esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Materials and Methods  
### Materials  
The material used in the experiment are the following:  
* Twelve wireless devices (i.e., GSM mobile phones) of 4 different brands (Sony Experia, HTC One, Samsung S5 and Apple iPhone): three phones were used for each of the four models.
* An OpenBTS software was used to activate the GSM communication from each of 12 wireless Devices Under Test (DUT) and to generate the signal in space.
* A Universal Software Radio Peripheral (USRP) type N200 receiver (RX) configured with a sampling rate of 1 MHz is used to collect the signal in space from each of the 12 transmitting wireless devices. The wireless devices were linked with a GSM base station implemented using OpenBTS running on a USRP N200. The base station and digitizer were fully disciplined and synchronized using a Global Positioning System (GPS) receiver with a Global Positioning System Disciplined Oscillator (GPSDO). To support repeatability and stability, the same USRP digitizer, as well as the same base station were used for all tests. All tests were performed after a minimum half hour lock after the Global Navigation Satellite System (GNSS) receiver was properly synchronized on at least four satellites. In the Software-Defined Radio (SDR), the signal is received and down-converted using a WBX, flexible frequency front-end compatible with the USRP with a passing bandwidth of 40 MHz and tuning capabilities from 20 MHz to 2 GHZ. Then, the signal is digitally down-converted by the built-in Digital Down Converter (DDC) employing half band and Cascaded Integrator Comb (CIC) decimators from 100 MHz to 1 MHz.  
A summary of the parameters and settings used for the collection of the signal in space is provided in Table 1.
## 5 Low-Power Cellular Technologies for IoT
### NB-IoT  
Narrowband Internet of Things (NB-IoT) is an LTE-based radio access LPWAN technology for IoT applications which was standardised in 3 GPP releases 13 through 16, as well as release 17 due in 2021 [62]. NB-IoT is increasingly used to support machine-type LTE, and 5G communication, such as by creating networks of sensors and other ultra-low IoT devices based on the benefits of enhanced coverage, deployment flexibility, deep penetration, and reduced energy consumption. The flexibility in deployment options comes from the wide range of frequency bands that LTE networks can support. For instance, the minimum bandwidth of 180 kHz requirements for both uplink and downlink make it possible to use one physical resource block (PRB) within an existing LTE network for NB-IoT deployment [63]. Refs. [20; 48] compared the network performance of licensed and unlicensed LPWAN technologies. The findings show that NB-IoT has good penetrating power in all deployment modes with a maximum coupling loss performance of 164 dB. Other important features of NB-IoT are presented in Table 3. A comprehensive review of frequency bands, price, and other features of IoT devices that can be utilised for NB-IoT deployment were contained in our previous work [8].  
In the southern part of Nigeria, where untapped natural gas and crude oil deposits stand at a record high, water and air pollution resulting from oil and gas exploration activities in and around these localities has been endemic as a result of illegal exploration activities such as pipeline vandalism and oil theft [26]. Crude oil spills and other chemical wastes have induced major water pollution threatening the health of those who live and work in these regions. With NB-IoT and other LTE-based sensor networks, it is possible to remotely monitor such parameters. An integrated wireless LPWAN presents the varieties of interoperable protocols for water and environmental data gathering before sending it over to a central data centre for analytics. Through the survey of wireless technologies available in the region, the authors were not aware of any LoRa and NB-IoT-based environmental monitoring system in and around the oil-polluted regions in Nigeria for either private or public use. The availability of wide LTE and GSM network coverage is what this study is  
\begin{table}
\begin{tabular}{c} \hline \hline
**Benefits of NB-IoT** \\ \hline Improved indoor coverage \\ New radio technology from 3 GPP release 13 and above \\ Supports massive low data devices \\ Low latency sensitivity \\ Ultra-low device cost \\ Low device power utilisation \\ High assurance of quality of service \\ Inherited LTE security \\ \hline \hline \end{tabular}
\end{table}
Table 3: NB-IoT features.  
leveraging to demonstrate the advantages of using an unlicensed spectrum to complement licensed technologies for scaling IoT deployment in developing countries.
## 4 Technical Review and Discussion
### Evaluation Metrics
#### 4.1.3 Evaluation Metrics for Capacity  
Capacity metrics measure how much information can be embedded within an image while maintaining the watermark's imperceptibility and robustness. Traditional watermarking usually involves a balance between capacity and imperceptibility, often limited by pixel-level distortions. In contrast, LDM watermarking operates in latent space, which offers opportunities for embedding larger amounts of information without affecting visual quality directly.  
* **Payload Size:** Payload size is the total number of bits that can be embedded within an image. For LDM watermarking, this is a critical parameter, as a higher payload means more information can be embedded without compromising the image quality. For instance, in the Gaussian Shading method [22], the watermarking capacity is quantified in bits, and the authors demonstrate successful embedding of up to 256 bits per image without visible quality degradation.
* **Embedding Rate (Bits per Pixel--bpp):** This metric evaluates the density of information embedded in an image, usually expressed as bits per pixel [46]. A higher embedding rate indicates that more watermark information is present per unit of image data, which can affect robustness and image quality if not properly balanced.
# Fracture Threshold Analysis and Parameter Matching of Cut-Out Induced Bolts for Subway Couplers
## 2 Experiment and Simulation Analysis
### Simulation Analysis  
#### 2.2.1 Finite Element Model  
The numerical simulations were performed employing a finite element model constructed in LS-DYNA. As shown in Figure 9, the structure of the train coupler mainly included the coupler head, expansion tube, buffer, cut-out induced bolts, coupler yoke, and coupler seat. To simplify the calculation of the model, beam elements were used to simulate the dynamic characteristics of the expansion tube and buffer, and *MAT_GENERAL_NONLINEAR_6 DOF_DISCRETE_BEAM (MAT_119) was selected as  
Figure 8: Force-displacement curve of experiment.  
Figure 7: The appearance of the bolts after experiment.  
the material model for the beam elements. The coupler head and coupler seat underwent less deformation during collisions and were not the focus of the study, so shell elements were used for simulation, and \"MAT_RIGID (MAT_020) was selected as the material model for the shell elements.  
Bolts, coupler yoke, and buffer front plate were simulated using solid elements, as shown in Figure 10. The bolts used \"MAT_PIECEWISE_LINEAR_PLAISTICITY (MAT_024) as their material model. The buffer front plate and coupler yoke underwent less deformation during collisions and could be considered rigid bodies in the calculation process, so \"MAT_RIGID (MAT_020) was selected as the material model. The connection between the buffer front plate and the buffer beam element was achieved through \"CONSTRAINED_EXTRA_NODE.  
As shown in Figure 11, the finite element simulation model for the test conditions established contact relationships between the coupler heads, crushed tube, buffers, and coupler yoke using \"CONTACT_AUTOMATIC_SURFACE_TO_SURFACE, and the friction coefficient was set to 0.2, referencing relevant literature [30]. Additionally, to ensure that  
Figure 10: The model of overload protection device.  
Figure 9: The finite element model of coupler.  
the impact of kinetic energy remains unchanged during the collision, corresponding mass points are placed on the test trolley, and the total model mass is adjusted to match the test mass.  
The finite element model consists of 157,728 nodes and 140,369 elements. With the exception of the overload protection device (composed of pentahedral and hexahedral elements) and the mechanical models of the expansion tube and buffer (beam elements), the model primarily employs a combination of triangular and quadrilateral shell elements. The finite element model features a global average mesh size of 10 mm, with local refinement to 1-3 mm at the bolt cut-out zone locations (corresponding to the structural dimensions of the cut-out zone).
## 3 Case Study 1: Robo-Chef  
This Robotic Chef is an intelligent robotic arm which is able to cook food dishes in an ordinary home environment or restaurants. The robotic arm recognizes the utensils and ingredients required for the particular recipe [45]. Robotic cooking is a difficult task, as the translation from raw recipe instructions to robotic actions involving precise motions and tool-handling is challenging [46]. Robotic chefs are starting to replace human chefs in the restaurant industry [28].  
The aim of the case study is to point at the potential of integrating various technologies into the Robo-chef functionality. In particular, the potential of using conversational capabilities which currently are missing form most Robo-chefs will be highlighted. Such integration will bring Robo-chef closer to the look and feel of human beings, and their behavior and reactions could be even more effective--as advanced robotic and navigation abilities combined with access to vast knowledge, deep learning, and other artificial intelligence technologies could enhance their ability to converse and guide the user.  
The literature about technologies integration in robo-chefs is very limited and concentrates in a limited number of applications. For example, [PERSON] and [PERSON] [28] examined the robotic chef anthropomorphism on food quality prediction. [PERSON] et al. [29] presented a robot that initialized with a set of ingredients placed on the table and a set of natural language instructions describing how to use these ingredients in cooking a plate-like cookies, a salad, or a meatloaf.  
[PERSON] et al. [47] describe five different robot chef systems, none of which has speech recognition, voice recognition, CBR, gesture recognition, machine learning and Cobots capabilities; however, they mentioned implicit sensing capabilities and it was not clear how these were achieved.  
[PERSON] et al. [11] lists the following AI characteristics for service robots: personification, autonomy, deep learning, complex interaction, people-oriented, understanding of aesthetics, and emotional abilities; however, except for the complex interaction, none of these abilities are mentioned in the literature about Robo-chefs.  
We did not find evidence for vast adaptation of robot chefs in the food industry; moreover, we did not find evidence of wide usage of the following technologies with Robo-chefs:  
1. Voice/speech recognition;
2. Emotion recognition;
3. Face recognition;
4. Navigation;
5. Gesture recognition;
6. Cobots capabilities.
## 2 Heart Conduction System and Mathematical Models
### Conduction System of the Heart  
The cardiac conduction system shown in Figure 1a is composed of a group of muscle cells characterized by their own electrical activity that generates the contraction of the cardiac muscles; the Sino-Atrial node (\(SA\)), the Atrio-Ventricular node (\(AV\)), branches of the right and left beam, and Purkinje fibers.  
The \(SA\) node is placed in the right atrium and it is the natural cardiac pacemaker. These cells are known as pacemakers because they manifest a spontaneous depolarization. They are able to reach the action potential threshold faster than every other cell. Thanks to this feature, they manage the heart rhythm [32]. The depolarization rises from the SA node, spreads in the whole right and left atria and finally reaches the \(AV\) node.  
Then, it proceeds along the bundle of his spreading to both left and right bundle branches depolarizing the left and right ventricles. The electrocardiogram (ECG) is a measure of how electrical activity changes during each cardiac cycle [33]. Figure 1b shows a typical ECG waveform.  
Figure 2: Heart mathematical models. (**a**) Ryzhii model [22], based on three oscillators; (**b**) modified model, based on four oscillators.  
The atrial depolarization phase is represented by the P wave, the ventricular depolarization by the QRS complex, and the ventricular repolarization by the T wave. In physiological conditions, the \(SA\) node works at 60-100 ppm, faster than any other cell; the only conducting path from atria to ventricles is provided by the \(AV\) node. If the \(SA\) node has a disease and it fails to generate impulses, the cells of the \(AV\) node can stimulate the heart at a rate of about 40-60 bpm [32]. If this node also presents damaged cells, the atrial-ventricular conduction block occurs. Depending on the entity of the damage, the block can be complete or partial. In this case, not all the electrical signals generated by the \(SA\) node are transmitted to the \(HP\) system. If no excitation signal is delivered from \(SA\) or \(AV\) nodes, the \(HP\) cells can fire at a rate of about 20-40 bpm. However, if the right or left bundle branch of the \(HP\) system is corrupted, the respective ventricle is not contracted.  
This condition is known as Bundle Branch Block (BBB) and it implies an alteration of the pathways for ventricular depolarization. When this condition occurs, the electrical impulse can move through muscle fibers both slowing the electrical activity and changing the propagation direction of the pulses [34].
# Boundary-Inner Disentanglement Enhanced Learning for Point Cloud Semantic Segmentation
## 4 Experimental Results and Discussion
### Toronto-3D Outdoor Scene Segmentation
For example, as shown in the first column, RandLA-Net tended to broaden the width of road markings, whereas BIDEL outlined the boundaries more accurately. Compared to outdoor Toronto-3D scenes, objects were labeled in more detail and connected more densely in indoor scenes such as S3 DIS [7], resulting in more semantic boundary points, enabling the effectiveness of BIDEL to be adequately demonstrated. Therefore, fewer gains were obtained for the Toronto-3D dataset. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Input** & **Methods** & **mIoU(\%)** & **OA(\%)** & **Road** & **Maxking** & **Natural** & **Building** & **Utility Line** & **Pole** & **Car** & **Fence** \\ \hline \multirow{8}{*}{xyz} & PointNet++ [34] & 41.8 & 84.9 & 89.3 & 0.0 & 69.0 & 54.1 & 43.7 & 23.3 & 52.0 & 3.0 \\  & DCCNN [41] & 61.8 & 94.2 & 9
## 2 Materials and Method
According to the definition given by Reference [14], the OOB correction factor is the ratio of the signal at the nominal center wavelength of each band, \(R_{rs}\Big{(}\lambda_{i}^{(N)}\Big{)}\), to the actual signal observed by the corresponding band \(R_{rs}^{(Total)}(\lambda_{i})\), as shown in Equation (5). The closer the correction factor is to 1.0, the smaller the OOB effect. \[\text{Corr}(\lambda_{i})\ =\ \frac{R_{rs}\Big{(}\lambda_{i}^{(N)}\Big{)}}{R_{rs}^{ (Total)}(\lambda_{i})}\times 100 \tag{5}\]  
After OOB\((\lambda_{i})(\Delta)\), OOB\((\lambda_{i})(\%)\), and \(\text{Corr}(\lambda_{i})\) were derived for each station in turbid waters, these parameters were averaged for each band. The above calculations were confined to the blue, green, and red bands due to the fact that the signal in the CZI near infrared band (750-905 nm) is quite small and can usually be neglected, except the highly turbid waters with a high load of suspended sediment. From the spectral response function shown in Figure 3, the nominal central wavelength \(\lambda_{N}\), which is defined as the center of the two wavelengths with 50% peak response, was determined for the four bands of the two CZI cameras (Table 1). Also shown in this table is the in-band spectral domain bounded with 1% spectral response around the peak response. Through comparison of the specifications of the two cameras, it can be found that for the blue (B1), red (B3), and near infrared (B4) bands, the two cameras share almost the same nominal central wavelengths, and the spectral interval with 50% (1%) peak response. The obvious difference between the two cameras was only found in the green band (B2), in terms of the wavelength with 1% response shorter than the peak response (464 nm vs. 511 nm, shown as bold numbers in Table 1), and thus the band interval (144 nm vs. 97 nm). As the significant OOB contribution will lead to the variability of the measured \(R_{rs}(\lambda)\) at the nominal center wavelength, the effective band center wavelength for the \(i\)-th band \(\lambda_{i}\), \(\lambda_{i}^{(E)}\), is determined using Equation (6), and then the central wavelength shift (\(\Delta\lambda\) = \(\lambda_{E}\) - \(\lambda_{N}\)) is quantified accordingly [14]. Figure 2.0
# Filter Media-Packed Bed Reactor Fortification with Biochar to Enhance Wastewater Quality
## 3 Results and Discussion
### Monitored Wastewater Qualities
#### 3.2.3 Filter Efficacy in DOC Removal  
As presented in Table 2, an average of 25.25 mg/L was loaded onto each filter every month. On average, the reference Sa, C, and GC filters discharged about 12.1, 10.1, and 9.4 mg/L while the SaC and GCC released 10.6 and 10.0 mg/L. respectively. Therefore, the order of their performances could be written as Sa \(<\) C \(<\) SaC \(<\) GC \(<\) GCC. Moreover, when using monthly means, it was obtained from the statistics that the Sa, GC, C, SaC, and GCC were able to reduce the influent DOC by an average of 52%, 63%, 60%, 58%, and 61%, respectively. Basing on this data, it could be said that the Sa was the least while GC was most efficient at trapping the DOC. Even though the difference between the GC and GCC filter efficiency was almost insignificant, it was distinct between that of the reference Sa and fortified Sa filters. This suggested that the presence of the C boosted the capacity of the Sa in removing the DOC from the treated wastewater. Comparing this to findings in [PERSON] and Samara [42], who achieved 73% removal of DOC using activated sludge, it could be said that since the C had not been activated, the efficacies of the C and SaC filters were quite substantial.  
As wastewater is continuously infiltrated through porous media, organic matter in it is accumulated on surfaces of particles of the media, forming a biomat. The biomat formed can contribute to reduction of DOC by absorption and biological degradation [44]. It is believed that the formation of biomat around the filter particles contributed to the substantial DOC removal efficiency of Sa, that is, \(>\)50%. Because of the high porosity of the GC and C media, it was highly likely that much of DOC was trapped in them by micro-pore filling mechanisms. In advanced treatment, whereby GAC is used [41], mechanisms of adsorption and ion exchange are normally responsible for removing dissolved substances. Even though the chemical composition of C was not analyzed in this study, its pH characteristics gave indications that it possessed some of these properties, hence the significant DOC removal by the Sa with C. However, its hydrophobicity may have played a part reducing chances of higher DOC removal in the beginning, that is, before it became colonized by fungi and thus fully wetted or pore-filled with water [45]. In general, the DOC removal by all filters with C was found to be substantial.
# A Survey on Optimal Channel Estimation Methods for RIS-Aided Communication Systems
## 3 Channel Estimation in the RIS-Assisted Communication Systems
### S-CSI and I-CSI  
In [9], it has been proven that large-scale antenna systems may be impaired from pilot contamination impact with many antennas. In frequency division duplex (FDD) systems, the BS accesses the CSI using a feedback channel. When users have high mobility, S-CSI is used, which varies more slowly than I-CSI [88]. The BS receives feedback for a longer time to reduce the amount of feedback [74]. The RIS-assisted communication systems are often hampered by the difficulty of obtaining accurate I-CSI [89]. Therefore, there are several studies on I-CSI in [90; 91] and channel reciprocity in TDD systems and uplink training at the BS have been exploited, respectively. S-CSI has a much more balanced quantity and changes very gradually. First, channel statistics are collected and updated in sufficient time. Furthermore, signal exchange overhead and computation overhead can be decreased because the state of the RIS does not need to change often. Doing so, signal exchange overhead and computation overhead can be reduced, and S-CSI is used to verify RIS coefficients over an extended period. For all of these reasons, the CSIs of the BS-RIS and RIS-UEs links must be punctual to optimize the reflection coefficients of RIS-assisted systems. In addition, CSI can be difficult to determine, due to the large number of passive reflective elements in BS-RIS and RIS-UEs links. As a result, it is necessary to provide low training loads while maximizing the operation profits provided by the RIS. Overall, S-CSIprovides an efficient direction in system design because it varies relatively slowly and can be obtained quite easily [74; 75].