esa-sceva/satcom-chunk-collection huggingface dataset:
# A Security Framework for the Internet of Things in the Future Internet Architecture
## 6 Delegation-Based Key Provisioning Protocol
### Proof of Concept
#### 6.8.3 Demo of the Prototype  
In this section, we present the demo output (shown in Figure 11) at Child, Guardian and Parent respectively. The demo was run on a single Linux machine so that we used different port number to differentiate the different communication channels. In practice, IP addresses and port numbers are usually used instead of purely port numbers.  
At the beginning, the Parent \(P\) configured itself as a server and opened a listening socket waiting for the Guardian \(G\). \(G\) played a dual role and thus configured itself as a server for the Child \(C\) and as a client of \(P\). \(C\) was configured as a client of \(G\). In the demo, we used the TCP socket provided by the mbed SSL library as the networking socket. In a practical IoT network deployment, this can be replaced by other networking sockets customized for IoT environments, e.g., DTLS [59]. Also, there is an additional Message 0 before the first message of the protocol to initiate the connection from the client (Child) to the server (Guardian).  
Below we briefly illustrates the message exchange flows shown in Figure 11.  
* **Step 1**: \(G\) initiates the protocol and sends the first message \(<\)_Guardian Request_\(>\) to \(C\).
* **Step 2**: \(C\) replies to \(G\) with \(<\)_Child Response_\(>\) indicating the identity of \(P\).
* **Step 3**: \(G\) sends \(<\)_Delegation Request_\(>\) to \(P\).
* **Step 4**: \(P\) performs SIGMA protocol and sends \(<\)_Authentication Challenge_\(>\) to \(G\).
* **Step 5**: \(G\) replies \(P\)'s challenge with \(<\)_Authentication Challenge Response_\(>\).
* **Step 6**: \(P\) grants two tokens to \(G\) in \(<\)_Contract_ (_TK1_, _TK2_) \(>\). \(P\) close the socket.
* **Step 7**: \(G\) forwards \(<\)_Contract_ (_TK2_) \(>\) to \(C\).  
Figure 11: A demonstration example illustrating the message flow and the session information printout at the Guardian, the Child and the Parent.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Cryptographic Parameter** & **Settings** \\ \hline symmetric key size & 32 bytes \\ key identifier size & 32 bytes \\ identifier size & 32 bytes \\ MAC tag size & 32 bytes \\ AES-GCM tag size & 16 bytes \\ SHA-256 digest size & 32 bytes \\ RSA key size & 2048 bits \\ Diffie-Hellman key size & 2048 bits \\ \hline \hline \end{tabular}
\end{table}
Table 3: Cryptographic Parameter Settings.  
* **Step 8**: \(C\) starts the final handshake to confirm the key agreement in \(<\)_Signed Contract_\(>\) with \(G\).
* **Step 9**: \(G\) finishes the handshake with \(<\)_Contract Review_\(>\) to confirm the key agreement with \(C\).
* **Step 10**: \(C\) confirms the success of the handshake and key agreement.  
At the end, we can see the key ID, which is derived from the session data, and the key material are consistent at both the Child and the Guardian.
# Regional Crop Gross Primary Productivity and Yield Estimation Using Fused Landsat-MODIS Data
## 3 Results
### Regional GPP Estimation over Montana
The seasonal variations and magnitudes of GPPM for barley were relatively close to the MODIS GPP record (\(r=0.87\); bias \(=-0.14\) g C m\({}^{-2}\) day\({}^{-1}\)). Significant differences in magnitude between GPPM and MODIS GPP occurred during the peak growing season (mid-June to August) for maize, peas and the three wheat species (e.g., Figure 4). However, these crop types all showed strong correlations between GPPM and MODIS GPP during the 2008 to 2015 growing seasons (\(r\geq 0.89\), Table 4). Moreover, the GPPM results showed the lowest correlations (\(r=0.38,p<0.05\)) and largest positive bias (\(2.69\) g C m\({}^{-2}\) day\({}^{-1}\)) with MODIS GPP for maize compared to the other six MT crop types. These results are consistent with a generally low LUE and productivity bias in the MODIS GPP record for croplands [17; 51; 52].
# Fuzzy MCDM Methodology for Analysis of Fibre Laser Cutting Process
## 2 Materials and Methods
### Experimental Research
\begin{table}
\begin{tabular}{c c c c c} \hline
**C** & **Mn** & **P** & **S** & **N** & **Cu** \\ \hline
0.17 & 1.40 & 0.035 & 0.035 & 0.012 & 0.55 \\ \hline \end{tabular}
\end{table} 
Table 1: Chemical composition of S235 steel (% maximum). \begin{table}
\begin{tabular}{c c c c c} \hline
**Material** & \begin{tabular}{c} **Minimum** \\ **Yield Strength** \\ **R\({}_{eH}\) [N/mm\({}^{2}\)]** \\ \end{tabular} & \
\begin{tabular}{c} **Tensile** \\ **Strength** \\ **R\({}_{m}\) [N/mm\({}^{2}\)]** \\ \end{tabular} &
\begin{tabular}{c} **Elongation at** \\ **Fracture** \\ **A\({}_{5}\) [\%]** \\ \end{tabular} & **Hardness HB** \\ \hline S235 & 235 & 360–510 & 26 & 120 \\ \hline \end{tabular}
\end{table} 
Table 2: Mechanical properties of S235 steel. After measurements, it was observed that in the covered experimental space, the mean kerf width on a straight cut ranged from 0.33 to 0.506 mm, kerf width on a curved cut from 0.353 to 0.503 mm, surface roughness from 3.56 to 40.55 \(\upmu\)m and surface productivity from 5.4 to 10.8 mm\({}^{2}\)/min. Although similar patterns for kerf width were observed, there was no experimental trial (alternative) with a particular combination of laser cutting parameter values which would result in minimal kerf width on both a straight and curved cut profile, minimal surface roughness and maximal surface productivity at the same time. As an example, alternative A3, conducted while focusing the laser beam at \(f=+1\) mm with the combination of a cutting speed of \(v=3.6\) m/min and oxygen pressure of \(p=0.85\) bar, is characterized by minimal mean kerf width values (0.33 mm on a straight cut and 0.353 mm on a curved cut) and maximal surface productivity (10.8 mm\({}^{2}\)/min), but at the same time results in the highest surface roughness (40.55 \(\upmu\)m). Therefore, a fuzzy MCDM methodology was proposed in order to consider expert opinions regarding relative criteria importance, handle uncertain attribute values related to the use of measurement systems for experimental data acquisition, rank alternative cutting conditions, determine the best possible trade-off solution and perform a more comprehensive analysis.
# Atomic, Molecular and Cluster Science with the Reaction Microscope Endstation at FLASH2
## 2 Experimental Setup
### IR Laser  
For XUV-IR pump-probe experiments, an 800 nm OPCPA (optical parametric chirped pulse amplification) IR-laser is operated by DESY. Pulse energies up to 500 \(\upmu\)J and pulse durations \(<\)15 fs can be delivered at a high repetition rate. The IR laser is synchronized to the pulse pattern of the FLASH and presently runs at a repetition rate of 100 kHz in 800 us bursts, repeating every 100 ms (further specifications can be found in Ref. [24]). The IR laser is coupled into the FEL beamline under 90 degrees by a silver-coated planar mirror. At this position, the FEL is focused down to less than 3 mm in diameter and passes through a 4 mm hole in this incoupling mirror. From this point on, the FEL and the IR beam travel collinearly. An IR-focussing lens with a focal length of 50 cm is placed outside the vacuum. Spatial overlap is set by means of a Ce:YAG-powder coated screen in the focal plane inside the REMI. The two foci of the FEL and the IR-laser on the screen can be observed with a CCD camera and magnifying optics [19]. Temporal overlap with nanosecond precision can be found with a fast photodiode downstream of the REMI. In order to exactly overlap the pulses in time, one uses a target with a well known pump-probe signal. The chosen target (e.g., helium, xenon) depends on the FEL  
Figure 1: Overview of the reaction microscope (REMI) endstation. The free-electron laser (FEL) is delivered from the left, while the infrared (IR) laser is delivered from the rear, crossing below the beamline. The IR is coupled into the beamline right before the REMI with a holey mirror. The HHG (high harmonic-generation) light is coupled by a grazing angle about 3 m upstream (indicated by the two red arrows).  
photon energy and usually involves a specific excitation and a following ionization step which is just possible for the right temporal order of FEL and IR.
# Simulation of Track-Soft Soil Interactions Using a Discrete Element Method
## 2 Material and Methods  
### Parameters Calibration of Scaled-Up Particles  
#### 2.1.1 JKR Model in the DEM  
The Johnson-Kendall-Roberts (JKR) contact model was developed from the Hertz-Mindlin contact model. The Hertz-Mindlin contact model is the basis of DEMs, and is accurate and efficient. Here, the Hertz contact model is applied for the calculation of normal forces [20], and the Mindlin contact model is applied for the calculation of tangential forces [21]. As a supplement, the damping component should be considered based on the damping coefficient and recovery coefficient [22]. Additionally, tangential friction and rolling friction should complied with the Mohr-Coulomb law and the contact-independent directional constant torque model [23].  
Based on the theory of the Hertz-Mindlin model, the load analysis for interactions among particles is derived as five categories: normal elastic force, normal damping force, tangential elastic force, tangential damping force and rolling friction. For soft soil with a high moisture content, the liquid bridge force has a great influence on the interaction between particles, which is ignored by the Hertz-Mindlin model. In the JKR model, the normal elastic force is developed and modified, with a comprehensive analysis of cohesive forces caused by the action of water. The JKR model is a method of contact simulation for cohesive particles, which can be used to simulate the cohesive effects among wet particles, as shown in Figure 1.  
In order to characterize the cohesion between particles, a new parameter of surface energy is introduced to calculate the cohesive forces. The equivalent JKR normal elastic force can be expressed as [24]:  
\[F_{JKR}=-4\sqrt{\pi\gamma E^{*}}\alpha^{\frac{3}{2}}+\frac{4E^{*}}{3R^{*}}\alpha^ {3}, \tag{1}\]  
\[\delta=\frac{\alpha^{2}}{R^{*}}-\sqrt{\frac{4\pi\gamma\alpha}{E^{*}}}, \tag{2}\]  
where \(E^{*}\) is the equivalent elastic modulus, \(R^{*}\) is the equivalent radius, \(\gamma\) is the surface energy, \(\delta\) is the overlap and \(\alpha\) is the interaction parameter.  
The equivalent elastic modulus and equivalent radius could be calculated with Hertz [20] model.  
\[E^{*}=\frac{\left(1-v_{i}^{2}\right)}{E_{i}}+\frac{\left(1-v_{j}^{2}\right)}{ E_{j}}, \tag{3}\]  
\[\frac{1}{R^{*}}=\frac{1}{R_{i}}+\frac{1}{R_{j}}, \tag{4}\]  
where \(E_{i}\) and \(E_{j}\) are the elastic moduli of two contacted particles; \(v_{i}\) and \(v_{j}\) are the Poisson ratios of two contact particles and \(R_{i}\) and \(R_{j}\) are the radii of two contacted particles.  
In the JKR contact model, even if the particles are not in contact, cohesion forces still occur between particles. The maximum clearance that produces non-zero cohesion force between particles can be expressed as [24]:  
\[\delta_{c}=\frac{\alpha_{c}^{2}}{R^{*}}-\sqrt{\frac{4\pi\gamma\alpha_{c}}{E^{* }}}, \tag{5}\]  
\[\alpha_{c}=\left[\frac{9\pi\gamma{R^{*}}^{2}}{2E^{*}}\bigg{(}\frac{3}{4}-\frac {1}{\sqrt{2}}\bigg{)}\right]^{\frac{1}{3}} \tag{6}\]
## 2 Model Development
### Reproduction Rendering  
The rendering of the immission signals for reproduction strongly depends on the type of reproduction system. For surround reproduction via multiple loudspeakers, techniques, such as Ambisonics [55,56] or amplitude panning (e.g., Vector Base Amplitude Panning (VBAP) [57] or Multiple-Direction Amplitude Panning (MDAP) [58]), are possible candidates. For binaural reproduction over headphones, generally, head-related transfer functions (HRTF) should be applied. In this paper, for simplicity, a simulation of the \"ORTF\" stereo technique [59,60] is used. If the listener is facing the road, this allows for a reproduction with sufficient accuracy via headphones and a stereo speaker set-up. The cardioid microphone pattern and the time difference between the left and right channel are modeled by:  
\[L(t^{\prime}) = 0.5\left(1+\cos(\theta-55^{\circ})\right)\cdot y(t^{\prime}+u) \tag{29}\] \[R(t^{\prime}) = 0.5\left(1+\cos(\theta+55^{\circ})\right)\cdot y(t^{\prime}) \tag{30}\]  
with the time-varying time difference:  
\[u=\frac{0.17\cdot\sin(\theta(t^{\prime}))}{c_{0}} \tag{31}\]  
The immission angle \(\theta(t^{\prime})\) has to be evaluated on the receiver axis (see Equation (21)). \(y(t^{\prime}+u)\) is calculated using a windowed sinc interpolation strategy according to Equation (24). As a consequence of this interpolation, high-frequency attenuation, as shown in Figure 5, and nonlinear distortions are introduced to channel \(L\).
# DPtracer: Integrating Log-Driven Accountability into Data Provision Networks
## 5 DPTracer Protocol
### Inspector  
The dispute process on a DPN is triggered when an erroneous result is detected among the provided results. This process is application-specific and there are varying criteria for determining node misbehavior based on the context.  
Despite these variations, a comprehensive historical record of previous data provision processes is universally required across all applications to identify the origins of incorrect data. As illustrated in Figure 5(c) and detailed in Algorithm 3, the inspector interacts with the logger to manage this process through the following operations:1. (Inspector) Query\((r,s)\rightarrow(r,s)\): The inspector requests a log entry of version \(s\) from round \(r\). When a dispute is initiated, the inspector traces data flow in reverse. Starting with the final version of the submitter for round \(r\), denoted as \(\mathsf{v}(N)\) (from \(C^{r}_{\mathsf{v}(N)}\)), this process is iteratively conducted based on the findings from the \(\mathsf{Detect}()\) operation.
2. (Logger) Response\((r,s)\rightarrow(R,\mathbb{V},\pi)\mid\varnothing\): Upon receiving the request, the logger locates the log entry for a given version \(s\) within Process Tree for round \(r\). If the log entry is found, the logger sends the \(R\) and \(\mathbb{V}\) from the stored log entries \(X^{r}_{s}\), along with its interval proof \(\pi\), generated as \(\pi^{r}_{s,\mathsf{v}(N)}\). This proof verifies that the log entry has not been tampered with based on the final commitment for round \(r\), \(C^{r}_{\mathsf{v}(N)}\). If the log entry is not found in Process Tree, the logger returns \(\varnothing\).
3. (Inspector) Detect\((R,\mathbb{V},\pi)\rightarrow\top\mid(r,s^{\prime})\mid\bot\): The inspector first verifies the integrity of the received \(R\) and \(\mathbb{V}\). The inspector retrieves the stored submission for round \(r\), which includes \(R^{r}_{N}\), \(\mathbb{V}^{r}_{N}\), and \(C^{r}_{\mathsf{v}(N)}\), and checks the received \(\pi^{r}_{s,\mathsf{v}(N)}\). The inspector confirms the inclusion of \(R\), \(\mathbb{V}\), \(R^{r}_{N}\), and \(\mathbb{V}^{r}_{N}\) in \(\pi^{r}_{s,\mathsf{v}(N)}\) and verifies that its computedcommitment matches \(C^{\prime}_{\forall(N)}\). If the log entry is validated, the inspector assesses node errors or misbehavior based on all retrieved log entries. The specific criteria for this assessment can vary depending on the application. If the inspector identifies an erroneous node, it returns \(\top\) and triggers further dispute steps, such as voting to determine the penalties, isolating the node, or slashing its deposit. If further tracking is required, the inspector selects the next target version \(s^{\prime}\) from the inputs of this version \(\mathbb{V}\) and executes \(\mathsf{Query}(r,s^{\prime})\) again. If the integrity of the log entry cannot be verified based on the proof and commitment or if there is no further node to traverse, the inspector returns \(\bot\). This result might involve alternative dispute resolution methods, such as additional manual inspections.
## 2 Related Work  
### Skeleton-Based Human Motion Prediction  
Skeleton-based human motion prediction has attracted increasing attention in various fields such as robotics, autonomous driving, human-computer interaction, and public video surveillance. Early approaches primarily relied on statistical and probabilistic methods, including nonlinear Markov models, Gaussian processes, and restricted Boltzmann machines [21]. These methods have proved effectiveness for predicting simple and periodic motions, like walking and running [22]. With the advent of RNNs and their capabilities for modeling sequential data, some RNN-based models generate future motions step-by-step by updating and propagating hidden states [9; 23]. However, a straightforward application of RNNs alone fails to capture the spatiotemporal relationships among joints.  
Since human skeleton sequences can be viewed as a series of isomorphic graphs interconnected in time domains, graph neural networks (GNNs) have been naturally applied to tasks such as action recognition and motion prediction [11; 24]. [PERSON] et al. [25] employed GCNs to adaptively learn the connectivity among joint nodes for modeling the spatial structure of the skeleton, and used a discrete cosine transformation (DCT) to predict human motions. [PERSON] et al. [18] proposed a learnable multiscale graph computational unit (MGCU) to extract features at a single scale and fuse features across multiple scales. Similar multiscale modeling approaches have been demonstrated by extensive experiments to be beneficial for human motion modeling.  
To address the limited modeling capabilities of fixed graphs, recent methods have attempted to construct more diverse graph structures to capture the complex spatiotemporal relationships among joints more thoroughly. [PERSON] et al. [17] proposed an adaptive multi-level hypergraph convolution network that comprehensively captures various dependencies among different human joints during motion, including local dependencies, high-level motion features, and long-distance dependencies. [PERSON] et al. [20] constructed three types of adjacency matrices--predefined, learnable, and adaptive--to aggregate the features of each node. The adaptive adjacency matrix indirectly influences the input features, thereby enabling adaptive adjustments for different actions. However, we argue that, regardless of whether the graph structure is predefined or adaptively learned from the input, these approaches all tackle the modeling problem from the perspective of human joint kinematics. While such methods may be highly effective for action recognition, they do not directly benefit motion prediction, as they omit dynamic information such as motion trends. To enable the learned spatio-temporal representations to directly influence motion prediction, we propose applying graph convolution to the intermediate features in the temporal domain, which allows for the aggregation of joint motion relationships along the time dimension.
# Modelling of Failure Behaviour of 3D-Printed Composite Parts
## 2 Materials and Methods  
### Materials  
The 3D-printed parts can be considered laminate structures since their behaviour is similar to that of traditional laminates [16]. Laminate behaviour in 3D-printed parts is mainly contributed by two factors: printing direction (raster angle) and materials used in 3D printing. Therefore, in this work, we investigated the effects of variation in the printing direction and materials on the nonlinear behaviour of the printed parts during failure.  
We considered two different commercially available filament materials for failure modelling of 3D-printed parts; the first one is a pure Acrylonitrile butadiene styrene (ABS) polymer, an isotropic material. The second material is a composite material that comprises 20% short Carbon fibres (sCF) in the ABS matrix; the materials data is available in [6; 16]. Further, two printing strategies were considered (See Table 1), one resembles the cross-ply lay-up architecture, and the other is the angle-ply lay-up architecture in 3D-printed parts.  
In cross-ply lay-up architecture, the printing direction is \(0^{\circ}\) and \(90^{\circ}\) in the subsequent layers, and in angle-ply architecture, the printing direction is \(45^{\circ}\) and \(-45^{\circ}\) in subsequent layers. 3D-printed parts considered in this work are shown in Figure 1. The printing direction, which is a raster angle, indicates the orientation of deposited material in each layer with respect to the length of the part. In our damage modelling, tensile loading is applied along the length of 3D-printed tensile coupons, which means that the raster angle is with respect to the loading direction. The above two printing strategies produce bidirectionally parts, which means that the subsequent layers in 3D-printed parts have different printing directions. On the other hand, unidirectionally 3D-printed parts have the same printing direction for all layers, for example, \(0^{\circ}\) raster angle for all layers.  
The 3D-printed parts' behaviour of the aforementioned materials and printing strategies was assessed experimentally in the works [6; 16]. Experimental data of unidirectionally 3D-printed parts are adopted in one of the computational models of the present work, and more details are discussed in the following section. Furthermore, experimental work on bidirectionally 3D-printed parts is utilized for validating the results obtained from the present computational damage modelling. The bidirectionally 3D-printed parts with printing strategies [\(0^{\circ}/90^{\circ}\)]2S and [\(45^{\circ}/-45^{\circ}\)]2S are discussed in the damage modelling.
## 2 Methods
### GPS PPP Observation Models
#### 2.1.4 Proposphere- and Ionosphere-Constrained Single-Frequency GPS PPP Observation Model  
If the external troposphere and ionospheric products are both used as virtual observations to constrain the observation equation, the following observation equation is obtained:  
\[\left[\begin{array}{c}p_{r}^{1}\\ l_{r}^{1}\\ \vdots\\ p_{r}^{m}\\ l_{r}^{m}\\ \Lambda\\ I_{r}\\ \Lambda^{1}\\ I_{r}\\ \vdots\\ \Lambda^{m}\\ I_{r}\end{array}\right]=\left[\begin{array}{cccc}-\mathbf{\mu}_{r}&\mathbf{I }_{t}&M_{w}&\mathbf{K}&\mathbf{R}\\ \mathbf{0}&\mathbf{0}&\mathbf{I}_{T}&\mathbf{0}&\mathbf{0}\\ \mathbf{0}&\mathbf{0}&\mathbf{0}&\mathbf{I}_{l}&\mathbf{0}\\ \end{array}\right]\left[\begin{array}{c}\mathbf{x}\\ \tilde{d}t_{r}\\ \tilde{\varepsilon}_{r}\\ \tilde{L}_{r}\\ \tilde{L}_{r}^{1}\\ \tilde{\mathbf{N}}_{r}\end{array}\right]+\left[\begin{array}{c}\varepsilon_{ r}\\ \varepsilon_{r}\\ \varepsilon_{r,prop}\\ \varepsilon_{r,ion}\end{array}\right],\quad\mathbf{Q}_{L},\quad\mathbf{Q}_{T}, \quad\mathbf{Q}_{l}\enspace. \tag{10}\]