esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Joint Localization Method for TDOA and Beamforming  
From the above analysis, it can be known that if the four sensors are used for TDOA locating, the approximate location of AE source can be located. Then, the region located by the TDOA method is divided elaborately, and the beamforming method is used to locate AE source. This joint localization method is more accurate than the TDOA method, and also reduces the calculation cost in comparison to the beamforming method.  
The specific steps are:  
1. As shown in Figure 7, four sensors are placed on the plate which is divided according to the midpoint of each two adjacent sensors. Then the plate can be divided into four regions: <1>, <2>,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sensor Spacing** & **Actual Location of \#1** & **Localization** & **Actual Location of \#2** & **Localization** \\
**(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** \\ \hline
50 & (300, 600) & (330, 690) & (500, 800) & (560, 940) \\
100 & (300, 600) & (300, 610) & (500, 800) & (490, 800) \\
150 & (300, 600) & (300, 610) & (500, 800) & (500, 820) \\
200 & (300, 600) & (300, 610) & (500, 800) & (500, 820) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Beamforming localization results.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**AE Source** & **Change in TODA** & **Change \(\Delta t_{2}\)** & **Change \(\Delta t_{3}\)** & **Change \(\Delta t_{4}\)** \\
**Location** & **(\%)** & **Unchange \(\Delta t_{3}\), \(\Delta t_{4}\)** & **Unchange \(\Delta t_{2}\), \(\Delta t_{4}\)** & **Unchange \(\Delta t_{2}\), \(\Delta t_{3}\)** \\ \hline \multirow{8}{*}{(550, 600)} & \(-10\) & (560, 243) & N.A. & (525, 365) \\  & \(-8\) & (558, 280) & N.A. & (530, 396) \\  & \(-6\) & (556, 326) & N.A. & (534, 432) \\  & \(-4\) & (554, 386) & N.A. & (539, 476) \\  & \(-2\) & (552, 468) & (554, 1222) & (544, 530) \\  & \(2\) & (548, 880) & (547, 429) & (556, 696) \\  & \(4\) & (546, 4811) & (544, 338) & (563, 840) \\  & \(6\) & N.A. & (542, 279) & (570, 1096) \\  & \(8\) & N.A. & (540, 236) & (578, 1801) \\  & \(10\) & N.A. & (538, 202) & N.A. \\ \hline \multirow{8}{*}{(750, 600)} & \(-10\) & (660, 112) & N.A. & (638, 299) \\  & \(-8\) & (675, 168) & N.A. & (653, 330) \\  & \(-6\) & (691, 233) & N.A. & (670, 369) \\ \cline{1-1}  & \(-4\) & (709, 315) & N.A. & (691, 421) \\ \cline{1-1}  & \(-2\) & (728, 427) & (835, 1702) & (717, 492) \\ \cline{1-1}  & \(2\) & (774, 940) & (702, 372) & (795, 784) \\ \cline{1-1}  & \(4\) & (802, 2844) & (672, 262) & (861, 1195) \\ \cline{1-1}  & \(6\) & N.A. & (651, 195) & (965, 4644) \\ \cline{1-1}  & \(8\) & N.A. & (637, 146) & N.A. \\ \cline{1-1}  & \(10\) & N.A. & (626, 108) & N.A. \\ \hline \hline \end{tabular}
\end{table}
Table 3: _Cont._\(<\)3\(>\), and \(<\)4\(>\). The sensors 1-4 are used for TDOA locating. The x-coordinate of the AE source can be obtained.
2. The localization based on the beamforming method is performed on the corresponding region according to the x coordinate. For example, if the x-coordinate of the TDOA locating is in the region \(<\)1\(>\), the region \(<\)1\(>\) is subjected to the beamforming elaborate scanning for locating.
## 2 Background
### Identification of objects of interest and data groups  
After identifying the functional processes, Table 3 illustrates the objects of interest in the functional process (e.g., software applications, humans, sensors, and other hardware) and their data groups or attributes (e.g., employee ID for a given employee).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Object of** & **Description** & **Related Data Groups** & **Example** \\ \hline User & \begin{tabular}{c} Represents any \\ interaction by a user \\ on the website or app, \\ such as clicks, page \\ views, form \\ submissions, etc. \\ \end{tabular} & \begin{tabular}{c} Button ID, Timestamp \\ of interaction, Page \\ URL, Event type \\ (e.g., \"click\", \\ “view”, “submit”). \\ \end{tabular} & \begin{tabular}{c} A user clicks a button, \\ generating event \\ details like button ID, \\ time of click, and \\ page URL, which \\ GTM captures \\ and processes. \\ \end{tabular} \\ \hline Tag & \begin{tabular}{c} A snippet of JavaScript \\ that sends collected \\ data to external \\ analytics platforms \\ (e.g., Google \\ Analytics). A tag is \\ fired based on \\ trigger conditions. \\ \end{tabular} & \begin{tabular}{c} Tag ID, Data sent by \\ the tag (e.g., page \\ URL, button name), \\ Trigger ID \\ (identifying which \\ condition led to the \\ tag firing). \\ \end{tabular} & \begin{tabular}{c} A tag fires when a \\ page view occurs and \\ sends the page URL \\ and timestamp to \\ Google Analytics. \\ \end{tabular} \\ \hline Trigger & \begin{tabular}{c} Defines the conditions \\ under which a tag is \\ fired (e.g., a button \\ click, form submission, \\ or page view). \\ \end{tabular} & \
\begin{tabular}{c} Trigger ID, Condition \\ (e.g., event type, scroll \\ depth, form ID), \\ Associated tag ID. \\ \end{tabular} &
\begin{tabular}{c} A trigger fires when a \\ user submits a form, \\ leading GTM to send \\ form data to an \\ analytics system. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 3: Objects of interest and data groups.
# Modal Identification of Train Passenger Seats Based on Dynamic Tests and Output-Only Techniques
## 2 Modal Identification Methodologies
### Transmissibility Function  
Seat dynamics is quantified regarding transmissibility, which is an indicator of dynamic comfort [6; 7; 11; 12; 21; 32]. Transmissibility is defined as the ratio between the response amplitude at a specific seat location under an external forced vibration and the excitation amplitude at the seat base, both expressed in the frequency domain [40]. Therefore, the transmissibility function between the user-seat interface and the floor, \(H(f)\), is a non-dimensional parameter calculated by the following equation:  
\[H(f)=\frac{Go(f)}{Gi(f)} \tag{1}\]  
where \(G_{o}(f)\) is the output acceleration spectrum at the seat-user interface, and \(G_{i}(f)\) represents the input acceleration spectrum at the floor level [23].  
As the human body is a highly complex nonlinear biomechanical system, the vibration transmissibility varies with the direction and location of the measurements [41]. Therefore, seat transmissibility can be measured according to the axis (vertical, lateral, or longitudinal) and location (e.g., seat surface, seatback, armrest). Most published research involves the study of vertical transmissibility measured at the floor and seat surface, as these are the axes and locations most affecting passenger comfort [40]. Conventional train seats show a typical seat transmissibility curve, as displayed in Figure 1, and resonances comprehended between 4 and 6 Hz [25].  
In Figure 1, a resonance peak (red dot) is noticed that characterises, in this case, the highest vibration transmission to the user, but in other situations, it can also be associated with a local maximum. In contrast, the antiresonance valley (blue dot) occurs when the responses of certain system components under forced excitation present a local minimum, in most situations, quite close to zero [39; 40; 42; 43].  
Regarding the train seat, the resonance frequencies can be associated with the single or combined movements of the foam and structural seat frame, while the antiresonance frequencies may be associated with the transition between the distinct type of seat vibrations.
## 2 What Is the Definition of Vertical Resolution?  
Firstly, it is worthwhile questioning the use of the word _vertical_. While it is true that often the sample is horizontal and therefore surface heights are vertical, there is obviously no requirement to measure surface heights with respect to the distant horizon. The term appears informally in the context of stylus instruments in ISO 25178-601 [5]; however, a normative definition is absent from the areal texture standards. To correct for this, the most recent draft of ISO 25178-600 notes that the instrument \(z\)-axis that nominally corresponds to surface heights is sometimes referred to as the vertical axis, regardless of the orientation [6].  
Next, in the international vocabulary of metrology (VIM) and in the guide to uncertainty in measurement (GUM), resolution refers to the smallest difference between displayed indications that can be meaningfully distinguished (VIM 4.15 [7]; GUM F.2.2.1 [8]). A traditional interpretation of this guidance in computer-based metrology is that the resolution corresponds to the least significant bit (LSB) of the height-sensor digitization. As an example, a sensor may have an analog-to-digital (A/D) converter that reads 0 or 1 nm for the LSB. If used for profiling surface heights, this sensor would have a vertical resolution of 1 nm--you cannot measure a smaller height difference than this in a single measurement with this particular A/D converter.  
The definition of resolution is not as clear with full-field instruments that derive surface height data from a sequence of tens or even hundreds of digitized camera frames, as is the case with confocal and coherence scanning interference microscopes. A decade or two ago, when heights were computed as 16-bit integers, the tradeoff between height range and resolution often led to a minimum measureable height that corresponded to the data storage format. Today, however, computer storage and display capabilities have advanced to the point where these factors are no longer a limit in distinguishing surface heights. As an example, ZYGO Mx(tm) software uses 64-bit, double-precision internal representation, with an LSB of one-billionth of a nanometer (\(10^{-9}\) nm). It is unlikely that such a value would be understood and accepted as useful information on a specification sheet. The conclusion is that currently there is no standardized definition of vertical resolution in optical surface topography measurement.
# Responses of Medicinal and Aromatic Plants to Engineered Nanoparticles +
## 4 Effect of Metal-Based NPs on Medicinal and Aromatic Plants
### Iron-Based NPs
#### 4.3.2 Elicitor Effect
FeNPs applied foliarly to _Mentha piperita_ L. at a dose of 1.5 g/L, three times of the interval of 15 days up to flowering stages, considerably enhanced contents of Chl and EO. Application of 0.5 g/L NPs increased the EO content and oil production by 60 and 50%, and menthone and menthol contents by 65% and 30%, respectively, compared to the control. On the other hand, at a threefold higher dose of FeNPs, menthofuran concentration was 2.5-fold higher than in the control [133]. Spraying of basil plants with 0.2% Fe-urea nanocomplex increased the content of epi-\(\alpha\)-cadinol and _trans_-\(\alpha\)-bergamotene to 27.09 \(\pm\) 2.5% and 14.93 \(\pm\) 1.77%, respectively, although n-decane content was reduced strongly even by 99.1% and the concentration of RA achieved 5.81 \(\pm\) 0.18 mg/g [223].
# Beacon Success Rate versus Gateway Density in Sub-GHz Sensor Networks
## 6 Numerical Results
The field measurements show that a CCDF equal to 1 (100% PSR) is not commonly observed due to non-zero interference, even when in the vicinity of the GW. Figure 21 depicts the simulation results for both the beacon collision probability and beacon loss rate. The beacon loss rate is simulated with the path-loss model presented in the System Model section (Section 2.C). A collision is identified if there is a non-zero temporal overlap between the packets. Not every collision results in packet loss. The relationship between the collision rate and packet loss rate is presented in Equation (10) in Section 4. The beacon loss rate simulation results are presented in Figure 21. They are obtained by assuming a fixed area size while increasing the GW density. The simulation result aligns with the theoretical outcome from Equation (10), illustrated with the green '+' markers. Calculations are derived from the distribution of C/I samples. The simulation results demonstrate that 1000 GWs can coexist (at \(\sim\)\(\frac{35\times 35\) GWs}{km^{2}}\) density) with a 5% beacon loss rate, equivalent to a beacon success rate of 95%. Figure 21: Relationship between beacon collision rate and beacon error rate versus number of gateways. Figure 20: CCDF of PSR in relation to the distance from the GW in urban terrain, considering 1 GW, 1 EN, and a 64-byte payload.
## 4 Identification of Recent Developments and Possible Solutions
### Solution Pump Solutions  
Cavitation is a major challenge for the solution pump in ACHP cycles. The saturated liquid coming from the liquid-vapor separator is very sensitive to changing low-pressure conditions caused by the compressor. The evaluation of the sources given in Table 1 shows that mainly centrifugal pumps for industrial pilot plants and diaphragm pumps for small laboratory facilities are used as solution pumps. To reduce the risk of cavitation, different approaches have been investigated. Besides the generally important reduction of pressure losses upstream of the pump inlet, the external subcooling of the lean solution, as used  
Figure 5: Schematic representation of a plate heat exchanger operated as absorber in (**a**) falling film and (**b**) bubble mode (adapted from [PERSON] et al. (2019) [75]).  
by [PERSON] et al. (1989) [51], is one possible solution. Additionally, experiments with an upstream booster pump or a special design of the separator to provide enough static height of the liquid level for the pressure increase upstream of the solution pump have been investigated by various researchers [44; 55; 56]. Furthermore, [PERSON] et al. (2004) [23] and [PERSON] et al. (2019) [56] introduced an option to control the liquid level in the high-pressure receiver by regulating the expansion valve to keep the low pressure stable and reduce the risk of cavitation due to rapid pressure changes.
## 3 Results
### Pig Behaviors as Detected by Sensors  
#### 3.3.1 Sensor Detection Characteristics  
The sensors demonstrated a rapid response time (approximately 1 s) to the activities of the sow and piglets within the pen, quickly generating a voltage signal upon detection of movement. The sensors were also able to immediately lower the signal when the pigs ceased moving. The sensor outputs enabled high-resolution and real-time visualization of pig activity patterns, accessible both on-site and remotely, as depicted in the screenshot of the 2 h measurement history at 1 Hz (Figure 6).  
The analog output voltages from the sensors reflected the intensity of pig movements, with the higher voltages representing more vigorous activity. While Figure 6 shows some signal saturation at 2 VDC, processing these 1 Hz signals into 15 s averages clarifies the variations in activity intensity for behavioral studies [17]. This method of monitoring individual pens with a single sensor provided more accurate data on pig behaviors by addressing limitations observed in previous studies, such as the impact of the pig's distance from the sensor on the sensor outputs.  
Figure 5: Comparison of different behaviors of sows (**left**) and piglets (**right**) between heat stress and cooling treatments.  
The sensor-based data collection and initial processing were continuous, automated, and required minimal labor, with the system operating unattended. The daily time-series graphs and basic statistics for each pen generated by AirDAC software facilitated quick and direct inspections of the temporal distribution of building environment conditions and their correlation with pig activities.  
One of the key advantages of sensor detection is its non-invasive nature, which assisted in revealing activity variations related to pen locations within the rooms. Pens positioned near the doors (i.e., pens #1, 2, 9, 12, 16, 17, and 19) produced higher sensor outputs than average, with a 21% increase in sensor outputs compared with the average of all pens. This was supported by the pig-moving scores, where pens near the doors had an average score higher than the overall average. However, pens near wall fans and the air circulation fan did not show considerable differences in sensor outputs or pig-moving scores. This suggests that sows located near doors may experience disturbances from researchers and workers entering and exiting the rooms. This interpretation aligns with findings from a previous microenvironment study [27], indicating that variations in thermal, lighting, and acoustic environments within a farrowing room can influence the welfare and behavior of sows and piglets.  
Figure 6: An example of real-time sensor outputs at 1 Hz in an AirDAC 2 h data history, showing different early morning pig activity patterns in 10 pens (#13–22) in Room B.
## 5 Discussion
### Limitations and Prospects  
Although the accuracy of \(k\)NDVI estimates at the site scale was higher than that of NDVI, unfortunately, ChinaFLUX shared flux observations only for certain stations. This makes it difficult to rely on observational data alone when assessing the quality of NPP data at the national scale. In the future, more measured data will be sought as much as possible to improve the quality of the estimated NPP data.  
For accuracy validation, direct real NPP data were not available in this research, and were obtained by converting flux data. It is worth noting that the value of the \(NPP/GPP\) ratio \(\alpha\) used in conversion varies with geography, meteorology, and vegetation type, while in this study we used the regional average \(NPP/GPP\) ratio (\(\alpha\) = 0.64), which could potentially have an impact on the NPP conversion value.
# Context-Based Dynamic Meshed Backhaul Construction for 5G Heterogeneous Networks
## 2 Traffic Adaptive Dynamic Meshed Backhaul Construction For Outdoor Dynamic Crowd
### Traffic Adaptive Mesh Construction
#### 2.3.2 Initial Path Creation for Backhaul Network  
The goal of this step is to create multi-hop paths from each sector of the mmWave GW to relay traffic to the mmWave SC-BSs via activated mmWave backhaul interfaces determined in previous step. Using load balancing approach, we determine such routes by solving the following linear programming problem:\[\text{find}: \quad\mathbf{f}\text{ s.t.}\] \[\text{minimize}: \quad\mathbf{h}^{T}\mathbf{f}\] \[\text{subject to}: \quad\mathbf{t}_{\text{GW}}\equiv\mathbf{M}_{\text{GW}}\mathbf{f} \leq C_{\text{GW}}\mathbf{1} \tag{1}\] \[\quad\mathbf{t}_{\text{S}}\equiv\mathbf{M}_{\text{S}}\mathbf{f} \geq\mathbf{a}\odot\mathbf{t}_{\text{D}}\] (2) \[\quad\mathbf{f}\geq\mathbf{0}, \tag{3}\]  
where the number of SC-BSs and the number of sectors of GW are denoted by \(N_{\text{S}}\) and \(N_{\text{GW}}\) respectively. Thus, the total number of flow \(N_{\text{V}}=N_{\text{S}}\times N_{\text{GW}}\) equals the product of these. \(\mathbf{f}\in\mathcal{R}^{N_{\text{V}}}\) stacks the amount of load to be transferred from a sector of the mmWave GW to a SC-BS. \(\mathbf{h}\in\mathcal{R}^{N_{\text{V}}}\) weights the number of relay hop from GW against \(\mathbf{f}\). \(\mathbf{t}_{\text{GW}}\in\mathcal{R}^{N_{\text{GW}}}\) stacks the summation of traffic load transfered via each sector of the mmWave GW. Similarly, \(\mathbf{t}_{\text{S}}\in\mathcal{R}^{N_{\text{S}}}\) stacks the total traffic supply to each SC-BS, \(\mathbf{a}\in\mathcal{R}^{N_{\text{S}}}\) expresses the ON/OFF state predetermined in the previous step. \(\mathbf{t}_{\text{D}}\in\mathcal{R}^{N_{\text{S}}}\) stacks all the traffic demands of each SC-BS. \(\mathbf{M}_{\text{S}}\in\mathcal{R}^{N_{\text{V}}\times N_{\text{S}}}\) is a mapping matrix between \(\mathbf{t}_{\text{S}}\) and \(\mathbf{f}\). Similarly, \(\mathbf{M}_{\text{GW}}\in\mathcal{R}^{N_{\text{V}}\times N_{\text{GW}}}\) is a mapping matrix between \(\mathbf{t}_{\text{GW}}\) and \(\mathbf{f}\). The objective function is to find the optimal load balance \(\mathbf{f}\) with as small as possible number of hops from the GW (to reduce latency) under three constraints. The first constraint Equation (1) guarantees that the traffic transferred via each sector of the mmWave GW should not exceed the sector's capacity. The second constraint Equation (2) ensures that the supply traffic to each SC-BS should higher than the total traffic load of that SC-BS to satisfy UEs' demands. Finally, the third constraint Equation (3) assures that non-negative value of traffic load. From solving for \(\mathbf{f}\), we can get the optimal combination of traffic load between multiple sectors of the mmWave GW working as the sources of information and all the SC-BS working as the sinks of information.
## 2 Internet of Things (IoT): Specifications & Security Challenges
### Challenges of IoT Security and Potential Attacks  
The original design of the Internet was not considering the emergence of modern technologies which in turn make the deployment of IoT hard on top of the existing networks and security solutions. Moreover, the underlying infrastructure of the Internet has several limitations in terms of scalability, complexity, configuration, and resource constraint. A lot of applications are presented by IoT networks for a vast number of end-users making security and privacy more challenging topics in such an environment. The limitation of the computational power of IoT devices complicates the process of providing efficient security mechanisms. Moreover, the variety of IoT devices increases the attack surface in which they are being targeted by different attacks [22,23]. According to the aforementioned IoT architecture layers, three possible attack surfaces can be summarized as follows [24]:  
* Perception Surface. In this direct surface, physical devices are found where attacks are conducted on units such as sensors, actuators, microcontrollers, RFID readers, and others. Identification, communication, and collection of information are performed by these devices making them targeted by several physical and logical attacks including vandalism, Denial of Service (DoS), eavesdropping, jamming, and others.
* Network Surface. Wired and wireless sensor networks are used to connect IoT devices which reflects the necessity of integrating them at a large scale. Due to that large-scale topology, IoT networks' surface is exposed to attacks while the data is transmitted using low-efficient security protocols [25]. Several attack scenarios can be seen such as scanning open ports to access the victim's networks and steal sensitive information. The attack types include man in the middle, spoofing, DoS, traffic analysis, jamming, and others.
* Application/Web Surface. Web mobile software-based applications are used increasingly to control and share the services provided by IoT devices via either clouds or servers. Several mobile platforms ease the process of deploying relative applications due to the use of open architecture such as the Android operating system. However, this introduces a new vector for exploiting threats and launching attacks on IoT devices. Such attacks include bluejacking, eavesdropping, blue-snarfing, DoS, and others. Additionally, cloud computing presents an additional attack vector where end-users can get data breaches, DoS, flooding attacks, and others on the cloud surfaces.