esa-sceva/satcom-chunk-collection huggingface dataset:
# Fire Performance of Self-Tapping Screws in Tall Mass-Timber Buildings
## 3 Results and Discussion
### Effect of the Withdrawal Resistance on Self-Tapping Screws
The capacity of screw connections, N\({}_{r}\), loaded in shear by taking the critical component, is  
\[N_{r}=\text{N}^{\prime}\cdot\text{n}_{\text{F}}\cdot\text{n}_{\text{R}}\cdot \text{J}^{\prime}\cdot\text{K}^{\prime} \tag{5}\]  
where N\({}^{\prime}\) is the factored axial resistance of a shear-tension screw in main member (P\({}_{\text{rw},\alpha}\) cos(\(\beta\))), \(\beta\) is the angle between screw axis and line of the force, n\({}_{\text{F}}\) is the number of effective fasteners in a row (max \(\{n^{0.9};0.9\cdot\text{n}\}\)), n is the number of screws acting together in a row(2), n\({}_{\text{R}}\) is the number of screws acting together in a row(2), J\({}^{\prime}\) = J\({}_{\text{F}}\)]\(\lambda_{\text{c}}\), the composite modification factor for fastener installation (1) and K\({}^{\prime}\) = K\({}_{\text{D}}\) K\({}_{\text{SF}}\) K\({}_{\text{T}}\), the composite modification factor for connections. In an attempt to fairly compare the equations to the test data, some adjustments are required. The load-duration factor, K\({}_{\text{D}}\), was set to 1.15 in Equations (4) and (5) in accordance for short-term tests [8]. For a spruce-pine glulam, the oven dry density and relative density were 470 kg/m\({}^{3}\) and 0.47, respectively. Since the wood moisture content was 12%, the service condition factor (K\({}_{\text{SF}}\)) was set equal 1.0. The STS diameter and penetration length values were the applicable values from the tests. The withdrawal resistance equations are calculated as 31.3 kN, 17.8 kN, and 4.6 kN at ambient conditions, one and two hours of fire exposure, respectively. The decrement is 43% between ambient conditions and one hour of exposure, and 85% between ambient conditions and two hours of exposure for the \(\mathcal{O}\)8 mm STS connections, respectively. The comparison between the design methods and the experimental data did not showed the same decrement. Equations (4) and (5) significantly over-predict the withdrawal resistance of STS connections with greater decrement for longer exposures. Therefore, a more appropriate design equation is needed for the withdrawal resistance of STS connections, which include a better characterization of the penetration length of the fastener into timber and the steel behavior at elevated temperatures.
# A Study of LoRa: Long Range & Low Power Networks for the Internet of Things
## 4 The LoRaWAN Protocol
### End-Device Setup  
In order to participate in a LoRaWAN network, an end-device must be activated. LoRaWAN provided two ways to activate an end-device: Over-The-Air Activation (OTAA) and Activation By Personalization (ABP).  
The activation process should give the following information to an end-device:  
* End-device address (_DevAddr_): A 32-bit identifier of the end-device. Seven bits are used as the network identifier, and 25 bits are used as the network address of the end-device.
* Application identifier (_AppEUI_): A global application ID in the IEEE EUI64 address space that uniquely identifies the owner of the end-device.
* Network session key (_NwkKey_): A key used by the network server and the end-device to calculate and verify the message integrity code of all data messages to ensure data integrity.
* Application session key (_AppSKey_): A key used by the network server and end-device to encrypt and decrypt the payload field of data messages.  
For OTAA, a join procedure with a join-request and a join-accept message exchange is used for each new session. Based on the join-accept message, the end-devices are able to obtain the new session keys (_NwkKey_ and _AppSKey_). For the ABP, the two session keys are directly stored into the end-devices.
## 5 Design Overview
### Security Mechanisms  
Our security mechanisms are summarized in Figure 2. First, we propose a _two-tier authentication scheme_ based on patient biometric and physiological data, in order to handle spoofing or physical compromise of motes. We assume that each mote is connected to a small fingerprint scanner or a comparable biometric identification device. The base station first verifies the patient's identity via a biometric signature, then checks incoming data against the patient's profile for consistency. Our authentication scheme is resilient against spoofing and physical compromise of motes, because it is difficult for an attacker to both forge a valid patient's biometric signature and generate physiological data that is consistent with the patient's own data.  
Figure 2: Three security mechanisms.  
Second, _queries and data are encrypted and integrity protected using dynamically generated symmetric keys_, in order to defend against attacks that compromise the privacy and integrity of patient data. As mentioned earlier, motes are very resource-constrained, so it is more efficient to use pairwise _symmetric keys_ shared between the base station and motes for data protection.  
Third, we propose an _elliptic curve cryptography (ECC) based key agreement protocol_ to allow base stations and motes to securely derive symmetric keys without any prior shared secrets. In order to handle spoofing of base stations, motes are pre-configured with base stations' public keys so that they can establish symmetric keys only with valid base stations. If there are many base stations in the facility, we can use the _group public-key scheme_ proposed in [24]. This scheme allows each base station to have its own private key, while allowing the motes to use a _single_ public key for the entire base station group. In the next three sections, we present the specific design, implementation, and evaluation of each mechanism.
# An Efficient Framework Model for Optimizing Routing Performance in VANETs
## 5 Simulation and Performance Analysis
### Results and Discussion
#### 5.3.1 Packet Loss  
Figure 6 describes the impact of the proposed BOLSR-PSO configuration on the number of packets loss under different traffic loads. This result explains the advantage of the proposed strategy, which presents the relation between the size of packet loss and the increase in node density with load traffic. In low traffic loads, BOLSR-PSO shows a reasonable packet loss compared to the OLSR RFC-3626, OLSR-Gomez, and OLSR-RAND algorithms. The reason for this is that the proposed configuration is able to adjust the communication time until a link failure occurs, while the trade-off strategy works as a metric to balance the natural dynamic mobility and the efficiency of the routing functionality. The chart clearly shows that OLSR-PSO always has poorer performance than the other routing protocols under the most traffic loads, but with an increase in the traffic load and number of vehicles, it remains the same. Table 5 reflect the statistical results indicating a packet loss mean of 82,793, median 61,094, mode of 28, and the proposed algorithm achieved the best packet loss score of 28. Comparing the proposed algorithm to OLSR, BOLSR-PSO achieve a slightly better mean than  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameters** & **Value** \\ \hline Local Coefficient & 2 \\ Social Coefficient & 2 \\ Inertia Weight & 0.50 \\ Maximum iterations & 500 \\ Swarm size & 40 \\ \hline \hline \end{tabular}
\end{table}
Table 4: PSO Parameters Settings.  
OLSR. Since a higher packet loss implies a low packet delivery number, this makes the functionality of protocol generate additional packets leading to network conjunction especially in high density network.
# Three-Dimensional High-Energy Electron Radiography Method for Static Mesoscale Samples Diagnostics
## 3 The Start-to-End Simulation Studies of TDHEER Method
### The HEER Simulation Studies with Rotating Sample  
The important interaction processes between electrons and samples are multiple Coulomb scattering (MCS), ionization energy loss, radiative energy loss, and bremsstrahlung. The MCS, ionization energy loss, and straggling processes in electron radiography are very similar to those in proton radiography, an analogous process and well-understood radiography technique used around the world for thick object imaging. However, bremsstrahlung interactions are dominant in the formation of electron radiography when the electron beam energy is above the critical energy. The scattering angle distributions and the energy spectrum of the electron beam after passing through the target are important considerations for HEER [7]. Here we use the electron gamma shower (EGS) code [13] for the electron beam and sample interaction studies and PARMELA code [14] for particles tracking. EGS is a general-purpose package for the Monte Carlo simulation of the coupled transport of electrons and photons in an arbitrary geometry for particles with energies from a few keV up to several hundreds of GeV.  
The sample used in the simulation is specially designed for internal structures diagnostics. The sample is a spherical aluminum ball with radius 0.01 cm and a small spherical hollow ball with radius 0.003 cm placed inner at a defined position. The sample parameters are shown in Table 2 and the structures are shown in Figure 2a, the red one is aluminum and the blue one is hollow. With HEER, the samples would be reverse imaged both in the \(x\) and \(y\) plane on the detectors, which is shown in Figure 2b, where the green ball (two images) is the reverse image of the blue object ball (one object).
# An Analysis of Rock Bolt Dynamic Responses to Evaluate the Anchoring Degree of Fixation
###### Abstract  
Rock bolting in underground environments is used for different fundamental reasons, including suspending potentially loosened blocks, clamping small wedges together, inducing a protective pressure arch along the contour of excavated voids to improve the self-supporting capacity of the ground, and providing passive pressure in integrated support systems. In this study, we describe a testing procedure that was developed to investigate the grouted annulus of a rock bolt using a low-cost investigation method. This diagnostic technique was based on the dynamic response of the system, where mechanical vibrations were induced within the rock bolt and the response was recorded by using geophones/accelerometers on the protruding element of the bolt (the collar and head). The collected signal was then processed to estimate the spectral response, and the amplitude spectrum was analyzed to detect the resonance frequencies. A 3D finite element model of the rock bolt and grouting was established to simulate the quality of the coupling by varying the mechanical properties of the grouting. The model's response for the studied geometry of the rock bolt suggested that a poor quality of grouting was usually associated with flexural modes of vibration with a low resonance frequency. Good-quality grouting was associated with a frequency higher than 1400 Hz, where the axial vibration was mainly excited. Our analyses referred to short rock bolts, which are usually adopted in small tunnels. The interpretation of the experimental measurements assumed that the spectral response was significantly affected by the quality of the grouting, as demonstrated by the modeling procedure. The resonant frequency was compared with the results of the model simulation. The method was used to test the quality of rock bolts in a small experimental tunnel carved from andesite rock in Chile. Low-cost shock sensors (piezoelectric geophones) with low sensitivity but a wide frequency band were used. The main research outcome was the development of a reliable method to model the dynamic response of rock bolts in mines or for experimental applications in tunnels. Albeit limited to the current specific geometries, the modeling and testing will be adapted to other anchor/bolt options.  
rock bolts; grouting; modal analysis; support system; anchoring 2025
## 4 Case Study
### Control Group Design and Results
#### 4.2.3 Projection Angles  
The relative angle between the mass models and the projector were changed by rotating the models horizontally (see Figure 6). This session illustrates the influence of tangential distortion (keystoning) when the projected object has an angled surface. The projector was placed to throw the images more parallel to the front wall of the building model in a horizontal direction (as shown in the right side of Figure 6). When the building model was rotated horizontally to a certain angle (as presented in the left side of Figure 6), differences in projection could be detected. From the displays on the frontal wall of the building, the details seemed blurrier after rotation. Moreover, the relatively big difference caused by tangential distortion could be found in the flank wall, where the texture of the wall was projected much more clearly on the left-hand side in the figure.
# Application-Aware Network Traffic Management in MEC-Integrated Industrial Environments
###### Abstract
The rationale of eINP is to provide the network with additional capabilities for packet payload inspection and processing through edge computing, either on-premises or at MEC nodes. Although eINP is characterized by longer transmission and processing times than INP, it enables degrees of expressiveness that INP-enabled NDs cannot support at their current maturity stage. The overall infrastructure thus consists of a joint orchestration of eINP modules (which implement the computing capabilities, i.e., data aggregation, filtering, and encryption) across on- and off-premises ENs and SDN-enabled NDs for providing flow prioritization and steering. In our previous work, we first designed an SDN-based middleware for dynamic rerouting and per-flow traffic prioritization in spontaneous wireless mesh networks [6]. Then, we explored data formatting of industrial traffic based on a standardized and widely accepted syntax for subsequent payload inspection and processing [7]. Subsequently, we proposed the concept of eINP along with the SDN-eINP interplay [8]. Lastly, we investigated the feasibility of eINP modules deployed through a container-orchestration system in industrial environments where strong mobility is present [9]. This article, which relevantly and originally extends our previous work, specifically focuses on MEC-integrated industrial environments, where on- and off-premises ENs are used in a coordinated way for enabling application-aware network traffic management. In our previous papers, network traffic was delimited within the industrial boundaries (on a locally owned and managed infrastructure); here, by concentrating on MEC integration in future Internet industrial scenarios, given that the considered resources are not only outside the industrial boundaries but also possibly owned by third parties, the confidentiality of information becomes critical. Indeed, in addition to symmetric cryptography, this work originally proposes to apply homomorphic cryptography in this context and describes the related tradeoffs in terms of performance and confidentiality. The rest of the article is structured as follows. Section 2 introduces the related work. Section 3 discusses MEC-integrated industrial environments, focusing on the manufacturing sector. Section 4 discusses the drivers of the proposal. Section 5 outlines the main architectural components of the proposed solution. Section 6 provides implementation details, describes the testbed setup, and lays out performance results achieved on top of the implemented proof-of-concept prototype. Finally, Section 7 provides concluding remarks.
# On the Role of Matrix-Weights Elements in Consensus Algorithms for Multi-Agent Systems
## 2 Problem Formulation
### Diagonal Matrix Weights
#### 3.3.2 Positive Semi-Definite Matrix Weights  
When diagonal positive semi-definite (PSD) matrices constitute the matrix-weights in the weight set \(\mathcal{W}=\{W_{ij}:v_{j}v_{i}\in\mathcal{E}\}\), it implies that for any \(W_{ij}\
eq 0\), there exist \(k\) and \(l\) such that \(w_{kk}^{ij}=0\) and \(w_{ll}^{ij}\
eq 0\). In this case, the edge \(v_{j}^{[k]}v_{i}^{[k]}\) does not exist in the \(k\)th states between agents \(v_{i}\) and \(v_{j}\), which leads to \(\mathcal{C}^{k}\
eq\mathcal{C}^{l}\) in general.  
**Theorem 4**.: _Assume that the matrix weights are diagonal PSD matrices, then for any state \(k\). The following statements hold:_  
1. _If the_ \(k\)_-state graph is connected, there will be_ \(k\)_-global consensus (KGC)._
2. _If the_ \(k\)_-state graph is not connected, then there is a_ \(k\)_-cluster consensus (KCC). Moreover, the number of clusters of the states is determined by the number of connected components of the_ \(k\)_-state graph._  
Proof of Theorem 4.: For a diagonal PSD matrix weight, the \(k\)-state graph has no incoming links from other state graphs. Hence, there will be KGC if the \(k\)-state agents are connected and a KCC otherwise.  
Hence, by the choice of the diagonal entries of the matrix-weights as zero or non-zero, the graph structure of the states can be made different for different states.
## 2 Materials and Methods
### Method of Establishing the SSC Prediction Model  
The SSC prediction model based on hyperspectral images is key to the detection accuracy. Its main function is to accurately estimate the SSC of a single grape based on its spectral reflectance characteristics. The main process of the research using this model is as follows:  
1. Establish the input set: collect images in the 400 nm-1000 nm spectral band of grapes of different levels of maturity through the hyperspectral acquisition platform; perform black and white correction on the hyperspectral images to obtain the standard reflectance.
2. Establish the output set: detect the SSC of each grape that has completed hyperspectral imaging; select the RoI to obtain the average spectral data of each grape; take the SSC as the label value of the average spectral data.
3. Establish the modeling set: based on the input set and the output set, a modeling set and a test set are obtained for the establishment and evaluation of the SSC prediction model.
4. Establish the SSC prediction model: adopt different methods to select characteristic wavelengths and establish different models; evaluate different models using the test set to select the best model.
# Discovering Trends in the Digitalization of Shipping:
## 5 Conclusions
The consistent presence of the keyword \"service\" throughout the years points to a continuous focus on service delivery improvements, while the increasing prominence of \"management\" highlights an escalating concern with managing technology and cybersecurity risks. The analysis of these presentations also addressed those presentations discussing solutions as it might provide insight into the maturity of the topics. As such, \"Cybersecurity and risk management\" showed a strong presence of solutions in 2022, indicating a proactive stance towards addressing cybersecurity challenges. In contrast, \"Smart ports and supply chain management\" showed a declining presence of solutions, possibly reflecting a shift towards exploring new challenges or the implementation phase of previously discussed solutions. In terms of weak signals, the identification of \"subsea\", \"drone\", \"diesel\", and \"blockchain\" as weak signals pointed to nascent areas of interest that may become more prominent in the future. These areas represent potential innovations and shifts in the industry, such as the exploration of undersea technologies, the use of drones for surveillance or logistics, the focus on diesel alternatives or efficiency for environmental concerns, and the application of blockchain for secure and transparent transactions. Finally, an alternative approach leveraging supervised machine learning, trained on expert-derived insights from historical data, uncovered a cluster of trending topics within maritime operations. These prominent themes encompassed energy efficiency, Energy Consumption Data Information System (ECDIS) utilization, crew well-being, greenhouse gas emissions, and voyage data recording practices. Conversely, the analysis identified \"Virtual disaster recovery\" and \"ERP Projects\" as emerging weak signals, warranting further investigation due to their potential to significantly impact the maritime domain. Future research should therefore pivot towards critical areas such as Environmental, Social, and Governance (ESG) and sustainability, where the industry's digital transformation could have significant impacts. Additionally, exploring automation's role in enhancing operational efficiency, alongside the pivotal aspects of security and safety in digitalization, will provide valuable insights into the industry's progression towards a more sustainable and technologically advanced future. In conclusion, the findings from this study not only shed light on the current state of digitalization in the shipping industry but also chart a course for future research endeavors. By focusing on areas like ESG, sustainability, automation, and security, the industry can continue to harness the power of digital technology to address emerging challenges and capitalize on new opportunities.