esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Analysis  
### Detection Performance  
These four methods are all based on the framework of tracking-by-detection or joint-tracking-and-detection, meaning that the performance of detection largely determines the performance of the tracking algorithm. Therefore, we first analyzed the detection performance of these four trackers (the main reference indicator is DetA).  
Trackor++ adapts the classic Faster-Rcnn method, which has relatively poor detection accuracy from the current point of view, so the DetA score of Trackor++ in each scene lower than other methods.  
TransTrack uses the latest transformer based DETR method. The transformer network structure to extract features is very complex, and the amount of network parameters increases rapidly with the increase of input image size. When the image input size is relatively small, the detection performance is still excellent. However, when the input size exceeds a certain value, the model will have too many parameters to train. The detection effect of this model decreases significantly with the increase of the input image size.  
Both CenterTrack and FairMOT use anchor-free based detectors in their works. The anchor-free method is scale invariant to image size, which is currently the most efficient and accurate method. Table 5 shows that these two trackers' detection performance on NPSPT are competitive with each other for the best method.
# Complex-Eigenfrequency Band Structure of Viscoelastic Phononic Crystals
###### Abstract  
The consideration of material losses in phononic crystals leads naturally to the introduction of complex valued eigenwavevectors or eigenfrequencies representing the attenuation of elastic waves in space or in time, respectively. Here, we propose a new technique to obtain phononic band structures with complex eigenfrequencies but real wavevectors, in the case of viscoelastic materials, whenever elastic losses are proportional to frequency. Complex-eigenfrequency band structures are obtained for a sonic crystal in air, and steel/epoxy and silicon/void phononic crystals, with realistic viscous losses taken into account. It is further found that the imaginary part of eigenfrequencies are well predicted by perturbation theory and are mostly independent of periodicity, i.e., they do not account for propagation losses but for temporal damping of Bloch waves.  
Keywords: phononic crystal; band structure; viscoelasticity; complex band structure +
Footnote †: journal: Applied Sciences
## 3 Methodology
### Formulation of Measurement
#### 3.3.3 Computation Of Deterioration Score  
The condition ratings of bridge decks can exhibit consistency, deterioration, or improvement over time. These variations in condition ratings are typically the result of natural deterioration, subjective ratings provided by bridge engineers during inspections, or improvements made through bridge maintenance activities (interventions). To account for the decrease in condition ratings over time (deterioration), we assume that a decrease in condition ratings corresponds to a negative slope with respect to both condition rating and deck age. Thus, in addition to creating separate bridge segments based on the new year-built, we further split each segment into monotonically decreasing segments along the bridge timeline, enabling a comprehensive understanding of bridge deck deterioration.  
In order to define the deterioration score of a bridge deck, we focus on the monotonically decreasing segments within the bridge deck's timeline. By calculating the difference in condition ratings over time, we can determine the deterioration of the bridge component. This deterioration score is derived through a simple calculation of the slope, capturing the extent of deterioration experienced by the bridge deck over its lifespan.  
Let  
\(y_{1}\) be the condition rating of Bridge \(B\) at the start of the segment.  
\(y_{2}\) be the condition rating of Bridge \(B\) at the end of the segment.  
\(x_{1}\) be the deck age of Bridge \(B\) at the start of the segment.  
\(x_{2}\) be the deck age of Bridge \(B\) at the end of the segment.  
\[\text{Slope (Deterioration)} =\frac{\Delta\left(\text{ deck condition rating}\right)}{\Delta\left(\text{ deck age}\right)}\] \[=\frac{y_{2}-y_{1}}{x_{2}-x_{1}}\]  
The slope represents the rate of change in deck condition rating between two points with respect to deck age. The final deterioration score of a bridge deck is the average of all segments and their monotonically decreasing sections of the bridge deck ratings.
## 6 Simulation Setup and Results  
First, the simulation scenario, baseline reference scenario, adopted assumptions and selected KPIs are presented. Afterward, the DSS techniques are compared to a baseline reference scenario with fixed spectrum allocation by running an extended set of simulations. Simulations were performed in a satellite environment based on the components and functionalities being reused and extended from Satellite Network Simulator 3 (SNS3) [31]. SNS3 is a satellite network extension based on the well-known ns-3 simulator environment [32].
# The Impact of GCP Chip Distribution on Kompsat-3A RPC Bias Compensation
## 3 Experimental Results
### Minimum Distribution Requirement  
We further subdivided the three-zone cases into four sub-cases, as illustrated in Figure 17. Sub-cases 1 and 2 are column-direction and row-direction distributions, respectively. As indicated by the earlier error analysis revealing significant error changes along the image line direction (as demonstrated in Figure 13), we can expect that sub-case 1 would result in the worst accuracy. Sub-cases 2, 3, and 4 aim to encompass at least one zone along the image line direction  
Sub-case 1: 1 2 3; 4 5 6; 7 8 9  
Sub-case 2: 1 4 7; 2 5 8; 3 6 9  
Sub-case 3: 1 5 7; 2 6 8; 2 4 8; 3 5 9  
Sub-case 4: 1 6 7; 3 4 9  
Figure 15: Error change in RMSE for the number of GCP zones.  
Figure 16: Error histogram for the number of GCP zones (Image 3).  
Figure 17: Sub-cases of the three GCP zone.  
Figure 18 illustrates the errors observed in the proposed sub-cases. Sub-case 4 demonstrates the most favorable outcome, followed closely by sub-case 3 regarding maximum error. The error range within sub-case 4 remains within \(\pm\)3 pixels (equivalent to 1 pixel in RMSE) across the entire dataset. In contrast, sub-case 1 exhibits errors ranging from 1 to 1.5 pixels, yet recording a maximum error of up to 5 pixels (observed in Image 5). As a result, we concluded that achieving accurate RPCs-bias compensation for Kompsat-3A necessitates GCP information covering at least three zones along the image line direction. Additionally, it is advisable to avoid a linear zone distribution, such as along the sample or line direction (horizontal or vertical).
## 2 Experimental Design
### Delivery Routes and Vibration Profile  
To collect the field vibration data four different route conditions were used. The measurements were performed on motorway, arterial road, country road and urban roads, respectively. The reason for choosing these categories is to compare the vibration levels between varied road conditions and to search for that vibration level which is closest to the accelerated laboratory circumstances. Each measurement was taken for an approximately 60 min period per routes. The measurements were executed at night only, so the van could maintain a relatively higher travelling speed due to less traffic. This is important due to the fact that a higher vehicle speed produces higher vibration intensity [4; 12; 13]. The routes for the measurements can be seen in Figure 4 and Table 2 contains information of distances and average vehicle speeds. The experiment was conducted from 6 to 7 August (2020) in the county of Gyor-Moson-Sopron, Hungary.  
Figure 3: (**a**) Sample stacked unit in van and (**b**) in laboratory.  
The laboratory measurement was performed at the Packaging Laboratory, Hungary (University of Gy\(\ddot{\rm o}\)r). The ASTM D7386 Standard Practice for Performance Testing of Packages for Single Parcel Delivery Systems (Figure 5) was programmed for the vibration system. This testing procedure is the one generally used for packed-products for parcel delivery shipment. This PSD spectrum simulates small vehicle vibration in the test procedure. The frequency range and overall G\({}_{\rm rms}\) of this profile is 1-200 Hz and 0.46.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Distance** & **Average Speed** \\ \cline{2-3}  & **(km)** & **(km/h)** \\ \hline Motorway & 120.9 & 114 \\ Arterial & 80.2 & 73 \\ Country & 51.5 & 54 \\ Urban & 38.4 & 35 \\ \hline Summary & 291 & 69.1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Specifications of routes and vehicle speeds for the van used in this study.  
Figure 4: Measured routes (a) Motorway, (b) Arterial road (c) Country road (d) Urban road.
# Distributed MIMO Measurements for Integrated Communication and Sensing in an Industrial Environment
## 5 Measurement Campaign
### Ground Truth  
To know where the channel samples are taken and to be able to quantify the accuracy of the radio-based position estimates, a _ground truth_ position is needed. This ground truth usually comes from high-quality global navigation satellite system (GNSS) signals when measurements are performed outdoors. In indoor scenarios, different approaches exist, e.g., camera-based motion capture, use of inertial measurement units (IMU), or a sensor fusion approach using cameras, lasers, and IMUs. However, the acquired ground truth positions must be at least an order of magnitude better than the estimates that are being evaluated.  
In our case, all measurements were performed indoors, which ruled out a GNSS solution. Therefore, a combination of a lidar sensor (Ouster OS-Dome 128, Ouster Inc., San Francisco, CA, USA) and an IMU (Microstrain 3 DM-GX5-25 (AHRS), Microstrain by HBK, Williston, VT, USA) was used to track the odometry of active and passive users. Thesensors were connected to a laptop running Ubuntu 20.04 and the Robot Operating System (ROS) [31] Noetic. All raw sensor messages were saved on disk to allow for the evaluation of different standard SLAM algorithms. In this work, we used the open-source algorithm _FAST-LIO2_; see [32] for details. The sensors were mounted on a robot to track its position and orientation. During each measured scenario, a new map was constructed as the robot moved. Then, all maps were merged to obtain a single coordinate and reference system. At the mounting point on each of the distributed antennas, we put reflective tape to allow for the easier localization of the antennas on the map; see Figure 6.
# Middle Rock Pillar Stability Criteria for a Bifurcated Small Clear-Distance Tunnel
## 2 Project Overview  
The Xiamen Haicang Shugang evacuation channel underground interchange tunnel, known as the Lushu underground interchange, is located in Haicang District, Xiamen city, China (Figure 1). Luao Road runs along a north-south axis and intersects with the Haicang evacuation channel, forming an underground interchange with ramp tunnels A, B, C, and D. The A and D ramp tunnels are formed through the gradual bifurcation of Tunnel 2# of the Haicang evacuation channel. During this bifurcation process, a section of ultrasmall clear-distance tunnel, with a minimum clearance of 1.60 m, transitions into small clear-distance tunneling and then separated tunneling. Our research focused on the small clear-distance segment between the left line of Tunnel 2# and Ramp A. The small clear-distance segment of the main line of Tunnel 2# spans from ZK2+665 to ZK2+775 with section type number SX3 III, whereas the segment for Ramp A extends from AK1+600 to AK1+490 with section type number ZX III. The total length of this small-clearance study section is 110 m.  
In the SX3 III section, the maximum excavation span was 16.17 m, with a height of 11.32 m and a clearance span of 14.45 m. In the ZX III section, the maximum excavation span was 12.62 m, with a height of 10.40 m and a clearance span of 11.10 m. This SX3 III-ZX III section transitioned from a large cross-section segment.  
Figure 1: The layout of the Xiamen Haicang Shugang evacuation channel underground interchange tunnel. (**a**) Map of China; (**b**) Fujian Province, China; (**c**) Research section; (**d**) Layout plan of Lushu underground interchange project. “#” denotes tunnel number.  
According to the geological survey report, the surrounding rock in this section was primarily classified as grade III, based on the Chinese Standard for Engineering Classification of Rock Masses (GB 50218-2014). Classification is determined by factors such as weathering degree, rock mass structure, joint development, and groundwater influence. The small clear-distance section of the tunnel bifurcation passes through the second intrusion of granite stratum \(\gamma_{5}^{3(1)b}\) in the late Yanshan period, which is of medium-coarse grained structure, blocky structure, and dominated by medium weathering. It is characterized by joints and fissures, with the main joints oriented N10-39\({}^{\circ}\) E/65-85\({}^{\circ}\) S. Some fissures are filled with quartz veins, and the rock mass is relatively intact. The groundwater consisted of fissure water in bedrock, which is mainly stored in the joints and fractures of the granite.
## 2 Methodology
### Unit Commitment and Dispatch Model Formulation
#### 2.2.2 Inertia by Synchronously Connected Generators  
The first source able to provide inertia in the unit commitment and economic inertia dispatch model of the future German power system are synchronously connected generators. As indicated by the name, synchronously connected generators provide synchronous inertia. The inertia representation of synchronously connected generators and its characteristics are expressed via two constraints in this model. Shown by Inequation (9) is the first constraint. The energy flow variable, \(x_{g}^{flow}\), of the synchronously connected generator has to be greater than the product of the generators nominated power, \(P_{g}^{rated}\), and its lowest possible stable operation point, \(P_{g}^{min\_stable\_op}\), per unit. Whether the synchronously connected generator is connected or disconnected from the power system is expressed by the binary variable \(x_{g}^{source\_inertia}\).  
\[x_{g}^{flow}\geq x_{g}^{source\_inertia}\cdot P_{g}^{min\_stable\_op}\cdot p_{g }^{rated} \tag{9}\]  
The second constraint determines the value of the binary connection variable, \(x_{g}^{source\_inertia}\), and is expressed by Inequation (10). The right side of Inequation (10) is a proportional expression of the synchronously connected units' flow, \(x_{g}^{flow}\), and the units' nominated power, \(P_{g}^{rated}\). As soon as the generators' flow is greater then zero, the unit is connected to the system and thus supplies it total moment of inertia, i.e., \(x_{g}^{source\_inertia}\). Thus, \(x_{g}^{source\_inertia}\) is set to the numeric value of one. Otherwise, the binary variables value is set to zero.  
\[x_{g}^{source\_inertia}\geq\frac{x_{g}^{flow}}{p_{g}^{rated}} \tag{10}\]2.2.3. Inertia by Synchronously Connected Storage Units  
Another source for synchronous inertia is synchronously connected storage units, and in this case, more precisely, synchronous condensers. Synchronous condensers are a source of inertia already in application but also used to provide ancillary services such as reactive power [42; 43]. Since reactive power support is not part of the model, synchronous condensers in this model are only used for synchronous inertia provision. Synchronous condensers in the energy system model are either connected to the power system, i.e., providing full inertia, or are not connected to the power system and hence do not provide a synchronous inertial response.
## 4 Simulations
### Effect of the Regularization on the Proposed Method  
The regularization parameter has an impact on the performance of the beamformer. To demonstrate this, we compare the WNGs and DIs of the sixth-order synthesized beamformer with different regularization parameters. The WNGs and DIs are plotted in Figure 5. As seen in Figure 5a, in the frequency range above 1 kHz, the WNG does not change with the increase in \(\mu\), indicating that there is no ill-conditioned problem existing in solving the optimal weighting and the solution is robust at high frequencies. Below 1 kHz, the larger the value of \(\mu\), the more frequency points have the WNG above 0 dB. When \(\mu\) equals \(10^{-12}\), the WNGs are below \(-20\) dB at some frequency points, indicating the beamformer has poor anti-perturbation ability. When \(\mu\) equals 0.01, the WNG is above 0 dB above 300 Hz, which is considered robust enough for practical applications. Figure 5b shows the impact of the regularization parameter on the DI. As seen, the regularization parameter mainly affects the DI of the proposed method at low frequencies. Below 1 kHz, as the regularization parameter increases, the fluctuation amplitude of the DI increases. This indicates that the improvement of the WNG at low frequencies comes at the cost of increased fluctuation of the DI. Additionally, it is noted that, at high frequencies, the DI corresponding to \(\mu\) equals 0.01, which is slightly higher than the DI when \(\mu\) is set to a smaller value. This indicates that a larger regularization parameter, although affecting the frequency-invariant characteristic of the beampattern at low frequencies, improves the proposed method's robustness at low frequencies and slightly enhances the directivity performance at high frequencies.
# Bounded Attitude Control with Active Disturbance Rejection Capabilities for Multirotor UAVs
## 1 Introduction
### Motivations and Background
An advantage of DOBC techniques against robust control is determining exogenous torques and forces from the system and canceling them via feedforward actions [24]. Moreover, the estimated information can be used for motion control or environment interaction when there are no available sensors [25]. DOBC and Kalman-filter-based approaches have been used and applied in the last years for successful attitude control of spacecraft and quadrotors, e.g., [26; 27; 28; 29; 30; 31; 32; 33]. A particular case of DOBC is the active disturbance rejection control (ADRC), which is based on the extended state observer (ESO) [12]. ESO is a fundamental part of the ADRC because it is charged to estimate the lumped disturbances of both unknown uncertainties and external disturbances [34; 35]. Nowadays, ADRC has been applied in almost every domain of automatic control. Therefore, it has gained popularity in fields as diverse as the motion control of humanoid robots [36], an induction motor without a speed sensor [37], electric traction [38], power electronics [39], and many more. ADRC simplifies the plant description; this approach groups all disturbances, endogenous and exogenous, into a single term. Total disturbance can have many characteristics, including unknown, time-varying, and unstructured. The main virtue is to make the disturbances indistinguishable from each other and of the same nature. Using a suitable additional dimension, that disturbance term, which is treated as a fictitious state, is estimated with an ESO. ADRC reduces a complex process to a possibly linear disturbed plant, harassed by a total disturbance term, which becomes easy to regulate using an output feedback control law that can be both linear and non-linear [40]. For UAV control, ADRC algorithms have become a popular choice. Some researchers have proposed inspiring ADRC-based approaches to regulate the attitude in multirotors, e.g., [41; 42; 43; 44; 45; 46; 47; 48]. Besides, the method can be extended to the automatic landing or trajectory tracking; for example, in [49; 50], where the safe landing of a UAV under wind effects con ditions is proposed. The work in [45] presents how an ADRC is employed for stabilization and obstacle avoidance of a quadrotor unsettled by external disturbances. The trajectory tracking problem of a quadrotor with disturbances is presented in [51]. Although the ADRC-based attitude controllers mentioned before have shown excellent results, the analysis of stability with the system in a closed-loop (control-observer-plant) is not always established; only a few works have taken this issue into account. The main reason comes from the nonlinearity of the system in a closed loop. Furthermore, none of the reported ADRC approaches considered the physical limits of actuators, which is a crucial topic for real applications.
## 6 Performance Analysis
### Communication Cost vs. Security  
The communication cost of our protocols decreases when \(h\) decreases, where \(r<h\leq H\). Under the extreme case when \(h=r+1\), both our protocols achieve their minimum communication cost. Similarly, the security performance of both protocols decreases as \(h\) decreases. Since the number of random directed paths decreases as \(h\) decreases, the attacker can trace the source easily from the phantom location. However, \(h\) should be assigned with a reasonable value so as to balance the communication cost and the security performance.  
Usually, \(r/H\) is no more than 1/5 for a large sensor network [9]. According to Section 6, if \(r/H\leq 1/5\), the communication cost of SLP and SLP-E are comparable, both of which grows as \(h\) grows. Table 2 shows that when \(2<h\leq 20\), the number of random directed paths increases significantly. In particular, the number of random directed paths increases drastically when \(h\leq 20\) but gradually slows down when \(h>20\). Table 2 shows that as \(h\) grows from 20 to 40, the number of random directed paths increases by only 6%. Thus, it is reasonable to define \(h=20\) to achieve a good balance between the communication cost and security performance.
## 2 Proposed System
### Safety Measurements  
As the success of our system is based on the assumption that either the action or gesture recognition result is correct, in the case that neither is correct, we select the following four measurements to minimize the flight risk: (1) virtual fence (also called electronic fence as illustrated in Figure 18) to restrict the flight area; (2) a limitation of the UAV top speed, acceleration, and flight height (as per Table 1); (3) automatic hovering when all data are lost; and (4) the pilot continuing to watch the screen of the ground station and switch to the manual remote-controlling model when the UAV appears to lose control.
## 5 The Mode of Action/Plant Response Mechanisms to the Crude Oil-Induced Abiotic Stresses
### Water (Drought) and Osmotic Stresses
#### 5.1.2 Osmotic Stress  
In line with [PERSON] et al. [80], osmotic stress may be used to refer to conditions in which there is a limitation on plant growth and development as a result of shortage of water availability. Although osmotic stress can result from drought, excessive salt in water, chilling, and freezing [80], [PERSON] and [PERSON] [81] identified drought and high salinity as the chief causes of stress to plants under natural conditions.  
Plants respond to osmotic stress at the morphological, anatomical, and cellular levels [80] which results in alterations in their development (such as the plant life cycle, limitation of shoot growth, and enhancement of root growth), regulation in ion transport as well as metabolic changes which may involve carbon metabolism and production of compatible solutes [45; 81]. Some of these responses according to [PERSON] and [PERSON] [81] are triggered by the primary osmotic stress signals while others can be linked to secondary stresses/signals resulting from the primary signals. Examples of the secondary signals are phytohormones [such as abscisic acid (ABA) and ethylene], ROS as well as intracellular second messengers (e.g., phospholipids) [81].  
Generally, the response of plants to osmotic stresses can be classified into three different forms involving (a) the maintenance of homeostasis; (b) detoxification of harmful elements; and (c) efforts towards growth recovery [81].