source Data from Smartphones
## 2 Materials and Methods  
This section focuses on the characterization of data from GNSS, cellular network systems, optical sensors, magnetometers, and Wi-Fi sensors, all of which are commonly used in smartphones and are less affected by variations in terminal devices. These five sensors were chosen due to their widespread use and reliability. The temporal, spatial, and mathematical/statistical features of the data vary depending on whether the environment is indoor or outdoor, and thus, this paper emphasizes data mining for these five sensors to account for these differences.  
We divide the indoor-outdoor scenarios into four scenarios according to the scenarios, namely, open outdoor, semi-outdoor, semi-indoor, and indoor environments, as shown in Figure 1, in which open outdoor is defined as the positioning terminal not being surrounded by obstructions, semi-outdoor is defined as the location being partially obstructed by the building or other objects, semi-indoor is defined as the indoor environment near the window or the entrance/exit, and the indoor environment is defined as the location inside a building being partially obstructed by buildings or other objects.  
Figure 1: Four scene classifications: (**a**) outdoor, (**b**) semi-outdoor, (**c**) semi-indoor, and (**d**) indoor.
## 2 Materials and Methods
### Linear Cutting Test  
When the rock was cut using a cutting tool, the three-axis reaction forces, namely, the cutting, normal, and side forces, were measured, as shown in Figure 2b. In general, cutting forces are related to the torque and specific energy of the tunnel excavation machine. The normal force is related to the thrust force and capacity of the machine, whereas the side forces are related to its vibration and stability [17].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Name** & **Clearance Angle, \(\alpha\)** & **Rake Angle, \(\beta\)** \\  & **(Deg.)** & **(Deg.)** \\ \hline C00R20 & 0 & 20 \\ C05R20 & 5 & 20 \\ C10R20 & 10 & 20 \\ C15R20 & 15 & 20 \\ C05R00 & 5 & 0 \\ C05R05 & 5 & 5 \\ C05R10 & 5 & 10 \\ C05R15 & 5 & 15 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Names and specifications of the cutting tools.  
Figure 3: Basic shape of a cutting tool.  
Tunnel excavation machines, such as tunnel boring machines (TBMs) and roadheaders, use the concept of specific energy when determining excavation efficiency. The specific energy is the energy required to cut a unit volume, and its lower values indicate high excavation efficiency. This energy can be obtained using Equation (1):  
\[SE=\frac{F_{c}\times l}{V} \tag{1}\]  
where  
\(SE\) = specific energy (J/m\({}^{3}\));  
\(F_{c}\) = average cutting force (N);  
\(l\) = cutting length (m);  
\(V\) = cutting volume (m\({}^{3}\)).  
The equation reveals that the specific energy is significantly affected by the cutting force and cutting volume. Considering these influencing factors, this study focuses on the cutting force, cutting volume, and specific energy. Since the cutting speed of the rock had no apparent effect on the cutting performance [17; 21], it was maintained at 12.5 mm/s, and the force was measured every 25 ms.  
The cutting volume was measured and analyzed only for rocks at 40 and 50 MPa. The volume was measured by scanning the surface of the rock after cutting using a Calibry mini from Thor3d (Figure 4).  
Cutting tests were performed on each rock model using the cutting tools listed in Table 1. The cutting depth was set to 3, 6, and 9 mm, and at least three cuts were made. The s/p ratio, which is the ratio of cutting spacing and depth, was set to nine or more to avoid interaction between cuttings (Figure 5).  
Figure 4: 3D scanning for volume measurement: (**a**) Calibry Mini and (**b**) example of 3D scanning.  
Figure 5: Cutting spacing by penetration depth: (**a**) depth: 3 mm, spacing: 30 mm, (**b**) depth: 6 mm, spacing: 55 mm, and (**c**) depth: 9 mm, spacing: 85 mm.
## 2 Materials and Methods
### Force Sensor (FS) Device  
Force Sensor (FS) Model 6A68D was supplied by Interface Force Measurements Ltd. (Scottsdale, AZ, USA) (Figure 1A). The reasons why we selected this device were that (1) it is portable and easy to set in different positions and (2) the sensor can measure a wide range of forces and moments, covering the demand of this project. The FS measures the force and torque values in three-dimensional space. It is usually used in industrial and scientific applications, such as medical research (orthopedics and biomechanics). It contains three axis forces and three torques or moments of the axes (Fx, Fy, Fz, Mx, My and Mz), and thus it is called a 6-axis sensor. It measures all parameters simultaneously in three perpendicular axes and exports six-channel outputs (Table 2).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **TCC (_n = 20_)** & **NTCC (_n = 25_)** & **Min-Max** \\ \hline Gender (M/F) & 9/11 & 11/14 & \\ Age (years) & 59.55 (2.01) & 59.56 (1.79) & 42–78 \\ Body mass (kg) & 75.37 (3.49) & 74.62 (3.00) & 50–119 \\ Height (cm) & 167.69 (1.64) & 166.77 (1.79) & 150–183 \\ \hline \hline \end{tabular} Note: TCC, Tai Chi Chuan; NTTC, Non-Tai Chi Chuan; F, female; M, male; Min, Minimum; Max, Maximum; Mean (Standard Error of Mean or SE). All _p >_ 0.05.  
\end{table}
Table 1: Baseline characteristics of the participants in the study.  
Figure 1: (**A**) Force Sensor (FS) device, (**B**) Force Sensor with the top and base parts.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{2}{c}{**The Force Sensor Characteristics**} \\ \hline Fx (N) & Max to 10 kN \\ Fy (N) & Max to 10 kN \\ Fz (N) & Max to 20 kN \\ Mx (Nm) & Max to 100 Nm \\ My (Nm) & Max to 100 Nm \\ Mz (Nm) & Max to 100 Nm \\ Diameter & 83 mm \\ Height & 64 mm \\ Mass & 1.050 kg \\ Materials & Stainless Steel (SS) \\ \hline \hline \end{tabular}
\begin{tabular}{c} \hline \hline \multicolumn{2}{c}{Specifications *} \\ Accuracy class \\
0.2\% \\ Relative linearity error \\
0.1\%FS \\ Relative zero signal hysteresis \\
0.1\%FS \\ Temperature effect on zero signal \\
0.1\%FS/K \\ Temperature effect on characteristic value \\
0.05\%RD/K \\ Relative creep \\
0.1\%FS \\ Relative repeatability error \\
0.5\%FS \\ Nature frequency \\
3.3 kHz \\ Mounting Details \\ \hline \hline \multicolumn{2}{c}{Live End} & Dead End \\ \hline \hline \multirow{3}{*}{Threads} & M10 \(\times\) 1.5 \\  & 6 Places \\  & 12 mm Deep \\ \hline \hline \multirow{3}{*}{Dowel Pin Holes} & 6 H7 \\  & 2 Places \\ \cline{1-1}  & 2 Times \\ \cline{1-1}  & 12 mm Deep \\ \hline \hline \end{tabular}
\end{table}
Table 2: Force Sensor Model 6A68D Characteristics.
## 4 Results and Discussion
### Point Cloud Properties  
Figure 7 shows an example of the point cloud generated for Transect 2a. This point cloud has a point density of 42.2 points per \(\rm m^{2}\). Table 3 gives a summary of the point cloud properties for each of the eight transects. The average point density including all three returns over every flight was 43.21 points per \(\rm m^{2}\). This density is significantly higher than the data that is currently used in forestry management and research (8 points per \(\rm m^{2}\)) [43]. The number of second and third returns across the entire study area was low, however, in the areas of dense vegetation the percentage of second returns increased up to 24% and third returns to 4.2%. This increase along with visual inspection of the point clouds is sufficient to suggest that points are being measured from within the canopy for use in the modelling of key forest metrics such as leaf area index and above ground biomass. An average area of 11,288 \(\rm m^{2}\) was mapped in each flight, which allows multiple transects to be flown over individual forest plots in a single flight.  
The internal precision of the point cloud is highlighted by the standard deviations across planar surfaces. The standard deviation from a least squares best fit planes fitted to each surface was found to be 0.04 \(\rm m\),which is within the measurement precision of the Ibeo LUX (0.1 \(\rm m\) at 1 \(\sigma\)). This is consistent with the results from [14] who reported a similar variation with a Ibeo LUX sensor.  
Figure 6: _Cont._
# Context-Based Dynamic Meshed Backhaul Construction for 5G Heterogeneous Networks
## 2 Traffic Adaptive Dynamic Meshed Backhaul Construction For Outdoor Dynamic Crowd
### Network Architecture and System Models
#### 2.2.2 IEEE802.11 ad Based mmWave Meshed Backhaul  
As depicted in Figure 3, we consider IEEE802.11 ad based mmWave meshed backhaul for ODC. The mmWave SC-BSs overlaid on conventional macro cell are assumed to have multiple IEEE802.11 ad based wireless interfaces for either purposes of access or backhaul. According to [31], the band is divided to 4 channels. This paper assumes access and backhaul links, each use 2 channels to avoid their mutual interference. Since access and backhaul interfaces are usually co-located, intra-channel interference avoidance via very high directivity antenna or other non-linear methods to reduce loop interference will incur high cost and thus is out of scope of this paper.  
This paper also assumes that backhaul interfaces of SC-BSs are located e.g., on streets' lamp posts such that Line of Sight (LoS) condition is guaranteed. Practical deployment is tackled in [32] and thus is out of scope of this paper. To overcome the high path loss issue, this paper assumes high gain antennas are employed at both transmit and receive sides of the mmWave backhaul link. Moderate link distance in the order of 100m is also considered. Furthermore, multi-hop communication is also adopted for SC-BSs which cannot communicate directly with the mmWave GW [25].  
Figure 3: Self-backhauling mmWave overlay HetNet.  
As mentioned above, the GW located at the macro BS can be connected to other SC-BSs either directly or indirectly in a multi-hop manner, which enables adaptive topology formation of meshed backhauling via beam switching. In addition, path loss attenuation can also be overcome via multi-hop relay since the weaken signals are re-boost at each hop of relay. For the sake of simplicity, this paper assumes relay links between mmWave backhaul interfaces can only be established if and only if their link budgets are sufficient to support the highest data rate defined in IEEE802.11 ad standard [31]. This assumption also facilitates the realization of a homogeneous backhauling rate for ease of analysis.
## 2 The \(\alpha\)-\(\beta\)-\(\gamma\)-\(\delta\) Filter
### Fundamental Properties Obtained in Conventional Studies  
#### 2.2.1 Transfer Function and Stability Conditions  
In [14], [PERSON] et al. derived the transfer function and Bounded Input-Bounded Output (BIBO) stability conditions of the \(\alpha\)-\(\beta\)-\(\gamma\)-\(\delta\) filter using the \(z\)-transform of definitions (1)-(8). Considering the difference in the definition of the smoothing process described above, the transfer function for the input \(x_{\rm o,k}\) and output \(x_{\rm p,k}\) is expressed as  
\[G(z)=\frac{X_{\rm p}(z)}{X_{\rm o}(z)}=\frac{\left(\alpha+\beta+\frac{\gamma}{ 2}+\frac{\delta}{6}\right)z^{3}+\left(-3\alpha-2\beta+\frac{2}{3}\delta\right) z^{2}+\left(3\alpha+\beta-\frac{\gamma}{2}+\frac{\delta}{6}\right)z-\alpha}{f_{ \alpha\beta\gamma\delta}(z)}, \tag{9}\]  
where \(X_{\rm p}(z)\) and \(X_{\rm o}(z)\) are the \(z\)-transforms of \(x_{\rm p,k}\) and \(x_{\rm o,k}\), respectively, and  
\[f_{\alpha\beta\gamma\delta}(z) =z^{4}+\left(\alpha+\beta+\frac{\gamma}{2}+\frac{\delta}{6}-4 \right)z^{3}+\left(-3\alpha-2\beta+\frac{2}{3}\delta+6\right)z^{2}\] \[\quad+\left(3\alpha+\beta-\frac{\gamma}{2}+\frac{\delta}{6}-4 \right)z+(1-\alpha), \tag{10}\]is a characteristic polynomial of the \(\alpha\)-\(\beta\)-\(\gamma\)-\(\delta\) filter.  
Based on (10), the stability condition of the \(\alpha\)-\(\beta\)-\(\gamma\)-\(\delta\) filter was derived as [14]:  
\[0<\alpha <2, \tag{11}\] \[0<\beta <\frac{13}{6}(4-2\alpha),\] (12) \[0<\gamma <\frac{2\alpha\beta}{(2-\alpha)},\] (13) \[0<\delta <4(2-\alpha), \tag{14}\]
# Machine-Learning-Based Path Loss Prediction
## 2 Theory
### Machine-Learning-Based Predictive Modeling  
In this study, several machine learning models are used to predict path loss in V2V communication. The input parameters used in all machine learning methods are weather conditions, number of antennas, distance, antenna position, and vehicle direction. The output parameter for all models is path loss. A common dataset was used in machine learning methods.
# An Enhanced System Architecture for Optimized Demand Side Management in Smart Grid
## 3 Proposed CHEMA and its Implementation
### Water Heater and Refrigerator Modules  
The water heater is another important load with thermal storage capability used in CHEMA for energy management. Water heaters are considered the second largest load among household appliances and consume about 30% of the total energy consumed [24]. Electrical energy supplied to water heaters is divided into two parts: one is used for heating of cold water and the other is consumed for compensation of heat losses from tank to ambient. There are two ways for heat loss in water heaters: one is conduction losses through walls of the heater and the other is temperature drop by water drawn for usage and entry of colder water for compensation. Energy flow analysis of the water heater provides the following differential equation to determine the inside temperature of water. On the basis of heat flow, the water heater model is given by the following equation [24]:  
\[T_{H}(t)=T_{H}(\tau)e^{-(\frac{1}{R^{\circ}})(t-\tau)}+GR^{\prime}T_{out}+BR^{ \prime}Tin+QR^{\prime}*[1-e^{-(\frac{1}{R^{\circ}})(t-\tau)}], \tag{8}\]  
where  
\(\tau\): initial time (h)  
\(T_{H}(\tau)\): initial temperature (\({}^{\circ}\)F)  
\(T_{in}\): incoming water temperature (\({}^{\circ}\)F)  
\(T_{out}\): ambient air temperature outside tank (\({}^{\circ}\)F)  
\(T_{H}(t)\): temperature of water in tank at time t (\({}^{\circ}\)F)  
Q: energy input rate as function of time (W)  
R: tank thermal resistance (m\({}^{2\circ}\)F/W)  
SA: surface area of tank (m\({}^{2}\))  
G = SA/R (W/ \({}^{\circ}\)F)  
\(W_{D}\): water demand (L/h)  
C\({}_{p}\): specific heat of water (W/\({}^{\circ}\)Fkg)  
D: density of water = 1 kg/L  
B: D.\(W_{D}\).C\({}_{p}\) (W/ \({}^{\circ}\)F)  
C: (volume of tank)(density of water)C\({}_{p}\) (W/ \({}^{\circ}\)F)  
\(R^{\prime}=1/(B+G)\) (W/\({}^{\circ}\)F).  
Water temperature inside the heater is represented on left hand, ambient losses, energy required for water heating and total input are taken on the right hand side of the equation. Refrigerator modeling is based on Equation (9) as follows:  
\[T_{w}=\int(T_{o}*2.9863)dt+\int(T_{r}*2.9863)dt-\int(T_{w}*5.9726)dt, \tag{9}\]  
where \(T_{o}\), \(T_{r}\) and \(T_{w}\) represent the outdoor temperature, the room temperature and wall temperature, respectively. This equation gives the approximate temperature of the refrigerator wall.  
Figure 4: Heater subsystem.  
The refrigerator system consists of three subsystems. The first and second subsystems are used to implement the wall and indoor temperatures, respectively. The third subsystem compares the indoor temperature and set point to turn ON/OFF the refrigerator. The same module is used to convert the joules into watts and perform subsequent cost calculations according to given rates.
## 4 Discussion
Finally, Au NPs can be useful for the image identification of M13 phages and of other biological nanostructures because the heavy element permits to be easily identified bydifferent fluorescence characteristic lines, which can be detected by high energy resolution detectors. Thus, near nano structures, such as the phages, or macroscopic structures, such as the organs of animals, can be detected thanks to the fluorescence response of Au nanoparticles, for example detecting the L and M characteristic lines of X-ray maps and to the SPR absorption bands in the visible region of optical absorbance spectra [28; 29]. Further information about the M13 phage structure and their interaction with the Au NPs can be evinced by Raman spectroscopy. Such spectra show some characteristic peaks in the (600-1600) cm\({}^{-1}\) region due to the presence of proteins, alanine, C-C, C-N, C-O, CH\({}_{2}\) and CH\({}_{3}\) chemical bonds. These peaks depend on the presence of the Au NPs, indicating that proteins and other molecules can be altered by their presence, as reported in the literature [12]. As an example, Figure 5a shows the X-ray image of the Au L fluorescence lines detected in the TEM image of Au NPs adherent to a phage filamentation (yellow dots), while Figure 5b shows a typical Raman spectrum in the (600-1800) cm\({}^{-1}\) region of the phage with the Au NP solution. With respect to our previous measurements, some of which are published elsewhere [12], the advancements reported in this paper concern the use of solutions containing a different concentration of surfactant to reduce Au NPs coalescence, the different preparative of the SEM and TEM samples to better observe Au NPs adhesion to the M13 phages, the evaluation of the average number of nanoparticles adherent to the phages per unit of length and of the average size and shape of the produced NPs. Such investigation confirms the previous data analysis, i.e., that the Au NPs are bonded to the phages head and tail and to their lateral surface, and that the NPs are spherical and generally of an average size of about 20 nm. Such size and the spherical shape may confer high penetrability of the cell membrane; however, further investigation should be performed to assert that this size is the best. The uptake effect seems high and limited to a maximum of about 12 particles/phage, a very low number with respect to that of nanoparticles immersed in the solution which is orders of magnitude higher. The performed measurements are only preliminary and up to now, do not allow to establish the level of stability of the Au NPs-phage adhesion. It probably depends on a combination of different factors, such as the time, the temperature, the PH of the biological liquids and by other effects which were not investigated in this paper. Figure 5: Characteristic X-ray image of the Au L lines in the TEM underlayer image (**a**) and typical Raman spectrum of the solution containing the M13 phages with Au NPs (**b**).