esa-sceva/satcom-chunk-collection huggingface dataset:
# Exploiting the Capture Effect to Enhance RACH Performance in Cellular-Based M2M Communications
## 1 Introduction
Moreover, we discuss the beneficial points of the proposed scheme when used with other RA-related schemes. The rest of the paper is organized as follows. In Section 2, we introduce our system model. The motivation of this study and an introduction to the Msg3 PR scheme are presented in Section 3. In Section 4, we derive the analysis models for the RA procedure, considering the capture effect, and provide the performance of the proposed scheme, in terms of several performance metrics. In Section 5, the simulation results demonstrate the performance of the Msg3 PR scheme and the accuracy of the proposed analysis. Finally, Section 6 presents the conclusions.
## 4 Performance Analysis  
### Derivation of the CRLB  
According to [27,35], we derive the CRLB for the above positioning scene.  
\[\mathbf{z}(k)=\left[\mathbf{z}_{1}^{T}(k),\cdots,\mathbf{z}_{\text{G}}^{T}(k)\right]^{T} \in\mathbb{R}^{2\text{MG}\times 1} \tag{60}\]  
The corresponding signal vector \  
Eventually, the CRLB can be obtained by  
\[CRLB(\mathbf{p})=\frac{\sigma_{n}^{2}}{2}\Biggl{\{}\sum_{k=1}^{K}\left[\mathbf{F}^{H}(k) \mathbf{D}^{H}\mathbf{P}_{\mathbf{H}}^{\perp}\mathbf{D}\mathbf{F}(k)\right]\Biggr{\}}^{-1}, \tag{68}\]  
where  
\[\mathbf{F}(k)=\mathbf{I}_{2}\otimes\text{diag}(\mathbf{s}_{0}(k)), \tag{69}\]  
\[\mathbf{I}_{2}=\begin{bmatrix}1&0\\ 0&1\end{bmatrix}, \tag{70}\]  
\[\mathbf{D}=\Biggl{[}\frac{\partial\mathbf{H}^{H}}{\partial x_{1}},\frac{\partial\mathbf{ H}^{H}}{\partial y_{1}},\ldots,\frac{\partial\mathbf{H}^{H}}{\partial x_{Q}}, \frac{\partial\mathbf{H}^{H}}{\partial y_{Q}}\Biggr{]}, \tag{71}\]  
\[\mathbf{P}_{\mathbf{H}}^{\perp}=\mathbf{I}-\mathbf{P}_{\mathbf{H}}=\mathbf{I}-\mathbf{H}\Bigl{(}\mathbf{H}^{H} \mathbf{H}\Bigr{)}^{-1}\mathbf{H}^{H} \tag{72}\]
## 2 Engineering Background and Ground Reinforcement Strategies  
In this investigation, the Mangzhou Tunnel Project in Zhuhai, Guangdong Province, China, was considered for the research background. As shown in Figure 1, the project consists of a pair of paralleled tunnels, with the preceding tunnel (on the right) of 1995 m and the following tunnel (on the left) of 2032 m. A slurry-balance shield with a diameter (\(D\)) of 15 m was used to excavate the twin tunnels. The minimum space between the centers of the twin tunnels is 1.6 \(D\) and the minimum burial depth is 0.5 \(D\). The tunnels were locatedin the ground, which consisted mainly of soft clay, which has an effective friction angle of \(13.3^{\circ}\) and effective cohesion of \(13.1\) kPa.  
Due to the disadvantages of the natural geological conditions, the reinforcement method was implemented in the actual construction process to ensure the stability of the excavation face for the twin tunnels. The influence mechanism of the left tunnel excavation (following excavation) on the right tunnel (preceding excavation) is taken as the main focus of this paper. In particular, the effect of reinforcement strategies will be emphasized and compared. As shown in Figure 2, four different reinforcement strategies were investigated: (a) ring reinforcement of right tunnel (denoted by RR) [26; 27; 28]; (b) ring reinforcement of left tunnel (denoted by LR); (c) ring reinforcement of twin tunnels (denoted by TR); (d) cement wall reinforcement between the left and the right tunnels (denoted by CR). The yellow zone in Figure 2 is the reinforcement layer and the symbol \(t\) represents the thickness of the reinforcement. Furthermore, to obtain the suitable thickness of reinforcement, six cases, i.e., \(t\) = 0 \(D\), 0.05 \(D\), 0.10 \(D\), 0.15 \(D\), 0.20 \(D\), 0.30 \(D\), were studied and compared for each reinforcement strategy.  
Figure 1: Location of the Mangzhou Tunnel project in Zhuhai, China.  
Figure 2: \(Cont\).
## 3 Methodology  
### The Schema of Research Procedure  
The schema of this research procedure was illustrated in Figure 3. Four steps were involved in this procedure. First of all, for detecting the wheat canopy C\({}_{\text{ab}}\), we developed the new modified ratio vegetation index (MRVI), based on the field canopy reflectance, C\({}_{\text{ab}}\), and TN data in exp1, at Yucheng Station. Secondly, the key parameters in the GPP estimation models were inversed, for example, MRVI; ScaledWDRVI; ScaledLST; ScaledVSDI (visible and shortwave infrared drought index), Equations (5) to (7); and the green normalized difference vegetation index (GNDVI). Thirdly, the new GPP estimation model (ACPM) was developed for the winter wheat in Hengshui City. Fourthly, the yield and DAM were simulated based on the ACPM, GPP1, and GPP2 models and the accuracies of ACPM for DAM and yield estimation were verified.  
Figure 3: The schema of this research procedure for the winter wheat production estimation based on the environmental stress factors from the satellite observations. MRVI—modified ratio vegetation index; exp1—experiment 1; exp2—experiment 2; DAM—dry aboveground biomass; GPP—gross primary productivity; MODIS—Moderate Resolution Imaging Spectroradiometer; VSDI—visible and shortwave infrared drought index; WDRVI—wide dynamic range vegetation index; GNDVI—green normalized difference vegetation index.  
Figure 2: Winter wheat sowing area of Hengshui City with the computed percent of the wheat value (% of a pixel occupied) at 1 km scale in 2017.
## 4 w-SMNO: Performance Analysis
### Evaluation Metrics
It encompasses the transmission delay (\(TD\)), propagation delay (\(PD\)), queuing delay (\(QD\)), and processing delay (\(PrD\)). For defined factors, including the population and subgroups' size, the average delay (\(\tau\)) is shown in Equation (33). \[\tau_{Agg}=\left[\sum_{test=1}^{r}\left[\int_{1}^{LL}\left[\int_{1}^{n}\left(TD+PD+QD +PrD\right)*\frac{1}{r}\right]\right]\right] \tag{33}\]  
Collision rate (\(\sigma\)): The collision rate refers to the proportion of collision occurrences in a communication system, particularly in scenarios where multiple devices contend for access to a shared communication medium. For the collision count (\(CC\)), among the total number of transmission attempts (\(T\)), the average of \(\sigma\) is expressed in Equation (34). \[\mu_{Agg}=\left[\left(\sum_{test=1}^{r}\left(\int_{1}^{LL}\left[\int_{1}^{n} \frac{CC}{T}\right]\right)*\frac{1}{r}\right)\right] \tag{34}\]
## 3 The Proposed Method and Scenarios
### Initial Authentication Phase
#### 3.4.4 TAS and RSU Interaction  
TAS and RSU has a non-direct interaction in the initial authentication phase and the handover process. In the initial authentication phase, as shown in Figure 6, it indicatesthat the TAS and RSU do not directly communicate; however, their functions are interrelated. The TAS comprises two components: the Authentication Server (AS) and the Ticket Granting Server (TGS). The AS is primarily responsible for managing the initial authentication of vehicles requesting RSU services within the secure network. Its functions include verifying vehicle identities, issuing Ticket Granting Tickets (TGTs), and encrypting user credentials for security. The TGS then issues service tickets, which vehicles use to access RSU services. The service server validates these tickets to authenticate the client and authorize service access. In the handover process, the TGS generates the \(AU_{VITGS}\), serving as the credential required during the procedure. This credential is uploaded to the blockchain, which is managed via a smart contract to control the uploading and access of the \(AU_{VITGS}\). The smart contract's Solidity code includes parameters such as entity name, entity ID, and network name. The TAS is the designated entity authorized to upload the \(AU_{VITGS}\). This credential is accessed by the RSU when a vehicle submits a handover request. The RSU retrieves the \(AU_{VITGS}\) by entering its entity name, which is verified through a consensus mechanism. Upon validation, the transaction is added as a new block, granting the RSU access to the \(AU_{VITGS}\).
# The State-of-the-Art of Sensors and Environmental Monitoring Technologies in Buildings
## 5 Discussion: Application, Challenges and Future Perspectives
### Data Analysis and Network Security  
Within the operational phase, buildings are now producing more data than ever before, and this data can be leveraged to reduce the energy consumption and operational costs of the building [137]. Datasets can provide useful information relating to energy, demand, occupancy, weather and pricing, that can be used to aid the development of demand-side response techniques, appropriately schedule building loads, maximize profit from peer-to-peer energy trading, or optimize the performance of energy harvesting-battery systems.  
Managing and creating value from large volumes of data can be a cumbersome task which involves communicating collected data over a network, storing data in database systems, cleaning and filtering data to identify missing, erroneous or duplicate values and applying machine learning techniques to make predictions and classifications [138]. The granularity of datasets required to capture transient behavior of certain parameters can be very high, and therefore, data processing can be an intensive and energy-consuming process. This data can be processed locally where it is collected so that no raw data is communicated to the remote server--increasing the overall energy performance by reducing communication needs, and can also be processed on the 'edge' of a network, where network devices are utilized to store and process data, or on the cloud, which provides highly scalable computing resources as a pay per use service [139]. In the future, we can expect further growth in these approaches as the sheer volume of data acquired within buildings continues to increase.  
Some of the prominent challenges associated with unlocking value from building datasets which need to be overcome in the future include the lack of a common data structure--such as database schema and naming methodologies--which limits the interoperability of information between buildings. This has been recognized by experts and is currently the driver behind the ASHRAE 223P specification for standardized building schema [140; 141]. The security of the sensor networks is also a concern as sensors can become points of intrusions, malicious attacks and other cyber threats [142]. This problem is particularly acute when public networks are used to transmit data.
## 5 Rendering Textured Surface with Vibration and Pressure
The whole rendering system is provided in Algorithm 1, where \(P_{\text{x,y,z}}\) denotes the tracked finger position in 3D space, \(L_{\text{x,y,z}}\) denotes one of the predefined texture surface locations in 3D space, \(R^{3}\) denotes the predefined location of three texture surfaces, \(S\) denotes any surface in the VR environment, \(I_{\text{s}}\) denotes the interacted surface, and \(T_{\text{s}}\) denotes the predefined texture surface. The algorithm constantly tracks any contact between the finger model and the virtual surface. A collision event is triggered in case of contact between the finger model and the surface. During the collision, pressure feedback is presented for the feeling of rigidity using the air chamber at the lower layer. When the user's lateral movement hits one of the grids, a thread is created for each unit of the grids. For every thread, a single vibrotactile signal is generated using the upper layer air chamber for texture rendering. The algorithm sends an opening command to appropriate valves for the two air chambers, generating actual feedback. ```
1:Input: User finger position
2:Step 1: Haptic texture detection
3:if \(P_{\text{x,y,z}}==L_{\text{x,y,z}}\) where \(L_{\text{x,y,z}}\subset R^{3}\)then
4:\(I_{\text{s}}\ =\ T_{\text{s}}\)
5:endif
6:Step 2: Rendering feedback
7:while\(S=\ ^{\prime}T_{\text{s}}\)\"do
8:ifTouch\(T_{\text{s}}=\ ^{\prime}T\)\"then
9:Get pressure feedback;
10:endif
11:ifMoving along\(T_{\text{s}}=\ ^{\prime}T\)\"then
12:forEvery number\(of\) gridsdo
13:Create a thread;
14:Get vibration\(feedback\);
15:endfor
16:endif
17:endwhile
18:Finish
```  
**Algorithm 1** Texture-rendering process  
Figure 7: Rendered simultaneous pressure and vibration feedback for Type A, Type B, and Type C Surfaces in the time and single-sided frequency domain.
## 2 Related Work
### Multicast Architectures
#### 2.1.2 HIP-Based Multicast  
Host Identify Protocol (HIP) is a 3.5 layer protocol which introduces a host identity name space aiming to provide a host layer between network layer and transport layer, and sets up the mapping between host identity and IP to fill the gap between the IP and DNS. By using Host Identity Tag (HIT) as an identifier in transport layer, HIP can provide mobility, multi-homing, and security supports [36]. In this design, host identity represents an abstract concept assigned to a computing platform (end point), and there may be multiple host identities for a given computing platform. While host identifier is a public key which is used as the name of a host identity, and each host identity exactly has one host identifier. HIT is a 128-bit datum created by taking a cryptographic hash over a host identifier plus bits to identify which hash used [37]. HIT is self-certifying and used as the operational representation in the HIP packet header.  
The related work and experiences of HIP are described in [38], and some HIP-based multicast schemes were proposed in the past few years. [PERSON] and [PERSON] [39] proposed a Host Identity Specific Multicast (HISM) model which includes architectural elements, access control, mobility support, and interaction with IP multicast. HISM adopts HIT to replace IP, and introduces HIT-S and HIT-R to represent multicast source and multicast receiver, respectively. Furthermore, HISM proposes a Version Independent Group Management Protocol (VIGMP) to perform group membership management. In term of multicast routing, it inherits the network layer multicast routing protocol such as PIM-SM. Besides, [PERSON] and [PERSON] [40] proposed a secure HIP-multicast model by introducing the HIP Multicast Agent (MA) and two-level administrations to provide the authentication/authorization of multicastmembers and ensure confidentiality of multicast packets. After that, [PERSON] et al. [41] extended the two-level administrations, and decomposed the HIP-multicast tree into a centre tree and several sub-trees to optimize the multicast delivery of HIP. More recently, [PERSON] proposed the BloomCasting [42] based on Bloom filter, in which multicast source handles the group management and control the receivers to enable controlled multicast packet forwarding. The HIP-based multicast is still under discuss and neither of them have been adopted by HIP working group, while Bloom filter based scheme may be a promising way for HIP multicast forward [36].  
Compared with SDN-based multicast, HIP-based multicast can provide better mobility and security support; however, it lacks of the effective control of multicast tree and the separation mechanism of control and forwarding, which may cause large reconstruction cost when group members frequently join/leave multicast group.
# Construction of Geosynthetic-Reinforced Pavements and Evaluation of Their Impacts
## 3 Construction Impacts on the Geosynthetic-Reinforced Pavements
### Moisture  
The readings of the moisture probes installed at the top of the granular base layer, middle of the granular base layer, and 10 cm below the subgrade were taken during the construction of the asphalt surface layer (SP 12.5). Data were collected every minute during construction. Lower moisture values represent a more saturated probe, which can provide information about the drainage capability of both fibreglass geogrid and geogrid composites.  
The water potential profile in the pavement structure based on each section and each layer is plotted in Figures 13 and 14. The measured water potential in the subgrade in all three sections is close to 0 (i.e., almost fully saturated) while the drainage behaviour can be observed in the geogrid composite section (Section 3). It can also be observed that the water potential in the subgrade in the geogrid composite is slightly higher, indicating a drier condition. At the middle of the base layer, the geogrid composite section generally shows a drier condition, with the control section showing a saturated condition. Lastly, at the top of the base layer, the geogrid composite is very much drier. The water potential readings in the fibreglass geogrid and control sections are close, with a slightly higher water potential in the fibreglass geogrid section. As the geogrid composite material serves as both a filter and separator, the water drains downwards more quickly, but it also prevents the upward migration of moisture and fine soils. As the same granular and asphalt material was used in all sections, the improved drainage behaviour can be attributed to the geogrid composite material installed in Section 3, thus, demonstrating the potential of the geogrid composite to mitigate the consequences of freeze-thaw cycles in the future. This observation can be confirmed when monitoring the water potential of the geogrid composite section during the colder winter months after construction.  
Figure 12: Asphalt strain gauge reading during surface course construction.
## 2 Definitions  
Let \(G_{n}^{d}=(V_{n}^{d},E_{n}^{d})\), or \(G=(V,E)\), denote the integer grid of size \(n\) in dimension \(d\). The coordinates of the vertices are from \(0\) to \(n-1\). For any vertex \(v\in V\) we denote its _closed neighborhood_ by \(\mathcal{N}(v):=\{w\in V\mid w=v\vee\{v,w\}\in E\}\). Paths \(\pi\) in \(G\) are parameterized by time. Vertex \(\pi(t+1)\) may be equal to \(\pi(t)\), or to one of its direct neighbors in \(G\). For a given set of \(k\) lion paths \(\pi_{j}\), let \(W(t)\) denote the set of vertices contaminated at time \(t\).  
That is, \(W(0)\) contains all vertices of \(G\) except the lions' start positions, and \(W(t+1)\) consists of all vertices \(p\
ot\in\{\pi_{1}(t+1),\ldots,\pi_{k}(t+1)\}\) that  
* belong to \(W(t)\), or
* have a direct neighbor \(q\) in \(W(t)\) that is not visited at time \(t+1\) by a lion coming directly from \(p\).  
**Clearing question in \(G_{n}^{d}\)**. Given \(k\) lion paths over \(\{0,\ldots,T\}\) in \(G_{n}^{d}\), does there exist a time \(t\in\{0,\ldots,T\}\) such that \(W(t)=\emptyset\)? We say that \(G_{n}^{d}\) has _clearing number_\(k_{d}(n)\), if  
* for arbitrary large \(T\) and arbitrary \(k_{d}(n)-1\) lion paths, we have \(W(t)\
eq\emptyset\) for all \(t\in\{0,\ldots,T\}\), **and**
* there exist \(k_{d}(n)\) lion paths and a time \(t\) such that \(W(t)=\emptyset\).  
Let us now consider \(k\) arbitrary paths \(\pi_{1},\ldots,\pi_{k}:\{0,\ldots,T\}\to V\). If, for some time \(t\in\{0,\ldots,T\}\), we have \(v\
ot\in W(t)\), then we call the vertex \(v\)_cleared_ at time \(t\). Figure 1 shows an example. Let \(\mathcal{C}(t)\) denote the set of cleared vertices at time \(t\).  
As [PERSON] _et al._[6] observed for \(d=2\), it is easy to verify that  
\[k_{d}(n)\leq n^{d-1}\]  
holds, by sweeping the grid with a hyperplane manned with \(n^{d-1}\) lions.  
There are different types of cleared vertices depending on their neighborhood. A cleared vertex \(v\in\mathcal{C}(t)\) is a cleared _interior_ vertex if all of its neighbors are also cleared, i.e., \(\mathcal{N}(v)\subseteq\mathcal{C}(t)\). Otherwise it is a cleared _boundary_ vertex. More generally, for any vertex set \(C\subset[n]^{d}:=\{0,\ldots,n-1\}^{d}\) we define the set of _boundary vertices_ as \(\partial C:=\{v\in C\mid\mathcal{N}(v)\cap\overline{C}\
eq\emptyset\}\) where \(\overline{C}:=[n]^{d}\setminus C\) denotes the complement of \(C\).
# Resource Profiling and Performance Modeling for Distributed Scientific Computing Environments
## 4 Evaluation
### Quantitative Model Evaluation  
In this section, we discuss comparative evaluation results of AFOLR and AFMV schemes with classical linear regression methods and moving average technique. To illustrate quantitative accuracy, we use the _Root Mean Squared Error (RMSE)_ analysis. \(RMSE\) can be calculated using following Equation (17) where \(Y_{t}\) is the actual value at time \(t\), \(\hat{Y}_{t}\) is the predicted one at time \(t\), and \(p\) is the number of data points.  
\[RMSE=\sqrt{\frac{1}{p}\sum_{t=1}^{p}(Y_{t}-\hat{Y}_{t})^{2}} \tag{17}\]  
Figure 5 shows evaluation results of five prediction techniques including AFMV, AFOLR, MV, OLR, and standard linear regression LR (depicted as \"Static LR\") in termsof \(RMSE\). As we can see from the results, our proposed schemes clearly outperform well-known classical forecasting methods. Interestingly, AFMV shows better prediction accuracy than AFOLR since a relatively short interval \((w=5)\) is used for estimation. AFOLR is basically founded on online linear regression which requires more data over a longer period (i.e., larger \(w\)) to estimate the coefficient parameters. Nevertheless, both of our proposed methods generate smaller margin of errors compared to the classical regression and moving average techniques.  
In order to further investigate the accuracy of our proposed models, we also compared them with the most widely used linear regression and moving average models respectively. Figure 6a presents the comparative analysis of AFOLR model and conventional Linear Regression (Static LR). It shows that distances from actual data (depicted as \"Actual Core\") which means the errors of static LR are relatively high and almost move forward statically. On the other hand, our proposed AFOLR scheme can capture the recent trend and predict the data very close to the actual values (\"Actual Core\" in Figure 6a). This is because it is capable of adapting to recent fluctuations in computing resource availability. Therefore, the AFOLR model clearly outperforms the LR technique and demonstrates its usefulness in predicting even when the datasets are highly variable. We also compared the AFMV approach against a class of time series-based forecasting techniques such as the moving average. The Figure 6b shows a comparison of our AFMV and moving average. Similar to the results of AFOLR in Figure 6a, it is clear that the AFMV model outperforms the traditional moving average, despite the fact that moving average is a well-known approach for predicting in the short term from highly fluctuating data.  
Figure 5: Root Mean Squared Errors of AFMV, AFOLR, MV, OLR, and Standard Linear Regression.  
Figure 6: Sensitivity Analysis: (**a**) AFOLR vs. Traditional Linear Regression Technique (Static LR) and (**b**) AFMV vs. Traditional Moving Average.