esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Statistical and Probabilistic Evaluation  
Statistical analyses yielded a mean structural crack width of \(M=2.68\) mm, a variance of \(\sigma^{2}=1.12\) mm\({}^{2}\), and a standard deviation of \(\sigma=1.06\) mm (\(n=12\)). The relationship between the probability of the optical fiber breakage, \(p\), and the width of the structural crack in the test specimen that caused the fiber breakage, \(\delta_{c}\), determined for sequential intervals of a crack width of 0.5 mm is illustrated in Figure 9.  
As can be seen in the diagram, all optical fibers broke at cracks 1-5 mm wide. Half of all of the optical fibers broke at cracks 1-2.5 mm wide, and three-quarters of all optical fibers broke at cracks 1-3.5 mm wide. Cracks of this width present a medium-to-severe structural hazard, which is targeted in this work.  
Furthermore, the probability that at least one fiber of an array of \(n\) optical fibers breaks due to a crack with a width in the range \(\Delta\delta_{c}\), \(P_{1}\), is [66]  
\[P_{1}\ =\ 1-(1-p)^{n} \tag{1}\]  
where \(p\) is the probability of the optical fiber breakage in the range \(\Delta\delta_{c}\) (see Figure 9), and \(n\) is the number of optical fibers.  
Table 1 shows the probability of breaking at least one fiber in an array of \(n\) optical fibers, \(P_{1}\), calculated for some ranges of crack widths \(\Delta\delta_{c}\). Calculations were made for the detection arrays of 1, 2, 4, 6, 8, 10, and 12 optical fibers.  
The data in Table 1 show that the probability of the breakage of at least 1 fiber of an array of 12 optical fibers, due to a crack in different parts of the 1-5 mm range, is high (0.888 \(<\)\(P_{1}\)\(<\) 1.0). A high probability of the breakage of at least one fiber exists even for rather small cracks 1-1.5 mm wide (\(P_{1}\)\(\cong\) 0.888). Reducing the size of the detection array even by a factor of two (to 6 fibers) reduces the probability \(P_{1}\), but only the probability of detecting the narrowest cracks degrades significantly (\(P_{1}\)\(=\) 0.665@\(\Delta\delta_{c}\)\(=\) 1-1.5 mm). The above data illustrate the high probability of detecting cracks with detection arrays of a moderate size, as well as the possibility of trade-off between the size of the detection array and the desired probability of detecting cracks of a certain width.  
Figure 9: Probability of breakage of the optical fiber, \(p\), as a function of the width of the structural crack that caused the fiber breakage, \(\delta_{c}\), measured in the test specimen of brick masonry and determined for sequential intervals of a width of 0.5 mm.
# Advances in Simulating Radiative Transfer in Complex Environments
## 4 Conclusions  
In order to improve microclimatological analysis of complex urban environments, a new radiation scheme is introduced into the microclimate model ENVI-met. In contrast to the old AVF scheme, where view factors are averaged over the whole model area, the new IVS scheme is able to analyze the surrounding objects of each grid cell and calculate their contribution to the local radiation budget. The high accuracy of modeling secondary radiative fluxes such as emitted longwave radiation and reflected shortwave radiation by using IVS was demonstrated by a proof-of-concept simulation. By taking into account shading of surfaces or a higher reflection by high-albedo materials, for example, fluxes of secondary shortwave and longwave radiation can be simulated in great detail. Several comparisons of different parameters, timesteps, and height levels between AVF and IVS showed that accurate surface properties and conditions affected the modeled microclimate conditions not only locally and for a specific timestep but also over larger distances and on the long-term when using IVS. Furthermore, the comparisons of PET indicate that a precise simulation of secondary radiation is very important to understand and prevent localized negative effects of, e.g., multiple shortwave reflections. These highly detailed results could be accomplished for a large, high-rise model area featuring yet rather low IVS resolutions. To gain an even higher accuracy in modeling results, the model could be simulated with an even higher IVS resolution and an even more accurate digitization of building surfaces and objects. However, the large demand in RAM limits the application  
Figure 9: Comparison of proof-of-concept simulation results between AVF and IVS at 03.00 in a height of 1.5 m for longwave radiation received from the lower hemisphere (**a**) and for PET (**b**), respectively.  
potential for simulating larger model areas. In summary, the advantages provided by this new radiation scheme should, however, overturn this shortcoming. Future urban planning scenarios, architectural material questions, or heat mitigation measure studies using the microclimate model ENVI-met should, thus, be allowed to obtain more plausible and accurate results with the trade-off of a higher RAM demand.  
Conceptualization, H.S., T.S., and M.B.; methodology, H.S., M.B., and T.S.; software, H.S., T.S., and M.B.; validation, T.S.; formal analysis, T.S. and H.S.; investigation, T.S. and H.S.; resources, M.B.; data curation, T.S. and H.S.; writing--original draft preparation, T.S. and H.S.; writing--review and editing, H.S. and T.S.; visualization, T.S.; supervision, H.S., T.S., and M.B.; project administration, H.S. and T.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 3 Motivation
### Challenge 2: Parallel Workflows  
Multi-tenancy and the implications of parallel FL workflow execution on a single administrative domain is another point of consideration for our healthcare provider use case. When users opt to run parallel FL workflows on the same physical resource pool, complexities arise that can impact the convergence speed and overall efficiency of the FL process or even cause unexpected system failures. To alleviate these threats, users need to consider the Resource Contention of parallel workloads, the Communication overhead that may be caused due to the repeatable patterns of FL workloads, and possible optimization opportunities for Workflows Synchronization.  
Resource ContentionWhen multiple users choose to run parallel FL workflows on the same physical resource pool, resource contention issues arise. In limited resources, lightweight virtualization technologies like containers are used to share the resource pool among multiple users. However, when multiple FL workflows run simultaneously, the capacity of the nodes to handle the resource requirements becomes a bottleneck. If multiple workflows request the same constrained resources, they face resource starvation and contention problems. This conflict over shared resources (i.e., CPU, memory) hampers performance, impacts training efficiency, and hinders convergence rates.  
Communication Overhead On SynchronizationFL involves frequent communication between the central server and the distributed client services. This interaction becomes critical when training models with a large number of parameters. Communication overhead and contention must be carefully examined to identify potential bottlenecks during FL cycles. For example, in a scenario where FL clients from different workloads attempt to update the global model simultaneously, the network remains idle while the clients are training the model locally, with network spikes occurring at the end of the round, during which the clients submit the training results to the central server. This strain on the network impacts overall performance and saturates the network bandwidth. By analyzing the communication overhead, optimization opportunities can be identified. For instance, shifting the starting point of one FL workflow or isolating FL servers and clients on different nodes may help avoid bandwidth congestion and minimize peak network traffic.
## 2 Related Work
### Geographic Information Routing  
Geographic information routing uses the location information of sensor nodes to forward data packets from a source node to a destination node. [PERSON] _et al._[15] proposed the Depth Based Routing (DBR) protocol, which can extend the lifetime of an entire UWSN by addressing in the depth direction and performing routing adjustment based on depth difference. However, the DBR protocol is only suitable for relatively dense networks as the depth of two nodes are not significantly different in sparse networks.  
[PERSON] _et al._[16] contributed the Vector-Based Forwarding (VBF) protocol, where during a routing process, each node does not need to save status information; instead it uses a forwarding factor to calculate the suppression time before forwarding is carried out in order to increase network energy efficiency by avoiding unnecessary forwarding, while the routing information is included in each data  
Figure 1: A network coding pattern.  
packet. However, the VBF protocol is also susceptible to network density, which impacts on the efficiency of creating a pipe from a source node to a destination node as there may be few nodes in a pipe for forwarding packages. In addition, the radius of pipe may significantly influence the routing performance. [PERSON] _et al._[17] later presented the Hop by Hop VBF (HH-VBF) protocol to alleviate VBF's problem of finding no forwarding node by creating a routing pipe for each forwarding node and by adopting redundant control in a self-adaption process. As a result, HH-VBF outperforms VBF in terms of both node energy consumption and data delivery rate.  
The Focused Beam Routing Protocol (FBR) [18] is another protocol based on location information, which aims to reduce energy consumption in data transmission by restraining flooding of packets. It is suitable for both mobile and static UWSNs as it does not require synchronizing clocks at the sensor nodes. The Multi-Path Routing (MPR) protocol [19] solves the data collision problem at receiving nodes by preventing them from receiving packets from different relay nodes through constructing a routing path consisting of multiple subpaths between the source node and the destination node. Compared to both the VBF and HH-VBF protocols, the MPR protocol shows a higher throughput in dense networks but at the same time leads to a higher energy consumption as it uses a substantial number of matrix operations. HH-VBF shows a higher overhead as it relies on flooding to discover neighboring nodes.  
[PERSON] _et al._[4] proposed the GEDAR routing protocol, which adopts geographic and opportunistic routing and uses depth adjustment based topology control for communication recovery over void regions. Greedy opportunistic forwarding is employed to route packets and move void nodes to new depths for the adjustment of topology. GEDAR outperforms the baseline solutions in terms of packet delivery ratio, but it exhibits high energy consumptions for low-density UWSNs.
# XAI Systems Evaluation: A Review of Human and Computer-Centred Methods
## 4 Human-Centred Evaluation Methods
### Trust
Follow up evaluations to these results could be understanding in what way personal explanations are different from impersonal ones, and how the latter could be improved to either be more trustworthy or mimic the former. As previously stated, Trust is not a static property. Besides the system itself, user's experience and learning over time can make the user's Trust evolve positively or negatively. [PERSON] et al. [23] assessed Trust and reliance in multiple stages of working with an explainable text-mining system. Results showed that, as the user became more familiar and experienced with the system, the level of Trust also changes over time. [PERSON] et al. [24] also explored how the level of Trust evolved as the user experience with the system increased utilizing two different Trust measures on their experiments. For this reason, monitoring the levels of users' Trust over time appears crucial when evaluating any ML system, with or without an explanatory component, to help understand how Trust progresses with experience over time. Finally, [PERSON] et al. [25] conducted a case study that explores how model features can influence user Trust and the joint Performance of the Human-AI system. The study was divided into two experiments, each one with a particular goal in mind. The first experience was based on an income prediction task, where subjects had to predict whether a person's annual income would exceed $50K or not, based on a set of attributes. Three experimental factors were used across different scenarios: the AI's confidence level, the AI's prediction and the amount of user additional knowledge compared to the system. For each scenario, user Trust was measured using two behavioural indicators that are not reliant on a subjective user self-report. The first one was a \"switch percentage\", which shows the proportion of trials were the participant chose to use the AI's prediction instead of his own. The second on was the \"agreement percentage\" is the percentage of trials were both the participant's and AI's prediction was equal. Results showed that the main influence factor was whether the user had access to the AI's confidence level. In those cases, the user would switch to the AI's prediction more often. The second experience focused on the effect of local explanations on user Trust. The setup was equal to the previous experiment, but local explanations generated using SHAP values were presented to the user instead of confidence levels. Using the same indicators as in the previous experiments, results did not indicate that explanations had a significant impact on user Trust, compared to the baseline scenario.
## 3 Methodology
### Markovian Queueing Networks
#### 3.1.2. A Markovian Queueing Network with Higher Demands
Although the complexity increases with the increasing number of nodes in the system, general formulas are concluded. A three-node system:  
\[\alpha_{(m)}=\frac{2}{3}\lambda\frac{K-m}{K-m+1},\quad m=0,1,\cdots,K \tag{1}\]  
A four-node system:  
\[\alpha_{(m)}=\frac{3}{4}\lambda\frac{K-m}{K-m+2},\quad m=0,1,\cdots,K \tag{2}\]  
A \(N\)-node system:  
\[\alpha_{(m)}=\frac{N-1}{N}\lambda\frac{K-m}{K-m+N-2},\quad m=0,1,\cdots,K \tag{3}\]  
Figure 1: A reduced \(N\)-node network with the same probabilities and the same arrival rates at the nodes which are aggregated into a virtual node. Figure 2: The state transition diagram with a high-demand node. \(\beta_{(m+1)}\) is given by  
\[\beta_{(m+1)}=\lambda_{i}\frac{N-1}{N},m=0,1,\cdots,K-1 \tag{4}\]  
The steady-state probabilities are given by  
\[p_{l}=\frac{\alpha_{(m)}}{\beta_{(m+1)}}p_{l-1},(l,m)=\{(1,0),\cdots,(K,K-1)\} \tag{5}\]  
Based on \(\sum_{l=0}^{K}p_{l}=1\), the formula of \(p_{0}\) is yielded by  
\[p_{0}=\frac{1}{1+\frac{\lambda}{\lambda_{i}}\frac{K}{K+N-2}+\frac{\lambda^{2} }{\lambda_{i}^{2}}\frac{K(K-1)}{(K+N-2)(K+N-3)}+\cdots+\frac{\lambda^{K}}{ \lambda_{i}^{K}}\frac{K!}{(K+N-2)\cdots(N-1)}} \tag{6}\]  
which leads to the formula of \(p_{l}(l=1,2,\cdots,K)\):  
Figure 1.0  
The value of \(p_{0}\) tends to be \(1\) when \(\lambda_{i}\) is far larger than \(\lambda\).
# Dual-Circularly Polarized 60 GHz Beam-Steerable Antenna Array with 8 \(\times\) 8 Butler Matrix
## 3 Performance of the Proposed Antenna Array System
### Experimental Results
Figure 18: Measured results of reflection coefficient and radiation pattern: (**a**) reflection coefficient for port 2 model, (**b**) radiation pattern for port 2 model, (**c**) reflection coefficient for port 4 model, and (**d**) radiation pattern for port 4 model. the connector model. The values of the measured axial ratio for each port model agree reasonably well with the simulated ones, but the measured values are slightly worse than simulated ones, reflecting the real-world manufacturing tolerances. The comparison of various antenna array systems integrated with a Butler matrix is given in Table 3. Compared with previous works, it is proven that the proposed antenna array system demonstrates good beam-steering capability and polarization characteristics without a big difference in size and gain.
# Multiscale YOLOv5-AFAM-Based Infrared Dim-Small-Target Detection
## 3 Experiments
### Experiments and Analysis
#### 3.3.3 Ablation Study  
To verify the performance of each proposed module in a separate manner. We conduct the ablation study based on YOLOv5s (baseline model). Here, we first compare the effectiveness of a single module. The results are shown in Figure 9.  
We then analyze the effectiveness of different combinations of modules in detail. The results are shown in Table 8. Here, we will analyze the following points. (1) By joining the AFAM module into the backbone network, the feature extraction capability and detection accuracy of the network increases significantly, e.g., [EMAIL_ADDRESS] rises to 82.7% (increase by 1.3%), and the GFLOP drops to 14.7. The experimental results demonstrate a significant improvement in detection capability. (2) The feature extraction capability is significantly enhanced after applying DownC. The values of [EMAIL_ADDRESS], [EMAIL_ADDRESS]:0.95, P, and R evaluation metrics are 82.3%, 37.8%, 82.6%, and 79.7%, respectively. The accuracy rate P is the highest value among all the improved models. This result proves the feasibility of switching to the DownC downsampling method. (3) When applying \"four-scale feature fusion\", \"four-scale shallow feature fusion\", and \"three-scale shallow feature fusion\" on the baseline model, the results will also be obviously improved. (4) Our proposed multiscale YOLOv5-AFAM achieves the best results. When combining \"AFAM\", \"DownC\", and  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]:0.95** & **P** & **R** \\ \hline YOLOv5s & 0.589 & 0.281 & 0.624 & 0.653 \\ \hline YOLOv5m & 0.629 & 0.311 & 0.704 & 0.683 \\ \hline YOLOv5l & 0.635 & 0.321 & 0.684 & 0.673 \\ \hline YOLOv7 & 0.640 & 0.335 & 0.736 & 0.687 \\ \hline Mask R-CNN & 0.577 & 0.298 & 0.716 & 0.634 \\ \hline RoITransformer & 0.593 & 0.305 & 0.692 & 0.702 \\ \hline RetinaNet & 0.523 & 0.285 & 0.595 & 0.672 \\ \hline Faster R-CNN & 0.563 & 0.298 & 0.706 & 0.634 \\ \hline Cascade Mask R-CNN & 0.559 & 0.273 & 0.657 & 0.659 \\ \hline ours & 0.638 & 0.328 & 0.735 & 0.677 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison experimental results on DroneVehicle.  
Figure 8: Comparison of mAP-parameters on ISVD.  
\"three-scale shallow feature fusion\", [EMAIL_ADDRESS] and [EMAIL_ADDRESS]:0.95 improve by 4.3% and 2%, respectively, with a slight decrease in parameters.  
As a whole, the ablation study demonstrates that the multiscale YOLOv5-AFAM improves the detection accuracy of the infrared dim small target, even in some scenarios where the environment is complex and the target object is blurred.
## 6 Empirical Results and Analysis
### Robustness Tests  
To validate the validity of the results, for this paper, we employed three methods for robustness tests. Firstly, considering the potential bias caused by the existence of outliers in the sample, the number of samples was reduced from 284 to 264, excluding the top 20 cities with the largest average number of green patents. The detailed regression results appear in column (1) of Table 7. Next, column (2) displays the result of the method of replacing the main explanatory variables, in which the logarithm of high-speed rail service frequency was replaced by dummy variables for the opening of high-speed rail. Finally, in consideration of the hysteresis effect of high-speed rail on the number of green invention patents granted, the variable of high-speed rail service frequency was treated with a lag of one period. The result is presented in column (3). The coefficients of high-speed rail always remained notably positive, showing that high-speed rail considerably effects the growth of green technology innovation in cities. Hence, it can be said that the core results of this paper are reliable and robust.
# Optimization of Clustering in Wireless Sensor Networks: Techniques and Protocols
## 6 Recent Advancements in Clustering Optimization Algorithms
### Meta-Heuristic  
The aim of developing optimization techniques is to provide solutions to NP-hard problems that cannot be addressed using traditional methods within a specified period. Meta-heuristics provide a global solution to such NP-hard problems, even though they sometimes fail to prove the best solution [92; 93]. Combining clustering techniques with meta-heuristics is used to achieve optimal energy usage in WSNs, as they work together to identify the optimal solutions [94]. Researchers have used several meta-heuristics, including swarm intelligence, approximation algorithms, and evolutionary algorithm-based methods.
# Research on an Index System for the Earthquake Disaster Resistance Capability of Highway Tunnels
## 2 Analysis of Seismic Damage Scenes in Highway Tunnels  
This paper uses a literature investigation method to investigate and calculate statistics on seismic damage in highway tunnels. A total of 56 typical earthquakes that have occurred worldwide since 1923 are investigated. The seismic damage cases of highway tunnels with relatively detailed records are mainly in Japan, the United States, China and other regions. In view of the damage conditions of highway tunnels in 12 typical earthquakes, detailed statistics were obtained on the damage conditions of 42 tunnels [15; 16; 17; 18; 19; 20; 21; 22; 23], and the damage sites of the tunnels were classified after the occurrence of tunnel earthquake disasters. The statistical results show that the seismic damage sites of highway tunnels are mainly concentrated in the portal section, the body section and the section passing through the fault crushing belt, of which the portal section accounts for 56.14%, the body section accounts for 33.3% and the section through the fault crushing belt accounts for 10.53%. Further statistical analysis of the disaster results in the above cases shows that the concrete forms of highway tunnel seismic damage mainly include the following:  
A: Tunnel-side slope collapse; B: Portal cracking; C: Open cut tunnel breaking; D: Lining structure cracking; E: Concrete chipping; F: Lining structure collapse; G: Pavement cracking; H: Inverted arch cracking; I: Inverted arch dislocation; J: Construction joint cracking; K: Lining structure dislocation; L: Lining seepage.  
The statistics of the percentage of each type of earthquake disaster are shown in Figure 2.  
Figure 1 shows that the main damage types of highway tunnel earthquake disasters in the investigated cases are tunnel-side slope collapse, which accounts for 20.51%; portal cracking, which accounts for 14.53%; and lining structure cracking, which accounts for 13.68%. According to the damage locations and types, highway tunnel seismic damage can be specifically divided into three kinds of damage sites and a total of 17 kinds of seismic damage scenes in highway tunnels, as shown in Figure 3.  
Figure 2: Percentage of each type of seismic damage in highway tunnels.  
Identification of the Factors Influencing the Earthquake Disaster Resistance Capability of Highway Tunnels