esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Earthquake Displacement
#### 3.2.2 Dynamic Deformation of Lushan Earthquake
Figure 6 depicts the dynamic deformation for the three directions in the first 10 min of the eight selected stations. It can be seen that deformations of SCTQ, SCSM, and SCXJ are relatively evident, especially in the horizontal direction. Meanwhile, the deformation amplitude values of those stations degrade clearly along with the distance between epicenter and track stations. For instance, the deformation values for the SCTQ station in the north and east components are 47.4 mm and 25.4 mm, respectively. By comparison, the deformations of SCSM and SCXJ are less significant, especially in the vertical direction. For the SCMB station, only the signal in the east direction can be identified, while signals in the north and up directions are submerged by noises. However, although the SCSN station is located far from the earthquake area, it has an obvious change in the amplitude and frequency when the seismic wave approaches. This phenomenon is due to the fact that seismic signals far from the epicenter are difficult to identify directly from the time series. In addition, because the positioning accuracy of the GNSS in vertical is worse than that in horizontal, the seismic signals in the vertical component are submerged by the noise, which makes it difficult to detect. Generally, since the Lushan earthquake belongs to the thrust earthquake with the character of levorotation, the mainshock occurred on the thrust fault with a large dip angle, and the scale of earthquake rupture was minor.
# A Review of Foot-Terrain Interaction Mechanics for Heavy-Duty Legged Robots
## 1 Introduction
Studying the foot-terrain mechanical behavior of the heavy-duty legged robots and establishing an appropriate foot-terrain mechanical model are meaningful. The reasonable landing area of the heavy-duty legged robots is increased. The optimization of control strategies is achieved. The parameters selection of foot structure design is facilitated. By designing foot configurations for different working conditions, the mobility performance of the heavy-duty legged robots is improved. The area of some irregular feet in contact with the terrain is not equal to the overall size of the feet. An accurate area is essential in model design. Compared to the light-duty legged robots, the heavy-duty legged robots have a larger leg mass. Both the supporting and the swinging legs can withstand greater torque during the movement, and that puts forward better technical requirements for maintaining the stability of the robots. Thus, it is particularly important to study the mechanical behavior of foot-terrain interactions of the heavy-duty legged robots. The forces acting on the robot's feet are divided into normal and tangential forces. In order to make the robot's walking smoother, the study of gait planning for heavy-duty legged robots also relies on the study of foot-terrain mechanics [9; 10; 11]. [PERSON] and [PERSON] have also pointed out that it is necessary to study foot-terrain mechanics of the legged robots to improve terrain adaptability [12; 13]. [PERSON] studied the multimodal information fusion of robots, which has a significant effect on improving their terrain recognition ability [14]. The supporting foot structures of the heavy-duty legged robots directly affect foot-terrain interactions. Based on the research process of the mechanical behavior of foot-terrain interactions in heavy-duty legged robots, the supporting foot structures and mechanical models of foot-terrain interactions are reviewed for the heavy-duty legged robots. In Section 2, the supporting foot structures of the heavy-duty legged robots are discussed. The foot configurations and plantar pattern shapes of the heavy-duty legged robots' supporting feet are compared and analyzed. In Section 3, the key technologies related to the foot-terrain characteristics of the heavy-duty legged robots are provided. The development of foot-terrain mechanics is narrated. In Section 4, the challenging works in the study of terrain behavior mechanics for heavy-duty legged robots are described. In Section 5, the conclusions are presented. The future development trends are projected. The overall framework of the article is shown in Figure 1. The purpose of this paper is to ensure the terrain adaptability of heavy-duty legged robots by studying the foot-terrain mechanics mechanism.
# Simulation of an Indoor Visible Light Communication System Using Optisystem
## 3 Optisystem Simulation Tool for Indoor VLC System
### NLOS Propagation Model
#### 3.2.1 Case 1. Single LED NLOS Propagation Model  
In the Single LED NLOS propogation model, we employ a single LED as the transmitter and consider both the direct and reflected pathways of the light. Figure 3 depicts the propagation model.  
The main difference between this model and the LOS propagation model is that we considered both a direct and reflected light path rather than just a direct one. In each situation, the following equations provide the distance between the transmitter and receiver, the incidence angle, and the irradiance angle.  
\[l_{1}=11m=\{\theta_{1s}=22^{\circ},\theta_{1d}=25^{\circ} \tag{2}\] \[l^{\prime}_{2}=9m=\{\theta^{\prime}_{2}s=50^{\circ},\theta^{ \prime}_{2}d=40^{\circ}\] (3) \[l^{\prime}_{2}=5m=\{\theta^{\prime}_{2}s=60^{\circ},\theta^{ \prime}_{2}d=40^{\circ}\] (4) \[l^{\prime}_{3}=5m=\{\theta^{\prime}_{3}s=43^{\circ},\theta^{ \prime}_{3}d=48^{\circ}\] (5) \[l^{\prime\prime}_{3}=5m=\{\theta^{\prime\prime}_{3}s=40^{\circ}, \theta^{\prime\prime}_{3}d=60^{\circ} \tag{6}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Low Pass Bessel Filter** & **Specification** \\ \hline Cutoff frequency & 0.75 \(\times\) symbol rate \\ Insertion loss & 0 dB \\ Depth & 100 dB \\ Order & 4 \\ \hline
**PIN Photodiode** & **Specification** \\ \hline Responsivity type & Silicon \\ Responsivity & 1 A/W \\ Dark current & 10 nA \\ Thermal power density & \(100\times 10^{-24}\) W/Hz \\ Absolute temperature & 298 K \\ Load resistance & 50 ohm \\ Shot noise distribution & Gaussian \\ Junction capacitance & 3 pF \\ Modulation bandwidth & 2 GHz \\ Center frequency & 193.1 THz \\ \hline
**3R Regenerator** & **Specification** \\ \hline Delay compensation & 0 s \\ Decision instant & 0.5 Bit \\ Absolute threshold & 0.5 a.u \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._This model is identical to the LOS propagation model except that it considers the reflection point. As seen in Figure 4, we apply attenuation (5 dB) in the reflection point. The simulation started with the same configuration in the LOS propagation model, and we used a power splitter at the transmitting side. The NLOS simulation uses the same diffuse channel used in the LOS propagation model with the values specified in Table 1. We used the power combiner component on the receiver side before feeding it into the PIN photodiode. The simulation uses the same lowpass filter used in the LOS model for filtering the detected electrical signal from the photodiode.
# Routing Protocols for Mobile Internet of Things (IoT): A Survey on Challenges and Solutions
## 4 Solutions to the Issues Faced by RPL during Mobility
### Trickle timer-Based Solutions
#### 4.1.1 Co-RPL  
Authors [43] carry out several simulations with static nodes in an RPL-based network. Due to loss of connectivity, a high packet loss ratio of more than 20% was observed in the network. This number got worse in a Mobile Wireless Sensor Network (MWSN) when there is a continuous change in topology. Hence, the authors proposed a Co-RPL that addresses the Quality of Service (QoS) requirements of MWSNs. Co-RPL [43] works on the Corona mechanism which reduces frequent node failures by improving the localization of mobile nodes. Corona architecture divides the network area into Coronas. A Corona is made up of a central region centered with a DAG root in a certain radius which is equal to the maximum transmission range of a sensor node. The author considered mobile routers as nodes running with RPL protocol and there are four DAGs with each DAG associated with a Corona.  
In RPL, DIO messages are sent by the Trickle timer mechanism for exchanging information with local nodes. Here transmission interval is bounded and is increased with the network stability. Hence, to be aware of the position of the nodes in real-time, the author proposed this mechanism where the DAG root sends DIO messages periodically. The interval between two DIO messages is based on the node's speed. Furthermore, without waiting for the periodic timer expiration, neighboring nodes start sending DIO messages. DIS and DIO messages have been modified as control messages used in Co-RPL by adding flags that distinguishes standard control messages used in RPL: Bit \(F\) is used in DIS messages, if \(F=0\), it is a standard RPL control message, when if \(F=1\), it is from the Co-RPL node. Each Corona is assigned a corona ID (CID), which helps in detecting the mobility of a node and triggering neighbor discovery. DAG root as well as other nodes maintain a list of the Node ID, DAG ID, Corona ID, and the link quality by tracking its neighbors. Simulation results show a lower packet loss of 20% in Co-RPL as compared to 45% in  
Figure 3: Classification of Solutions.  
standard RPL due to the Co-RPL forwarding strategy that uses Corona to detect mobile nodes and find alternative parents. The authors noticed that the energy consumption in Co-RPL was 50% less compared to the standard RPL, owing to the reason of high packet loss. In addition, it results in an average delay 2.5 s lower than standard RPL due to the forwarding mechanism in Co-RPL.
# A Query Understanding Framework for Earth Data Discovery
## 4 Methodology
### Named Entity Recognition  
Geospatial search query consists of significant entities, including geophysical variable, version, sensor, and processing level, etc. In addition to the query input search box geospatial data portals usually provide filters (e.g., processing level, sensors) to narrow the search context. With search filters, users can filter and customize search results to certain content types, e.g., to only obtain images as search results on Google, to get data only in NetCDF format from the PO.DAAC data portal. If these entities are automatically detected from user queries, it is unnecessary for a user to set filters to help the search engine efficiently retrieve datasets. When a user sets filters, entities recognized from a query can support filter verification. For example, when a user searches \"sea surface temperature level 2\", but sets the processing level filter to \"level 3\", the search engine cannot return datasets of processing level 2. The processing level detected from the input query in this example can help the search engine to figure out the issue by either changing the filter to \"level 2\" or giving prompts about the mismatch between query and filter settings.  
Name entity recognition is a process where an algorithm identifies specific types of nouns from a string of text [39]. While people, places, and organizations are the most common types of entities a NER algorithm detects, a NER algorithm can also recognize user-defined entities with the trained data. In our research, pre-defined entities fall in following types: variable, parameter, processing level, version, collection, and sensor. Queries with entities manually labeled by domain experts are expensive  
Figure 4: Workflow of -concept recognition.  
and ineffective to collect. Thus, this research proposes a method that automatically generates train data from metadata attributes to train a customized NER model.  
Each metadata attribute belongs to one of the following categories: entity attribute; query attribute; and, irrelevant attribute. Irrelevant attributes are ignored. Entity attributes (e.g., metadata processing level) serves as phrases sources to entities. For example, the processing level entity consists of level 1, level 2, level 2p, level 3, and level 4 in PO.DAAC metadata. The query attributes provide numerous query-like sentences or expressions. Entities that are discovered from entity attributes are labeled in these sentences for training. Many metadata fields are regarded as query attributes (e.g., short name, long name, title of citation). A NER model is trained with the Stanford CoreNLP package [40], an open source software providing various Natural Language Processing tools. This research focuses on incorporating the advanced research in computer science with existing geospatial data to solve issues in Earth science. For an input query, the NER model recognizes user-defined entities, which are leveraged to automatically set or optimize filters (Figure 5).
## 3 Results and Discussion
### Dry Matter, Total Fat and Fatty Acid Profile of Teff Varieties
The low TI and AI values of teff compared to the oils of some cereals  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Fatty Acids**} & \multicolumn{6}{c}{**Teff Varieties**} \\ \cline{2-9}  & **DZ-01-1278** & **DZ-01-1681** & **DZ-01-2053** & **DZ-01-2423** & **DZ-01-2675** & **DZ-01-99** & **DZ-Cr-387** \\ \hline SFA & 24.33 \(\pm\) 0.26 \({}^{\rm{\,d}}\) & 23.81 \(\pm\) 0.39 \({}^{\rm{\,e}}\) & 23.61 \(\pm\) 0.16 \({}^{\rm{\,e}}\) & 23.13 \(\pm\) 0.12 \({}^{\rm{\,b}}\) & 23.45 \(\pm\) 0.31 \({}^{\rm{\,b}}\) & 23.82 \(\pm\) 0.23 \({}^{\rm{\,c}}\) & 22.35 \(\pm\) 0.19 \({}^{\rm{\,e}}\) \\ UFA & 75.62 \(\pm\) 0.25 \({}^{\rm{\,a}}\) & 76.14 \(\pm\) 0.40 \({}^{\rm{\,a}}\) & 76.34 \(\pm\) 0.17 \({}^{\rm{\,a}}\) & 76.83 \(\pm\) 0.12 \({}^{\rm{\,a}}\) & 76.52 \(\pm\) 0.30 \({}^{\rm{\,a}}\) & 76.15 \(\pm\) 0.22 \({}^{\rm{\,a}}\) & 75.45 \(\pm\) 3.50 \({}^{\rm{\,a}}\) \\ MUFA & 25.40 \(\pm\) 0.48 \({}^{\rm{\,a}}\) & 26.49 \(\pm\) 0.17 \({}^{\rm{\,b}}\) & 26.75 \(\pm\) 0.45 \({}^{\rm{\,b}}\) & 28.51 \(\pm\) 0.38 \({}^{\rm{\,d}}\) & 26.89 \(\pm\) 0.37 \({}^{\rm{\,b}}\) & 25.07 \(\pm\) 0.04 \({}^{\rm{\,a}}\) & 27.32 \(\pm\) 0.10 \({}^{\rm{\,c}}\) \\ PUFA & 50.22 \(\pm\) 0.23 \({}^{\rm{\,a}}\) & 49.65 \(\pm\) 0.28 \({}^{\rm{\,a}}\) & 49.58 \(\pm\) 0.31 \({}^{\rm{\,a}}\) & 48.32 \(\pm\) 0.26 \({}^{\rm{\,a}}\) & 49.63 \(\pm\) 0.63 \({}^{\rm{\,a}}\) & 50.46 \(\pm\) 0.19 \({}^{\rm{\,a}}\) & 48.14 \(\pm\) 3.57 \({}^{\rm{\,a}}\) \\ Omega 6/Omega 3 & 6.50 \(\pm\) 0.17 \({}^{\rm{\,b}}\) & 70.4 \({}^{\rm{\,a}}\) & 6.69 \(\pm\) 0.16 \({}^{\rm{\,b}}\) & 6.90 \(\pm\) 0.42 \({}^{\rm{\,b}}\) & 5.86 \(\pm\) 0.18 \({}^{\rm{\,a}}\) & 6.43 \(\pm\) 0.17 \({}^{\rm{\,b}}\) & 6.42 \(\pm\) 0.22 \({}^{\rm{\,b}}\) \\ SFA/UFA & 0.32 \(\pm\) 0.00 \({}^{\rm{\,c}}\) & 0.32 \(\pm\) 0.01 \({}^{\rm{\,d}}\) & 0.31 \(\pm\) 0.00 \({}^{\rm{\,a}}\) & 0.30 \(\pm\) 0.00 \({}^{\rm{\,a}}\) & 0.31 \(\pm\) 0.01 \({}^{\rm{\,a}}\) & 0.31 \(\pm\) 0.00 \({}^{\rm{\,d}}\) & 0.33 \(\pm\) 0.02 \({}^{\rm{\,a}}\) \\ PUFA/SFA & 2.06 \(\pm\) 0.01 \({}^{\rm{\,a}}\) & 2.08 \(\pm\) 0.05 \({}^{\rm{\,a}}\) & 2.10 \(\pm\) 0.01 \({}^{\rm{\,a}}\) & 2.09 \(\pm\) 0.00 \({}^{\rm{\,a}}\) & 2.12 \(\pm\) 0.05 \({}^{\rm{\,a}}\) & 2.12 \(\pm\) 0.03 \({}^{\rm{\,a}}\) & 2.15 \(\pm\) 0.19 \({}^{\rm{\,a}}\) \\ TI & 0.41 \(\pm\) 0.01 \({}^{\rm{\,a}}\) & 0.41 \(\pm\) 0.01 \({}^{\rm{\,a}}\) & 0.40 \(\pm\) 0.01 \({}^{\rm{\,a}}\) & 0.39 \(\pm\) 0.01 \({}^{\rm{\,a}}\) & 0.38 \(\pm\) 0.01 \({}^{\rm{\,a}}\) & 0.39 \(\pm\) 0.00 \({}^{\rm{\,a}}\) & 0.43 \(\pm\) 0.12 \({}^{\rm{\,a}}\) \\ AI & 0.25 \(\pm\) 0.01 \({}^{\rm{\,c}}\) & 0.25 \(\pm\) 0.01 \({}^{\rm{\,c}}\) & 0.24 \(\pm\) 0.01 \({}^{\rm{\,b}}\) & 0.23 \(\pm\) 0.00 \({}^{\rm{\,a}}\) & 0.24 \(\pm\) 0.01 \({}^{\rm{\,b}}\) & 0.25 \(\pm\)and oilseeds enable the crop to be considered as a healthy alternative for nutraceuticaloid food development.
## 3 Results and Discussion
### Electro-Fenton-like Tests
#### 3.3.3 Reusability Study of Electro-Fenton-like Treatment  
Finally, as with the study of the photocatalytic activity of ZnO/g-C3N4 composites, it is crucial to evaluate their stability, recyclability, and reusability. For this purpose, four electro-Fenton-like cycles were conducted at 25 mA and with 25 mg of catalyst. At the end of each cycle, a more concentrated dye solution was injected to ensure that the initial conditions were maintained while minimizing the catalyst losses throughout the process.  
As illustrated in Figure 7e, the degradation capacity of the ZnO/g-C3N4 composites remained practically stable during the four cycles performed, with a slight decrease more noticeable in the last cycle that could be attributed to the loss of catalyst in the sample collection. The degradation achieved in all cycles after 90 min of treatment was greater than 97%, making the material suitable for multiple reuses. Furthermore, not only did its degradation capacity remain unchanged, but there was also practically no change in the kinetic rate constant values throughout each cycle (Figure 7f). Although a minor reduction in degradation efficiency was noted during the final cycle, the overall results validate the reusability of the ZnO/g-C3N4 composites. These findings underline the potential of this material for extended use in large-scale or continuous water treatment processes, offering a viable and sustainable solution for the degradation of pollutants.
## 3 Results
### Simulation  
#### 3.1.1 Design of Non-Smooth Structure Parameters  
CFD simulations were conducted across a flow rate range of 50-150 m\({}^{3}\)/d for the three dimple configurations (Figure 8). Key findings include the following:  
* **Flow-Dependent Head Enhancement:** Maximum head gain of 12.98% occurred at 150 m\({}^{3}\)/d, representing a 515% increase compared to the 2.11% improvement at 50 m\({}^{3}\)/d.
* **Consistent Efficiency Trends:** Efficiency gains mirrored head improvements, with 8.55% enhancement at 150 m\({}^{3}\)/d versus 1.88% at 50 m\({}^{3}\)/d.
* **Nonlinear Size-Performance Relationship:** The d = 0.9 mm configuration achieved synchronized optimization at 150 m\({}^{3}\)/d, delivering 12.98% head gain and 8.55% efficiency improvement over the baseline, validating the engineering applicability of the theoretical optimal diameter.  
Figure 7: Schematic diagram of the experimental setup.
## 3 Results and Discussion
### Batch Studies  
#### 3.2.1 Effect of pH on Adsorption  
Figure 6 demonstrates a comparison of NGC's capacity to absorb MB at different pH levels. The removal efficiency \(R\)% of MB increased from 3.134% to 83.647% as the pH level rose from 4 to 8, with the highest adsorption of MB (83.647%) achieved at pH 8. The increase in pH resulted in the deprotonation of the composite material, leading to a negative charge. This change in charge enhanced the material's electrostatic attraction affinity towards the cationic MB dye, consequently, increasing the adsorption capacity. Conversely, at low pH levels, protonation of the composite material created a competitor to the cationic MB dye, reducing adsorption capacity. These observations can be explained by  
Figure 4: TEM images of NGC (**a**) before adsorption and (**b**) after adsorption.  
Figure 5: **(a,b)** EDAX spectrum of NGC; (**c**) map of gold distribution in NGC.  
the protonation and deprotonation processes of the composite material, which align with previous studies [44; 45; 46].
## 4 Experimental Setup
### Hyper-Parameters  
**CNN pre-training:** 13D Res-50 backbone [37] pre-trained on Kinetics [43] ([[https://github.com/deepmind/kinetics-i3d](https://github.com/deepmind/kinetics-i3d)]([https://github.com/deepmind/kinetics-i3d](https://github.com/deepmind/kinetics-i3d)), accessed on 20 August 2020 ).  
**Optimization:** The BLSTM is trained with Vanilla SGD, a fixed momentum of 0.9, initial learning rate \(10^{-2}\) and reduced down to \(10^{-4}\) every time the error plateaus. Following [44], the fully convolutional network and Transformer are trained with the ADAM optimizer, initial learning rate \(10^{-3}\), reduced down to \(10^{-4}\) on plateaus.  
**Transformer embedding:** The information about the sequence order of the encoder and decoder inputs is fed to the model via fixed positional embedding in the form of sinusoid functions. The Transformer is trained using teacher forcing--the ground truth of the previous decoding step is fed as the input to the decoder, while during inference the decoder prediction is fed back.  
**Dropout:** Following [45], the BLSTM is trained with dropout probability \(p=0.3\) on the units of the inputs and recurrent layers. The fully convolutional network is trained with a dropout probability \(p=0.8\) on the units of batch normalization layers. The Transformer is trained with dropout probability dropout \(p=0.1\).  
**Termination:** The loss function no longer drops on the validation set between 2 consecutive epochs.  
**Software and Hardware:** All the models are implemented in TensorFlow and trained on a single GeForce GTX 1080 Ti GPU with 11 GB memory.
## 5 Performance and CPU Usage Analysis
### Sequential Read/Write
Through profiling, we find that virtiofsd of the Kata containers performs a memory copy frequently to deliver the requests of the VM to the filesystem of the host OS and executes file I/O operations, so it burns the CPU heavily in the user and system categories as shown in Figure 5(a),b.  
Firecracker and gVisor utilize 112.8% of the CPU on average, which is similar to the usage of runc (112.4% on average). However, the categories that consume most of the CPU resources are different between them due to their architectural differences. Firecracker mostly consumes the CPU resources in Guest (i.e., 87.6% for read and 92.2% for write), which means that most of the file operations are conducted in the VM. On the other hand, in gVisor, the CPU spent in Guest (i.e., the container in gVisor) is smaller than Firecracker, which is 27.2% for read and 21.5% for write. This is because the container in gVisor does not perform file I/O requests, but Gofer executes the filesystem-related system call that conducts actual memory copy and file I/O processing, which is counted in the System category. As a result, in gVisor, the CPU spent in System increases as the block size increases.
###### Abstract  
This paper presents an innovative and cost-effective solution for the absolute localization of mobile robots using ultrasound beacons. The proposed system addresses the challenge of precise positioning within a controlled environment by employing Differential Time-of-Flight (ToF) measurements to determine the relative distances between the robot and optimally placed beacons. Unlike other ToF methods that require synchronization pulses, the proposed approach eliminates this requirement, significantly simplifying the setup and reducing system complexity. Furthermore, the system achieves a higher sampling rate than conventional synchronization-based systems, enhancing real-time performance. Detailed analysis and simulation demonstrate the system's ability to provide accurate and reliable localization. The results highlight the potential for broad application in various robotic environments, offering a robust solution for absolute positioning without complex synchronization strategies. This work underscores the advantages of using ToF measurements with ultrasound beacons and contributes to the ongoing development of efficient and cost-effective robotic localization systems.  
mobile robot localization; ultrasound beacons; time-of-flight (ToF) measurement; cost-effective robotics; real-time localization; position estimation; absolute positioning Article