esa-sceva/satcom-chunk-collection huggingface dataset:
# Real Cyclic Load-Bearing Test of a Ceramic-Reinforced Slab
## 1 Introduction  
Ceramic slabs were intensively used in Spain during the 1950s and 1960s. The possibility of building rigid and resistant slabs with almost no concrete turned this material type into a very attractive option at that time due to the shortage of materials, especially concrete and steel. A wide range of ceramic cassettes were used for these slabs over more than three decades; some of them have not yet been catalogued or well identified. The idea consisted of building rigid horizontal slabs with the same requirements as precast reinforced concrete ones, but avoiding the beams, with only the minimum amount of in situ concrete needed (only enough to fill the interface between the ceramics and therebar). Thus, steel reinforcements become crucial in the structural response of these slabs, as almost no concrete contributes; besides this, most of these slabs were built with smooth bars for reinforcing, which was quite typical at that time [1; 2; 3]. In 1955, the Spanish Ministry of Labor of that time approved the \"Ordenanzas Tecnicas y Normas Constructivas para viviendas de renta limitada\", which was a document which established the Building Regulations for public housing. To be more specific, it was a document that authorized architects and engineers to prescribe any type of slab regardless of the amount of concrete and steel, always under their responsibility and after proper design [4]. From that moment, the use of ceramic slabs spread and became generalized around Spain, especially for housing, since these solutions meant a significant lower amount of concrete and even steel. The use of ceramic slabs was later extended to office buildings and industrial hangars, in combination sometimes with steel or RC frames [5; 6]. Despite the extensive implementation of ceramic slabs, the literature on their real structural performance and durability is scarce. Although it is difficult to analytically justify their compliance by using only the current calculation criteria, it is surprising to see how most structures based on this material type remain impeccable, even with very surprising load capacities. In this study we analyze the structural behavior of these slabs by means of a real loading test performed in an industrial building made of metal frames and ceramic slabs (a typical case of industrial heritage of that time in Spain). As mentioned below, the loading test was performed by repeating several loading and unloading cycles in order to describe the damage evolution under cyclic loading.
# Segmentation of Rice Seedings Using the YCrCb Color Space and an Improved Otsu Method
## 2 Materials and Methods
### Improved Otsu Method for Rice Seeding Segmentation
#### 2.5.2 Improvement to the Otsu Method
The minimum value of the weighted within-class variance shown by the dark green curve is \(T=33\). According to Figure 8B, the threshold determined by the weighted Otsu method is close to the desired threshold. Based on the above analysis, the calculation of the within-class variance traverses all gray levels in the image. Thus, the computational time increases with an increase in image resolution. To improve the efficiency of the weighted Otsu method, the optimal threshold is searched within a limited range. Using \(\mu\), \(\mu_{1}\) and \(\mu_{2}\) as mentioned above, \(D\) is defined as the class difference of rice seedling and background:  
\[D=|\mu-\mu_{1}|-|\mu-\mu_{2}|. \tag{16}\]  
From Figure 8A, the rice seedling area is much smaller than the background area, and the gray value of seedlings is greater than that of the background. From Figure 8B, we also observe that \(\mu_{1}<\mu\) and \(P_{1}(t)>P_{2}(t)\). Equation (16) can therefore be expressed as:  
\[D=2\mu-\mu_{1}-\mu_{2}. \tag{17}\]  
Figure 8: (**A**) original image; (**B**) histogram and threshold values of (**A**). According to Equations (7), (8) and (10), the difference, D, is rewritten as:  
\[ \begin{split} D&=2\mu-\mu_{1}-\mu_{2}\\ &=2\sum\limits_{i=0}^{L-1}ip_{i}-\sum\limits_{i=0}^{t}ip_{i}/P_{1}( t)-\sum\limits_{i=t+1}^{L-1}ip_{i}/P_{2}(t)\\ &=\frac{2P_{1}(t)P_{2}(t)\sum\limits_{i=0}^{L-1}ip_{i}-P_{2}(t) \sum\limits_{i=0}^{L}ip_{i}-P_{1}(t)\sum\limits_{i=t+1}^{L-1}ip_{i}}{P_{1}(t) \sum\limits_{i=t+1}^{L-1}ip_{i}}\end{split}  \tag{18}\]  
For the case of \(P_{1}(t)+P_{2}(t)=1\), Equation (18) is rewritten as:  
\[ \begin{split} D&=\frac{2P_{1}(t)(1-P_{1}(t))(\sum \limits_{i=0}^{L}ip_{i+}\sum\limits_{i=t+1}^{L-1}ip_{i})-(1-P_{1}(t))\sum \limits_{i=0}^{L}ip_{i}-P_{1}(t)\sum\limits_{i=t+1}^{L-1}ip_{i}}{P_{1}(t)(1-P _{1}(t))}\\ &=\frac{(1-2P_{1}(t))(P_{1}(t)\sum\limits_{i=0}^{L}ip_{i}+P_{1}( t)\sum\limits_{i=t+1}^{L-1}ip_{i}-P_{1}(t)\sum\limits_{i=0}^{L}ip_{i})}{P_{1}(t)-P _{1}(t))}\\ &=\frac{(1-2P_{1}(t))P_{1}(t)(\mu-\mu_{1})}{P_{1}(t)(1-P_{1}(t))} \end{split}  \tag{19}\]  
Under \(\mu_{1}<\mu\), \(P_{1}(t)>P_{2}(t)\) and \(P_{1}(t)+P_{2}(t)=1\), the difference \(D\) satisfies \(D<0\). Thus, the total mean \(\mu\) is closer to the background class. Moreover, the histogram of rice seedling image is a near-unimodal distribution. Hence, the optimal threshold \(T\) of segment rice seedling should be within the interval \([\mu,L-1]\). As a result, the search range is set to \([\mu,L-1]\) in the weighted Otsu method.
## 3 Single-Particle Imaging with X-ray Lasers
### Sample Selection  
For the development phase of SPI, the selection of samples is key. To make things easier, choosing a high \(Z\) material is tempting, but this is not very representative of samples that are the ultimate goal of the method with the scattering strength of high \(Z\) nanoparticles being stronger than bioparticles and biomolecules. Nevertheless, as initial steps towards developing aspects of the SPI methods, such artificial test samples can prove useful. Nanofabricated samples present the challenge of producing particles with regular shapes and sizes, a key requirement for putting together a consistent 3D diffraction volume from multiple copies of an object. This is also a challenge for bioparticles but less so.  
The sample size should be smaller than the focused beam size. If the X-ray pulse only illuminates part of a single particle, one does not get a diffraction pattern representative of the whole object and therefore cannot merge this data reliably. As the sample quantities needed are large, the cost, mass production capacity should also be considered. High concentration should also be easy to achieve to improve the hit rate. Last but not least, the particles should be able to be aerosolized stably for injection to the XFEL beam. Under these principles, gold octahedra with size range from 2 \(\upmu\)m to 100 nm were produced by a polyol process [164]. For bioparticles, RDV, PR772 and MS2 were selected. RDV (Rice  
Figure 6: Pulse energy fluctuation with time. The intensity and position fluctuations come from the origin of SASE. The common fluctuations are typically 10% but sometimes as high as 30%.  
Dwarf Virus, Figure 7a) is an icosahedral RNA virus of about 70 nm in diameter [165]. A 3D structure of the capsid was determined by X-ray crystallography with a resolution of 3.5 A. The associated PDB ID is 1 UF2 [166]. PR772 (Figure 7b) is a lipid-containing bacteriophage that infects _E. coli_[167]. The diameter of this icosahedral virus is about 69 nm. Unfortunately, the inner arrangement and structures of lipid layer and DNA are also not clearly known. MS2 (Figure 7c) is a bacteriophage that infects F + _E. coli._ The diameter of this icosahedral virus is about 26 nm [168]. The internal arrangement of the complex is unknown.
# 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
#### 3.2.1 Idle Patterns and Brightness Control  
The operation of IEEE 802.15.7 devices of infrastructure type--such as the coordinator--must be compatible with its use as a lighting source. According to the standard, infrastructure devices must transmit idle patterns during the time intervals they receive data or do not have transmission activity, in order to maintain visibility, ensure brightness control, and avoid visible flicker to the human eye. The standard allows idle patterns to be in-band or out-of-band, in relation to the frequency spectrum of the optical carrier modulating signal, _i.e._, according to the spectrum of the optical detector output signal. In-band idle patterns do not require changes in the frequency of the optical clock, and remain within the frequency range the receiver circuits can accept. Out-of-band idle patterns maintain visibility using lower clock frequencies (can even use direct current) and do not lie in the receiver's modulation-domain.
# Structure and Principles of Operation of a Quaternion VLSI Multiplier
## 4 Quaternion Multiplier Structure  
Figure 5 shows a rough outline of the structure of a quaternion multiplier that implements the proposed approach.  
The quaternion multiplier works as follows. At the beginning of the system operation, the coefficients of the multiplied quaternions, naturally ordered, are written to the corresponding buffer memory cells, as seen in Figure 5 on the left and at the bottom. The  
Figure 4: The detailed schematic view of the matrix–vector multiplication of appropriate vectors by factorized \(4\times 4\) Hadamard matrix.  
Figure 5: The common schematic view of the quaternion multiplier.  
multiplicand is written to the upper-left group of registers, and the multiplier is written to the registers, which are located at the bottom of the figure.  
In the next time period, the DM demultiplexers prepare and transmit data, as shown in the figure. In this case, the first half of the entries of the first data vector, using a demultiplexer located in the upper-left part, goes to the fast Hadamard transform block, and then to the first inputs of the upper half of the multiplication blocks. The reordered second half of the entries in this vector goes directly to the first inputs of the lower half of the multiplication blocks. In this case, the appropriately prepared data are fed through the lower demultiplexer to the second inputs of the lower half of the multiplication blocks (see the left half of the demultiplexer outputs). In turn, the right half of the outputs of this demultiplexer is connected to the inputs of the fast Hadamard transform block; for these outputs, the data are supplied to the second inputs of the second half of the multiplication blocks.  
In the last time period, data from the outputs of the first four multiplication blocks arrive to the inputs of the upper-right block of the fast Hadamard transform, and then to the first inputs of the corresponding adder subtractors. In this case, data from the outputs of the four lower multiplication blocks arrive at the second inputs of the adder subtractors. The result of the calculation goes into the output buffer.  
As you can see, the main working blocks of the proposed structure are three blocks that implement the four-point fast Hadamard transform algorithms, eight multipliers, and four two-input adder subtractors. To avoid problems with data input/output, four register buffers are located at the input and output. Data are supplied to the binary multipliers using two special demultiplexers, the operation of which is clear from Figure 5. The quaternion multiplier operates under the control of a master device.
# Vendor-Agnostic Reconfiguration of Kubernetes Clusters in Cloud Federations
## 8 Discussion of Limitations
### CNI Plugin Reconfiguration in Hosted K8s Products  
The presented CNI plugin reconfiguration tactic is based on existing industry practice and procedures that are used by CNI plugin vendors to install their software out-of-the-box to a running K8s cluster. For example, Cilium [29] uses this technique in its Quick Installation Procedure that targets mainstream K8s vendors of the Hosted K8s clusters.  
In hosted K8s vendors, however, there is a trade-off to be made with respect to which type of overlay network solution to use when installing the CNI plugin. Typically, there are three options: UDP tunneling, IP-in-IP encapsulation, and direct mode where Pod IP packets directly hit the wire of the host network that interconnects the nodes of the K8s cluster.  
As stated in Section 5.3, when the master control plane of a cluster cannot be directly controlled and modified by the cluster administrator (which is typically the case in hosted K8s vendors), direct mode is preferred so that master control plane components can talk to Pods that are managed by the CNI plugin. It is not a common requirement, however, that master control plane components are able to talk to Pods directly, as all management of Pods on a node is performed by the local Kubelet agent. Furthermore, note that some components of the master control plane also run as Pods, but they communicate with each other and the Kubelet agents on worker nodes via the host network, not the CNI-based network. There is one exception, namely when pods serve as plug-in component of the control plane in order to customize the control plane. For example, a Pod can run an admission web hook server as illustrated by the API Server Reconfiguration tactic in Section 5.1.  
The flip side of using direct mode is that the CNI plugin can only operate correctly in K8s clusters of which the nodes all run in the same subnet, i.e., all nodes can be reached via Layer 2 (Ethernet routing) without having to pass an IP router. Of course, when no Pods must deployed as plug-in components of the control plane, then a tunneling or IP-and-IP encapsulation can still be used.
## 3 Results and Discussion  
### Asymptotic Behavior of the Numerical Solution  
Numerical simulations for the four oils were initialized from preliminary steady-state runs; after reaching a quasi-stationary solution, the transient solver was activated. It is worth to point out that each oil exhibits a different dynamical behavior, ranging from an asymptotically steady-state solution (Midel 1215) mirroring the initial condition to weakly oscillatory regimes (reference oil and Midel 7131) and regimes where local instabilities are present over most of the domain (Nytro Taurus). As an example, Figure 4 reports colormaps of the temperature field in the oil region around the top discs of Pass 1. It can be observed that, for all the oils except Midel 1215, the temperature distributions reveal that the thermal boundary layers in the horizontal channels are locally disrupted by plume-like formations, typical of Rayleigh-Benard convection, whose characteristic length scale is of the order of the channel height. Such structures appear to have a regular distribution in the cases of the reference oil and Midel 7131, indicating that the oscillatory behavior has the form of a traveling wave. On the contrary, the unsteady behavior of the Nytro Taurus is fully chaotic, as hinted at by the large high-temperature spots appearing in the channels.  
These behaviors can be broadly correlated with the variation of fluid properties with temperature. In particular, by observing the trend of the Prandtl number as a function of temperature (Figure 5), it can be observed that, for Midel 1215, Pr always exceeds a value of 100 for temperatures below the maximum oil temperature (highlighted by circular markers in the graph). Such a value can stand as a possible threshold for the triggering of instabilities. In fact, all other oils show a transitional behavior already in the first pass of the winding, where locally the Prandtl number is already below such a threshold. This is most evident for the Nytro Taurus oil, where the solution is highly oscillatory, and the Prandtl number is already around 100 at the inlet.  
Figure 4: Instantaneous oil temperature colormaps in the top region of Pass 1 for all the four oils considered. Thermal structures in the horizontal channel hint at the occurrence of instabilities and oscillatory behaviors.
## 5 Conclusions
* When compared with the BiGRU (Bidirectional Gated Recurrent Neural Network) method and the CNN-LSTM (Hybrid Model of Convolutional Neural Network and LSTM Network) method, the proposed prediction model, when validated using the PHM2012 dataset and the XJTU-SY dataset, yielded smaller RMSE and MAE values in the prediction results (Tables 8 and 10) than the other two comparative methods. Although the other two methods could also predict RUL results, the RUL curves exhibited greater fluctuations. Generally speaking, smaller RMSE and MAE values indicate better model performance. The R\({}^{2}\) value of the proposed prediction model is closer to 1, and the model's fitting ability is also superior to that of the comparative methods. Therefore, the proposed model further enhances the performance of RUL prediction. Additionally, this also demonstrates that the proposed prediction model is less influenced by data variations and possesses strong generalization capabilities and practicality. * The proposed method performs well under different operating conditions and across various datasets; therefore the proposed method demonstrates higher effectiveness in predicting RUL. By employing this method for RUL prediction, potential bearing failures can be detected in a timely manner, providing sufficient time to prompt bearing repairs. It guides personnel in formulating comprehensive maintenance strategies, thereby preventing further deterioration of faults that could lead to equipment shutdown or damage, and reducing economic losses caused by fault-induced downtime. Conceptualization, Y.Z. and W.S. ; methodology, Y.Z. ; software, Y.Z. ; validation, Y.Z., H.W., T.X. and B.W. ; formal analysis, Y.Z. ; investigation, B.W. and T.X. ; resources, W.S. and H.W. ; data curation, Y.Z. ; writing--original draft preparation, Y.Z. ; writing--review and editing, Y.Z. ; visualization, Y.Z. ; supervision, Y.Z. ; project administration, Y.Z. ; funding acquisition, W.S. and H.W. All authors have read and agreed to the published version of the manuscript. This research is supported by National Natural Science Foundation of China (grant no. 51565055); Autonomous Region Key Research and Development Program (grant no. 202112142); Autonomous Region Natural Science Foundation (grant no. 2022D01C390). Not applicable. The raw data supporting the conclusions of this article will be made available by the authors on request. We would like to thank all the authors in the laboratory. The authors declare no conflicts of interest.
# Self-Organizing Networks for 5G and Beyond: A View from the Top
## 5 Future SON Research Directions for B5G
### SON for 5G Key Use Cases--The URLLC Use Case  
Possibly the most difficult part to ensure and implement in 5G networks will be the URLLC use case. URLLC will be the key enabler for emerging applications and services including tactile internet, mobile factory automation, and inter-vehicular communications for improved safety such as autonomous driving. In order to achieve a successful deployment of the former applications, stringent requirements such as reliability, latency, and availability must be met [115].  
Existing SON systems are mostly service agnostic; thus, the authors of [12] proposed SON usage as an approach for the successful implementation of the strict low-latency and reliability requirements through the selection of specific parameter sets as input to the system. The focus of this specific paper is on the Device to Device (D2D) technique, which regards mainly direct short-range communication MTC applications such as Wi-Fi Direct, Bluetooth Low Energy (BLE) or Near Field Communication (NFC).  
On the other hand, Machine to Machine (M2M) communications regard mainly long-range Low-Power Wide-Area (LPWA) networks such as SigFox, Long-Range Wide-Area Networks (LoRaWAN) and Narrowband-IoT (NB-IoT), with applicability in smart cities, supply chain management, smart metering, security and surveillance, automotive communications, and e-healthcare scenarios, and it is expected that M2M traffic shall occupy around 45% of the total traffic of the Internet. Thus, currently operators focus on the deployment of M2M networks such as NB-IoT [116; 117; 118; 119]. Based on these factors, we believe that future research on SON application in M2M applications is an interesting scenario that should be investigated.
## 1 Introduction
Even though machine learning methods are powerful, they often lack the desired interpretability. The path that a Decision Tree follows to reach its predictions can be readily visualized; however, the same does not hold for more complex ML models. Thus, explainability techniques and models have been employed in ML [17] in order to analyze how these models weigh their input parameters when making a decision, thereby increasing the reliability of their predictions. Among others, [PERSON] et al. [18] recently employed Shapley additive explanations (SHAP) [19] to quantify the effect of each input parameter on damage predictions of bridges in California. [PERSON] et al. [14] performed multiclass classification to predict the damage category of structures and binary classification to predict whether the damage was recoverable or reconstruction was needed. Subsequently, they employed SHAP values to identify 19 of the top 20 most important features for both tasks. However, the features they employed significantly deviated from those in the RVS procedure, and lack many of the features employed in the present study. The features employed in the present study have been used previously [20; 21]; however, there is no consensus on the magnitude of the effect that each feature has on the vulnerability ranking, with different researchers and different seismic codes employing different values. In this paper, we implement explainable machine learning techniques and SHAP values to analyze features' contribution to the relative classification of structures in the respective damage categories. To the best of our knowledge, the novel contribution of the introduced approach is that it does not attempt to directly predict the damage category. Instead, it considers the well known thresholds of the Serviceability Limit State (SLS), Ultimate Limit State (ULS), and Collapse Limit State (CLS) to distinguish structures that not only surpass the ULS threshold but suffer partial or total collapse which could potentially lead to loss of human life. Moreover, machine learning is used to develop binary classification models capable of distinguishing between adjacent damage categories. The benefit of this modeling research effort in comparison with the previously established literature is twofold. On the one hand, the obtained binary classifiers have significantly improved accuracy compared to previous models. This higher accuracy enhances the reliability of the extracted feature importance coefficients, which is the main focus of the present study. On the other hand, the binary classification approach allowsus to examine each of the damage thresholds separately. This allows us to answer the following questions: What are the deciding factors that lead a structure which would have otherwise suffered minimal to no damage to cross the serviceability limit threshold? If a structure does cross the serviceability threshold, what factors prevent it from crossing the ultimate limit state threshold as well? Finally, if it does cross the ULS threshold, what factors prevent it from ultimately collapsing?