esa-sceva/satcom-chunk-collection huggingface dataset:
## 3. Freestanding Position-Based Routing Protocol
### FPBR State Machine and Flow Chart  
In order to explain the interaction among the different FPBR modules (_i.e._, Hello_Mechanism, Location Service and Data Forwarding) a flow chart for each one of these modules is depicted in Figure 5. The Figure 5(a) presents the Hello Mechanism flowchart (named Hello Proc module). This module is in charge of sending the hello messages. The waiting period, T1, used in this module is calculated in accordance with the fixed beacon rate mentioned in Section 3.2. The Location Service flowchart is shown in Figure 5(b) (named Loc_Serv). This module is in charge of performing the procedures described in Section 3.4. Lastly, the flowchart for the data forwarding mechanism explained in Section 3.5 is introduced in Figure 5(c) (named Data_Fwd).  
The interaction between the different FPBR modules is controlled by means of a state machine (SM), as depicted in Figure 6. Every transition of the SM is in the format condition/procedure. A specific module from Figure 6 is launched when the corresponding condition of the state machine is fulfilled. The Hello timer condition is satisfied when the T1 waiting period ends, then the Hello proc is invoked. If the network layer receives a data packet from the application layer, then the App_pkt condition is fulfilled and the Loc_Serv module is invoked. Finally, if a node receives a data packet sent by a neighbor node, the Nb App_pkt condition is satisfied and the Fwd_Data module is invoked.
## 2 Overviews of In Situ Stress and Fifty-One Underground Powerhouses in China  
Owing to strong uplift of the Tibetan Plateau, the southwest region (Sichuan, Yunnan and Guizhou province) of China is under a high in situ stress concentration. In situ stress is the natural stress that exists in the stratum, which is firstly put forward by the Swiss geologist [PERSON] in 1912 [48]. In addition, the horizontal stress component has stronger effect than the vertical stress component under a certain thickness of the upper crust [49]. Especially in the southwest region of China, the maximum horizontal principal stress increases with the depth by 1.1-8.8 MPa per 100 m, and the lateral pressure coefficient (horizontal stress/vertical stress) is generally 1-1.5 [48]. To express the complicated stress distribution throughout the world, [PERSON] et al. [50; 51] considered faulting as the main cause of stress concentration and drew the world stress map. Figure 3 shows the detailed distribution of high in situ stress field in China where the main incentives include NormalFaulting (NF), Strike Slip (SS) and Thrust Faulting (TF), etc. At present, there is no unified standard for the division of in situ stress levels. Generally speaking, when the maximum principal stress reaches 20-30 MPa, it is considered that the rock mass is in the state of high in situ stress. Generally, when the local stress is at a small value, the surrounding rock stability is less affected by in situ stress while the surrounding rock will be greatly affected with the local stress being higher. Since the late 20 th century, the deep-rock excavation has gradually become a continuous trend for geotechnical engineering development, including hydropower underground powerhouses. Globally, there are more than 560 established underground powerhouses of large hydropower stations (see Figure 4) [52], accounting for about 18% in China, 10% in Italy, 8% in Japan, and both 7% in Norway and Sweden. In China, there are nearly 100 underground powerhouses of large-scale hydropower projects (including pumped storage). Figure 5 shows the detailed distribution, where about 44% of them are located in Sichuan, Yunnan and Guizhou province under high in situ stress field. Figure 4: Distribution of underground powerhouses all over the world. Figure 3: Stress distribution map of China [50,51]. To perform research into the underground powerhouses in China, fifty-one of them are collected and listed in order of in situ stress levels to introduce the stress environment and other characteristics, as shown in Table 1.
## 2 Instrumentation and Methods
### Empirical Model of Gross Primary Production (EMGPP)
To reduce the influence of errors in the carbon flux (i.e., NEE, mg CO\({}_{2}\) m\({}^{-2}\) s\({}^{-1}\)) and solar radiation measurements in the EMGPP model development, the following data were not used: (1) NEE larger than two times the standard deviation calculated from all individual measurements and the corresponding GPP and (2) global solar radiation with a solar zenith angle greater than 75\({}^{\circ}\). The EMGPP model was developed utilizing a dataset that includes GPP, with PAR, photochemical and scattering terms (3-factor model, Equation (2)). Considering the practicality of the EMGPP model when direct or diffuse solar radiation is not available, a 2-factor model was developed (i.e., without the scattering term in Equation (2)). \[e^{-0.1\mathrm{aGPPpm}}\times\mathrm{cos}(\mathrm{Z})=\mathrm{C}_{1}\mathrm{ PAR}+\mathrm{C}_{2}e^{-\mathrm{kWm}}\times\mathrm{cos}(\mathrm{Z})+\mathrm{C}_{0} \tag{3}\]  
Therefore, the EMGPP models were developed for these two conditions (3-factor and 2-factor).
# Investigation on Eddy Current Sensor in Tension Measurement at a Resonant Frequency
## 4 Experimental Results and Discussion
### Sensor Performance at the Resonant Frequency
#### 4.2.2 Impedance Phase  
The impedance phase results are shown in Figure 11. As can see from Figure 11, each point of the curve is the average of six repetitive experiments and contains two coordinates; one is the phase value and another is the frequency. As the external tension increased, the relative permeability of the test specimen changed and the sensor resonant frequency decreased.  
The resonant frequency at each tension is plotted and a good linear relationship is observed, as shown in Figure 12. The sensitivities are found to be around 0.468 kHz/kN in the loading process and 0.443 kHz/kN in the unloading process, which are slightly different due to the residual stress in the test specimen.
# Survey on Wireless Technology Trade-Offs for the Industrial Internet of Things
## 4 Wireless Technologies for Industrial Applications
### Long Range Multi-hop Networks  
Multi-hop networks not only extend the range but also substantially contribute to the reliability as in mesh networks there are (typically) 2 or more paths from the source to the destination, meaning that the loss of a link will not result in a communication failure like in single-hop networks. However, nodes in multi-hop networks consume more energy because aside from their own packets, they also must transmit and receive (forward) other nodes' packets. Routing the packets adds up to latency and complexity. Thus, even though the scalability of multi-hop networks is not explicitly limited, deployments typically do not include more than 5 hops. The theoretical limit on the maximal network density is based on the addressing space, which for most of the analyzed technologies is 2[32], thus considered unlimited. However, the practical capacity limit is determined by the network latency and individual device's energy consumption. They both increase with the number of the communication links in the network. In addition, bottlenecks can occur at one or more devices that communicate directly with the gateway in a mesh network. Because of this, data traffic can significantly increase resulting in the increase of the devices' energy consumption. Also, the maximum achievable update rate is greatly influenced by the network density given that high update rates generate more traffic than low update rates.
## 2 Motivation and Background Analysis
### Edge Computing
#### 2.1.1 Classification of Edge Computing  
The centralized Cloud computing (CC) [9] is unable to provide quality service to the end-users due to the enormous traffic load. Therefore, different solutions were proposed to decentralize the network traffic and to support the computational services to the edge of the network. There are several categories of EC based on the network structure, service types, and traffic of a specific area of the network. Figure 1 shows the classification of EC. Mobile Cloud computing (MCC) [10; 11] is one of the EC paradigms, and it offers an offloading mechanism for the mobile devices into a combined system based on the integration of mobile Internet, CC, and mobile computing. Offloading a task to the cloud servers from a mobile user was the major goal to overcome the source mobile devices computational and storage resources limitations. Moreover, the lifetime of a battery of the mobile user is also increased due to the offloading of the task.  
Figure 1: Types of edge computing.  
As the extension of MCC, Cloudlet [12] was introduced as a type of EC, and it is reflected as the key enabling technologies for MCC. Although the concept of MCC was working fine, the distance between the source of mobile devices for offloading a task and the cloud server is very long. Therefore, the communication latency is very high, and the real-time task requires low latency that is not possible based on MCC. As a result, the task can be offloaded into the Cloudlet instead of the cloud servers to meet the lower latency communication and to provide the computation power to the end-users. The deployment of Cloudlet is considered to the nearby mobile devices with single hop proximity and it works as a virtual machine (VM).  
FC [13; 14] is another type of EC where the preprocessing happens before sending to the cloud servers. FC also provides other computational and resource-oriented services (e.g., offloading a task, caching, location awareness, and mobility information). European Telecommunications Standards Institute (ETSI) proposed the concept of mobile edge computing (MEC) [15; 16], aiming to reduce communication latency and providing the location awareness services to the mobile users. ETSI also ensured that the requirements of EC will be fulfilled by the 5G mobile networks and beyond.
## 4 Experiment Design  
The chosen rock test-piece was a sandstone cylinder 50 mm in diameter and 100 mm in height. As mentioned, a CCD black-white camera Basler SCA1600 14 FM was used for shooting images. It is a 1/1.8\" CCD Firewire camera having \(1628\times 1236\) pixel resolution with a maximum frame rate of 14 fps and a pixel size of \(4.4\times 4.4\) mm. All the during-process figures were automatically recorded and stored onto an HP-Z600 workstation in bmp format at the frequency of 2 fps. The loading range of the adopted universal testing machine was 0-600 kN. During the compression process, the compressing head of the machine can automatically adjust its pressure direction corresponding to the dynamic specimen movements so as to satisfy the required experimental conditions of uniaxial compression. Our specific goal was to obtain the profiles of the strain field of the specimens to trace relevant failure modes. Therefore, the robust two-dimensional digital image correlation system of VIC-2D was adopted for image analysis due to its sufficiency for dealing with in-plane deformations and its greater convenience when compared to three-dimensional DIC procedures. The recorded images were uploaded into VIC-2D software, which is installed on the HP-Z600 workstation for image analysis. For experimental purposes, in total, the same experimental protocol was repeated on 12 identical sandstone specimens to secure reliable outcomes.  
Figure 4 displays the tested samples and the image recording system. While Figure 4a shows the raw tested specimen, Figure 4b presents the corresponding specimen, which was manually painted to highlight the related surface speckles for better testing effects. First, the surfaces of the sandstone test-piece were thoroughly cleaned. Matte black paint was subsequently sprayed to the surface, resulting in randomly scattered speckles for the sandstone test-piece. After ventilating to dry, the painted specimen was placed at the center of the testing machine. Figure 4c shows an example of specimen failure. The measurement system is shown in Figure 4d. The recording camera was calibrated to be able to view the surface facing the camera and acquire quality images on the piece by adjusting its focal length. Meanwhile, an external lighting source with uniform luminance was added. The testing machine was turned on and then adjusted to enable the machine head to just touch the specimen. Loading rate was first controlled at 0.1 MPa/s by an automating process, and then at 0.3 MPa/s to see if a loading rate change can alter the value of failure strain for sandstone. The CCD-based system was used to record the whole loading process at the image shooting rate of two images per second until the compression process ended.  
Figure 3: Relationship between yield strength and off-axis angle.
## Materials and Methods
### Characterization of the Nanoparticles (NPs)  
#### 2.3.1 Particle Size and Zeta Potential  
NP size and zeta potential were determined using the Zetasizer Nano ZS90 (Malvern Instruments, Malvern, UK), which uses dynamic light scattering (DLS) to measure particle size at a 90-degree scattering angle and static light scattering to measure zeta potential. In brief, the NPs (encapsulated with or without geranitin) were dissolved in deionized water at a 1:3 ratio and filtered using a polytetrafluoroethylene (PTFE) membrane syringe filter of diameter 13 mm and pore size 0.25 um (Millex HV; Milipore, Milford, CT, USA). The pH was measured, and 1 mL of the solution was added to the folded capillary zeta cell to be read. The mean value of three readings was reported [21].  
Figure 1: W/O/W double emulsion method used to prepare nanoparticles (NPs) and typical structures of (**a**) blank and (**b**) geranitin encapsulated poly(ethylene glycol)-_block_-poly(lactic-co-glycolic acid) (PEG-b-PLGA) NPs. The percentage yield was calculated using a formula derived by [PERSON] and [PERSON], 2013 [20]: % yield = [NPs obtained after synthesis/Total solid weight (PEG-b-PLGA + geranitin)] \(\times\) 100.
# Two-Dimensional Permutation Vectors' (PV) Code for Optical Code Division Multiple Access Systems
## 2 The 2D Wavelength-Hopping/Time-Spreading System  
Utilizing a wavelength-hopping/time-spreading (WHTS) as encoding technique for 2D representation provides numerous advantages over other incoherent schemes such as cardinality and code interference mitigation. In this approach, the codes were spread in both the time and wavelength domains simultaneously.  
Three different cases were demonstrated to represent the wavelength-time slots matrices in Figure 1. Firstly symmetric case (SC) is shown in Figure 1a, in which all assigned wavelengths were utilized and every wavelength appeared only once in each code sequence at different time slots. Secondly, asymmetric case (AS) occurred if at least two different wavelengths were located at the same time slot or if the same wavelength appeared twice or more at different time slots (Figure 1b,c).  
3 The 2-Dimensional Wavelength-Time (W-T) Permutation Vectors' (PV) Code Construction and Properties  
The new 1D PV code was constructed based on permutation vectors. It is characterized by the code length (\(L\)), the code weight (\(w\)), and by zero cross-correlation. \(L\) depends on the number of users (\(K\)) and \(w\). The relationship of these variables is given as follows:  
\[L=wK. \tag{1}\]
## 3 Experiments and Results
### Dataset Description  
For the purposes of this study, we employed two widely recognized remote sensing image datasets: the Massachusetts road dataset and the DeepGlobe road dataset. Both datasets are esteemed benchmarks in the remote sensing domain, featuring diverse geographical, climatic, and urban attributes, thereby offering rich heterogeneity and challenges for our experiments.
# Train Me If You Can: Decentralized Learning on the Deep Edge
## 2 Background  
### Stochastic Gradient Descent (SGD)  
The training of an ANN is an optimization problem [55] where the optimization targets are the weights and the biases. They are iteratively optimized to produce the minimum prediction error [56]. For this purpose, a loss function calculates how far the current values are from the optimal solution [57]. In supervised learning applications, a loss function calculates the distance between the prediction of the ANN and the corresponding true label. This error is optimized using the so-called training algorithms. From the set of training algorithms available, the most prominent is the SGD, which provides information about the direction of the weight update. According to SGD, the update of a given weight of the ANN detailed in Figure 1 is calculated as described in Equation (1).  
\[w_{i}^{+}=w_{i}-\eta*\frac{\partial E_{total}}{\partial w_{i}} \tag{1}\]  
\[\frac{\partial E_{total}}{\partial w_{1}}=(\frac{\partial E_{o_{1}}}{\partial in _{o_{1}}}*\frac{\partial in_{o_{1}}}{\partial out_{h_{1}}}+\frac{\partial E_{ o_{2}}}{\partial in_{o_{2}}}*\frac{\partial in_{o_{2}}}{\partial out_{h_{1}}}* \frac{\partial out_{h_{1}}}{\partial in_{h_{1}}}*\frac{\partial in_{h_{1}}} {\partial w_{1}} \tag{2}\]  
\[in_{o_{1}}=out_{h_{1}}*w_{5}+out_{h_{2}}*w_{6} \tag{3}\]  
\[in_{o_{2}}=out_{h_{1}}*w_{7}+out_{h_{2}}*w_{8} \tag{4}\]  
\[\frac{\partial in_{o_{1}}}{\partial out_{h_{1}}}=w_{5},\ \ \ \ \ \frac{\partial in_{o_{2}}}{\partial out_{h_{1}}}=w_{7} \tag{5}\]  
SGD starts with a forward pass, where the ANN is iteratively fed with input samples and the model prediction is calculated [58; 59; 60]. After comparing the model prediction with the true label, the loss function calculates the loss that will be used to update the parameters (weights and biases) in the backward pass [61; 62]. In the backward pass, the effect of each weight in the prediction error is calculated. This way, each weight is updated proportionally to its error magnitude effect. At a given training iteration, the weight variance tends to be lower than in the previous one. The main goal is to reduce the error disrupted by each weight to ideally zero. Since an error at a given layer propagates to the forwarding layers, the weights are updated only after the computation of the error propagation effect. The error propagation effect is represented in Equation (1) by the term \(\frac{\partial E_{total}}{\partial w_{i}}\). The calculus of this term follows a chain-rule as detailed in Equations (2)-(5). As can be observed for the particular case of \(w_{1}\) in the ANN depicted in Figure 1, the chain-rule depends on the values returned in the previous training iteration for the weights \(w_{5}\) and \(w_{7}\). This pattern is verified for any weight in a hidden layer of an ANN and is responsible for making SGD require the allocation of a memory block that at least doubles the size of the weights.  
Figure 1: SGD overview.
# Dynamic Layered Dual-Cluster Heads Routing Algorithm Based on Krill Herd Optimization in UWSNs
## 2 Models and Definitions
### Underwater Acoustic Energy Consumption Model  
UWSNs vary from terrestrial wireless sensor network because they adopt a unique manner of underwater acoustic communication. Therefore, considerable research has looked into the energy consumption model to adapt the underwater environments. To develop an underwater acoustic energy consumption model, the underwater acoustic signal attenuation model \(A(d)\) is expressed as follows:  
\[A(d)=d^{q}.a^{d} \tag{1}\]  
where \(q\) is the diffusion factor, and it usually sets 1.5.  
\[10\text{log}\;a(f)=\frac{0.11\times f^{2}}{1+f^{2}}+\frac{44\times f^{2}}{4100 +f^{2}}+2.75\times 10^{-4}\times f^{2}+0.003 \tag{2}\]  
where Equation (2) represents \(a=10a(f)/10\). In this case, \(a\) is determined by the absorption coefficient \(a(f)\) in \(\text{dB}/\text{m}\) and \(f\) is the carrier frequency in kHz.  
The information transmission distance between a node and the other node is far from \(d\) meters. The node energy consumption is depicted as follows:  
\[E=E_{send}+E_{rec}+E_{int} \tag{3}\]  
\[E_{send}=lP_{0}A(d) \tag{4}\]  
\[E_{rec}=lP_{r} \tag{5}\]  
\[E_{int}=lE_{da} \tag{6}\]  
where \(E_{send}\), \(E_{rec}\) and \(E_{int}\) are the energy consumption of the transmitting data, received data, and data fusion respectively. \(P_{r}\) normally sets as a constant, it is the energy consumption of each unit. \(l\) is the data packet, and \(P_{0}\) is the minimum power required for the underwater node to receive unit information, and \(E_{da}\) is the energy consumption which compresses every package.
## 1 Introduction
Thus such type of strategy can easily cripple a segment of a network and possibly make it inaccessible. To overcome this problem and to increase the reliability and lifetime of the network various multipath routing protocols have been developed. An on-demand multipath protocol discovers multiple paths in a single route discovery request and later can utilize each one of these if the best link fails. As a result next route discovery is only needed when all of the previous discovered routes fail. This helps in increasing the lifetime and reliability of the network with fewer interruptions. Moreover, end-to-end latency is reduced at the cost of minimal memory overhead at each routing node in the routing path. In this paper, we have developed a new on-demand multipath routing protocol called WEAMR using a hotline-based architecture. WEAMR is based on a well-known and well accepted protocol AODV. WEAMR extends the concept of AODV to use multiple paths between source and destination nodes. The multiple path discoveries ensure loop free and disjoint routing paths. In this architecture, the gateway nodes are connected with highly reliable hotline links. The use of hotlines reduces the number of average hops between a source and a destination and provides better reliability as compared to traditional WSN routing [5]. WEAMR takes into account energy depletion criteria and switches to a less costly alternate routing path in order to increase the life time of a network. A route cost calculation mechanism has been proposed. This cost calculation mechanism does not require additional control packets. The rest of the paper is organized as follows: Section 2 reviews the AODV protocol and hotline-based topology. In Section 3 we discuss related work. In Section 4 we introduce the WEAMR routing protocol. In Section 5 we compare the performance of our protocol with AODV using ns-3 simulations. Section 6 concludes this paper with our future work.