esa-sceva/satcom-chunk-collection huggingface dataset:
# Router Activation Heuristics for Energy-Saving ECMP and Valiant Routing in Data Center Networks
## 2 Communication Management in a Data Center
### Data Center Networks Architectures  
The network topology is vital to network performance and reliability [1; 14]. It determines the length of network paths and backup connections that can be used in the case of failure. In general, network architectures employing simple topologies, i.e., tree, complete graph, or star, do not allow for an adequate level of reliability and scalability to ensure the efficient operation of a large data center. In the case of the tree topology, the failure of even a single switch results in an inability to communicate some servers due to the lack of backup paths. This problem does not occur in the complete graph topology. A path passing through any other computing node can be used. The number of connections equals \(n(n-1)/2\), where \(n\) denotes the number of network nodes. However, ensuring the direct connection between each pair of nodes in the data center network is costly. The lack of switches forces the need for a large enough number of ports to handle all existing connections to a given server. Using a single switch in the star topology creates a single point of failure in the network. Moreover, it is also difficult to scale such a network due to the limited number of switch ports.  
The fat-tree topology [15] is an extension of the tree topology, providing alternative paths. Multiple switches are used instead of one. The path length between each pair of servers is the same as in the case of the original tree topology. Due to the use of multiple paths between servers, higher transfer rates can be achieved. Moreover, the architecture is robust and less costly. It enables the use of multiple cheaper switches with lower bandwidth ports. For example, the maximum number of servers connected using switches equipped with \(k\) ports equals \(k^{3}/4\). The fat-tree topology can be adjusted to the number of server rack groups in real data centers. Communication within the same group does not employ the switches from the top level. The typical approach to increasing (e.g., doubling) server bandwidth and transmission reliability is multiplying its links similar to in multirail fat-tree or multiplane fat-tree; see, e.g., [16; 17]. In the case of a flattened butterfly topology [18] depicted in Figure 1, a greater number of network layers compared to the fat-tree are used to connect the same number of server racks. Equipping each layer with switches guarantees efficient communication between remote servers. The topology can be customized regarding the number of layers and switch to devices connections. The butterfly topology is an efficient, scalable, and cheap architecture that can be used in large-scale data centers.
# Laser Inter-Satellite Link Visibility and Topology Optimization for Mega Constellation
## 4 Results and Discussion
### Simulation Modeling  
As shown in Figure 7a, which is the LEO layer of the OneWeb constellation, and the simulation's five-element parameter set is {40, 18, 9, 1200 km, 86.4\({}^{\circ}\)}, with 18 orbital planes and 40 satellites per orbit, and the optimal phasing factor is set to 9. As shown in Figure 7b, which is the LEO layer of the Starlink constellation, the simulation's five-element parameter set is {66, 24, 11, 550 km, 53\({}^{\circ}\)}, with 24 orbital planes and 66 satellites per orbit, also the optimal phasing factor is set to 11. In the simulation figure, satellites of the same color represent that they are in the same orbit. The effects of various satellite numbers and phasing factors on the coverage performance are discussed using the OneWeb constellation as a comparison group. The simulation begins at 4:00:00.000 on 1 July 2022 and ends at 4:00:00.000 on 2 July 2022, lasting 24 h.  
Figure 6: Solving for the optimal Phasing Factor. (**a**) The variation of the OneWeb constellation, where \(0\leq F\leq 17\); (**b**) The variation of the Starlink constellation, where \(0\leq F\leq 23\).  
Figure 7: Modeling diagram of Starlink and OneWeb constellation (Data on the orbits of satellites in the constellation are detailed in the link in the Supplementary Materials). (**a**) OneWeb constellation; (**b**) Starlink constellation.
## 3 Construction of Citrus Target Recognition Model
### Module Design of Attention Mechanism  
When detecting the images captured by UAV low altitude aerial photography, due to the difficulty in grasping the flight height and the uncertain scale transformation of the collected data, which is easy to cause the problem of significant differences of citrus size in different images, at the same time, there are some problems between different citrus, such as mutual occlusion and leaf coverage, which makes it a great challenge to detect such data. This paper used a lightweight attention mechanism module CBAM to strengthen attention to occluded objects. This architecture is inserted into many CNN models without disturbing the model training steps. Through the feature map output by the model, the attention mechanism carries out in channel and space simultaneously. The architecture diagram of CBAM is shown in Figure 5.  
Among them, the formula for calculating channel attention is shown in Equation (1). The input characteristic map is obtained by global pooling operation and average pooling operation of width and height, respectively. Then, they are sent to a two-layer neural network to share parameters at the same time. After fusion, they activate by sigmoid activation function to generate the characteristic map required for channel attention.  
\[\mathrm{M_{c}(F)}=\sigma(\mathrm{MLP(Avgpool(F))+MLP(Maxpool(F))}) \tag{1}\]  
The formula for calculating spatial attention is shown in Equation (2). The feature mapping and original mapping of computational channel attention fusion are used to calculate spatial attention. Perform global average/maximum pool operation on the channel to obtain feature mapping; this structure uses concat to fuse the features of the two processes, and the final feature mapping is obtained through the sigmoid process.  
\[\mathrm{M_{s}(F)}=\sigma(\vec{t}^{\prime\times 7}([\mathrm{Avgpool(F);MLP(Maxpool(F) ]})) \tag{2}\]  
The insertion of CBAM in the model structure is shown in Figure 6, and we added CBAM to the feature pyramid before each extracted feature fusion to improve the model performance.  
Figure 5: CBAM architecture is shown in the figure, which is composed of two serial modules: channel attention and spatial attention.
## 4 Hardware-in-the-Loop Real-Time Simulation
### Continuous Curve Passing Performance  
The adaptability of the proposed control strategy was verified through the continuous curve performance of the SRT. Figure 10 shows the test line used in the simulation, which mainly includes four sections: Section S1 represents lane change, precisely two connected curves with a radius of R20 m. Section S2 is a circular curve with an R50 m radius representing the large radius curve. Section S3 is a circular curve with an R30 m radius representing the small radius curves. Section S4 is a straight line. The vehicle speed is also 5 m/s.  
Figure 11 shows the lateral deviation, heading error, wheel steering angle, and hinge force under the continuous curve. The lateral deviation results indicate that the tracking error of the first module is opposite to the subsequent modules, with a maximum tracking error of no more than 0.14 m, a maximum heading error of no more than 0.014 rad, and a maximum hinge force of less than 6000 N. The wheel steering angle of each axle shall not exceed 0.241 rad, with the first, fourth, and sixth axles having the same wheel angle direction and the second, third, and fifth axles having the same wheel angle direction.  
Figure 9: Circular curve performance of SRT: (**a**) heading error; (**b**) sideslip angle; (**c**) yaw rate; (**d**) folding angle.
## 4 Discussion
### Linking the Results with Behaviour Change Frameworks
#### 4.1.4 Trip Factors as a Reflexive Motivation  
A behaviour may be driven by an individual's beliefs, identity and goals which may form a reflexive motivation for the specific behaviour [70]. In the case of AST, parents' decisions for their children commuting to school may be a result of their social identity and personal priorities [80], which may have an impact on children's experiences. According to our results, children's affective experiences may differ depending on how they travel to school. Similar to [PERSON] et al. [43], we found that children actively commuting to school mentioned a wider range of sub-themes, both for physical and social environments, than those who are commuting by car. In addition, our results indicate that different ways of commuting may encourage other types of activities, with children walking to school to enjoy active activities (e.g., skipping and running) and children going to school with a car to enjoy more passive activities (e.g., listening to music and looking outside the window). Therefore, it is clear that the mode of commuting to school can create different experiences and different interactions with the surrounding physical and social environment. In the case of independent mobility, parents' decisions define what kind of experiences their children may have along their journey to school. On the other hand, as [PERSON] [22] suggested, independent mobility could enable children to explore their localities. Therefore, designing neighbourhoods and school streets that support no-escort trips could allow children to develop their own experiences of AST without depending on their parents' social identity. It would be interesting to further explore whether a supportive built environment for AST could shape children's reflexive motivation for future trips. However, since parental influence is important [44; 73] and working parents are more likely to drive children to school [40], it is also recommended that AST behaviour change interventions should, in parallel, target parents' travel behaviours and their reflexive motivations [55; 70].
## 3 Theory and Methods  
### Data and Sample Selection  
During a given geological time period, variations in Earth's orbital parameters occur gradually and remain relatively stable. The proportional relationships among these parameters--eccentricity, obliquity, and precession--are critical indicators for identifying Milankovitch cycles in sedimentary records. It is widely accepted that, when stratigraphic periodicities exhibit ratios corresponding to these three orbital components, the strata are considered to have been influenced by Milankovitch forcing. However, three essential conditions must be met for a reliable astronomical interpretation: (1) Sedimentary continuity: the sequence must be continuous and free from significant hiatuses or unconformities to ensure the integrity of the cycle analysis. (2) Relatively stable sedimentation rate: only when the rate is stable can thickness ratios in the depth domain accurately reflect time ratios. (3) Climatically sensitive proxies: the selected indicators must reflect variations in climate or lake level. The thick mudstone layers of the Yixian Formation in the Naiman Depression represent stable, organic-rich lacustrine fine-grained deposits that are particularly sensitive to climatic fluctuations, making them well suited for studying Milankovitch cycles and their sedimentary expressions. In such studies, data selection is critical: proxies that effectively capture climate variability are prioritized. Among these, the natural gamma ray (GR) logging curve stands out due to its high resolution and ability to preserve climate-related signals, making it a preferred proxy for astronomical cycle identification [15; 16]. In selecting a representative well, preference is given to those located in structurally stable settings with minimal tectonic disturbance and complete stratigraphic records [17; 18]. Well Nai-10, the focus of this study, is situated in such a stable area, characterized by continuous well logging data and abundant core samples. This research utilizes GR log data from the thick mudstone interval of the Yixian Formation in Well Nai-10 as a proxy for analyzing orbital-scale sedimentary cycles. By integrating logging data, major and trace element geochemical analyses, and petrographic observations, the study investigates stratigraphic cyclicity and shale depositional responses governed by astronomical forcing. The GR data were acquired using the CPLog logging system and processed using CIFLog 2.0 software. The samples collected this time were mainly from the depth of 2045-2080 m of Well Nai-10, with a sampling interval of one sample every 0.5 m. The major and trace elements and TOC experimental analyses were carried out on the collected samples. For mineralogical study, thin sections were prepared by cutting samples perpendicular to bedding, mounting them in resin on glass slides, drying them in an oven, and examining them under an OLYMPUS microscope (Olympus Corporation, Tokyo, Japan)at magnifications of 10\(\times\), 20\(\times\), and 50\(\times\).
## 2 Contaminants of Emerging Concern (CECs)  
Contaminants of emerging concern (CECs) is a general term for the organic pollutant(s) including: human and veterinary pharmaceuticals (PhACs), endocrine disruptors (EDs) as bisphenols and steroids hormones, personal care products (PCPs), illegal drugs, antifungals, biocides, pesticides, herbicides, surfactants, and nanomaterials [28]. The term of CECs characterizes classes of unregulated or not completely regulated chemicals [29]. CECs are generally chemicals previously known to be present in the environment but exhibiting new documented impacts, recombination of known chemicals or mixtures of chemicals which, in combination, are hazardous for the environment, pharmaceuticals, and pharmaceuticals metabolites [30]. The main contamination sources are untreated wastewater, the wastewater treatment plants, waste of medical centers, animals and livestock, fertilization practice with manure, poorly treated raw materials, and different industries [29; 30]. Potential concerns of the environment contamination with CECs include abnormal physiological processes and reproductive impairment of aquatic biota, the development of antibiotic-resistant bacteria, and the potential increased toxicity of chemical mixtures [29; 31].  
Due to the large number and diversity, the continuous discharge and long-term persistence of CECs pose a significant challenge to the scientific community and policy regulators. Prioritization criteria have been set according to the occurrence, exposure routes, chemical properties, toxicological relevance as results of in vitro and in vivo studies, current regulator state, and current research [31]. REACH regulatory approach [32] and the NORMAN prioritization focused on eco-toxicity endpoints [33] are the most common methodologies applied in Europe. However, the lack of information on hazard and risk of CECs makes the prioritisation process a research field with many unknowns [34].  
Currently, according to the European Union Directive 2013/39/EU [35] the Watch List of Decision 2018/840/EU [36] and recent Directive 2020/1161/EU [37], several CECs including non-steroidal anti-inflammatory drug (NSAID) diclofenac, macrolide antibiotics, synthetic estrogen 17-ethinylestradiol (EE2), two natural estrogens, estrone (E1) and 17\(\alpha\)  
Figure 1: The study area in the Danube River and NW Black Sea Basins.  
estradiol (E2), methiocarb, metaflumizone, several neonicotinoids, and azoles antifungals should be monitored in Europe in surface water. Acceptable method detection limits (as lowest PNEC) were set by the European Commission as Environmental Quality Standards (EQS) [36; 37].  
Among CECs, pharmaceuticals, PCPs, EDs, and pesticides (other than organochlorine pesticides) were more often monitored in the Danube River basin and represent the subject of the research in the present review.
## 3 Method
### Judgment Strategy  
In the defect detection phase, the patches are detected one by one using the CNN networks. After that, each patch corresponds to an output, which also represents the defect probability of the patch. As shown in Figure 9, each element of the matrix represents the probability that there are no defects in the patch. On the right of Figure 9 is the low-frequency sub-image in the wavelet domain (256 \(\times\) 256 pixels) to be detected. It can be seen that our method can not only judge whether there are defects in the image, but also roughly find the location of defects.  
Figure 8: The sliding-window method in DAGM 2007. Extract all 64 \(\times\) 64 patches in the image in 32 strides for prediction.  
Figure 7: Label image (**left**) and defect image (**right**) in DAGM. The black spot is the center of the defect, the red rectangle is the defect patch we extracted after offset.  
It should be noted that the size of the sliding-window is 64 pixels, and the step is 32 pixels in DAGM, so there is partial overlap between patches. That means the element position in the probability matrix does not match the position of the patch in the image.  
If the prediction probability of 0.5 is used as the threshold to distinguish defective images from non-defective images, it can be found that the element in the lower right corner of the probability matrix of Figure 9 is 0.23, which means the CNN model predicts the patch is defective. However, as you can see from the right image in Figure 9, the patch in the lower right corner is non-defective. In our judgment strategy, the prediction error of a few patches will not affect the final judgment. Because of the overlap in the process of extracting patches by the sliding-window method, at least four defective patches are extracted from each defect area in DAGM. In the judgment strategy of the defect detection stage, a threshold can be determined by comprehensively considering the size of the defect area, the accuracy of model, the step of the sliding-window and other factors. When the number of defective patches is greater than the threshold, it is considered that the image to be detected is defective. Under this strategy, the defect detection method is robust to background interference. Figure 10 shows a non-defective image and its probability matrix as a comparison.  
Figure 10 shows a non-defective image (**right**) and its probability matrix (**left**). The number in the box indicates the probability that the patch is non-defective.  
Figure 9: A defective image (**right**) and its probability matrix (**left**). The number in the box indicates the probability that the patch is non-defective.
# Evaluation of the Effects of Hidden Node Problems in IEEE 802.15.7 Uplink Performance
## 3 IEEE 802.15.7 Overview
### PHY Layer Operation Modes  
The PHY layer supports three operation modes, which allow the use of different transmission rates and include support for different types of optical devices and operating environments:  
* **PHY I**: intended for outdoor usage with low data rates (from 11.6 Kbps to 266.6 Kbps).
* **PHY II**: intended for indoor usage with moderate data rates (from 1.25 Mbps to 96 Mbps).
* **PHY III**: for applications with multiple optical transmitters and receivers working on different frequencies (colors). It uses the technique of modulation Color-Shift Keying (CSK) [34], with transmission rates from 12 Mbps to 96 Mbps.  
Each operation mode of the PHY layer supports the use of different combinations of transmission rates, optical clock frequencies, modulation schemes, line codes, and error correction coding techniques. Each one of the aforementioned parameters settings is designated with an operation mode identifier or Modulation Coding Scheme Identifier (MCS-ID).
# Process Discovery for Event Logs with Multi-Occurrence Event Types
## 3 Proposed Schema Induction Method
### Loop Detection  
The loops cause special difficulties in the process discovery methods; the detection of loops is a hard task. Although finding exact repetition can be solved with direct sequence matching algorithms, the finding of nested loops or fuzzy (not exact repetition) loops is a complex problem. To enhance the efficiency of schema discovery, we also apply a loop detection mechanism as a pre-processing step, which identifies subsequences that are more likely to correspond to loops. This information is used as a component in the calculated context description matrix for equivalence mining.  
In the loop detection module, we apply a loop prediction mechanism using a neural network approach. Having a sequence \(s_{1},s_{2},\ldots,s_{m}\), the input context is given by a similarity matrix of size \(m\times m\), which denotes which positions contain the same value. The matrix element is equal to 1, if the elements are the same, otherwise, the matrix element is 0. Figure 1 shows examples for the construction of the similarity matrix. The applied convolutional network takes this similarity matrix as input, and the output is the prediction of the loop sections within the input sequence. With this method, we convert the loop detection problem into an image classification problem, namely, the similarity matrix can be considered as a two-dimensional image. In the case of loop segments, this image contains some characteristic lines parallel to the main diagonal. The applied convolutional network can detect these characteristic lines in the image.  
We can remark that as this approach requires an input matrix of \(O\big{(}n^{2}\big{)}\) size, it can be applied for moderate-sized sequences. In our problem domain, this condition is met; the maximal trace length in the practice was always moderate.
## 3 Results and Discussion
### Evaluation of the Heavy Metals Contamination Level  
#### 3.4.1 EF  
In agreement with [45], an EF < 2 indicates minimal enrichment. In particular, 0.5 < EF < 1.5 shows a natural-origin metal, while EF > 1.5 suggests a more likely anthropropic one [45]. Moreover, the values between 2 and 5 indicate moderate enrichment; between 5 and 20, significant enrichment; between 20 and 40, high enrichment; and an EF higher than 40, extremely high enrichment.  
The obtained EF values, reported in Table 4, were found to be <2 in all cases, indicating no or minimal enrichment.
## 4 Experimental Validation
The accuracy obtained by putting the generated data back into the classification model is also the highest, and the performance is relatively stable. Hence, suggesting that this type of architecture is well suited to detect features and patterns in the raw time-domain signals that can unequivocally determine the presence of defect reflections. For the signal data of this experiment, DDPM can extract features well, and generate new signals that are close to the original signal properties, which plays a great role in defect signal processing and recognition. At the same time, we evaluated the effect of DenseNet in the classification task and compared it with ResNet architecture. The feature extraction ability and classification effect of Densenet in this study are also stronger than Resnet, and these two methods can be better complementary and improved. To sum up, the signal recognition neural network framework proposed in this experiment combined with improved DDPM and DenseNet shows good comprehensive effect in the above tests. The experiment and test results show that the method designed in this study based on DenseNet classification and DDPM data enhancement can accurately detect and classify the damage signals of common defects in CFRP composite plates, and can achieve high accuracy. It is practical for the nondestructive testing of composite materials.