esa-sceva/satcom-chunk-collection huggingface dataset:
# Vendor-Agnostic Reconfiguration of Kubernetes Clusters in Cloud Federations
## 3 Related Work
### Transferability of Cloud-Native Applications in Cloud Federations  
Transferability is an important prerequisite to ensure elastic deployment of cloud-native applications across different cloud infrastructures and enable cloud federations [25; 26]. When realizing the transferability of cloud-native applications, heterogeneous cloud infrastructure imposes problems of vendor lock-in. That is to say, without Kubernetes, the realization of cloud-native elasticity depends on the APIs the cloud provider. Before the release of Kubernetes, there were several studies aimed at addressing achieving the transferability of cloud-native applications. [PERSON] et al. [2; 25] proposed a broker model where brokers sit among different cloud providers, aggregating and coordinating the services they provide and exposing provider-independent interfaces to applications. After the release of Kubernetes, [PERSON] et al. [4; 27; 28] are the first to recognize the power of container orchestration platforms for achieving vendor lock-in avoidance with cloud providers. First, they argued that the broker-based approach requires that application development artifacts may need to be modified to use the broker interfaces, yielding again a form of vendor lock-in with  
Figure 2: Cluster Federation based on KubeFed V2.  
the broker. This problem of modification of application development artifacts does not occur with container-based platforms as containers offer a portable application component representation. Second, they propose an approach that incorporates nodes from multiple cloud providers into one cluster powered by a container orchestration platform. Therefore, applications running on this container orchestration platform can be migrated painlessly regardless of which cloud provider they are on. However, this solution against vendor lock-in does not work for the case where cloud providers are K8s vendors themselves who offer hosted K8s clusters. This yields vendor lock-in due to the feature incompatibilities between different K8s vendors as explained in Section 2.
# Dynamic Properties of Calcareous Sands from Urban Areas of Abu Dhabi
###### Abstract  
Abu Dhabi (ABD) has recently developed its national building code that mandates dynamic analysis of large structures to seismic loadings. Large strain dynamic properties such as shear modulus (G) and damping ratio (D) of local undisturbed calcareous sands are not available. Designers are compelled to choose degradation models from studies on calcareous sands of other regions that are usually reconstituted. This research presents for the first time large strain dynamic properties of calcareous sands of urban ABD. Undisturbed samples are collected from different areas of urban ABD and are tested in cyclic triaxial (CT) devices fitted with bender elements (BE). The dynamic properties as functions of confinement are curve fitted with power models, whereas their variation with shear strain level is curve fitted with hyperbolic models. The results are compared with findings of previous studies on calcareous and silica sands. The results indicate that dynamic properties of all samples degrade with shear strain at almost the same rate irrespective of their spatial distribution. The proposed degradation models can therefore be used in dynamic analyses. The results from this study show smaller rates of degradation in dynamic properties compared to other studies on similar sands. The change in low-strain shear wave velocity (Vs) with confinement is significant among the tested samples; therefore, a site-specific evaluation of Vs is recommended. The dynamic properties of calcareous sands of ABD and previous studies however exhibit larger degradation with shear strain and a smaller increase in confinement compared to silica sands.  
calercous sands; cyclic triaxial; bender elements; shear modulus; damping ratio; United Arab Emirates +
Footnote †: journal: Applied
## 4 The DCPVP Protocol  
In the previous protocols, such as LEACH and HEED, the size of clusters is uniform regardless of the distance from the BS, which causes early death of some nodes. Since the energy consumption increases proportionally to transmission distance, multi-hop routing is used by CHs, so the neighbors of a BS have the duty of forwarding data packets of farther nodes to the BS. On the other hand, as the cluster size increases, the CH energy consumption increases too. According to the discussed issues, we assume the size of clusters is proportional to the distance from BS, which provides balancing of CHs' lifetime in different places. This means that closer clusters to the BS have smaller sizes. To avoid uncontrolled increases in the cluster number, as the distance from the BS increases, the cluster size will be bigger [30], as shown in Figure 2.  
On the other hand, in the places where nodes are distributed densely, by choosing one node as the CH, the energy consumption of that node increases extensively and may cause its death. To avoid that, the size of clusters should be smaller and the number of them should increase in such places. Therefore, the load of several nodes is prorated over several CHs and this avoids the death of a single node [30] (Figure 3).  
Figure 2: The cluster size is proportional to distance from BS.  
As mentioned, in the DCPVP method, nodes have similar roles in the clustering process, so the control manner of this protocol is distributed, which causes the protocol to be scalable and makes it more adaptive. However, in centralized methods by increasing the number of nodes, the manager node should be able to access all nodes and in some cases this is not possible. On the other hand, as the number of nodes increases, the time of the clustering process increases too and relative to steady state cycle times this becomes inefficient. According to all these issues we can mention that the main idea of DCPVP is choosing the cluster size based on the nodes' distance from the BS, the local density of nodes and the nodes' average distance from neighbors. We should mention that the important parameters such as residual energy times being elected as CH are also considered. The protocol includes five phases which are described in details.
## 4 Spatiotemporal Feature Extraction Techniques
### Application of Deep Learning Models  
#### 4.2.1 Convolutional Neural Networks (CNNs)  
CNNs exhibit significant advantages in processing spatial feature extraction. The convolutional and pooling layers within CNN can extract features from data and abstract them at higher levels. However, CNNs have limitations when dealing with time-series data, as they struggle to capture long-term dependencies, which restricts their application in handling complex spatiotemporal data [54, 55, 56].
# Development of Highly Sensitive Temperature Microsensors for Localized Measurements
## 2 Materials and Methods
### Numerical Simulations
The simulation uses a 3D mesh composed mostly of tetrahedral and triangular elements. The elements size was defined as extremely fine through a physics-controlled mesh. A stationary study was used to compute the numerical simulation. The direct solver MUMPS was used for the electric currents and the iterative solver GMRES (Multigrid) was used for the heat transfer. For the convergence criteria, the COMSOL predefined relative tolerance of 0.001 was adopted. To study the sensor self-heating, only the geometry with 24 windings was considered, as this effect is more evident due to its higher resistance in comparison with others. A parametric study for several excitation currents ranging from 50 uA to 1 mA was selected for a fixed temperature of 35 \({}^{\circ}\)C, and the maximum output temperature from the solved model was compared to the initial input temperature. Figure 3 shows the expected self-heating in temperature for a microsensor of 24 windings as a function of excitation currents. Figure S1 in the Supplementary Information shows the self-heating temperature distribution and electric potential of the simulated 24 windings sensor, with and excitation electric current of 100 uA and an ambient temperature of 45 \({}^{\circ}\)C. An exponential increase of the self-heating is showed until 1 mA and thus, small currents (-100 uA) are preferred to minimize the joule heating of the sensor. Therefore, to study the sensors' sensitivity, a parametric study with the temperature ranging from 35 \({}^{\circ}\)C to 45 \({}^{\circ}\)C was selected, for a fixed excitation current of 100 \(\upmu\)A performing a self-heating of 0.008 \({}^{\circ}\)C. Figure S2 in the Supplementary Information presents the sensors electric potential generated for a temperature ranging from 35 \({}^{\circ}\)C to 45 \({}^{\circ}\)C and respective sensitivity. The sensors show a high linearity and an increase of the sensitivity with the increase of windings number (Figure 4). The microsensor with 24 windings present the best sensitivity, however it has larger dimensions (400 \(\times\) 950 \(\upmu\)m). Despite this, its size is still appropriate to be integrated into the OoC. More than 24 windings will perform better sensitivity, but the bigger size will hamper the integration of the microsensors into the OoC. Additionally, the reduction of the individual width, spacing and thickness will also increase the sensitivity, but the first two, will increase the complexity of the fabrication process and the last one will compromise the electric current that can pass through the RTD and self-heating. Figure 4: Comparative study of the sensor’s sensitivities from the simulations results. Figure 3: Simulated microsensor (24 windings) self-heating with respect to excitation current.
# Proximity-Based Adaptive Indoor Positioning Method Using Received Signal Strength Indicator
## 3 Proposed Indoor Positioning Method
### RSSI Error Correction
It is difficult to handle RSSI outliers through outlier replacement, although outliers are replaced with a certain normal value. Therefore, OR (outlier removal) is adopted as an outlier handling method for unstable RSSI. where \(s_{t}\) is the predicted RSSI, \(s_{t}^{\prime}\) is the predicted RSSI, \(m_{t}\) is the measured RSSI, and \(K_{t}\) is a Kalman gain that is weighted to the predicted and measured RSSIs. \(K_{t}\) is calculated as follows:  
\[K_{t}=P_{t}^{\prime}\big{(}P_{t}^{\prime}+R\big{)}^{-1} \tag{5}\]  
where \(R\) and \(P_{t}^{\prime}\) are a measurement noise and an error covariance, respectively. \(R\) represents the reliability of the measurement. The measurement becomes more negligible as \(R\) increases. Typically, the optimal value for \(R\) is determined experimentally. The initial error covariance \(P_{0}^{\prime}\) is set to 1. Then, the error covariance is updated as follows:  
\[P_{t+1}^{\prime}=P_{t}+Q \tag{6}\]  
where \(Q\) represents a process noise and \(P_{t}\) is a calculated covariance as follows:  
\[P_{t}=(1-K_{t})P_{t}^{\prime} \tag{7}\]  
Figure 4 shows the RSSI correction through the KF. The gray, blue, and red solid lines represent the raw RSSIs, the corrected RSSIs through KF, and an RSSI at a distance of 5 m, respectively. The KF reduces distance estimation errors by smoothing the RSSIs.
# Underwater Wireless Sensor Network Performance Analysis Using Diverse Routing Protocols
### Fisheye State Routing (FSR)
Figure 3 depicts the main parts of a sensor node, which include the following subsystems: a communication (transceiver) subsystem, a computing (processing) subsystem, a sensor subsystem, and a unified power supply system. The main contribution of the manuscript is highlighted as follows:  
* Implementation of a source tree adaptive routing least overhead routing approach (STAR-LORA) protocol and a fisheye routing protocol for UWSN;
* Comparison of the STAR-LORA and fisheye routing protocols with standard routing protocols in the literature, namely, AODV, DSR, DYMO, LAR1, Bellman-Ford, OLSR, Fisheye, STAR-ORA, and ZRP;  
Figure 3: Internal architecture of sensor networks for underwater wireless communication. Figure 2: 3D- Visualization of underwater wireless communication. Analysis of the energy efficiency of the routing protocol with an increasing number of underwater wireless sensor nodes;
* Examination of the trade-off between average transmission delay, average jitter, utilization rate, and energy in transmitting and receiving modes;
* Recommendation of an appropriate routing protocol for UWSN based on the targeted performance metric. The remainder of this paper is organized as follows. Section 2 describes related work. The network scenario is covered in Section 3. The performance parameters are presented in Section 4. We present the findings of our results in Section 5. In Section 6, we finally concluded.
# SMARTerra, a High-Resolution Decision Support System for Monitoring Plant Pests and Diseases
## 2 Methods
### Back-End
#### 2.2.8 Data Storage  
The back-end pipeline generates a substantial daily dataset, approximately 5-6 GB in size. This dataset comprises interpolated measurements and predictions of three agrometeorological parameters (temperature, humidity, and rainfall) across various scales, from small rice-growing districts to the entire region of Sardinia. Additional weather variables may be included in the future, depending on the integration of new risk models in SMARTerra. Over time, the interpolated BOLAM predictions are phased out. This is because the locust egg hatching prediction model requires only data from meteorological stations, while the rice blast disease risk prediction model relies solely on BOLAM forecasts for the current and next day. Moreover, the BOLAM interpolated data are swiftly recalculated using cubic interpolation instead of the computationally intensive KED technique.
## 3 Results and Discussion
### Factors That Impact the Filtration Process
#### 3.2.1 Aspect Ratio
The normalized capture efficiency of the woven fibers can be expressed as \(\eta/\eta_{0}=\lambda M+\gamma\), in which a larger value of \(\lambda\) represents a faster rate of increase in the non-steady-state capture efficiency. Table 3 suggests that the normalized efficiency can be modeled as \(\eta/\eta_{0}=0.19M+1.00\) (Case 5), \(\eta/\eta_{0}=0.53M+1.00\) (Case 2), and \(\eta/\eta_{0}=1.02M+1.01\) (Case 6). Apparently, the rate of increase (\(\lambda\)) of elliptical fibers is somewhat higher than that of cylindrical fibers, indicating that elliptical woven fibers have a higher capture efficiency than normal cylindrical woven fibers. To accurately determine the relationship between the rate of increase (\(\lambda\)) and the fiber shape (aspect ratio), we performed multiple computations with 11 aspect ratios from 1-3 and present the slope \(\lambda\) and the error bars in Figure 9. Note that \(\lambda\) tends to grow slowly as the aspect ratio increases from 1 to 1.6. Once the aspect ratio exceeds 1.6, however, \(\lambda\) displays a fixed linear growth. Actually, this linear rate can be fitted as \(\lambda=0.50\varepsilon-0.47\), where R\({}^{2}>0.997\). This expression might be useful in determining effective mathematical equations for predicting the dynamic capture efficiency during the dust-loading process. Figure 8: (a) Logarithmic scaling of the normalized pressure drop and (b) dynamic changes in the normalized capture efficiency with different aspect ratios. Figure 9: Dynamic changes in slope (\(\lambda\)) with different aspect ratios (with error bars).
# SKVOS: Sketch-Based Video Object Segmentation with a Large-Scale Benchmark +
## 2 Related Work
### (Language-Based) Video Object Segmentation  
Recently, referring video object segmentation (RVOS) has gained significant attention from the research community. This task focuses on segmenting and tracking the mask of a target object in a video, guided by a language expression. The Ref-DAVIS dataset, introduced by [14], was the first to provide a benchmark for this task, containing 90 videos. The approach employed a complex model that initially located the object with a bounding box, followed by mask propagation. Building on this, URVOS [15] introduced the Ref-YouTube-VOS dataset, a large-scale collection for RVOS, and proposed an STM-style model incorporating a cross-modal attention block to fuse frame and text features. ReferFormer [34] achieved state-of-the-art results in RVOS by employing a top-down approach, supported by the popular Deformable-DETR detector [65]. In this method, the language expression is used as input to the Transformer decoder, playing a central role in the model. Hungarian matching [66] is used to link the instance tube across frames. However, due to its complexity, this method requires substantial computational resources.
# Iteratively Refined Multi-Channel Speech Separation
## 2 Proposed Method
### Iterative Separation  
The overall structure of the iBeam-TFDPRNN is shown in Figure 2. This new structure is divided into two stages, i.e., initial separation and iterative separation. The initial separation is described in Section 2.2. The iterative separation contains an MVDR beamformer and a post-separation network. For convenience, the pre-separation prior to the initial separation is called stage 0, the first combination of MVDR beamformer and post-separation after stage 0 is called stage 1. In the subsequent iteration separation, the first iteration is called stage 2, the second iteration is called stage 3, and so on.  
Specifically, in the first iteration, the initially separated signals \(\left\{\hat{y}_{q,c}^{(1)}\right\}_{q=1,c=1}^{\text{Q}\cdot C}\) of \(Q\) speakers on all \(C\) microphones and multi-channel mixed signals \(\left\{x_{c}\right\}_{c=1}^{C}\) are sent to the MVDR beamformer to obtain output \(\left\{\hat{z}_{q}^{(2)}\right\}_{q=1}^{Q}\), then, the output \(\left\{\hat{z}_{q}^{(2)}\right\}_{q=1}^{Q}\) and the original mixed signal \(\left\{x_{c}\right\}_{c=1}^{C}\) are sent to the post-separation network to obtain output \(\left\{\hat{y}_{q,c}^{(2)}\right\}_{q=1,c=1}^{Q\cdot C}\).  
Figure 2: The structure of iBeam-TFDPRNN.  
In the second iteration, the output \(\left\{\hat{y}_{q,c}^{(2)}\right\}_{q=1,c=1}^{Q,C}\) and mixed signals \(\left\{x_{c}\right\}_{c=1}^{C}\) are sent to the MVDR beamformer of the next stage to obtain output \(\left\{\hat{z}_{q}^{(3)}\right\}_{q=1}^{Q}\), then, the output \(\left\{\hat{z}_{q}^{(3)}\right\}_{q=1}^{Q}\) and the original mixed signal \(\left\{x_{c}\right\}_{c=1}^{C}\) are sent to the post-separation network of the next stage to obtain output \(\left\{\hat{y}_{q,c}^{(3)}\right\}_{q=1,c=1}^{Q,C}\), and this is continued by repeating the process \(N\) times. From this iterative separation, we can see that the post-separation output serves as an additional input for the MVDR beamformer in the next stage. Through this iterative loop, the results of the MVDR beamformer and the post-separation are fully employed to promote their individual optimization and ultimately improve the overall performance.
# Highly Compact 4 \(\times\) 4 Flower-Shaped MIMO Antenna for Wideband Communications
## 2 Single Antenna Design  
The layout of the single-element antenna is illustrated in Figure 1. The antenna is designed on a low-cost FR4 substrate with a dielectric constant of 4.4, a thickness of 0.8 mm, and a loss tangent of 0.027. The antenna is composed of a monopole antenna with a flower-shaped radiator on the top side and partial ground with a stair shape on the back side to enhance the antenna matching. Moreover, a vertical slot is etched from the partial ground plane to make an additional improvement on the impedance matching. The antenna is fed by a 50 \(\Omega\) transmission line with a 1.6 mm width and 4.7 mm length, and the antenna dimensions are optimized to produce the desired frequency band. The antenna has a compact size with total dimensions of 15 mm \(\times\) 15 mm. In order to investigate the leverage of the suggested flower-shaped antenna, the model is simulated using commercial software, a computer simulation tool (CST), and the return loss outcome (S\({}_{11}\)) of the antenna is depicted in Figure 2a. It can be observed from Figure 2a that the antenna achieved S\({}_{11}\)\(\leq\) -10 dB from 5.95 GHz up to 11 GHz with a resonance frequency of 7 GHz with a \(-\)27 dB S\({}_{11}\) result, confirming its appropriateness for operation in wideband applications. The realized gain and the radiation efficiency are displayed in Figure 2b, which shows that the antenna has a gain fluctuation between 2.5 dBi and 3 dBi while the efficiency fluctuates from 85% to 95%. The next step is to duplicate the same antenna on the same type of substrate with extended dimensions for MIMO operation. The upcoming section will introduce the 2-port MIMO antenna with the investigation of different arrangements' impact on the antenna impedance characteristics.  
Figure 1: Layout of single antenna: (**a**) front view; (**b**) rear view.
## 2 VLC System Model for Underground Mining  
In general, underground mines are divided into two environments: one where mining workers can walk and carry the extracted materials, called \"mining roadway\" [9; 16], and another where mineral extraction work is carried out, called \"mine working face\" [9; 16]. The model of a mining system is presented in Figure 1, which we describe below.  
1. Mining Roadway [9; 16]: This part of the mining environment is generally a tunnel or a narrow chamber, where workers are mobilized and materials extracted from the mine are transported. In addition, it is characterized by being very narrow, with a short visibility distance and irregularities in the shapes of the walls and roof. In our work, for simplicity purposes, we will use a horseshoe-shaped environment to represent this mine section, whereby the LEDs will be located in a row on the roof, equidistant from each other, to provide communication and lighting simultaneously, see Figure 1a. Notice that since this region does not allow work to take place, environmental interference caused by external factors will not be preponderant.
2. Mine Working Face [9; 16]: In this mine section, material extraction works are carried out by using specialized equipment. In the extraction process, electromagnetic radiation, water vapor, and flammable gases are produced. For these phenomena, wireless RF communication is not the best option, since it can produce interference or be absorbed. We will assume that this environment is rectangular, whereby the LEDs are located in rectangular form, equidistant from the center of the work section, to provide lighting and communication, see Figure 1b.  
Both sections are equipped with LED lamps, and in order to optimize the effect of communication, the light intensity in the spaces should be distributed as uniformly as possible. Therefore, multiple LED lamps must be installed on the roof of each mining section. The dimensions, LED lamp locations, and key parameters of the proposed underground mining VLC system are detailed in Table 1, located in Section 5. Taking as reference the communication scenarios proposed in [16], for our study, we will adopt the I2M communication scenario. This is because the LED lamps will be fixed on the ceiling of the mining sections; thus, acting as optical base stations that provide communication and lighting. In addition, this communication model considers LoS and NLoS propagation components, which favors us to propose a complete channel mode, as we can see in Figure 2.  
Figure 1: Underground mining VLC System Model. (**a**) Scheme of the mining roadway environment with the LED lamps row location and (**b**) scheme of the mine working face with the quadrature location of the LED lamps.  
Figure 2: Geometry of the LoS and NLos propagation model.