esa-sceva/satcom-chunk-collection huggingface dataset:
# Reliable Application Layer Routing Using Decentralized Identifiers
## 2 Background  
### Decentralized Identifiers  
Decentralized identifiers (DIDs) are a new type of globally unique identifier, recently standardized by W3C. DIDs are designed to enable individuals and organizations to generate their own identifiers using systems they trust [9]. Due to their intriguing security and privacy properties, DIDs have been investigated as a security solution for many types of systems, including IoT-based ones (see, for example, [10; 11; 12]).  
A DID systems is akin to a key-value lookup architecture, where the key is the decentralized identifier (DID) and the value is a DID _document_. The actual contents of a DID document, as well as how it is resolved, depend on the implementation of the specific DID _method_.  
A DID is a string consisting of three parts: (1) the \"did\" URI scheme identifier, (2) the identifier for the DID method, and (3) the DID method-specific identifier [9]. An example of a DID for the \"example\" DID method follows.  
\[did:example:fddc3256\]  
A DID document may include, among other things, public keys (or \"pointers\" to public keys) that can be used as _verification methods_, e.g., an \"authentication\" key used for authenticating the DID \"owner\", or an \"assertion\" key used for verifying digital signatures generated by the DID owner. A DID document is usually maintained by a DID _registry_. A registry implements proper security and access control mechanisms. Registries allow the DID document _owner_ to manage their corresponding DID documents, as well as third parties to look up DID documents. Additionally, registries provide _proofs_ of correctness (e.g., a proof can be a digital signature generated by the registry). In a typical DID system, any entity can verify that a digital signature has been generated by the owner of a DID by following a two-step process: (a) retrieve the corresponding DID document, and (b) check if the digital signature can be verified with (one of) the assertion key(s) included in the DID document.  
In this work, a new DID method is defined, and it is used to protect routing advertisements. The proposed DID method relies on ICN for managing the corresponding DID documents.
# Automatic Outdoor Patrol Robot Based on Sensor Fusion and Face Recognition Methods
## 2 Materials and Methods
### Face Recognition  
This study uses a webcam to detect the intruder who appears on the patrol path. For people detection, the important information is head, face, skin color, and so on. The image processing methods include pattern recognition, feature description, human face detection, and convolutional neural network. To start with, the webcam captures the desired color of the target in RGB color space. RGB color space uses additive color by primary color to produce many colors, but RGB color is easily influenced by the light source. Various changes will occur due to outdoor ambient light sources. Thus, the RGB image is transformed to the HSV color space, the use of HSV color space is to reduce the impact of light sources. The HSV space is similar to human vision. By color segmentation [33] we can obtain the HSV color space for pattern recognition. Then, the face image is processed by the Haar-like method [34; 35; 36]. The second is Adaptive Boosting (AdaBoost) learning algorithm and boosted classifiers that can have a better success rate for detecting faces [37]. The convolutional neural network (CNN) image processing is applied to face recognition. It has been proven to have a very good recognition rate. However, when CNN involves the operational nature of deep learning, it must have a huge amount of computing resources and needs a very long training time to support the final results. This study attempted to achieve generally acceptable effect verification with CNN that is as simple as possible, maintain the face recognition rate that can be generally accepted under general conditions after simplifying CNN, and use less computational resources and training time [38]. These methods are described as follows.
# CT-RPL: Cluster Tree Based Routing Protocol to Maximize the Lifetime of Internet of Things
## 4 Results and Discussion
### Packet Loss Ratio  
The packet loss ratio shows the proportion of the number of packets received out of the total number of packets sent. It also indicates the reliability of the data from the participant to the DODAG root. Figure 5 depicts the packet loss ratio for the different network nodes. The packet loss ratio of RPL, E2 HRC-RPL, and CT-RPL are 15%, 10%, and 7%, respectively, for the network size of 100. It reveals that the packet loss ratio of CT-RPL increases with the increase in the size of the network node. It is owing to the consideration of the metrics _QU_, _ETX_, _RER_, and _CHNC_ during the route establishment. It also results in reducing the path breakages in the data transmission.  
The packet loss ratio for different network sizes is represented in Table 4. In this table, a comparison is made with the different routing protocols, namely RPL, E2 HRC-RPL, and CT-RPL. Table 4 indicates that the packet loss ratio is low in CT-RPL as compared to RPL and E2 HRC-RPL. The proposed CT-RPL reduces the packet loss ratio by half when the number of network size is 100.
## 2 Methods
### Decoder Architecture  
#### 2.2.1 Spiking Neural Networks  
Spiking neural networks [34] are networks that propagate information through discrete spikes, where the spike output is controlled by a membrane potential modelled on differential equations. They can be trained in three distinct ways:  
1. [label=(0)]
2. Conversion from ANNs to SNNs; however, this can cause issues, leading to the need to increase the time period for inference latency [35].
3. Using a biologically plausible approach called spike-timing-dependent plasticity (STDP) [36]. This, however, has exhibited problems in terms of performance.
4. Backpropagation through time (BPTT), which was the method we used. BPTT uses surrogate gradients in the backwards pass to allow for differentiability [34].  
For this method, we used two different models of neurons. The main model used in this study is the leaky integrate and fire (LIF) model:  
\[\text{U}[T+1]=\underset{decay}{\beta U[T]}+WX[T+1]-S[T]\underset{ reset}{U_{thr}} \tag{1}\]However, we also undertook a brief comparison with the Synaptic current-based LIF model:  
\[\begin{split} I_{syn}[T+1]=\alpha I_{syn[T]}+WX[T+1]\\ U[T+1]=\beta\underset{decay}{U[T]}+I_{syn}[T+1]-R[T]\\ \text{Spikes are governed by the following equation:}\\ S_{out}[T]=\frac{1\,if\,U[T]>U_{thr}\\ 0\end{split} \tag{2}\]  
where \(\alpha=\) the decay rate of the synaptic current; \(\beta=\) the decay rate of the membrane potential; \(U=\) membrane potential; \(I_{syn}=\) synaptic current; \(W=\) weights; \(T\) is a discrete variable representing the current timestep; \(X\) represents the data being passed to the neuron.
## 7 Different Spatial Node Distributions  
In this section, the main parameters of MAC and network layers for simulations of three different arrangements of nodes are described. Table 11 exhibits the parameters to simulate the three mentioned scenarios.  
These scenarios depend on the location of the coordinator node and the jammer node. In the first configuration, when the coordinator node is located at one corner of the topology, every node in the network forwards and sends traffic to the coordinator. Routes are made longer here, and there is interference in the middle of the topology (jammer node). In the second configuration, the non-uniform random distribution, the coordinator node is located in the center of the topology, and the remaining nodes are distributed along the area. Sixty percent of the nodes, including the jammer node, are located near the central part of the topology, and the rest of the nodes, 40%, are distributed in the remaining area. In the third configuration, all nodes, the jammer and the coordinator are uniformly distributed. Analyzed metrics are tested under reactive jamming. These information and performance metrics are shown in Tables 12-14. The metrics shown here are those that have been working along the paper. These metrics are retransmissions, CSMA retries, tries from the coordinator, achievable nodes by the coordinator and energy. The measurements were made per node, _i.e._, data reported in the tables are average numbers per node in 100 s of simulation time. Results exhibit that the MPH protocol has a better performance in all metrics with respect to the other three protocols.  
The MPH protocol contribution is clearly seen in this comparison of performance metrics under the jamming attack. The three described scenarios help to visualize the impact of a jammer node with different node configurations better. A concrete contribution of the MPH protocol is self-configuration (this feature is demonstrated by achievable nodes by the coordinator), which helps to create links rapidly that were broken and to avoid packet loss (this feature is demonstrated by retransmissions and CSMA  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Parameter** & **Value** \\ \hline Maximum data rate & 250 kbps \\ Discovery packet time & 10 s \\ Transmission power & 0 dBm \\ Receiver sensitivity & \(-85\) dBm \\ Average frame length & 22 Bytes \\ MAC layer & CSMA/CA \\ Maximum number of backoffs & 4 \\ Packet loss & 1\% \\ Coverage radio & 50 m \\ Mode & Active \\ Jamming & Reactive \\ Scenario & Static \\ Area & 150 m \(\times\) 150 m \\ \hline \hline \end{tabular}
\end{table}
Table 11: Parameters for the tested scenarios.  
retries). Besides, we obtain better performance with low energy consumption, as we can observe in the energy metric for the three scenarios.
# Developing a Building Fire Information Management System Based on 3D Object Visualization
## 2 Background  
Building fire safety should be managed as part of overall disaster control or mitigation measures and requires an integrated management system [9]. In particular, unlike daily-use convenience facilities, it is important to maintain fire protection facilities on a routine basis because it is difficult to detect any functional failure without the occurrence of an emergency [10]. Facility managers, who are generally responsible for supporting buildings and related services, manage information and knowledge (i.e., financial, human, and physical resources) for strategic building operations and maintenance [11]. Above all, records of O&M work history and technical documents, such as building drawings, guidelines, and manuals, are important assets for O&M work. Throughout the digitalization and computerization process, the availability and accessibility of information can be improved sharply [12]. The conventional computer-aided facility management (CAFM) involves documentation, information provision and classification, and monitoring functions related to O&M tasks [13]. The amalgamation of CAFM and BIM tools is advantageous in that it can identify the location of the building components and provide real-time information relevant to O&M via 3D object models containing large amounts of physical information. In emergencies, BIM-based facility management systems can access data in real time using BIM, enabling emergency responders to identify potential countermeasures and risk factors [14]. In spite of these advantages, in practice, two-dimensional (2D) computer-aided design (CAD) drawings, spreadsheets, or web-based information systems, all of which may store crucial information, are seldom interlinked in an effective way.  
In addition, even buildings with their own respective CAFMs use them in management tasks primarily to optimize the building's operation cost. Thus, the information preparation and management for various disaster response activities are somewhat incidental. The absence of required information reduces the speed of fire responses and threatens the safety of emergency responders. Therefore, to easily obtain relevant information for effective building fire response, firefighting, and disaster prevention-related work, maintenance information should be integrated and managed in the O&M stage. In addition, in terms of information usability, an instant information output is required for each building space and internal facility. To ensure reliable information management, it is necessary to verify the quality of each data set, and data standardization should be performed to enhance the quality of information management [15]. Therefore, the facility manager can benefit from computerizing the information required in the fire response and continuously updating the changes during the building's life cycle [16].
## 2 About the Chosen Digital Signature Algorithms
### Sphincs+
#### 2.3.1 Selected Parameters and Performance Metrics  
The architecture of Sphincs+ allows for a multitude of trade-offs between the speed of the algorithm and the size of the digital signature, even at a given security level. Table 4 presents six distinct sets of parameters that, in conjunction with the cycle counts provided in Figure 4, demonstrate how these trade-offs manifest in practice.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Bitsec** & **NIST Security Level** & **Sig Bytes** \\ \hline Sphincs+-128s & 133 & 1 & 7856 \\ Sphincs+-128f & 128 & 1 & 17,088 \\ Sphincs+-192s & 193 & 3 & 16,224 \\ Sphincs+-192f & 194 & 3 & 35,664 \\ Sphincs+-256s & 255 & 5 & 29,792 \\ Sphincs+-256f & 255 & 5 & 49,856 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Examples of parameter sets for Sphincs+ targeting different security levels and different trade-offs between size and speed [25].  
Figure 3: Key and signature size for Falcon and some competing algorithms [24].  
Figure 4 shows the algorithm's performance metrics (minimum and maximum values) when run on a 3.1 GHz Intel Xeon E3-1220 CPU (Haswell architecture), with 32 GB of memory. Optimized implementations for platforms compatible with the AVX2 instruction set are also available. Specifically, for Haraka, the availability of the AES-NI instruction set is particularly noteworthy. Each of six implementations has its version for:  
* One, three and five NIST security levels.
* Small and fast parameter sets.
* Simple and robust instantiations of the tweakable hash functions.
* Offering various trade-offs.  
Table 5 outlines the sizes of the public keys, secret keys, and digital signatures in bytes. Regarding memory utilization, the reference implementation generally leans toward minimal stack consumption.
# Online Learning Based Underwater Robotic Thruster Fault Detection
## 6 Experimental Results and Analysis
### Sea Trial Fault Detection Results and Analysis  
To better demonstrate performance of the proposed method, a sea trial fault detection experiment was conducted. A portable AUV is used for sea trial, as shown in Figure 16, specifications of the portable AUV and its thruster studied in the sea trial is described in Table 3.  
During the sea trial, the thruster system worked in a fault-free state initially, the portable AUV sailing in shallow sea water. Near the 2400 th sampling period, the AUV encounters weeds, some weeds are caught in the propeller, causing minor failures of the thruster. Then, control variable of the portable AUV reduced to zero to protect the propulsion system. When the thruster restarts, more weeds are entangled in the propeller. The thruster was blocked and caused a serious malfunction, as shown in Figure 18.  
Figure 17: Thruster control variable and propeller speed of the portable AUV.  
Figure 18: Thruster of the portable AUV blocked by weeds during the sea trial.  
Model residuals obtained by the proposed fault detection method during sea trial of the portable AUV is shown in Figure 19. As described above, thruster of the portable AUV worked in a fault-free state before the 2400 th sampling period, and the online learning-based fault detection algorithm did not make any false alarms. When the portable AUV encounters weeds near the 2400 th sampling period and causes a minor failure of the thruster, the online learning-based fault detection algorithm detected the occurrence of fault in the 2427 th sampling period. When the thruster was blocked near the 2870 th sampling period, the proposed algorithm detected a severe fault in the thruster.  
Model residuals of the fixed model-based fault detection method during sea trial of the portable AUV is shown in Figure 20. As mentioned before, parameters in the fixed model-based fault detection are the same as that uses in the proposed online learning model-based method. As can be seen in Figure 20, residuals of the fixed model are similar to the online learning-based model after the fixed model has just been established. After the 580 th sampling period, due to the change of thruster work status and the influence of environment, residuals of the fixed model exceed the threshold, resulting in a series of misdiagnosis.
## 3 Results and Discussion
### Tensile Properties  
Figure 11 shows the typical stress-strain curves for the AZ31 base metal and GTA welded joints prepared with different weld speeds (3 mm/s, 4 mm/s, and 5 mm/s). The tensile results are listed in Table 3. The weldments prepared with 5 mm/s welding speed (low heat input) exhibit the highest (YS: 160 Mpa, UTS: 215 Mpa, % El: 8) values compared with 4 mm/s (YS: 140 Mpa, UTS: 198 Mpa, % El: 6.5) and 3 mm/s welding speeds (YS: 120 Mpa, UTS: 165 Mpa, % El: 5), and this is attributed to the fine equiaxed dendritic grains and \(\beta\)-precipitates present in the fusion zone. As discussed earlier, lower temperature gradients, G, are observed in the weld pool at higher welding speeds (Figure 9). This results in increased constitutional supercooling, and assists the survival of more of the nuclei and the formation of fine equiaxed grains. However, all welding speeds exhibit lower strength and ductility values compared with the cold-worked base metal. The weld metal shows a typical dendritic microstructure containing eutectic \(\beta\)-Mg\({}_{17}\)Al\({}_{12}\) (intermetallic) particles, which is entirely different from cold-worked wrought base metal. It is worthwhile to note that the weldments display considerably lower strength and ductility values at lower welding speeds (high heat input) than at high welding speed and compared to the base metal. This is attributed to the presence of the coarse columnar dendrites at the edge of the fusion line, and equiaxed dendrites present at the centre of the weld. The tensile fracture surfaces of the welded joints were fabricated with high and low welding speeds (5 mm/s and 3 mm/s), and these are shown in Figure 12a,b, respectively. Figure 12a shows that ductile and very fine dimple fracture features in the AZ31 welded joint prepared with a high welding speed of 5 mm/s (low heat input). Ductile materials show fine dimples on the fracture surface. The welded joints prepared with low heat input welds have a higher  
Figure 9: Temperature measurements in the centre of the fusion zone for three different welding speeds (3 mm/s, 4 mm/s, and 5 mm/s).  
ductivity compared with high heat input welds. Figure 12b reveals the low ductility fracture surface of welds made with high heat input (3 mm/s).  
Figure 11: Typical tensile curves for AZ31 base metal and GTA welded joints made at different welded joints (3 mm/s, 4 mm/s, and 5 mm/s).  
Figure 10: Hardness measurements across the welds were prepared with different welding speeds (3 mm/s, 4 mm/s, and 5 mm/s).
## 3 Numerical Analysis of the Thermal Performance
### Approximation of the Temperature Fields
#### 3.2.1 Temperature Filed on the Rake Face  
Figure 4 shows the temperature field on the rake face, where different colors represented the temperature distribution in the CFX simulation process. To ensure proper fitting of the field, three straight line paths, which are denoted by \(L1,\ L2,\ L3\), were deployed to probe the temperatures on them. The temperature distributions along the three straight lines are shown in Figure 5. Figure 5 shows their temperature distribution have little difference. Considering the characteristics of the temperature distributions, negative exponential function is suitable to illustrate it.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Insert** & **Tool-Holder** & **Water** \\ \hline Density (g/cm\({}^{3}\)) & 15 & 7.85 & 1 \\ Thermal conductivity (W/(m\({}^{\circ}\)C)) & 46 & 60 & 0.6 \\ Specific heat capacity (J/(kg\({}^{\circ}\)C)) & 203 & 462 & 4200 \\ \hline Air temperature (\({}^{\circ}\)C) & & 20 & \\ Water temperature (\({}^{\circ}\)C) & & 20 & \\ Viscosity of water (kg/(m-s)) & & 0.001 & \\ Inlet velocity (mL/min) & & 80 & \\ Heat transfer coefficient (W/(m\({}^{2}\).\({}^{\circ}\)C)) & & 8 & \\ \hline Cutting width (mm) & & 0.1 & \\ Cutting depth (mm) & & 0.02, 0.1, 0.2, 0.5 & \\ Contact area temperature (\({}^{\circ}\)C) & & 100, 400, 700, 900, 1200 & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameters.  
Figure 3: Temperature field with internal cooling structure created inside the tool-holder.  
Orthogonal experiment is carried out to fit the simulated temperature field. As such, the temperature field on the rake face can be expressed as:  
\[\left\{\begin{array}{l}T_{r}=P_{1}e^{P_{2}\sqrt{x^{2}+y^{2}}}+(T_{c}-P_{1})e^{P _{3}\sqrt{x^{2}+y^{2}}}\\ P_{1}=0.9546T_{c}-14.5e^{-0.06356a_{p}}-0.07785T_{c}a_{p}\\ P_{2}=-6.208+18.46a_{p}-24.92a_{p}^{2}+11.46a_{p}^{3}\\ P_{3}=-0.0103-0.02213T_{c}-5.771\times 10^{-5}a_{p}+3.302\times 10^{-5}T_{c}a_{p} \end{array}\right. \tag{3}\]
## 4 Online Icing Prediction Model Based on _Robust STL-BTSF_ and IBWO-SVR
### Evaluation Index  
To verify the superiority of the proposed model, four evaluation indexes, namely the Mean Square Error (_MSE_), Root Mean Square Error (_RMSE_), Mean Absolute Error (_MAE_), and Mean Absolute Percentage Error (_MAPE_), are used for comparative analysis. The formula for each evaluation index is shown in Equations (24)-(27):  
\[MSE=\frac{1}{n}\sum_{i=1}^{n}\left(\hat{y}_{i}-y_{i}\right)^{2} \tag{24}\]  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}\left(\hat{y}_{i}-y_{i}\right)^{2}} \tag{25}\]  
\[MAE=\frac{1}{n}\sum_{i=1}^{n}\left|\hat{y}_{i}-y_{i}\right| \tag{26}\]  
Figure 5: The online prediction model process is based on _Robust STL\(-\)BTSF & IBWO\(-\)LSSVR.  
\[MAPE=\frac{1}{n}\sum_{i=1}^{n}\left|\frac{\dot{y}_{i}-y_{i}}{y_{i}}\right| \tag{27}\]  
where \(MSE\) denotes a measure of the degree of difference between the estimated and the estimated quantity; \(RMSE\) is the square root of the ratio of the square of the deviation of the predicted value from the true value to the ratio of the number of observations, \(n;MAE\) denotes the mean of the absolute value of the absolute deviation of the values of the various measurements; and \(MAPE\) is the difference between the predicted value and the true value divided by the true value. The smaller the value of the above assessment indicators, the better the model is.