esa-sceva/satcom-chunk-collection huggingface dataset:
# 3D-Printed Concrete Bridges: Material, Design, Construction, and Reinforcement
## 1 Introduction
The project was a collaborative effort among Holcim, the Block Research Group at ETH Zurich, the Computation and Design Group at Zaha Hadid Architects, and incremental3D [11;12]. This paper provides a literature review on research related to 3D-printed concrete bridges, offering valuable insights for designers and researchers aiming to advance this  
Figure 1: Examples of 3D-printed concrete bridges: (**a**) Striatus bridge in Venice, Italy [13], and (**b**) Phoenix bridge in Lyon, France [11;14]. emerging field. The review focuses on key aspects, including material properties, design processes, construction techniques, and reinforcement strategies. Specifically, the paper addresses the following critical questions:  
* Design process: What are the design processes for 3D-printed concrete bridges? * Material: What additives/admixtures are utilized in the mixture, and how do they enhance the performance and printability of 3D-printed concrete for bridges? * Construction methods: What construction techniques are implemented, including prefabricated and in situ approaches, and what 3D printing systems, such as gantry-based or robotic arm setups, are used in bridge construction? * Reinforcement: What reinforcement strategies, such as embedded bars or fiber-reinforced concrete, are applied in 3D-printed concrete bridges, and how is reinforcement integrated? By addressing these questions, the paper aims to provide a foundational understanding of the current research in 3D-printed concrete bridges and guide future advancements in this innovative field.
## 7 Experimental Results and Discussion
### Real Time Implementation  
In a realistic NN scenario, linearization methodology is carried out at the Central Office (CO) where the BBUs are placed and a periodical re-training of the DPD system is in this case necessary, requiring however a negligible time with respect to the time of normal operation of the RoF system. Recently, a Xilinx DPD kit has been developed that can be used for this purpose [75].  
Bringing feedback signal from the BS to RAU is one of the main challenging task in the adaptive recompense of the RoF link. This is due to possible nonlinearity of the feedback link; actually, it can be as nonlinear as the RoF link, which is being compensated for. The present work is based on the fact that the predistorter sees only the non-idealities that it needs to compensate. Since it is assumed that the nonlinear feedback connection is uncompensated, therefore, it would destroy the performance of the compensation. In simple words, it means that an approach is utilized where the RoF link is first compensated for using a post distorter and known training signal from the RAU is used here. After that, the already compensated for downlink RoF link can be used as a feedback connection for the compensation [76]. However, as a possible feedback scenario, Figure 14 represents a realistic implementation of an adaptive DPD that shifts complex processing of the signals at the Central Office/Base Transmit Station (CO-BTS).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**DPD Method** & **Coefficients** & **\# Coefficients** & **Multiplications** \\ \hline GMP & \(K_{a}(Q_{a}+1)+K_{b}(Q_{b}+1)R_{b}+K_{c}(Q_{c}+1)R_{c}\) & 84 & 244 \\ CPWL & \((4M+1)\left(K+1\right)L\) & 260 & \((14M+2)\left(K+1\right)L=880\) \\ MSA & \(2(4M+1)\left(K+1\right)L\) & 520 & \((14M+2)\left(K+1\right)=220\) \\ NN & \((N-1)\)\(K^{2}+(4+N)\)\(K+6\) & 8526 & \((N-1)K^{2}+4K+4=8224\) \\ \hline \hline \end{tabular}
\end{table}
Table 9: Complexity Comparisons.  
It should be noted that DPD works as a black box, it counter acts the overall nonlinearities of the system including that of MZM (laser), fiber and photodiode. For tens of km, the combined effect of laser chirp and fiber dispersion becomes a major nonlinearity issue after tens of km [5]. Therefore, laser and possibly photodiode are the primary source of nonlinearity which is mitigated in this proposed bench. In future, it will be interesting to increase the length of fiber and linearize the link by mitigating the fiber nonlinearities such as Kerr effect.  
Indeed, with the higher modulation format and higher bandwidth similar to multiple LTE carriers or 5G new radio (NR) waveforms as discussed, they would lead to higher complexity of DPD operation due to stronger PAPR. Concomitantly, the elevation in bandwidth will lead to overall increase in the base-band memory of the system model. Nevertheless, the evaluated models are still valid. However, higher values of the \(Q\) and \(K\) will be indispensable as compared to the considered case.  
To summarize the discussion above, Table 10 lists the values of ACPR, MSE and EVM for all the utilized methodologies.
## 2 Materials and Methods
### Hyperspectral Imaging System  
The portable hyperspectral imaging system has been described in detail [19]. The system consisted of a C-mount lens (Rainbow S16 mm, 2/3\", Sensei Shoj Co., Tokyo, Japan), a liquid-crystal tunable filter (400-720 nm, a 20-nm full width at half maximum (FWHM); VariSpec VIS, Caliper Life Sciences, Waltham, MA, USA), back-correction optics (Channel Systems Inc., Oakland, CA, USA), and a monochrome GigE CCD camera (Prosilica GC1380, Allied Vision Technologies, Stadtroda, Germany). Due to the narrow viewing angle of the tunable filter, the filter was placed between the lens and the camera, which necessitated the use of back-correction optics to allow images to be appropriately focused on the camera CCD. Illumination for fluorescence imaging was provided by four 10-watt, 405 nm, LEDs (LED Engin, Wellington, MA, USA). LEDs were mounted in aluminum finned casings to help dissipate heat and can be switched on or off. Power for illumination was provided by an 18-V lithium-ion battery pack. Power for other components (12 V) was provided by using a portion of the battery pack. Factory calibration of the tunable filter was checked using standards [19].  
Image data were acquired using an X61 Thinkpad laptop and an acquisition program written in-house using Visual Basic version 6 (Microsoft, Seattle, WA, USA). The camera was connected to the laptop using the Ethernet port and the filter controller was connected using an USB port. The native resolution of the camera, 1360 (H) \(\times\) 1024 (V) pixels, was binned by three to yield a final resolution of 453 \(\times\) 341 pixels. Binning was accomplished to reduce noise, and to increase light sensitivity and dynamic range of acquisitions. The acquisition software facilitated the automated capture of a sequence of images from 465 to 720 nm at 5 nm increments. To acquire the sequence of images, the camera was mounted on a tripod so that the imaging field was identical for each of the sequential images that comprised the hyperspectral data set (Figure 1). Using a frame rate of 15 fps, a complete hyperspectral data set can be acquired in less than 30 s. The frame rate determined the maximum potential exposure  
Figure 1: Experimental setup used to capture hyperspectral images of bird droppings against a background matrix that included a spinach leaf, a romaine leaf, and soil.  
time for individual images. The 12-bit resolution of images was preserved by storing images using the 16-bit TIF grayscale format.  
The camera supported gain settings from 1 to 32, and the acquisition software allowed individual gains to be set for each acquisition wavelength. For reflectance images, the camera gain was set to 1 at all wavelengths, the lowest gain possible. Aperture was selected by looking at continuously acquired images at a selected wavelength, and reducing the aperture until there was no saturation. For fluorescence images, the aperture was set to an f-stop of about 2.0, and gains were set to a fixed number across all wavelengths.
# Neighbor Discovery for IPv6 over BLE Mesh Networks
## 1 Introduction
The design in [10] details the IPv6 address configuration mechanism for BLE nodes. However, the BLE network also follows the star topology. To enable the compression and decompression of the IPv6 header, context information is supposed to be exchanged between devices. In [PERSON] et al. [11], the authors proposed a context management mechanism based on the implementation of 6 LoBLE. Since context exchange could be initiated by either the router or the sensor node, the mechanism does not rely on the neighbor discovery process. This mechanism improves the flexibility of the network. [PERSON] et al. present an adaptive network platform to achieve the connectivity between BLE networks and the Internet, and an advertising data transmission protocol was designed to ensure the emergency data transmission in [12]. While the platform focuses on improving packet transfer, there is a limited part for the network construction. As mentioned above, there is a lot of research on the technology of IPv6 over BLE networks. However, BLE mesh networks have not been considered yet. BLE mesh networks can support more applications of IoT and may provide more advantages than simple star topology networks. This fact motivated us to study the solutions of 6 LoBLEmesh. BLE mesh networks bring new challenges to the neighbor discovery process. With the aim to apply the lightweight IPv6 technology to the neighbor discovery protocol of BLE mesh networks, this paper proposes a neighbor discovery protocol for the 6 LoBLEmesh network. The main contributions of this paper are summarized as follows. * A novel architecture for IPv6 over BLE mesh networks, which includes the 6 LoBLE star network and mesh network, and defines the node roles in the network domain according to the BLE mesh profile. * According to the BLE 5.0 standard and the 6 LoWPAN ND protocol, this paper provides the networking solution, and the IPv6 address auto-configuration and update mechanism. * The performance of the protocol is analyzed, and the 6 LoBLE simulation platform was built by [PERSON]. The experiment results verify the effectiveness of the proposed protocol. The remainder of this paper is structured as follows. Section 2 provides the networking solutions of IPv6 over BLE mesh networks, defining the domains and characters of the network. Then the detailed neighbor discovery protocol is designed in Section 3, including the device discovery mechanism, and the IPv6 address auto-configuration and update mechanism. Section 4 shows the performance simulation. Finally, in Section 6 we present our conclusions and ideas for further works.
# Integrating Multi-Access Edge Computing (MEC) into Open 5G Core
## 2 5G System
### MEC Reference Architecture  
Similar to the 5 GC, MEC is standardized by the European Telecommunications Standards Institute (ETSI), which is dedicated to the continuous standardization and expansion of MEC. Technical documents are regularly released, covering everything from the architecture to developing services within the MEC framework.  
The MEC reference architecture serves as the foundation for creating any MEC structure. Figure 1 depicts the MEC reference architecture in a simplified manner, showcasing only the essential components for the purpose of this study. This architecture is defined in the document [13]. Various associated components can be identified within the architectural setup, such as the MEC Platform (MEP), MEC Host, and MEC Platform Manager (MEPM).  
The MEC Host plays a comprehensive role in the MEC architecture, encompassing critical elements like the MEP, the Virtualization Infrastructure Manager (VIM), and MEC applications (MEC App). The VIM provides storage, processing, and networking resources for MEC applications. It is also where the DP resides [13]. The MEP plays a central role in management, especially concerning applications. It creates an environment conducive for applications to explore, offer, and use services. Moreover, it facilitates communication with the DP, adjusting it according to the traffic rules received. The MEP also hosts MEC services, including functions like a DNS server [13].  
The MEPM holds management-related responsibilities. This includes overseeing the lifecycle of applications and conveying information to the edge orchestrator, the Multi-Access Edge Orchestrator (MEO). Furthermore, the MEPM handles MEP component management, as well as addressing standardization and application needs [13]. The MEO, in turn, performs various functions, such as maintaining a comprehensive view of the MEC system based on deployed MEC Hosts, available services, resources, and the overall topology. The MEO is also responsible for selecting MEC Hosts based on requirements, implementing, transferring (if applicable), and terminating applications [13].  
Figure 1: MEC reference architecture summarized based on [13].
# UAV-Based Privacy-Preserved Trustworthy Seamless Service Agility for NextG Cellular Networks
## 4 Privacy-Preserved Trustworthy Seamless Service Agility
### Delegation Phase  
The 5G core network creates a contract document including a specific delegation policy, expiry date, and certificate public key and sign this certificate with the core network private key. This certificate is called a \"delegation warrant\", which describes the delegation \"message space\" or security policy under the delegated authority functions. This delegation  
Figure 3: The proposed authentication mechanism exchange messages.  
warrant is securely uploaded to the supporting UAV, the delegated proxy signer, along with two delegation keys \((r_{i},s_{i})\) for delegation warrant signature, as follows,  
* Let \(g\) be a generator of a multiplicative subgroup of \(Z_{p}^{*}\) with order \(p\). Then a random number \(k_{i}\in_{R}Z_{p}^{*}\) is selected from this set.
* The proxy signature delegation keys are generated as follows: \[r_{i} =g^{k_{i}},\] (1) \[s_{i} =x_{c}h(w_{i},r_{i})+k_{i},\] where, \(x_{c}\) is the home core network private key and \(h()\) is a collision resistant hash function. In addition, as part of this proxy signature, the core network creates a unique delegation warrant \(w_{i}\) for each UAV \(D_{i}\), as follows: \[w_{i}=\mathbf{S}(r_{i},s_{i}),\] (2) where \(\mathbf{S}\)() is a digital signature function. Note that this delegation warrant is specific to UAV \(D_{i}\) because it uses the \((r_{i},s_{i})\) assigned to this UAV.
* Then, the delegation parameters (i.e., the proxy parameters, the delegation warrant, and the home core network public key \(y_{c}\)) are assigned securely to the UAV \(D_{i}\) as a tuple of \((w_{i},r_{i},s_{i},y_{c})\).
the presence of rain clouds. On the one hand, weather radar can estimate rainfall with high resolution (20 \(\times\) 20 m\({}^{2}\)) over a large area [21] using the Z-R (reflectivity-rain rate) relationship [22, 23]. However, this empirical relationship is insufficient to capture rainfall variability in space and time [24, 25, 26]. In addition, several factors can affect the accuracy of rainfall estimation values: errors in estimating radar reflectivity factors [27], differences in sampling between weather radar and rain gauges [28], natural variability in raindrop size and distribution [29], and instrument errors [30, 31]. On the other hand, satellites have a wider coverage area than weather radar, particularly over oceans. Weather satellites estimate rainfall intensity by linking the peak cloud temperature produced through visible (VIS) or infrared (IR) light reflectivity [32]. The developed techniques cannot be universally applied because of the indirect relationship between rainfall and satellite-measured parameters [33].  
In measuring rainfall, wide spatial coverage with a high level of accuracy becomes a major issue. Indonesia's total surface area of rain gauges is only 1.29 \(\times\) 10\({}^{-9}\)% of the country's total area. In addition, the integrated national weather radar network continues to fall short of covering the entire observation area [34, 35]. The distribution of automatic rain gauge equipment and weather radar throughout Indonesia is shown in Figure 1. Moreover, weather satellite images with global coverage have resolution limitations [36]. This condition explains why using only one data source cannot fulfill Indonesia's requirement for reliable rainfall data.  
This article presents the study results of various sources of rainfall information data from several instruments used in the Indonesian region. Furthermore, it reviews recent studies on utilizing statistical algorithms, machine learning, and deep learning for rainfall estimation implemented in the Indonesian region. The results can be used as a guide for future study.
## 4 Conclusions  
The feasibility of using simple and low-cost culture media was demonstrated to monitor interactions between _S. cerevisiae_ and other yeasts, such as _T. delbrueckii_ and _Z. bailii_, isolated from the alcoholic fermentation of agave juice. However, testing these culture media with more yeast species is necessary. The results presented here demonstrate that _Saccharomyces cerevisiae_ exerts an antagonistic action on the non-_Saccharomyces_ yeast _Torulaspora delbrueckii_, which shows a more significant loss of viability. Nevertheless, the nutritional quality of the culture medium influences this effect since a nutritional-richmedium modulates negative responses and can improve the survival of non-_Saccharomyces_ yeasts. Finally, the survival time of non-_Saccharomyces_ yeasts influences the concentration and diversity of the minor volatile compounds produced. The prolonged survival of non-_Saccharomyces_ yeasts in the YPDA medium was associated with increased production of higher alcohols and esters. This was in stark contrast to the M2 medium, where the output of these two groups of compounds was poor. Likewise, the production of fatty acids was found to be influenced by the presence of _S. cerevisiae_ in the co-cultures.  
The following supporting information can be downloaded at [[https://www.mdpi.com/article/10.3390/](https://www.mdpi.com/article/10.3390/)]([https://www.mdpi.com/article/10.3390/](https://www.mdpi.com/article/10.3390/)) fermentation10080400/s1, Figure S1: Growth kinetics of monocultures; Table S1: Semi-quantification of volatile compounds (\(\mu\)g/L).  
Conceptualization, N.O.S.-C.; writing--original draft, N.O.S.-C. and E.D.A.-G.; methodology: E.D.A.-G., N.O.S.-C., M.R.M.-J. and A.R.-C.; investigation, E.D.A.-G. and E.A.V.-H.; formal analysis: E.D.A.-G., N.O.S.-C. and M.R.M.-J.; data curation, E.D.A.-G. and J.B.P.-L; writing--review and editing, all authors; project administration, N.O.S.-C.; funding acquisition, N.O.S.-C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Tecnologico Nacional de Mexico (TeNM), grant number 17235.23-P.  
Data available from the corresponding author upon reasonable request.  
The first author tanks CONAHCYT for a scholarship for doctoral studies.  
The authors declare no conflicts of interest.
## 4 Studies
### Study D  
This study investigated the detection of craving for gaming in individuals with IGD using multimodal biosignal measurements. The individual's craving was assessed with a self-report craving score. The photoplethysmogram (PPG), galvanic skin response (GSR), and the electrocoulogram (EOG) signals were recorded to observe the subsequent changes in biosignals recorded during gameplay. The signals recorded were then analyzed using a support vector machine (SVM) for classifying the individual craving states. This study found significant decreases in the standard deviation of the heart rate, saccadic eye movements, the number of eye blinks, and a significant increase in the mean respiratory rate. EOG is considered as a potential indicator of the individual craving state than the other biosignals adopted in this study.
# Research on O2O Takeout Delivery Route Optimization Problem Based on Improved Genetic Algorithm
## 3 Establishment and Handling of Mathematical Model  
### Problem Description  
Each instance of the O2O takeout delivery problem can be characterized by takeout orders, riders, and a distribution center. Each order consists of a pickup point and a delivery point. Let the number of orders be \(n\). Let \(I=\{1,2,\cdots,n\}\) and \(J=\{n+1,\cdots,2n\}\) represent the set of pickup points and the set of delivery points, respectively. Each order \(i\) requires picking up a quantity \(q_{i}\geq 0\) from the pickup point \(i\) and delivering it to the delivery point \(n+i\). For convenience, assume \(q_{n+i}=-q_{i}\). Each order \(i\) has an earliest acceptable delivery time \(e_{i}\), a promised delivery time \(l_{i}\), and a latest acceptable delivery time \(L_{i}\). All orders were divided into priority orders and ordinary orders according to whether customers have purchased the on-time delivery service. Let \(m\) riders be responsible for picking up and delivering takeout, and let \(K=\{1,2,\cdots,m\}\) represent the set of riders. Each rider's vehicle has a maximum cargo capacity \(Q\) and a travel speed \(v\), respectively. Initially, all riders are located at the distribution center \(0\). Let \(V=I\cup J\cup\{0\}\). The distance between any two points \(i\) and \(j\) in \(V\) is denoted by \(d_{ij}\geq 0\), and the unit delivery cost on the path between them is denoted by \(c_{ij}\geq 0\).  
The requirement of the problem is to plan a takeout delivery route for each rider such that (1) all orders must be served by riders; (2) all riders depart from the distribution center, complete their assigned delivery tasks, and return to the distribution center; (3) each order can only be delivered by one rider, and the rider must pick up the order before delivering it; and (4) the maximum cargo capacity of each rider's vehicle cannot exceed \(Q\) at any location. The objective of the problem is to minimize the platform costs, maximize the average customer satisfaction, and maximize the average rider earnings.
## 5 Mission Technical Implementation
### Payload Design: Radiometer
The mirror is used at the same angle of incidence for cold space and Earth views, so that it has the same emissivity in these two configurations, providing a very good zero radiance reference. Edge structures on the mirror are used to block the cold space aperture during Earth view. The warm black body is a deep-cavity black body, with an emissivity that will always be very close to unity. The black body temperature will be monitored precisely and provide a calibration accuracy better than 1 K.