esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Analysis
### Uniaxial Compressive Strength and Failure Mode
During rock sample deformation under load, failure is often accompanied by minor rock fragment ejection, as shown in Figure 7. Under uniaxial compression, the microcracks induced by freeze-thaw cycles coalesce, leading to partial surface spalling [27]. As the number of freeze-thaw cycles increases, the damage intensifies and the sample failure typically exhibits shear characteristics. After fewer freeze-thaw cycles, the interparticle cohesion within the rock is stronger and the overall structure remains relatively stable. After a high number of freeze-thaw cycles, the rock structure experiences significant damage and the internal cementation is substantially weakened. Under uniaxial compression, specimens subjected to 40 unidirectional freeze-thaw cycles exhibit not only shear cracks but also localized crushing failure resulting from freeze-thaw-induced inhomogeneities. Macroscopic frost-heaving cracks are already present in the samples subjected to 40 all-directional F-T cycles. The propagation of these cracks significantly impacts the compressive strength at failure. The different freezing directions influence the water migration and freezing processes within the sample. Consequently, the samples exhibit significant anisotropy and inhomogeneity upon failure. Figure 6: Stress-strain curve and compressive strength change. (The \(\varepsilon\) is the strain in USC process, and the \(\sigma\) is the stress value).
# A Review of Transformer-Based Approaches for Image Captioning
## 5 Vision-Language Pre-Training in Captioning
### VLP in Image Captioning
#### 5.2.2 OSCAR: Object-Semantics Aligned Pre-Training for Vision-Language Tasks  
[PERSON] et al. [38] introduce OSCAR, a VLP method to learn generic image-text representations for vision-language understanding and generation tasks. They employ anchor points (object tags detected in images) to ease the learning of semantic alignments between image regions and texts. The motivation is the observation that the salient objects in an image are often mentioned in the paired texts. These salient objects can accurately be detected using object detectors. The input of their method consists in the triplet of words, tags and image regions. The model is pre-trained with two losses: a masked token loss over words and tags, and a contrastive loss between tags and other polluted tokens. The model overview is illustrated in Figure 11. The model is pre-trained on a large dataset consisting of 6.5 m image-text pairs and then fine-tuned for five understanding and two generation downstream tasks, including visual question answering and image captioning.  
The authors point out that existing VLP methods take visual region features and word embeddings of the paired text as input and then rely on self-attention to learn the image-text alignments and produce cross-modal contextual representations. A limitation with this is that it results in ambiguity owing to the oversampled and overlapping regions from the object detector. They also mention the lack of grounding due to VLP being a weakly-supervised learning problem: there are no explicitly labeled alignments between image regions and words or phrases in the text. Their method of using object tags as anchor points seeks to overcome these two limitations.  
Figure 10: Unified VLP overview. The model is unified because the same multi-layer transformer network does the encoding and decoding; the same model is also fine-tuned for different downstream tasks, i.e., vision-language generation (image captioning) and understanding (VQA).
## 5 Packet Length Control for Sparse Signal Transmission
### Packet Length Effect on Mutual Coherence
While reconstruction accuracy is correlated with measurement number, as shown in Equation (3), when the measurement number meets the need for recovery, a longer packet length has larger coherence compared to shorter packet length. Further, Figure 8 shows the relationship between measurement number, mutual coherence and reconstruction error when different payload lengths are adopted. Generally, the measurement number has a direct effect on mutual coherence, which influences the reconstruction error. However, packet loss with different packet lengths also affects the mutual coherence, as shown in Figure 6. Figure 8 presents the combined effect of packet length and measurement number on mutual coherence (further on recovery error). Because a larger packet length decreases the mutual coherence, the measurement number guaranteeing the recovery error increases compared to the shorter packet length.
# Relay Selection Schemes for FSO Communications over Turbulent Channels
## 2 System Model
### Log-Normal (LN) Channels  
The probability density function (PDF) of the LN channels is given by [13]  
\[f_{h}=\frac{\xi^{2}h(\xi^{2}-1)}{2(A_{O}\beta)^{\xi^{2}}}\text{erfc}\left( \frac{\ln\left(\frac{h}{A_{O}\beta}\right)+q}{\sqrt{8}\sigma_{x}}\right)\exp \left(2\sigma_{x}^{2}\xi^{2}\left(1+\xi^{2}\right)\right) \tag{11}\]  
where \(\text{erfc}(\cdot)\) denotes the error complementary function in [14] and \(\beta\) denotes the normalized path loss coefficient, as given by  
\[\beta=\frac{h_{\beta}}{\beta_{h}}, \tag{12}\]  
where \(\beta_{h}\) is the path loss for the first-hop.  
\[\sigma_{x}^{2}=0.30545k^{\frac{7}{6}}C_{n}^{2}d^{\frac{11}{6}}, \tag{13}\]  
where \(C_{n}^{2}\) is the refractive index structure parameter, \(k=(2\pi/\lambda)\) is the wave number, \(\lambda\) is the wavelength of the transmitter, and \(\sigma_{x}^{2}\) is the variance of an independent and identically distributed Gaussian random variables.  
\[\text{where }q=2\sigma_{x}^{2}\left(1+2\xi^{2}\right), \tag{14}\]  
\[\xi=\frac{w_{zeq}^{2}}{2\sigma_{s}^{2}}, \tag{15}\]  
\(\xi\) is the ratio between the equivalent beam width at the receiver and the pointing error displacement standard deviation at the receiver, as in [14].  
The channel fading coefficient, \(h_{a}\) is given by  
\[h_{a}=\exp(2x) \tag{16}\]  
\(x\) being an independent and identically distributed (i.i.d.) Gaussian random variable (RV) with a mean \(\mu_{x}\) and a variance \(\sigma_{x}^{2}\). To ensure that the fading channel does not attenuate or amplify the average power, the fading coefficients are normalized [15].
# A Dynamic Distributed Deterministic Load-Balancer for Decentralized Hierarchical Infrastructures
## 5 The \(D^{3}\)-Tree
### Element Insertions and Deletions  
#### 5.4.1 Handling Element Updates  
Assume that an update operation (insertion/ deletion) is initiated at node \(v\) involving element \(a\). By invoking a search operation, node \(u\) with range containing element \(a\) is located and the update operation is performed on \(u\). Element \(a\) is inserted in \(u\) or is deleted from \(u\), depending on the request.  
To apply the weight-based mechanism for load balancing, the element should be inserted in a bucket node (similar to node joins) or in a leaf. However, node \(u\) can be any node in the structure, even an internal node of PBT. Therefore, if \(u\) is a bucket node or a leaf, \(a\) is inserted to \(u\), and no further action is necessary.  
If \(u\) is an internal node of the PBT, element \(a\) is inserted in \(u\). Then the first element of \(u\) (note that elements into nodes are sorted) is removed from \(u\) and inserted to \(q\), the last node of the bucket of the left adjacent of \(u\), to preserve the sequence of elements in the in-order traversal. This way, the insertion has been shifted to a bucket node. The case of element deletion is similar.  
After an element update in leaf \(u\) or in its bucket, the weight-based mechanism is activated and updates the weights by \(\pm 1\) on the path from leaf \(u\) to the root, as long as Invariants 1 or 2 do not hold. When the first node \(w\) is accessed for which Invariants 1 and 2 hold, then the nodes in the subtree of its child \(q\) in the path, have their weightsrecomputed. Afterwards, the mechanism traverses the path from leaf \(u\) to the root, in order to find the first node (if such a node exists) for which Invariant 3 is violated and performs a load-balancing in its subtree.  
```
0: BinaryNode currentNode
1:if currentNode.Contains(element) then
2:return currentNode;
3:endif
4:if currentNode is Leaf then
5:return currentNode.Bucket.Search(element);
6:endif
7:if currentNode.UpperValue \(\leq\) element then
8: rightLeaf = currentNode.RightMostLeaf;
9:if rightLeaf.Contains(element) then
10:return rightLeaf;
11:endif
12:if rightLeaf.UpperValue \(\leq\) element then
13:return rightLeaf.Bucket.Search(element);
14:else
15: /* search internal subtree */
16: currentNode = currentNode.RightChild;
17:while currentNode \(\
eq\) null do
18:if currentNode.Contains(element) then
19:return currentNode;
20:endif
21:if currentNode.Node.UpperValue \(\leq\) element then
22: currentNode = currentNode.RightChild;
23:else if currentNode.Node.LowerValue \(>\) element then
24: currentNode = currentNode.LeftChild;
25:endif
26:endif
27:return currentNode.Bucket.Search(element);
28:endif
29:else if currentNode.LowerValue \(>\) element then
30: /*... similar to previous case...*/
31:endif
32:return null;
```  
**Algorithm 10** Search(SearchSubtree(integer element))
## 4 Test and FE Analysis
### Stress Dynamic Response Analysis  
For the case of breakage of the 1# and 6# suspenders, respectively, the stress response of the arch rib and bridge deck system has been presented in Figure 14. The demonstrated  
Figure 12: The time history plots of displacement due to the long and short suspenders fracturing: (**a**) displacement of the arch rib when the 1# suspender breaks; (**b**) displacement of the bridge deck when the 1# suspender breaks; (**c**) displacement of the arch rib when the 6# suspender breaks; (**d**) displacement of the bridge deck when the 6# suspender breaks.  
Figure 13: The maximum response due to the breakage of different suspenders: (**a**) displacement; (**b**) stress.  
results indicate that the stress variations of the arch rib are large, such that their maximum values are reported to be \(-35.41\) MPa and \(-50.16\) MPa, respectively. Additionally, the stress changes of the main beam are relatively small, and their corresponding maximum values due to the breakage of the above-mentioned suspenders are \(4.14\) MPa and \(11.97\) MPa, respectively. Since the stresses of the arch ribs at the \(1\#\) and \(6\#\) suspenders under the action of the dead load are \(-75.24\) MPa and \(-63.28\) MPa, respectively, there is still a certain safety margin for the arch ribs composed of Q345 steel. However, for the bridge deck system with C50 concrete, the \(4.14\) MPa and \(11.97\) MPa tensile stresses caused by the fracture of the \(1\#\) and \(6\#\) suspenders may lead to the cracking or even fracture of the longitudinal beam. According to the results provided above, for the arch bridge structure with the second type of suspension bridge deck system, although the influence of the suspender fracture on the arch rib stress is much greater than that on the bridge deck system, the impact of the suspender fracture on the longitudinal beam stress is greater than that on the arch rib from a structural safety standpoint.  
When each pair of suspenders is broken, the maximum stress response of the arch rib and bridge deck system can be obtained per the plotted results in Figure 13b. The obtained results reveal that the maximum value of the stress response of both the arch ribs and deck system magnifies gradually when the suspenders \(1\#\) to \(6\#\) break. Actually, the effect of a long suspender break on the stresses in the arch ribs and the deck system is greater than the breakage of a short suspender. The main reason is basically the same as that given in Section 3.1 regarding the influence analysis of the displacement response during the suspender breaking.
# A Review of Nanomaterials' Effect on Mechanical Performance and Aging of Asphal Mixtures
## 1 Introduction
Nanomaterials can play a significant role in enhancing the performance of the existing materials by providing better resistance to traffic and environmental loads or mitigating incompatibility between some natural aggregates and asphalt binder, enabling more sustainable and durable pavement solutions [9]. The objective of this review is to analyze the effects of the modification with nanomaterials in the mechanical performance of the asphalt mixture. This review focuses on the modifications with nanosilica, nanoclay, and nanoiron. Firstly, the effects of the modifications in the properties and rheology of the modified bitumen are summarized, subsequently, the effects of the modifications in the mechanical performance of the asphalt mixture are analyzed as well as their contributions for aging resistance.
# Devising Mobile Sensing and Actuation Infrastructure with Drones
## 3 Simulations
### Network Performance
#### 3.1.3. Simulation Results in the DaaG Multi-Hop Network Method  
In this simulation, we select a nearest-based multi-hop network as a comparison target, which organizes a multi-hop topology to connect a sensor and an actuator that has the shortest distance to the sensor. Similar to the previous simulation, drones visit the sensor network and gather the sensing data. The differences are that the number of drones is increased to four, and they create a bridge for the communication.  
Since the distance between the sensor network and actuator network is too long to connect these two networks with a single drone, we use more drones to connect the networks with the multi-hop method.  
Figure 4b shows the result of the simulation. Our video [31] shows the whole simulation procedure and result. In the result, our drones create a communication bridge from the highest correlated actuator to the sensor that is closest to the actuator. However, the nearest-based path-planning scheme connects a sensor and the actuator that is closest to the sensor, but not the highest correlated actuator. As shown in Figure 4b, DaaG's communication has started 17.7% earlier than the nearest-based path-planning scheme. Furthermore, we can notice that DaaG's priority service throughput increased more quickly than the other. Moreover, the DaaG system quickly finishes the data transmission compared to the nearest-based path-planning scheme since all actuators complete their job of monitoring the problematic sensor. This is because the highest correlated actuator is the first monitor in the actuator network, but it is the third monitor in the nearest-based path-planning scheme. Following the analysis, we can claim that DaaG can provide new services faster than other path-planning algorithms in terms of both the data ferry and multi-hop network method. Moreover, DaaG can select the message transfer schemes according to the network status and service type.
# Convolutional Neural Networks: A Roundup and Benchmark of Their Pooling Layer Variants
## 2 Materials and Methods
### Let the Pooling Begin
#### 2.3.7 LP Pooling  
[PERSON] et al. (2018) proposed LP pooling as part of an architecture to recognize house numbers. It is essentially another alternative to the Average and Max pooling methods, closer to the one or the other depending on the value of P, a predefined parameter chosen during the setup of the layer. This method is a sort of weighted function ending up with higher weights for more important features and lower for the lesser ones, which can be applied by using Formula (9).  
\[O=(\sum\sum I(i,j)^{\text{P}}\times G(i,j))^{1/\text{P}} \tag{9}\]  
where \(O\) is the output, \(I\) is the input, and \(G\) is a Gaussian kernel. We should also note that when \(P=1\), it is essentially Gaussian averaging, while when \(P=\infty\), it is similar to Max pooling. Using this type of pooling, the authors managed to achieve an average of about 4% better accuracy than Average pooling for the Street View House Numbers (SVHN) dataset.
# Integrated Multifunctional Laryngoscope for Medical Diagnosis and Treatment
## 3 Experiments and Results  
### Evaluation of System Parameters  
The parameters of the imaging and ablation unit were tested at the designed working distance of 15 mm. As shown in Figure 3A, for the white light endoscope, the imaging range was 14 mm, and the resolution was 32 lp/mm at the central area by using a test target (1951 USAF test target). The OCT system transverse resolution was 121.7 \(\upmu\)m at the central field of view of the OCT system. The axial resolution was 12 \(\upmu\)m, which was obtained by analyzing the point spread function, as shown in Figure 3B. Since the light intensity decayed dramatically at the edge of the scanning area, the scanning range of the OCT B-scan was set to 10 mm, which was slightly less than that of the white light endoscope. The B-scan rate was 100 frames/second. This imaging range coincided with that of the white light endoscope. The beam diameter of the ablation laser was 2200 \(\upmu\)m at a working distance  
Figure 2: Front (left) and cross-sectional (right) views of the probe. L1: plano-concave lens \(-9.8\) mm; L2: achromatic doublet 10 mm; LMT: inner metal tube; OMT: outer metal tube.  
of 15 mm, as shown in Figure 3C. In conventional laser surgery, the light beam diverges from the multimode fiber. At a distance of 5 mm, the beam diameter is usually larger than 2000 \(\upmu\)m. Therefore, this spot diameter at the object distance of 15 mm is acceptable for laser ablation.
# The Fractal Characteristics of Ground Subsidence Caused by Subway Excavation
## 2 Theoretical Foundation  
### Wavelet Transform  
Wavelet analysis represents a relatively novel mathematical approach to time series analysis that builds upon the principles of Fourier analysis. Its ability to effectively localize both time and frequency enables it to effectively reconcile the challenge of preserving signal locality while suppressing noise ([PERSON] et al., 2019; [PERSON] et al., 2020).  
It is widely acknowledged that monitoring data are subject to noise pollution, with the signal being represented by s(i) and the noise by q(i). The former is the real signal with low frequency, while the latter is the noise signal with high frequency ([PERSON] et al., 2019). The wavelet transform was employed to separate the high-frequency noise signal from the relatively stable low-frequency signal. The following steps are required for the decomposition of a monitoring signal into N layers using a wavelet function ([PERSON] et al., 2019; [PERSON] et al., 2020; [PERSON] et al., 2020): (1) select the appropriate wavelet function to decompose the monitoring signal in N-layer; (2) choose the appropriate threshold to process; (3) reconstruct the low-frequency signal and the high-frequency signal after threshold processing.  
Let \(\Psi(t)\in L^{2}(R)\) and its Fourier transform be \(\hat{\Psi}(\omega)\). When the Fourier transform of the wavelet function, \(\hat{\Psi}(\omega)\), satisfies the tolerance condition, \(\Psi(t)\) is said to be a fundamental wavelet or mother wavelet. The mother wavelet, \(\Psi(t)\), can be stretched and translated to obtain a wavelet sequence. For the continuous case, the wavelet sequence is as follows:  
\[\Psi_{a,b}(t)=\frac{1}{\sqrt{|a|}}\Psi\bigg{(}\frac{t-b}{a}\bigg{)}a,b\in\mathbb{R} \tag{1}\]  
where \(a\) is the parameter associated with the telescoping process, while \(b\) represents the translational parameter (\(a,b\in\mathbb{R}\)), with \(a\
eq 0\). When \(a\) and \(b\) are continuous variables, \(\Psi_{a,b}(t)\) represents the continuous wavelet function that depends on \(a\) and \(b\)[42; 43; 44].  
In the context of settling data series, the useful signal is frequently characterized by low frequency or smooth characteristics. Conversely, the noise signal is often associated with high-frequency properties. The high-frequency coefficients derived from the decomposition process can be utilized to select a value for quantization, after which the noise-canceled signal can be reconstructed through wavelet reconstruction.
# Transitioning Broadcast to Cloud
## 4 Transitioning Broadcast to Cloud
### Cloud-Based Ingest, Playout, and OTT Delivery System  
In Figure 10, we show a more advanced architecture, in which not only OTT delivery, but also ingest, media asset management, and playout are offloaded to cloud.  
As it can be immediately grasped, the move of playout functionality to the cloud also enables the use of the cloud platform for ingest. This is particularly helpful on a global scale, as major cloud systems have data centers in all major regions, and so the contribution link is only needed to deliver content to the nearest local datacenter. Media asset management also naturally moves to cloud in this case.  
With cloud-based playout, there will still be a need in a control room with monitors, switch panels, etc., but it all will be reduced to a function of a thin client. All storage, redundancy management, media processing, etc., will happen in cloud, significantly reducing required investments in hardware and operating costs.  
In the system depicted in Figure 10, the broadcast distribution encoding, multiplexer and all subsequent operations stay on prem without any changes. This way, broadcasters can operate all current ATSC equipment until ATSC 3.0 matures or there is some other serious need to replace it. This is another hybrid cloud + on-prem architecture, which we believe will make sense in practice.  
To make such system work, in addition to all improvements mentioned earlier, what further needed is:  
* cloud-based broadcast-grade playout system,
* direct link connection to cloud ensuring low latency monitoring and real-time responses in operation of cloud playout system,
* improvements in cloud-run encoders, specifically those acting as contribution transcoders, sending broadcast-compliant streams back to the on-prem system.
## 1 Introduction
### Related Work  
Previously, some scholars proposed the use of a UAV as an aerial base station of a cellular data network [20]. A UAV base station can move freely in a network without considering the cell boundary. UAVs can be equipped with base station hardware, regardless of building boundaries, and they can be deployed in appropriate locations to provide cellular data networks for ground users. Therefore, we proposed a physical model in [21]--a low altitude UAV carries an IRS as a passive relay, providing a cascaded LoS channel that can be deployed in traditional mobile communication, and this can cover signal-blind areas well or provide additional channel gain for users with bad channel performance at the cell edge. However, when UAVs act as aerial base stations, the hardware payload limits their flight flexibility, and when they are deployed as signal generators at low altitudes, they produce extremely unstable signals. Therefore, the authors of [22] proposed an IRS-assisted UAV cellular data network communication model that used the flexible deployment of UAVs to solve the problem of small base station signal coverage. This paper determines the optimal distances between the BS, IRS, and UAV by simulating the relationships between the parameters and the received signal's SER at the UAV. Finally, by drawing three-dimensional images, performing function fitting, and calculating the extreme value of the condition function, the problem of the optimal deployment location of a UAV is solved and the signal coverage area is obtained.