esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Cluster Formation
### Event-Driven Clustering  
Numerous researchers have focused on generating energy-efficient clusters; most of these solutions offer pre-event clustering and pro-active routing algorithms. However, cluster formation in the entire field prior to occurrence of an event imposes a significant overhead in terms of energy and processing on the network, while it does not guarantee the better performance of the network in some applications. Hence, event-driven clustering solutions are proposed by [85; 86; 87; 88]. While the cluster formation procedure and performance in [85; 86; 87] is not evaluated in details, a comprehensive event-based clustering algorithm, called Event-to-Sink Directed Clustering (ESDC), is presented by [PERSON] and [PERSON] in [88], which considers both event location and direction of data flow from event area towards the sink node. In ESDC, clustering is triggered only within the event region and right after the detection of an event. The clustering continues in a corridor along the data-forwarding path, from the event region towards the BS. Another advantage of the algorithm is in its data transmission towards the sink node. Since the CHs of the event region and the ones located in the path towards the BS are responsible for relaying the generated data, the upstream node in each cluster is selected as the optimum CH of the cluster to minimize the number of data transmissions in data routing. In other words, in ESDC, similar to LEACH-B [89], nodes select a CH having the smallest distance to the BS to prevent data routing back and forth inside the clusters.
# A Full-Spectrum Registration Method for Zhuhai-1 Satellite Hyperspectral Imagery
## 3 Methods  
The proposed full-spectrum inter-band registration method consists of two parts, including relative and absolute registration. Relative registration involves matching feature points between adjacent spectral bands to obtain accurate matching points. Absolute registration involves selecting a spectral band as the reference image and performing registration for all other bands. This process involves transferring the affine transformation relationship between adjacent bands and differential correction from dense Delaunay triangulation. By comparing image quality evaluation metrics such as histogram, clarity, Shannon entropy, and Signal-to-Noise Ratio (SNR) of different bands, an image band with higher quality was selected as the reference image. Figure 4 displays the full-spectrum registration workflow used in this study.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Name** & **Long/Lat** & **Imaging Time** &
\begin{tabular}{c} **Minimum/Maximum** \\ **Elevation(m)** \\ \end{tabular} & **Lateral Angle** \\ \hline OHS-Arizona-USA & \(-\)114.7/32.6 & 2020-04-02 & 18/633 & \(-\)12.688 \\ OHS-Guang Xi-China & 107.4/22.6 & 2020-04-15 & 93/1023 & \(-\)1.88 \\ OHS-Xin Jiang-China & 89.8/43.0 & 2020-05-29 & \(-\)60/3801 & \(-\)5.703 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Image registration experiment data.  
Figure 4: Full-spectrum registration workflow.
# Uncertainty Quantification for Infrasound Propagation in the Atmospheric Environment
## 4 Quantitative Results Presentation and Analysis Based on the Actual Atmospheric Data
### Uncertainty Quantification Analysis for the Travel Time of Infrasound Propagation on a Phase Loop
From these two sensitivity indices, it can be seen that the atmospheric environmental parameter with the greatest influence on the infrasound propagation travel time is the mean atmospheric molar mass. From the term \(1/c^{2}\) of Equation (3), it can be seen that the atmospheric molar mass, as the numerator term, ranges from 18.45 g/mol to 30.62 g/mol after sampling. The atmospheric temperature and specific heat ratio as the denominator term parameters after sampling fall within the ranges of (134.06 K, 532.73 K) and (1.37, 1.61), respectively. The change as the numerator term has a more significant effect on the infrasound propagation time \(t\) than the change in the denominator term. Therefore, it can be inferred from Equation (3) that the main atmospheric parameter affecting the infrasound propagation time is the average atmospheric molar mass. As shown in Figure 5 (d), the average atmospheric molar mass \(m\) is almost constant below 100 km. This conclusion applies especially to rays propagating beyond an altitude of 100 km. The conclusions in this paper are drawn under the MSISE00 model and the HWM93 model, i.e., the three conclusions above are based on region-specific atmospheric data. Therefore, the above conclusions are not generalizable. This paper provides a method of quantifying the uncertainty of infrasound propagation for a specific region. For a specific region, the uncertainty quantification of infrasound propagation needs to be performed based on the acquisition of region-specific atmospheric data. The results of the above study can further understand the propagation law of infrasound, reveal the mechanism of infrasound propagation, and lay the foundation for further localization of infrasound sources in uncertain atmospheric environments. The uncertainty information of infrasound propagation can be incorporated into the Bayesian framework as a priori knowledge [32; 33]. In addition, infrasound source localization algorithms in a Bayesian framework have been developed [34]. Therefore, by combining the above two points, the uncertainty information of the infrasound propagation environment can be incorporated into the infrasound source localization algorithm to achieve enhanced localization results in the future. The nonlinear effect of the infrasound propagation process is not considered by the tau-p model. Considering the nonlinear effects of the infrasound propagation process can better reflect the real infrasound propagation process in the atmosphere [35; 36]. Future work on the quantification of infrasound propagation uncertainty should aim to consider the nonlinear effects of the infrasound propagation process.
# A Packet Collision Reduction Based on Reselection for LTE V2X Mode 4
## 3 Proposed Method  
In this paper, we propose new two resource selection methods in consideration of the fact that \(P_{rsvp}\) notified over PSCCH reaches 0 when the UEs reselect. The proposed methods can reduce the packet collisions without constraints such as additional information.  
Figure 6: An example of evaluating RSSI for released resources.  
Figure 7: Example of overlapping selection window.
## 4 Discussion
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Authors, Year** & **Advantages and Disadvantages** \\ \hline \multirow{4}{*}{1} & & - & Satisfactory patient cooperation \\  & & - & Minimal chairside adjustment \\  & & - & Lower cost \\  & & - & Improved time efficacy \\  & & - & Possibility of immediate replacement in case of fracture or failure \\ \hline \multirow{4}{*}{2} & & - & Increased patient comfort \\  & & - & More precise fabrication \\  & & - & Comfortable, fast, and effective treatment options \\ \hline \multirow{4}{*}{3} & & - & 3D printing in one unit and minimized breakages \\  & & - & Decreased chairside time \\  & & - & Reduced failure of appliance \\  & & - & Precise, easy, and quick digital design \\ \hline \multirow{4}{*}{4} & & - & Decreased laboratory procedures \\  & & - & Reduced number of appointments \\  & & - & Lowest chance of failure or breakage \\  & & - & No plaque accumulation or gingival inflammation compared to conventional \\  & & - & No plaque accumulation or gingival inflammation compared to conventional \\  & & - & Maximum precision with fewest possible flaws \\  & & - & Minimized human error \\  & & - & Improved patient satisfaction \\  & & - & Fine adaptation without disturbing occlusion \\  & & - & More predictable and less invasive \\ \hline \multirow{4}{*}{5} & & - & Sefer and easier manufacturing \\  & & - & Reduced deformation and error \\  & & - & Better compatibility of digitally fabricated RSMs \\ \hline \multirow{4}{*}{6} & & - & Better cooperation and acceptance by patient \\  & & - & Reduced production time \\  & & - & Reduced mistakes during manufacturing process \\  & & - & Stays stable, without decementation or fracturing \\  & & - & Smooth surface, easy to clean, and polish \\  & & - & No reported allergic reaction \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Advantages and disadvantages of using CAD/CAM technology in SM manufacturing.
# Simulation Tests on Granite Pillar Rockburst
###### Abstract  
Parallelepipeds specimens were made to further investigate the rockburst occurrence mechanism of ore pillars in underground mining units. The investigation was carried out with uniaxial compression systems and real-time testing systems, such as stress, video, and acoustic emission, combined with digital image correlation (DIC) and SEM electron microscope scanning technology, to systematically analyze the evolution of rockburst of ore pillars, strain field characteristics, acoustic emission characteristics, mesoscopic characteristics of the rockburst fracture, morphology of the bursting crater, and debris characteristics. The findings demonstrate that the pillar's rockburst process went through four stages, including the calm period, the particle ejection period, the block spalling period, and the full collapse period. According to DIC digital image correlation technology, the development of cracks in the rock is not obvious during the calm period, but during the small particle ejection and block spalling periods, the microcracks started to form and expand more quickly and eventually reached the critical surface of the rock, resulting in the formation of a complete macro-rockburst rupture zone. During stage I of the test, the rate of acoustic emission events and energy was relatively low; from stages II to IV, the rate gradually increased; and in stage V, the rate of acoustic emission events and energy reached its maximum value at the precise moment the rock exploded, releasing all of its stored energy. The specimen pit section primarily exhibits shear damage and the fracture exhibits shear fracture morphology, while the ejecta body primarily exhibits tensile damage and the fracture exhibits tensile fracture morphology. The location of the explosion pit is distributed on the left and right sides of the middle pillar of the specimen, and the shape is a deep \"V\". The majority of the rockburst debris is greater than 5 mm, and it mostly takes the shape of thin plates, which is comparable to the field rockburst debris's shape features.  
pillar rockburst; rectangular chamber; DIC technique; acoustic emission; rockburst crater; rockburst debris +
Footnote †: journal: Applied Sciences
## 3 Voltage Controlled Synthetic Inductors
### Voltage-Controlled Synthetic Inductor Based on an OTA  
The OTA LM13700 was used to implement the VCR stage of the VCSI, as shown in Figure 4a. The behavior of the OTA configured as a VCR was described by the equation:  
\[R_{VCR}=\frac{R_{8}+R_{4}}{19.2\,I_{ABC}R_{4}} \tag{5}\]  
where \(I_{ABC}\) is the amplifier bias current [16] and the constant value \"19.2\" is expressed in A\({}^{-1}\).  
Figure 4: Voltage-controlled synthetic inductor: (**a**) based on an LM13700 operational transconductance amplifier, and (**b**) the resonance frequency simulation results for the voltage control (\(V_{CTRL}\)) from \(-10\) V to \(10\) V with \(1\) V steps.  
Considering Equations (2) and (5), for \(R_{1}=R_{2}=R_{3}=R\), we obtained the equivalent inductance:  
\[L=\frac{R_{8}+R_{4}}{19.2\,I_{ABC}R_{4}}RC_{g} \tag{6}\]  
where the constant value \"19.2\" is expressed in A\({}^{-1}\).  
In Figure 4b, the frequency resonance of the RL shunt in an equivalent simulated situation used in Section 3.1 is shown. In contrast with the whole voltage control range from \(-10\) V to \(+10\) V, the linear behavior of the resonant frequency for the RL shunt with a VCSI based on an LM1700 was observed only for positive voltage values. For both versions of VCSIs, the PSPICE(r) simulated results showed a linear response for the resonance frequency of the RL shunt only in a limited voltage control range.
# Characterization of Dynamic Blockage Probability in Industrial Millimeter Wave 5G Deployments
## 2 Related Work
Although the blockage probability is provided in an analytical form, it features the binomial coefficients of high order, making the calculations cumbersome. Additionally, completely stochastic deployment of machines on the factory floor has been assumed, which contradicts conventional semi-regular deployment conditions [15]. The abovementioned review illustrates that blockage of propagation paths in IIoT deployments is a critical question affecting the performance of UEs. It is also a challenging task due to complex deployment conditions characterized by the semi-regular deployment of production lines on the factory floor, presence of metallic components in the environment, and also the periodic movement of machines. Furthermore, there is a clear lack of mathematical models allowing to build reliable performance evaluation scenarios. In this paper, we fill this void by proposing a simple model that captures these inherent properties of industrial deployments.
## 3 Results
### Effects of FF on IPGTT and AUC  
To assess glucose tolerance in each experimental group, IPGTT was measured at eight weeks. The results are presented as IPGTT change curves over time and an area under the curve (AUC) graph (Figure 3). According to the IPGTT results, a significant increase in blood glucose levels was observed in all groups 30 min after glucose injection (Figure 3A). The FF 250 group consistently exhibited significant differences compared with the HFD group throughout the test, with the GTE group showing significant differences at 30, 60, 90, and 120 min. Additionally, the FF group showed a dose-dependent difference from the HFD group across all time points, with the FF 250 group demonstrating significant differences in blood glucose levels throughout the test duration (Figure 3A). Analysis of the AUC graph for the IPGTT blood glucose curve revealed a dose-dependent decrease in the FF administration group. Furthermore, significant reductions were observed in the GTE and FF 250 groups compared to the HFD group (Figure 3B). These findings suggest that FF administration contributes to the improvement of blood glucose levels in HFD-induced obese mice.
## 2 Geological Setting  
Lithuanian basins comprise a number of subsurface reservoirs for the safe storage of CO2, and earlier studies [7; 27; 28] point to two most prominent reservoirs, namely, the Syderiai and Vaskai onshore aquifers. This study focuses on these two saline aquifers: Syderiai and Vaskai (Figure 2). These are sandstone aquifers of the Cambrian age, and the formation type is a Deimena formation. Mineralogically, the Deimena formation sandstone is identified as a mature rock, classified within the quartz arenite group due to the predominance of detrital quartz, constituting 90-99% of framework grains. The detrital quartz grains exhibit a size range of fine to medium, typically measuring between 0.15 and 0.5 mm, with occasional observations of coarse grains [7; 29].  
Syderiai structureAccording to the findings of both 2D and 3D seismic surveys, the Syderiai structure exhibits a northeast-southwest elongated dome, associated with the Telisai fault zone. The complex Telisai fault zone and its branching faults are situated within the southern region of the structure. Porosity estimates derived from seismic data inversions indicate a range of 14-18 percent for the Deimena series, exhibiting a gradual distribution. The thickness of this series varies from 20 to 60 m, with the maximum  
Figure 2: Location of saline aquifers of Lithuania, Syderiai and Vaskai. The depth contours indicate the top depth of the Cambrian structures.  
thickness observed in the southeast. According to Neutron Gamma Ray (NGR) logging data [7], the average porosity of the Middle Cambrian sandstone is 16%, and the average permeability is 400 mD.  
The oblong-shaped Syderaii structure spans an area of 12 \(\times\) 8 km\({}^{2}\), reaching a height of 80 m. Comprising sandstones from the Middle Cambrian Deimena series, the structure includes a Middle Cambrian layer situated around 1320 m deep at the structure's center. Towards the western part, the depth increases, reaching 1380 m. The aquifer's thickness measures 57 m and is sealed on top by a 560 m thick Ordovician-Silurian shale layer [7; 29].  
_Vaskai structure:_ The Vaskai structure extends in the west-east direction and is bordered by two smaller faults in the north and the Telsiai fault in the south, covering a length of 12 km. The sandstone's porosity within this structure varies from 19.5% to 24.5%, while the permeability ranges from 90 to 1628 mD. According to sonic logging data, the average porosity of the sandstone corresponds to 23%, and the average permeability corresponds to 280 mD [7].  
Encompassing an area of 11 \(\times\) 3.2 km\({}^{2}\), the Vaskai structure has a brachy anticlinal shape with a height of 36 m. The primary aquifer horizon consists of fine-grained-to-medium-grained sandstones from the Lower Cambrian Virbaliai suite (Cm1 vr), alongside clayey sand siltstones [7; 29]. The Cambrian aquifer's thickness is 60 m, located at a depth of 900 m, and is overlaid by Ordovician-Silurian clay layers with a thickness of about 360 m.
# Experimenting with Routing Protocols in the Data Center: An ns-3 Simulation Approach
## 4 Validation and Experimental Results
### Functional Evaluation  
Several test cases has been developed along the process of implementing changes and additions to ns-3 DCE to allow the execution of FRR, following an iterative and incremental approach.  
The core of FRR architecture is the zebra daemon, which manages IP routing by updating the operating system's kernel routing tables. It also permits to discover interfaces and redistribute routes among the different routing protocols running on the host [31].  
Thus, in order to verify the correct functionality of the implementation, it is necessary to run zebra and routing protocols' implementations together; in this case, we focus on BGP. Under normal operation, BGP will learn prefixes and install entries in the kernel routing tables via zebra, allowing the node data plane to forward IP packets. Note that BGP may run without zebra, if we only want to verify the control plane operation (without packet forwarding).  
Therefore, for any given scenario, the verification method consists in checking routing table updates. Likewise, connectivity tests can also be performed using, for example, the ping command.  
For this evaluation, we selected three scenarios and validate the correct execution:  
1. Running zebra and BGP in a single node: This scenario allows us to test that FRR with zebra and the chosen routing protocol can be loaded and executed in a node.
2. Running zebra and BGP in a network: We implemented some scenarios based on Kathara project labs [32], such as BGP Simple Peering and BGP Prefix Filtering, which permit us to test BGP update propagation and filtering among peers, and BGP Multi-homed Stub, which is a more complex scenario.
3. Running other routing protocols: Although our focus is on BGP, all the FRR daemons should run on ns-3 DCE. Therefore, we evaluate running OSPF in the same networksfrom the previous scenario. It is worth mentioning that no particular change was necessary in the OSPF case, and consequently, we expect that other protocols will also work correctly without the need to make further modifications to the implementation; this is reasonable since, by the architecture of FRR, most of the complexity is contained in the zebra daemon.  
After the correct execution of these scenarios, and having verified that the routing tables are correctly updated, we can conclude that zebra and BGP are running correctly in ns-3 DCE, validating our extension.
# Audio-Visual Action Recognition Using Transformer Fusion Network
## 4 Experimental Results  
### Implementation  
All our experiments were conducted in Intel i7-4790 CPU, NVIDIA TESLA V100 GPU, Pytorch 1.12.1 environments, and Ubuntu 20.4 LTS version. A pre-trained Swin transformer [25] with ImageNet [29] was adopted, and a trained model using AdamW [30] was used as the optimizer. The batch size was set to 16, with an initial learning rate of \(5\times 10^{-4}\), and a cosine annealing scheduler [31] was adopted.  
For the spatial stream, during training, a single frame was randomly selected from each video to enhance the model's ability to generalize. For testing, the central frame of each video was consistently chosen to ensure uniformity and comparability of results. For the temporal stream processing in the S3D module, we utilized uniform sampling to select 16 frames as inputs, ensuring diverse and representative coverage of the video content.  
The raw audio data were sampled at 22.05 kHz, and the input features were extracted using a short-time Fourier transformation (STFT) with a window size of 2048, an overlap of 50%, and 256 Mel bands. We used the augmentation methods of random crop, random flip, color jittering, and autoaug [32]. The default version of Swin transformer [25] is referred to as Swin-B. Additionally, there are versions Swin-T, Swin-S, and Swin-L, which have model and computational complexities of 0.25\(\times\), 0.5\(\times\), and 2\(\times\), respectively. Following the original Swin Transformer configuration, we set the window size to \(M=7\) and the query dimension of each head to \(d=32\) in our models. The other hyper-parameters of each model are as follows:  
* Swin-T: \(C^{*}=96\), \(L=\{2,2,6,2\}\),
* Swin-S: \(C^{*}=96\), \(L=\{2,2,18,2\}\),
* Swin-B: \(C^{*}=128\), \(L=\{2,2,18,2\}\),
* Swin-L: \(C^{*}=192\), \(L=\{2,2,18,2\}\),  
where \(C^{*}\) is the channel number of the hidden layers in the first stage and \(L\) is the layer number of each stage. The metric for evaluating model performance is accuracy.
# Design of a Chaotic Trajectory Generator Algorithm for Mobile Robots
## 1 Introduction  
Studies on food searching behavior of animal groups (especially ants) helps in the solution of many problems, primarily optimization problems. Recently, biologists have discovered that ant activity is mostly of a chaotic nature [1].  
Research has been carried out on chaotic trajectory design for mobile robots in order to imitate this behavior found in nature. Some researchers have used chaotic systems alongside the behavioral equations of differential drive mobile robots to obtain a mathematical model that causes erratic motion [2]; in other research, the use of a standard map (a well-known discrete two-dimensional chaotic system) to generate a series of chaotic way-points to be followed by robots is presented in [3]. There has been other research [4] that used a chaotic random bit generator. In addition, some research [5] has studied the generation of chaotic behavior in mobile robots while they follow a specified path. Other works [6; 7] have studied the emergence of chaotic behavior in mobile robots. Recently, [8] addressed the problem of surveillance from a confined area using a four-engine unmanned aerial vehicle by using chaotic systems to design an unpredictable velocity profile for the quadrotor.
# Measuring Torque and Temperature in a Rotating Shaft Using Commercial SAW Sensors
## 2 Materials and Methods  
The system that was used in this work is based on a 433 MHz kit fabricated by the French company SENSEOR; the kit includes a temperature sensor, two strain gages and the corresponding interrogation unit.  
The sensors were mounted on the surface of a load cell that consisted of a hollow cylinder fabricated in aluminum Al7075-T6. The cell was 90 mm long, inner and outer diameters were 74 and 80 mm, respectively. A flat 24 \(\times\) 50 mm\({}^{2}\) surface was machined to allow for the fixation of the sensors. Since the load cell was assembled together with a rotating shaft, the antenna was replaced by a large diameter RF coupler that keeps the sensors in line of sight with the interrogation unit.