esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
To date, most referenced studies applied experimental heel lift conditions with only heel inserts or flat/built in insoles, the interaction of arch-support and heel lift has yet to be tested and warrants further investigation. Considering that it is plausible to induce running injuries in occasional runners when running in shoes with higher heel lift or heel-toe drop [14; 29; 30; 31; 32; 33], it is believed to lower loading rate and thereby reduce the risk of running-related injuries [28; 34; 35]. Moreover, arch-support orthoses may provide support on the midfoot structure and thereby affect forefoot mechanics during push-off [36], but this was not investigated in previous biomechanical studies on the effectiveness of arch-support orthoses. The forefoot is commonly injured and can be an insidious comorbidity in lateral ankle strains and chronic ankle instability [37]. Both ankle sprains and chronic ankle instability are common musculoskeletal injuries that are a result of inversion injury during cutting and landing in various sports [37]. Forefoot impairment contributes to functional limitation and disability of individuals who experience lateral ankle sprain and chronic ankle instability [37]. Most arch-support orthoses are built with different contours of arch support (medial-lateral and rearfoot-midfoot offsets) to control flatfooted deformation [37]. The orthoses are suggested to influence midfoot kinematics possibly by minimizing arch motion during locomotion [38]. Most previous studies predominantly applied a single-segment foot model to evaluate primarily on rearfoot movements [39]. Furthermore, using shoe markers, instead of skin markers, for foot motion measurement do not reflect the actual foot motion inside the shoe [40]. It seems warranted to examine the combined effects of arch-support orthoses with varying heel lifts on forefoot joint kinematics and joint moment variables. Hence, the purpose of this study was to examine whether arch-support orthoses combined with various heel lifts (2-mm, 6-mm, and 10-mm) would influence GRF loading, spatiotemporal, sagittal, and frontal joint kinematics and joint moments using the skin marker set model. It is hypothesized that running in shoes with arch-support orthoses and lower heel lift can lead to smaller loading rate, stride frequency, larger stride length and contact time, and altered forefoot and rearfoot joint mechanics. Such information would aid coaches, physicians, and runners in understanding the effectiveness of foot orthoses with different heel lifts during running. The study results may also contribute to improving running shoes and training regimes to help prevent running-related injuries.
## 2 Related Work
### Existing Key Management Frameworks  
Lightweight Encryption and Authentication Protocol (LEAP) is a widely used key management framework that employs a public-key infrastructure (PKI) to generate, distribute, and revoke cryptographic keys. The LEAP protocol provides secure communication, but it has a high computational overhead for encryption and decryption, making it unsuitable for resource-constrained underwater sensor nodes [10]. [PERSON] et al. [11] present LEAP-based cryptographic framework for Controlled Area Network (CAN). Stream cipher for message encryption and key management mechanisms are used to protect the network from external attacks. The proposed framework solved the integrity and confidentiality problems but due stream cipher method the synchronization, high data rate and key distribution management are still addressable.  
Another key management framework is the pairwise key pre-distribution scheme (PKPS) which uses a pre-distribution of symmetric keys to ensure secure communication. PKPS distributes keys before deployment, and these keys are stored in the memory of the sensor nodes. However, PKPS suffers from scalability issues and requires many keys to be preloaded into the sensor nodes, making it unsuitable for large-scale UWSNs [12].  
Pairwise key pre-distribution creates problems in random deployments of sensors because of not knowing the locations in advance. The resiliency of node capture attacks and fast connectivity of sensor nodes, the authors presented a random small pool of key chains in the article [13].  
Several key management frameworks have been proposed in the literature, such as Hybrid Energy-Efficient Distributed clustering (HEED), Multimodal Scheme (MMS), and Quantum Key Distribution (QKD). QKD-based approaches are proposed for secure communication and according to the authors resist all known eavesdropping attacks. In terms of UWSNs quantum-based key distribution approaches can create complexities, vulnerabilities to noise interfaces and key management issues [14]. These frameworks use different approaches to ensure secure communication in UWSNs, such as clustering, multimodal sensing, and quantum cryptography. However, these frameworks have their limitations such as high processing overhead, limited scalability, and vulnerability to attacks [15]. Therefore, there is a need for a secure and lightweight key management framework that can overcome the limitations of existing frameworks and ensure the security of the network.
## 2 Experiment Details
### Data Acquisition with Smartphone
#### 2.1.5 Signal Quality  
It was identified through experimental investigations that the best way to collect video data without much motion is to let the subject hold the phone on their hand and then place their fingertip on the camera sensor. This introduces fewer motion interferences as people naturally hold a phone in this way. Other approaches were also attenuated that caused many movements of the finger from the subject during data collection. A little distortion may result in scattered plots and acquired signals may suffer from severe baseline variations. An example of such an event is shown in Figure 4. Videos were also captured with the flash on and off. PPG signals obtained with a flash were much cleaner than without flash, and the red channel yielded the best overall output (Supplementary Material Figure S5).  
Figure 3: Representative PPG signal extracted from the (Red) channel, (Green) channel, and (Blue) channel of two video frames of varying quality. The extracted PPG on the left side signal was observed to be more reliable compared to the right side.  
Even after careful considerations about the type of camera to be used and orientation of the placement of the fingertip, the acquired signals had certain varieties. This is evident in Figure 4, which provides three sample signals collected from different subjects. It can be seen that one of them is quite clean, another has few baseline variations, and the last one contains high-frequency noises and baseline variations. The accuracy of PPG signal is highly influenced by the motion artifacts resulting in inaccuracy in calculation of vital features. These motion artifacts, baseline wander, and high-frequency noise are due to the invariably interference of the contact between the smartphone camera and the finger, the voluntary or involuntary subject movement that includes respiration and extrusion between the finger and the smartphone camera. Therefore, it is crucial to develop a reliable series of preprocessing steps to enhance uniformity and reliability of PPG signal acquisitions.
## 3 Results and Discussion
In this study, a comparative analysis of the absorption properties at high-frequency region of various polymer composites based on epoxy resin and various fillers published in recent years was conducted (Table 3). It is noteworthy that the bandwidth of RL, a crucial factor for potential applications, was found to be one of the highest in (NiZn)\({}_{1-\mathrm{x}}\)Mn\({}_{\mathrm{x}}\)Fe\({}_{2}\)O\({}_{4}\)/CNT-ER. These results confirm that this type of composite is a very promising material for use as an electromagnetic wave-absorbing coating.
## 5 Development of the Models
### Analysis of Stationarity and Seasonality  
First, it was necessary to carry out an analysis to determine the stationarity and seasonality of the series using the following graphs obtained in GRETL (Figure 5).  
Figure 4: Data traffic measured in kbps for 200 s.  
Figure 5: PERIODOGRAM of the original series.  
The seasonal component (which is obtained based on the average number of lags between similar values) was deduced from the series periodogram, from where it can be confirmed that the series exhibited a cyclical behavior every 32 samples. The 200 analyzed samples were covered, which corresponded to omega = 0.03142, and from there the seasonal period was equivalent to 31.89.  
However, it was not possible to establish whether the series was stationary since a constant mean was not obtained over time, nor was an increasing or decreasing trend. However, in addition to the trend and variance analysis, it was also possible to perform iterated differentiations to observe which one had a lower standard deviation and, thus, determine the value of \"d\" [28]. Table 1 shows the variance values for different degrees of differentiation (d) of the original series.  
As can be seen, the variance began to increase after the second differentiation. Avoid over-differentiating the original series and eliminating valuable information that would be manifested in the autocorrelation function, since in a case of over-differentiation the autocorrelations become even more complicated to analyze, the model loses parsimony, the variance increases, and observations are lost. Therefore, it was determined that the series must be differentiated once.  
The seasonality of the series can be checked by observing its ACF (Autocorrelation function) [29] and PACF (Partial autocorrelation function) functions, which are shown in Figure 6.  
As can be seen, these functions yielded significant values in the delays close to 32 and other multiples of the frequency, which showed the periodic behavior of the series. Furthermore, as described in the following section, the order of the other parameters of each model was established from these graphs.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(\mathbf{d}\) & \(\mathbf{0}\) & \(\mathbf{1}\) & \(\mathbf{2}\) & \(\mathbf{3}\) \\ \hline Variance (s\({}^{2}\)) & 41440.7 & 70410.6 & 206997.7 & 681400.7 \\ \hline Typical deviation (s) & 203.57 & 265.35 & 454.97 & 825.47 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Variance and standard deviation for different values of d.  
Figure 6: ACF (Autocorrelation function) and PACF (Partial autocorrelation function).
## 2 Pointing Strategy and Model Establishment
### Dynamic Model
#### 2.2.4 Actuator Model  
For the telescope pointing mechanism, the selection of its drive device has a significant impact on the performance of the mechanism. According to the requirements of gravitational wave detection, the following restrictions are imposed on the drive device: (1) no electromagnetic noise is allowed, and (2) requirements for driving force, stroke, and precision must be met. Based on the above analysis, we chose a walking piezo actuator as the drive device for the pointing mechanism, as shown in Figure 5a. Figure 5b illustrates the working principle of the walking piezo actuator, where the motion displacement is formed by the alternating interaction of two pairs of piezoelectric legs with the drive rod.  
Figure 5: Walking piezo actuator. (**a**) Three-dimensional model of the piezoelectric actuator. (**b**) Schematic representation of the working principle of the piezoelectric actuator. (**c**) Equivalent model of the piezoelectric actuator.  
The expression for the driving torque of a telescope pointing system is as follows:  
\[\mathrm{M}_{\mathrm{T}}^{\mathrm{O}}=F_{\mathrm{d}}\times R, \tag{5}\]  
where \(R\) represents the moment arm, which refers to the distance between the actuator's point of action and the axis of rotation of the telescope pointing mechanism, and \(F_{\mathrm{d}}\) represents the driving force generated by the actuator. Figure 5c illustrates the equivalent model of the piezoelectric actuator, from which a mathematical model can be derived. Due to space limitations, this paper does not provide the specific derivation process [20].  
\[\left\{\begin{array}{l}F_{x}=K_{x}x_{\mathrm{p}}=K_{x}c_{\mathrm{p}x} \Delta U\\ F_{1}-2F_{x}=M\dot{x}_{r}+B\dot{x}_{r}+Kx_{r}\\ F_{1}=B_{\mathrm{c}}\big{(}\dot{x}_{r}-\dot{x}_{s}\big{)}+K_{\mathrm{c}}(x_{r}- x_{s})\\ F_{1}+F_{\mathrm{d}}=\mathrm{M}_{\mathrm{e}}\dot{x}_{s}+B_{\mathrm{e}}\dot{x}_{s}+K_{ \mathrm{e}}x_{s}\end{array}\right., \tag{6}\]  
where \(F_{x}\) is the piezoelectric electromotive force, \(F_{1}\) represents the external force applied to the piezoelectric actuator from the connection part, \(K_{x}\) is the equivalent spring coefficient, \(c_{\mathrm{p}x}\) is the voltage coefficient, \(\Delta U\) is the voltage difference, M, B, and K are the equivalent mass, damping, and stiffness in the horizontal direction of the piezoelectric actuator, \(K_{\mathrm{c}}\) and \(B_{\mathrm{c}}\) are the equivalent damping and stiffness of the connecting components, and M\({}_{\mathrm{s}}\), B\({}_{\mathrm{s}}\), and K\({}_{\mathrm{s}}\) are the equivalent mass, damping, and stiffness, respectively, of the mechanical stage in the horizontal direction.
## 2 Electrohydrodynamic Model
### EHD Thrust  
The force provided to a thruster device, named the thrust, results from the ejection of propellant from the engine exhaust into space and is the time rate of change of the momentum of the propellant. The thrust \(\mathbf{T}\) is defined as: \(\mathbf{T}=\dot{n}\mathbf{v}_{ex}\) where \(\dot{m}\) is the mass flow rate and \(\mathbf{v}_{ex}\) the exhaust propellant velocity. Then, the thrust is created by the expulsion of a propellant mass.  
The axial component of the steady-state mass flow velocity is \(v_{z}(r)\). An infinitesimal element of mass crossing in a time \(dt\) the circular area of the cathode outlet surface is \(dm=\rho_{f}[v_{z}(r)dt]dS\), where \(dS=2\pi rdr\), and \(dm/dt=2\pi\rho_{f}v_{z}(r)dr\). As the propulsion force through this surface element is \(dT=v_{z}(r)dm/dt\), the total thrust value delivered by the fluid passing is estimated by means of the following equation  
\[T=2\pi\rho_{f}\int\limits_{0}^{R}rv_{z}^{2}(r)dr \tag{13}\]  
where \(v_{z}(r)\) is the axial component of the steady-state velocity at the cathode's exit; \(r\) is the distance from the center of the thrust symmetry axis (\(r=0\)) to the cathode's wall, located at \(r=R\).
## 5 Pseudo-Dynamic Test Results and Analysis
### Maximum Seismic Response Load and Displacement  
Table 4 compares the pseudo-dynamic seismic response maximum load and maximum displacement results with the failure mode and the degree of seismic damage for the control specimen (PD-FR) under the seismic wave of 200 cm/s\({}^{2}\) and the PD-CCSS specimen under 200, 300, and 400 cm/s\({}^{2}\).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Specimens**} & **Input Seismic** & **Maximum** & **Maximum** & **Degree of Earthquake Damage * \\  & **Ground Motion** & **Strength** & **Displacement** & **[Failure Mode]** \\  & **(cm/s\({}^{2}\))** & **V\({}_{\text{U}}\) (kN)** & **\(\delta_{\text{u}}\) (mm)** & **[Failure Mode]** \\ \hline PD-FR & 200 & 272.5 & 70.5 & Collapse [Shear collapse] \\ \hline \multirow{4}{*}{PD-CCSS} & 200 & 488.6 & 10.5 & Light [Flexural crack] \\ \cline{2-5}  & 300 & 708.2 & 20.0 & Small [Flexural crack] \\ \cline{2-5}  & 400 & 864.9 & 35.2 & Moderate [Flexural-shear crack] \\ \hline \hline \end{tabular}  
* Earthquake damage degree was investigated based on the studies conducted by JBDPA [22] and [PERSON], [PERSON], and [PERSON] [23].  
\end{table}
Table 4: Comparison of the maximum response strength–displacement and degree of earthquake damage.  
Figure 13: Test results of the PD-CCSS specimen (400 cm/s\({}^{2}\), final).  
According to the test results of the unreinforced specimen (PD-FR), the seismic response of a maximum strength of 272.5 kN and a maximum displacement of 70.5 mm was observed under the seismic wave of 200 cm/s\({}^{2}\). The degree of seismic damage is expected to be at the collapse level according to the studies by [PERSON] [22] and [PERSON], [PERSON], and [PERSON] [23].  
Meanwhile, the PD-CCSS specimen showed a maximum strength of 488.6 kN and a maximum displacement of 10.6 mm under the seismic wave of 200 cm/s\({}^{2}\). It exhibited a maximum strength of 708.2 kN and a maximum displacement of 20.4 mm under the seismic wave of 300 cm/s\({}^{2}\) and a maximum strength of 864.9 kN and a maximum displacement of 35.2 mm under 400 cm/s\({}^{2}\). According to studies by JBDPA [22] and [PERSON], [PERSON], and [PERSON] [23], the degree of seismic damage to the CCSS-reinforced R/C framework specimens is expected to be light under the input ground motion of 200 cm/s\({}^{2}\), small under 300 cm/s\({}^{2}\), and moderate under 400 cm/s\({}^{2}\), indicating the high seismic strengthening effect of the developed CCSS method.
## 2 Continuous Formulation of Topology Optimization Problem Statements and Sensitivities
### Compliance Functional  
In the particular problem statement of compliance minimization, the objective function can be rewritten as:  
\[J(\rho,u(\rho))=\int_{\Omega}fu\ dx \tag{17}\]  
and its derivative in the direction \(w\in H^{1}_{0}(\Omega)\) is then:  
\[D_{u}J(\rho,u(\rho))w=\int_{\Omega}fw\ dx=l(w). \tag{18}\]  
Notice that, in the particular case of compliance-minimization problem statements, it is commonly assumed in the literature that the external loads do not depend on \(\rho\). Therefore:  
\[a(\rho,u,v)=l(v). \tag{19}\]  
In this particular case, the adjoint variable leads to the same expression shown in Equation (15), and introducing Equation (4), it is implied that:  
\[\begin{split} a(\rho,\lambda,w)=-D_{u}J(\rho,u(\rho))w,\quad \forall\ w\\ \Leftrightarrow-a(\rho,u,w)=-D_{u}J(\rho,u(\rho))=-l(w).\end{split} \tag{20}\]  
Note that Equation (20), which describes the adjoint problem of the compliance-minimization problem statement, returns the definition of the state problem, setting them equal to each other. Functionals that are \"self-adjoint\" lead to clear computational benefits since Equations (4) and (15) can be solved with the same computation, a single FEA in the case of the code provided.  
Since \(l(v)\) and \(l(w)\) do not depend on \(\rho\), the terms \(D_{\rho}J(\rho,u(\rho))\check{\rho}=D_{\rho}l(w)\check{\rho}=D_{\rho}l(v) \check{\rho}=0\), and thus, the derivative is just:  
\[DJ(\rho,u(\rho))\check{\rho}=-D_{\rho}a(\rho,u(\rho),u(\rho))\check{\rho}. \tag{21}\]  
Following the definition of the compliance, the derivative of the bilinear form is:  
\[D_{\rho}a(\rho,u(\rho),v)=\int_{\Omega}\bigtriangledown^{s}v\mathbb{C}^{\prime }(\rho)\bigtriangledown^{s}u\check{\rho}\ dx \tag{22}\]Finally, considering the stiffness penalization used, Equation (22) can be introduced into (21), obtaining:  
\[DJ(\rho,u(\rho))\tilde{\rho}=\int_{\Omega}\bigtriangledown^{s}u(P\rho^{P-1} \mathbb{C}_{0})\bigtriangledown^{s}u\tilde{\rho}\,dx=\int_{\Omega}g\tilde{\rho} \,dx \tag{23}\]  
where \(g\) is the gradient of the compliance objective function, thus obtained as \(g=\bigtriangledown^{s}u\)\((P\rho^{P-1}\mathbb{C}_{0})\bigtriangledown^{s}u\). For this particular case, the first three optimization steps can be rewritten as:  
* Find \(u(\rho)\in H^{1}_{0}(\Omega)\), the solution of: \(a(\rho,u(\rho),v)=l(v),\ \forall\ v\in H^{1}_{0}(\Omega)\);
* Take \(\lambda=-u\), since \(a(\rho,\lambda,w)=-D_{u}l(\rho,u(\rho))w=-l(v),\ \forall\ w\) is exactly the same problem as the first step;
* Compute: \(DJ(\rho,u(\rho))\tilde{\rho}=-a_{\rho}(\rho,u(\rho),u(\rho))\tilde{\rho}=\int _{\Omega}\bigtriangledown^{s}u(P\rho^{P-1}\mathbb{C}_{0})\bigtriangledown^{s} u\tilde{\rho}\,dx\).
## 4 Experiment and Analysis
### Parameter Settings  
The experimental parameters in this paper are set as follows, the dimension of item embedding and the training batch size are set to 100. Co-occurrence parameter \(\phi_{ij}\) of session interaction layer selection range is [0.0-0.9]. Use random initialization for each vector, with all parameters having a mean of 0 and a standard deviation of 0.1. All parameters are optimized using the cross-entropy loss function and the mini-batch Adam algorithm, where the initial learning rate is set to 0.001, the number of epochs is set to 30, and the decay will be 0.1 after every 3 epochs, using L2 regularization to prevent the algorithm from overfitting, L2 regularization coefficient selection range is [\(1\times 10^{-3}\), \(5\times 10^{-4}\), \(1\times 10^{-4}\), \(5\times 10^{-5}\), \(1\times 10^{-5}\)]. For a fair comparison, the dimension of the item embedding of the baseline algorithm is set to 100, and other basic hyperparameters follow what is shown in the baseline algorithm literature. The number of hidden units in both GRU4 REC and NARM is set to 100, the memory of CSRM is set to 100, the number of attention layers is 3 in FGNN, and the multi-head mechanism used is set to 8. The number of CoSAN neighbor sessions is set to 5.
# Envisaging a European Digital Building Renovation Logbook: Proposal of a Data Model
## 3 Results
### Energy and Water Performance, Renewable Energy, and SRI Information  
This category, made up of seven entities (Figure 11), is intended to store all the information related to the energy and water performance, clean energy production, and smart readiness of the building. The relationships between the entities are complex because each one has particular linkage needs. Relationships' details are described below, whereas all the attributes are presented in Appendix A.7.  
* In the \"DistrictHeatingCooling\" entity, it is expressed whether the building is connected to a district heating or cooling network. When the connection exists, the network is described, and the energy source is indicated. This entity is related to \"Building\" through a 1:N relationship, which indicates that each building can be connected to one district network, whereas each district heating or cooling network may serve several buildings.
* Similarly, in \"EnergyCommunity\", it is expressed whether the building is a part of an energy community, providing its description and energy source. The relationships of this table are identical to those of the previous one.
* \"WaterManagement\" collects data on water consumption within the building or unit and outline strategies for water savings and efficient management. This entity is linked to both \"Building\" and \"BuildingUnit\" in 1:1 relationships, enabling distinct information for each unit while also allowing an overall perspective of the building.
* \"EnergyPerformance\" stores all the data related to the energy performance of the building, including DHW, heating and cooling demands, lighting, GHG emissions, energy performance label and certificate, heated and cooled area and volume, and primary energy factors. This entity is related to \"Building\" and \"BuildingUnit\" by means of 1:1 relationships. This allows having different information and certificates for each of the units that make up the building and/or for the entire building.
* \"EMobility\" stores information on charging points for electrical vehicles, indicating whether chargers are installed or preinstalled in the building, as well as their quantity and description. The entity is linked to \"BuildingUnit\" through a 1:1 relationship.
* In the \"RenewEnergyProduction\" entity, information on the production of renewable energies at the building or building unit scale is stored, specifying the description of the technology, the energy source, the generated amount, and the storage possibilities. The entity is related to both \"Building\" and \"BuildingUnit\" tables by means of 1:N relationships, which implies that each building and building unit can have more than one renewable energy production system installed.
* In \"SmartReadiness\" entity, which is linked to \"Building\" and \"BuildingUnit\" through a 1:1 relationship, the SRI results are stored. Soon, when the SRI is further developed and implemented, this entity may be extended.