esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Overview of Supplementary Damping Systems
### Classification of Damping Devices
#### 2.1.1 Classification by Energy Dissipation Mechanism  
(a) Velocity-dependent devices:  
Velocity-dependent devices, notably viscous and viscoelastic dampers, function by responding to the relative velocity between different structural elements. Viscous dampers employ the principles of fluid dynamics to dissipate energy, utilizing specially formulated fluids that flow through meticulously designed orifices. Their force output is directly proportional to the velocity, rendering them especially efficient during rapid seismic activities. In contrast, viscoelastic dampers integrate both viscous and elastic characteristics, allowing for energy dissipation over a wider spectrum of movement speeds while also contributing a degree of restoring force to the structure.  
(b) Displacement-dependent devices:  
Devices that depend on displacement function by assessing the relative motion between structural elements. Friction dampers exploit mechanical friction through uniquely crafted interfaces to convert kinetic energy into heat energy. Typically, these systems deliver a uniform force output once activated, irrespective of the rate of movement. Metallic yield dampers are designed to absorb seismic energy through the plastic deformation of metals, primarily steel or lead, by enabling controlled yielding of specially engineered components.  
(c) Motion-activated devices:  
Devices that are activated by motion, such as tuned mass dampers (TMDs) and tuned liquid dampers (TLDs), operate on varying principles. These systems employ supplementary mass, either in solid or liquid states, that shifts in response to the movement  
Figure 2: Supplementary damping systems classifications.  
of the structure. The movement of this mass is meticulously adjusted to counterbalance the main structural motion, effectively lessening vibration amplitudes. Typically, TMDs are made up of large masses that are mounted on springs and dampers, while TLDs rely on the sloshing action of liquids within uniquely designed containers.  
(d) Phase transformation devices:  
Shape memory alloy (SMA) dampers: The primary materials used in these dampers are Nickel-Titanium (Nitinol) alloys, characterized by two distinct crystal structures: austenite at higher temperatures and martensite at lower temperatures. When subjected to seismic activity, the shape memory alloy (SMA) undergoes a reversible phase change between these states, which allows it to effectively absorb considerable energy while still being able to return to its initial form.
# Accounting for Energy Cost When Designing Energy-Efficient Wireless Access Networks
## 3 Results and Discussion
### Results: Strategy 2 Take Action According to the Energy Price  
In this part, results obtained with Strategy 2 are presented. Simulations are performed setting the 50 th percentile of the hourly energy price during the considered week as the threshold of _Take action according to the energy price_. As shown in Figure 13, the introduction of the proposed strategy reduces cost by more than 52%, from \(\in\) 48.08-\(\in\) 22.68, since the energy consumption is lower (Figure 13). In fact, the total power used drops by more than 45% with respect to the reference scenario. Nevertheless, the average user coverage is reduced by less than 3% (Figure 13).  
Take Action According to the Energy Price: Threshold Effects  
In Figure 14a, the total energy cost is shown, varying the threshold used for the deactivation of the microcell base stations. The threshold parameter is investigated in terms of the percentile of the hourly energy price during the week. Increasing its value (increasing the percentile), the total cost increases as well, since the energy price is less often greater than it. Therefore, the energy consumption of the network increases and consequently the total energy cost. At the same time, the increase of the analyzed parameter acts in favor of the user coverage (Figure 14b), since base stations are less likely to be deactivated.  
In fact, with threshold values lower than the 30 th percentile, more than a 65% cost reduction can be achieved, but this results also in a user coverage drop of more than 3.42%. When the threshold is equal to the 70 th percentile, the total outgo drops by approximately 43% and the user coverage by 2.47%, as summarized in Table 2. With values higher than the 80 th percentile, the users coverage drop is limited to approximately 1.8%, but with cost reductions lower than 33%.  
Figure 13: Take action according to the energy price: total outgo, power consumption and average user coverage.
# Eye State Identification Based on Discrete Wavelet Transforms
## 5 Experimental Results
### Scheme 1: One Feature  
The experiments for this scheme were carried out using the ratio \(R\) as the only feature for eye state classification. In order to compare the different wavelet types, we employed overlapped windows of 10 s and a decision delay of \(D-d=2\) s.  
Table 2 shows the mean accuracy from all the subjects obtained for each wavelet type, for both eye states and using the data from one and two channels. The results achieved for CE are significantly higher than those achieved for OE, regardless of the wavelet type and the number of channels. In the case of CE, all the accuracies are above 86%, while in the OE case some drop to 71% and none of them exceed 86%. For most of the wavelets, using more channels does not imply an improvement in the performance, since similar results are obtained using one- or two-channel data. In addition, the number of filter coefficients for each mother wavelet is also shown. The number of operations for applying the multi-resolution analysis of the input signal is directly related to this filter length.  
From Table 2, we can see that coif4 offers the highest accuracy for OE and high results that exceed 91% for CE; thus, it could be the best choice for implementing the system. In this regard, for robustness analysis, Table 3 shows the accuracy obtained for each subject using coif4 as the mother wavelet. The results follow the same pattern described before, where CE offers better classification accuracy than OE and similar results are achieved using one or two sensor data. All subjects except one (Subject 5) show accuracies above 80% for any condition and even some of them, such as Subjects 1, 3 and 6, present results higher than 89%.  
Figure 5: User’s experiment flowchart.  
A second set of experiments was conducted in order to determine the performance of the system for short delay times, which is an important aspect when implementing BCI in real-life scenarios. Figure 6 shows the accuracy obtained for each subject and ocular state as a function of the window size. In these experiments, we considered a constant overlapped time slot \(d\) with a duration of the 80% of the window size \(D\). Therefore, the decision delay of the system will be 20% of \(D\), i.e., if \(D=1\,\)s the delay would be \(D-d=0.2\,\)s. It is apparent that there exists a trade-off between the window size and accuracy of the system, i.e., as window size increases the obtained accuracy improves and vice versa. For short window sizes the classifier offers low accuracies, especially for the OE case, where none of the subjects exceeds 75% with the shortest window size, \(D=1\,\)s, and one of them, Subject 5 (Figure 6e), shows an accuracy below 50%. Moreover, as presented in the previous results in Tables 2 and 3, similar accuracies are achieved for one and two-channel data.
## 4 Control Design  
### Trajectory Tracking Control  
Recently, the fractional order calculus has become an interesting topic and extensively used in control design [31; 32; 33]. In comparison with the conventional controllers based on integer order integrator and differentiator, the fractional order controller offers more degree of freedom which can be utilized to further improve the performance of the control system. The basic definitions and practical implementation of the fractional order calculus are provided in Appendix A in detail. In this research, a modified computed torque controller is proposed to enhance the tracking performance of the robotic orthosis, in the sense that the conventional integer order derivative is replaced by a fractional order one.  
\[\tau=\bar{D}(\theta)\begin{bmatrix}\ddot{\theta}^{*}+K_{d}D_{t}^{A}e+K_{p}e \end{bmatrix}+\bar{C}(\theta,\dot{\theta})\dot{\theta}+\bar{G}(\theta) \tag{13}\]In Equation (13), \(\theta^{*}\) is the reference trajectory, \(e=\theta^{*}-\theta\) is the tracking error, \(K_{c}\) and \(K_{d}\) are positive definite gain matrices. \(D_{t}^{a}e\) is the differential of fractional order \(\alpha\in(0,1)\) of \(e\). By substituting Equation (13) into (12), the dynamics of tracking error is  
\[\bar{D}(\theta)(\bar{e}+K_{d}D_{t}^{a}e+K_{p}e)=0 \tag{14}\]  
Since \(\bar{D}(\theta)\) is positive definite, the dynamics of the tracking error actually only depends on  
\[\bar{e}+K_{d}D_{t}^{a}e+K_{p}e=0 \tag{15}\]  
By adjusting \(K_{d}\), \(K_{p}\) and the additional fractional order \(\alpha\in(0,1)\), the required tracking performance can be achieved. Besides, the control law Equation (13) can be rewritten as  
\[\tau=\underbrace{\bar{D}(\theta)\dot{\theta}^{*}+\bar{C}(\theta,\dot{\theta}) \dot{\theta}+\bar{G}(\theta)}_{\tau_{ff}}+\underbrace{\bar{D}(\theta)(K_{d}D_ {t}^{a}e+K_{p}e)}_{\tau_{fb}} \tag{16}\]  
It can be seen from Equation (16) that the computed torque controller incorporates a feedforward and a feedback loops. In practice, the design matrices \(K_{d}\) and \(K_{p}\) may be chosen diagonal which results in a decoupled multi-variable linear system. Hence, the control system actually consists of three channels. The block diagram of each channel can be reconstructed as depicted in Figure 4. Particularly, the reference trajectory for the third channel, which shows the contribution of the antagonistic bi-articular muscles to the motion of the robot, is the sum of the hip and knee reference values \(\theta_{3}^{*}=\theta_{1}^{*}+\theta_{2}^{*}\).
# Internal Backpressure for Onboard Crospoint-Buffered Switches with Port Multiplexing
## 3 Design of Internal BP Architecture
### BP Mechanism
When a node experiences congestion, the mechanism promptly identifies and restricts traffic attempting to traverse that node. This approach efficiently addresses localized congestion while maintaining overall system performance. * _BP load balance_: Building up BP fine, this mechanism assigns maximum and minimum thresholds to each output queue. When a queue exceeds its maximum threshold, BP is applied to the corresponding input ports sending traffic to that queue, temporarily halting new data transmission. Conversely, when the queue length falls below the minimum threshold, the BP state is lifted, restoring normal data traffic. By dynamically adjusting its traffic handling strategy, this mechanism maximizes the utilization of output buffer space and achieves load balancing.
# Adaptive Compensation for Robotic Joint Failures Using Partially Observable Reinforcement Learning
## 3 Methodology
### Reinforcement Learning Framework  
#### 3.2.1 PPO Algorithm  
In this study, we leveraged the Proximal Policy Optimization (PPO) algorithm to train the Franka robot for robust tasks, enabling robust task completion even in the presence of joint malfunctions. PPO is an on-policy RL algorithm that balances ease of implementation with strong empirical performance. It achieves stable learning by optimizing a clipped surrogate objective, which prevents large, potentially destabilizing updates to the policy.  
For PPO, we employed two neural networks--actor and critic networks--as illustrated in Figure 1. The policy network outputs the robot's action (\(a_{t}\)), which represents the angles for each joint (\(\theta_{i}\)) of the robot given its current state (\(s_{t}\)). Meanwhile, the value network estimates the expected return \(V_{\phi}(s_{t})\) from a given state (\(s_{t}\)). Both networks share the same architecture comprising three fully connected layers. The hidden dimensions of each layer are 256, 128, and 64, respectively, followed by ReLU activations. The final layer's output is either the action or the expected return, depending on whether it is the actor or critic network, and it uses a Tanh activation function [43].
## 8 Technical and Ethical Considerations for Effective Implementation of 5G Security  
### Technical Considerations  
The primary technical considerations for effective implementation of 5G security involve architectural modifications, cryptographic techniques, and adaptive security protocols. The 5G network architecture is fundamentally different from previous generations, enabling more devices and facilitating data-driven applications. However, this complexity increases the vulnerable surface area. Robust cryptographic techniques are thus essential to ensure data integrity, confidentiality, and secure user authentication. Further, the dynamism of 5G networks necessitates adaptive security protocols that can adjust to varying levels of threats and vulnerabilities. Machine learning and deep learning technologies, as delineated in previous sections, offer promising avenues to bolster adaptive security mechanisms but bring their own set of challenges like training data quality, model interpretability, and computational cost.
## 3 Results and Discussion
### CMM Measurements  
The measurement of the coordinates of the samples was conducted, and the data obtained using the CMM device are given in Table 3. The height of the part is 6 cm, and its width is 3.5 cm. To illustrate an example of the measuring points, PNT43, PNT 44, and PNT 45, and PNT 46 are shown in Figure 7. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Reference**} & **ABS FDM Absolute** & \multirow{2}{*}{**ABS-Injection**} & **Injection—FDM** & **Injection—FDM** \\  & **Measurement (mm)** & & **Absolute Measurement (mm)** & **Deviation** & **Deviation** \\  & & & **(mm)** & **(mm)** \\ \hline POINT1 & 0.011 & 0.065 & 0.054 & 0.004 \\ POINT2 & \(-\)0.199 & \(-\)0.153 & 0.046 & 0.02 \\ POINT3 & \(-\)0.281 & \(-\)0.264 & 0.017 & 0.101 \\ POINT4 & \(-\)0.185 & \(-\)0.335 & \(-\)0.15 & 0.001 \\ POINT5 & \(-\)0.25 & \(-\)0.309 & \(-\)0.059 & 0.014 \\ POINT6 & \(-\)0.18 & \(-\)0.304 & \(-\)0.124 & \(-\)0.016 \\ POINT7 & \(-\)0.149 & \(-\)0.255 & \(-\)0.106 & 0.009 \\ POINT8 & \(-\)0.034 & \(-\)0.153 & \(-\)0.119 & \(-\)0.113 \\ POINT9 & \(-\)0.007 & \(-\)0.07 & \(-\)0.063 & \(-\)0.199 \\ POINT10 & \(-\)0.342 & \(-\)0.138 & 0.204 & 0.037 \\ POINT11 & \(-\)0.197 & 0.043 & 0.24 & 0.046 \\ POINT12 & \(-\)0.19 & 0.107 & 0.297 & 0.053 \\ POINT13 & \(-\)0.181 & \(-\)0.382 & \(-\)0.201 & \(-\)0.256 \\ POINT14 & \(-\)0.013 & 0.214 & 0.227 & 0.071 \\ POINT15 & \(-\)0.079 & 0.109 & 0.188 & 0.159 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: CMM measurements. Figure 6: Compression test results. Our findings indicate that the ABS sample produced by plastic injection molding demonstrated dimensional accuracy closest to the 3D model. Only one measurement point for this sample exceeded the accepted tolerance range of \(\pm 1\) mm. Overall, the plastic injection method yielded results that were consistently closest to the 3D model.
## 1 Introduction
However, it is difficult to verify the accuracy of the proposed models and to validate multiple-scale remote sensing products due to the inadequacy of the footprint information for the carbon flux towers. Challenges still exist in interpreting and incorporating footprint information into these Earth system models due to the lack of footprint information for carbon flux sites ([PERSON] et al., 2018; [PERSON] et al., 2018). Besides, the biases and uncertainty are still not clear when validating these Earth system models and multiple-scale remote sensing products due to a lack of footprint information and because most flux tower sites are located in more-or-less heterogeneous landscapes. In this context, studying the representativeness of the carbon flux data for typical land cover types in the Heihe River Basin, is important for benchmarking these models and multiple-scale remote sensing products in arid and semi-arid areas, which cover more than one-third of China ([PERSON] et al., 2018). The aims of this paper were to study the spatial representativeness of field GPP for several typical land cover types in the Heihe River Basin in China, and to analyze the influence factors related to the footprint of field GPP. The results of this paper provide information about the footprint of field GPPs in arid and semi-arid areas in Northwestern China, and reveal a number of impact factors related to the footprint distances of GPP from field eddy covariance systems at flux tower sites in the Heihe River Basin, China.
# A Review of the Potential Climate Change Impacts and Adaptation Options for European Viticulture
## 3 Atmospheric Influence on Grapevine Development
### Agro-Climatic Indices and Viticultural Zoning  
The aforementioned effects of climate and weather on grapevines have been described by several agro-climatic indices. They commonly provide closer relationships between the grapevine development and atmospheric conditions than individual atmospheric variables, such as monthly mean temperatures, radiation, or precipitation. These indices were developed to integrate the plant-atmosphere interactions, thus following the plant physiological development more closely. Further, they can be used to assess the suitability of a given region or site for viticulture, in general, or a specific variety in particular. Due to the strong link between temperature and grapevine development, the agro-climatic indices are frequently based on temperature, such as indices using simple growing degree-day concepts (temperature integration). As a classical example, [PERSON] and [PERSON] [31] developed the Winkler growing degree-day scale for a base temperature of 10 \({}^{\circ}\)C. Following a similar concept, after some modifications in the temperature summation during the growing season and including a coefficient for day length, the Huglin index [77] was later developed. [PERSON] et al. [78] applied a trapezoidal model approach to simulate grapevine phenology (UniPhen), on the basis of cumulative degree-days, using three threshold temperatures (10 \({}^{\circ}\)C--lower threshold; 20 \({}^{\circ}\)C--upper threshold; 30 \({}^{\circ}\)C--heat threshold). Other indices based on hourly temperature accumulation were more recently applied [30]. To put more emphasis on the temperature conditions during the ripening period, which may ultimately influence the wine style, the cool night index [2; 79], based on the mean minimum temperature in September (NH), was proposed. The dryness index is another very common index in viticultural zoning [68; 79], which is based on an estimation of the potential soil water balance. Hence, viticultural zoning using agro-climatic indices is common to assess the suitability of a given region for viticulture and wine production for specific grapevine varieties.
## 1 Introduction
Survey results also illustrate that, because of lacking relevant information, manual input can take up to two years to put MEP-related data into computerized maintenance management systems after the handover stage [4]. Worse still, associating components with correct data in various databases manually is an error-prone process. In addition, inadequate data integration is a frequently observed issue amongst building information modelers due to the differences in syntax, schema, or semantics [4]. The Industry  
Figure 1: Typical logic chain in the subsystem of HVAC. Foundation Classes (IFC) [9] standard, an open and object-oriented 3D vendor-neutral data file format ([PERSON] 2011), supports data sharing and exchange between the construction and O and M phases of the building lifecycle [10]. However, the logic information within MEP systems is not explicitly considered in the widely used IFC standard even though spatial topology between components can be presented in property sets according to the IFC schema. To solve these problems, this study proposes an automatic approach to generate the logic chain of the MEP system from BIMs with the help of pre-defined or user-defined identification rules. The rest of the paper is organized as follows. A literature review concerning both the spatial topological analysis and logical inference through domain query language is presented in Section 2. Afterwards, three key processes to automatically generate the MEP logic chain are discussed thoroughly in Section 3, including the parametric and nonparametric spatial topological analysis within MEP models, the pre-defined and user-defined identification rules of logic chain in MEP systems and the logic chain completion of MEP models. Section 4 contains a description of the application of the proposed approach to a real project. Finally, conclusion remarks are presented in Section 5.
# Securing SDN-Based IoT Group Communication
###### Abstract  
IoT group communication allows users to control multiple IoT devices simultaneously. A convenient method for implementing this communication paradigm is by leveraging software-defined networking (SDN) and allowing IoT endpoints to \"advertise\" the resources that can be accessed through group communication. In this paper, we propose a solution for securing this process by preventing IoT endpoints from advertising \"fake\" resources. We consider group communication using the constrained application protocol (CoAP), and we leverage Web of Things (WoT) Thing Description (TD) to enable resources' advertisement. In order to achieve our goal, we are using linked-data proofs. Additionally, we evaluate the application of zero-knowledge proofs (ZKPs) for hiding certain properties of a WoT-TD file.  
crawd management; software-defined networking; linked-data signatures; Web of Things; zero-knowledge proofs +
Footnote †: journal: _Future Internet_  
* We design an IoT onboarding process that ensures that only authorized IoT devices participate in a group;
* We leverage linked-data signatures to provide advertisement validity and proof of ownership;
* We extend our solution to support selective advertisement of resources using zero-knowledge proofs.  
The remainder of this paper is organized as follows: In Section 2, we introduce the technologies used as building blocks of our system. In Section 3, we detail the design of our solution, and in Section 4, we present its implementation and evaluation. We discuss related work in this area in Section 5, and we conclude our paper in Section 6.