esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Simulation Results
### First Stage: One Day Evaluation
#### 6.1.4 First Order Low-Pass Filter (FOLPF) Method  
Simulation results for the FOLPF method are shown in Figure 14. It is noted that, although results were similar, there was a violation at t = 10:15 for T\({}_{\text{f}}\) = 450. A summary of the simulation results for the FOLPF method is shown in Table 6. The least degradation without having PRR violations occurred when T\({}_{\text{f}}=550\).  
Figure 13: Simulation results for the EMA method. (**a**) One-day data results showing the total output power (P\({}_{\text{out}}\)), the ESS output power (P\({}_{\text{ess}}\)), the PRR at the output (PRR\({}_{\text{out}}\)), and the state of charge (SOC). (**b**) Zoomed figures of the corresponding black squares.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Measurement** & \(\mathbf{\alpha}\) & \(\mathbf{\alpha=0.123}\) & \(\mathbf{\alpha=0.133}\) \\ \hline D(\%) & 0.0048 & 0.0053 & 0.0049 \\ \hline NC(80\%) & 0.4823 & 0.5315 & 0.4927 \\ \hline Violations (mins) & 13 & 0 & 1 \\ \hline SOC (end of the day) -SOC(ref) & 28.215 & 15.3441 & 6.2242 \\ \hline \(\sigma^{2}(\text{PRR}_{\text{out}})\) & 25.9067 & 5.3813 & 6.1433 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Summary of the simulation results for the EMA method.
# A Review on Upper Limb Rehabilitation Robots
## 3 Rehabilitation Robots and Their Classifications
### According to Robot Treatment Approaches  
Rehabilitation robots can be classified into two types, according to their treatment approaches. The first approach is continuous passive movement (CPM). CPM requires no volunteer effort performed by the patient where the limb is controlled and moved by the robot [26]. CPM treatment reduces muscle tone, which eventually improves the mobility of muscles, joints and tendons [7]. Furthermore, CPM activates the cortical area that has the corresponding sensorimotor and leads to an action similar to normal movement [7]. The second approach is active-assisted movement, where the robot requires a signal from the patient to perform the movement. This signal could be an electromyogram (EMG) and follows the patient's intention to move the limb [27]. Rehabilitation robots with active-assisted movement require voluntary effort from the patient and, consequently, provide significant motor improvement when they are compared to CPM rehabilitation [7]. Therefore, most of the current researchers focus on active-assisted rehabilitation using robots [7].  
The EMG-driven robots use an EMG signal an as \"on-off\" controller, which means that, once the patient voluntarily initiates the robot, the robot will repeat its action for a period of time. Then, the patient will be allowed another voluntary signal [28].
## 2 Data and Methods
### MODIS Land Products Subsets
#### 2.1.3 MOD17 GPP  
MOD17 is the 1 km 8-day average GPP and is based on a light use efficiency (LUE) model [31]:  
\[GPP=\varepsilon_{\rm max}*PAR*FPAR*f(VD)*f(Tmin) \tag{3}\]  
where \(\varepsilon_{\rm max}\) is the biome-specific maximum light use efficiency (g C/MJ), PAR is photosynthetically active radiation (MJ), and potential GPP is linearly scaled between 1 (no limitation) to 0 (no GPP) using biome-specific functions for Vapor Pressure Deficit (f(VPD)) and minimum temperature (f(Tmin)) [39]. FPAR is from MOD15 and includes filling of cloud-contaminated pixels, while meteorological inputs are from the Goddard Earth Observing System Model (GEOS) of the NASA Data Assimilation Office (DAO) [40]. In this study, MOD17 was used as a reference dataset since it had been well validated with flux tower data [31, 39].
# Comparison of Diagnostic Accuracy of Physical Examination and MRI in the Most Common Knee Injuries
## 3 Results  
The medial compartment was more prone to meniscal tears than the lateral compartment. In the study group, 65 patients were diagnosed with meniscal tears: 45 with medial meniscus, 17 with lateral meniscus and 3 patients with bilateral meniscal lesions. Meniscal lesions commonly coexisted with chondral lesions.  
ACL injuries were diagnosed in 42 patients, out of which 32 suffered from total tear and 10 patients presented subtotal injuries. The ACL injuries significantly correlated with medial meniscus tears (\(p=0.044\)), the medial compartment (\(p=0.03\)) and PFJ cartilage lesions (\(p=0.006\)). There have been no statistically significant differences in regard to sex (\(p=0.1\)) or dominant extremity (\(p=0.4\)). For the purpose of the study, only total tears of the ACL were correlated with the physical examination.
# Hyperspectral Python: HypPy
## 4 Discussion  
In addition to HypPy, several other packages offer functionality for processing hyperspectral images.  
To name a few, QUANTools was implemented using the ENVI/IDL programming language for the automatic detection of multiple absorption feature parameters [35]. However, to run QUANTools, a runtime version of ENVI/IDL is needed, and the QUANTools package offers limited functionality, similar to one specific tool in HypPy, which is called the wavelength mapper.  
PySptools is a Python module that implements spectral and hyperspectral algorithms [36]. However, this is a package or library, and you have to do coding before you can use it. In fact, HypPy uses psyptools for its linear unmixing classifier. Unlike psyptools, HypPy offers a user interface to the functionality.  
An additional noteworthy toolkit is known as spectral Python (SPy) [37]. SPy is a Python module designed for handling hyperspectral image data, featuring functions for tasks such as reading, displaying, manipulating, and classifying hyperspectral imagery. Notably, SPy is limited to interactive usage from the Python command prompt or through Python scripts, unlike HypPy.  
The Orfeo and EnMAPBox software packages are both powerful tools used in remote sensing and image processing applications [38; 39]. They are widely used in the remote sensing community and provide extensive capabilities for processing and analyzing satellite imagery. While these packages focus on the processing of satellite images, HypPy is also targeted at processing general hyperspectral images, such as proximal images recorded with hyperspectral cameras in the laboratory.  
Just like the Python language itself [6], HypPy was designed with simplicity rather than performance in mind [40]. Some operations in HypPy may be relatively slow. However, the modularity of the functions and tools can be used to build experimental programs or even prototypes of processing chains that run on any hardware.  
Regarding speed, future versions of Python may be faster. To name one ongoing project, the Mojo language is a superset of the Python language with the speed of the C language [41].
## 1 Introduction
### Contribution and Article Structure  
Throughout this paper, we employ commercially-available off-the-shelf equipment in a non-clinical setting to determine the user-specific QoE in binocular vision augmentation scenarios. Our approach resembles a practical conceptualization towards real-world implementations. The main contributions we describe in this paper are:  
1. A performance evaluation of predicting the QoE of individual human subjects in overall vision augmentation (augmented reality) settings based on EEG measurements,
2. An evaluation of how these data can be employed in future wearable device iterations through evaluations of potential complexity reductions and
3. A publicly-available dataset of human subject quality ratings at different media impairment levels with accompanying EEG measurements.  
The remainder of this paper is structured as follows. In the succeeding section, we review our general approach and methodology before presenting the underlying datasets in Section 3. We subsequently discuss the utilization of the datasets in an exemplary evaluation in Section 4 and describe the obtained results in Section 5 before we conclude in Section 6.
## 4 Simulation Results
### Beacon Losses and Link Errors  
The amount of beacons received from a next hop neighbor is shown in Figure 8. Only the beacons that are received when a node is associated to the next hop neighbor are considered, as a node can forward its data only after the association. Thus, the result indicates forwarding capacity of a node. TUTWSN is presented with 3 and 4 as the maximum number of successive beacon misses before a synchronization is considered lost. In ZigBee, the default value of 4 beacon misses was used.  
At low PERs, beacons are missed linearly according to the error rate. However, a high packet error rate increases the probability of missing several successive beacons, therefore causing a synchronization loss. On ZigBee, the synchronization is lost 2 and 8 times per hour on 30% and 40% PERs, respectively. On TUTWSN, the synchronization losses per hour are 5 and 12. After a synchronization loss, a node performs network scan and association procedures during which a node may not forward its data.  
Figure 7: Simulation topology. A link is drawn between nodes within communication range.  
Allowing more beacon misses before assuming a link break is beneficial when synchronization losses are caused only by link errors. However, in a practical network, beacon losses might be caused by malfunctioned neighbor node or node mobility. Therefore, it may not be feasible to increase catch attempts significantly.  
The frequency of synchronization losses is presented in Figure 9. At low PERs, synchronization losses are rare. However, when the PER is 20% or more, synchronization fails often.  
ZigBee and TUTWSN have similar behavior when the same number of beacon catch attempts is used. Still, on very high packet error rates (more than 40%) ZigBee receives less beacons than TUTWSN although it experiences slightly less synchronization losses. The behavior can be attributed to the complex association messaging of IEEE 802.15.4. During the association, a device (node) sends first an association message to a coordinator (cluster head). Then, the device requests for an association response with a data request. An association fails, if the transmission of any request or response fails. In comparison, TUTWSN proposes only one association request message, which is replied with an association response.
# Learning with Errors: A Lattice-Based Keystone of Post-Quantum Cryptography
## 3 Lattices
### Computational Lattice Problems and Complexity  
These lattice problems have all been extensively researched and they are of great importance as they are believed to be difficult aside from having very large approximate factors. A critical issue is finding an interesting, practical and useful basis of a lattice that, from a mathematical perspective, satisfies sufficiently strong features. In 1982, [PERSON], [PERSON] and [PERSON] proposed a polynomial time algorithm for basis reduction, LLL [26], which approximates, in small dimensions, the solution of the SVP. The LLL algorithm resolves lattice problems within exponential approximation factors \(\gamma(n)\).  
Various efforts have tried to improve the LLL algorithm; therefore, there are some variants of it in the literature [27]. Another famous lattice reduction algorithm is the blockwise Korkin-Zolotarev (BKZ) algorithm proposed by [PERSON] and [PERSON] in 1991 [28]. The BKZ algorithm is widely used, frequently found in software libraries and utilized in the majority of lattice record computations as well as cryptanalysis.  
The famous lattice problems, SVP, CVP and SIVP, cannot be solved in polynomial time, and the well-known algorithms that use polynomial factors \(poly(n)\), or even better, approximate factors, need superexponential \(2^{\Theta(n\log n)}\) time or exponential \(2^{\Theta(n)}\) space and time. An important fact is the existence of time-approximation tradeoffs that interpolate between these classes of outcomes to obtain \(\gamma=2^{k}\) approximation factors in \(2^{\Theta(n/k)}\) time, which symbolizes the most advanced quantum algorithms.  
Moreover, a cryptosystem whose security depends on the fact that no polynomial time algorithm can solve a given problem may become insecure if, for instance, a quasi-polynomial time solution for that problem is able to run quickly enough. So, in terms of complexity, various computational lattice problems are considered to be NP-hard and the cryptographic protocols that are based in lattice theory use polynomial approximation factors \(\gamma(n)\geq n\).
# ABORt: Acknowledgement-Based Opportunistic Routing Protocol for High Data Rate Multichannel WSNs
## 4 Simulations Environment and Performance Evaluation
### Packet Delivery Ratio  
We calculated the delivery ratio as the ratio between the number of received packets by the sink and the overall generated data packets in the network. Results presented on Figure 3 show that, in light data traffic scenarios, where each node generates one packet per second, the delivery ratios of all protocols are high (between 92% and 99%) and very close with a slight advantage for ABORt. Indeed, when the network is under-loaded, the medium is free most of the time, which results in collision-free transmissions. Thus, using multiple channels is not very useful and optimizing the routing protocol has very little impact. When traffic load is higher (generation of five or ten packets per second per  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameters** & **Values** \\ \hline Medium access & IEEE 802.15.4 CSMA/CA [23] \\ Number of channels & 16 \\ Duty-Cycle & Disabled \\ Radio model & MRM with random path loss \\ Packet queue size & 8 packets \\ Packet size & 50 bytes \\ Simulation surface & 100 \(\times\) 100 m\({}^{2}\) \\ Topology & Random mesh \\ Number of nodes & 10, 20, 40 and 80 \\ Packet generation rate & \(\{1,5,10\}\) packets/s/node \\ Number of repetitions & 10 iterations \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.  
node), the advantage of ABORt becomes more important as shown in Figures 4 and 5. The low delivery ratio of RPL and CoLBA is mainly due to high contention, interference and collision, leading to packet loss in a single channel shared medium. The delivery ratio of CoLBA outperforms RPL and the gap increases with the packet generation rate. Indeed, CoLBA is based on a load balancing routing and queue overflow avoidance that helps to mitigate packet loss due to queue overflow in a high traffic load scenario from which RPL suffers. The delivery ratio gap between ABORt and M-HopCount varies between 6 and 20 points. The low delivery ratio of M-HopCount and M-HopCount-Sync compared to ABORt is due to their static routing metric. In a static network, the minimum number of hops from each node to the sink is static and the shortest path from each node towards the sink is always the same. Nodes always use the same next hop to forward their data packets. Thus, some nodes are overloaded, which provokes congestion, queue overflow and packet loss leading to low delivery ratio.  
Figure 4: Packet delivery ratio for 5 pkts/s/node.  
Figure 5: Packet delivery ratio for 10 pkts/s/node.  
Figure 3: Packet delivery ratio for 1 pkt/sec/node.
## 3 Adaptive Routing Optimization Algorithm
### Analysis of Node Relations  
In OppNets for mHealth, nodes are human-carried intelligent devices, and their movement and contacts are carried out by the carriers. Hence, they are heterogeneous and have social relationships and behaviors of people, which are used to analyze their mobility and predict their contacts for reliable message delivery.  
Figure 1: Schematic diagram of the network model that is used in our study.
## 1 Introduction
Although the thermo-mechanical analysis is a primary concern, it is not all-inclusive for the novel TPS design. When the TPS outer surfaces are exposed to high-speed airflow, the aerodynamic load generated would interact with both structural dynamics and heat transfer [30]. Therefore, the effects of the fluid-structure-thermal coupling on the novel TPS need to be verified in the design process as early and accurately as possible, which is hindered by the coupling complexity. Generally, wind tunnel tests on scaled models of the TPS have great difficulties in simulating fluid-structure-thermal coupling environments [31], while flight experiments on the TPS prototype are rather costly. Instead, most of verification work employs detailed finite element analysis using commercial software at the expense of time-consuming computation. The approaches mentioned above are not conducive to the TPS preliminary design process where combinations of different design variables are considered in order to learn parametric influence rules. The main purposes of the present paper are (1) to establish a layerwise finite element model of the HCS plates with the fluid-structure-thermal coupling effects, and (2) to investigate the structural behaviors of the HCS plates based on the proposed model with main concerns on the mechanical stability. The paper is organized as follows. Section 2 presents the modeling procedure with key equations derived. Section 3 displays the numerical results of the coupled fluid-structure-thermal analysis. Section 4 discusses results obtained from the parametric study. Conclusions are drawn in Section 5. It is found that mechanical instabilities can be induced by fluid-structure coupling and further accelerated by thermal effects. The impacts of design variables on dynamic instability and thermal buckling instability usually show opposite tendencies, and thus a tradeoff is required in the novel TPS design to solve these two instabilities simultaneously. The present accurate and efficient model, as well as the coupled fluid-structure-thermal analysis results, are applicable to the preliminary design for the novel TPS.
## 4 Practical Explanations and Implications
### Expectation of Broadcast Matrices  
As indicated by (10), (18), and (19), the suggested architecture depends on knowing the expectation of each user's matrix \(\mathbf{Q}_{a(u),\Omega(u)}^{(u)}\). A crucial question that arises is one regarding the practical computation of this matrix. In accordance with (10), it is possible to calculate \(\mathbf{Q}_{a(u),\Omega(u)}^{(u)}\) simply by replacing every matrix entry with its a posteriori expectation. When considering (1) and (2), the a posteriori expectation of the sent sample \(s_{z}^{(u)}(m)\) is written as  
\[\begin{array}{c}\mathrm{E}\left[s_{z}^{(u)}(m)\bigg{|}\mathbf{y}_{u}, \widehat{\alpha}(u,\zeta),\widehat{\Omega}(u,\zeta),\widehat{\mathbf{h}}^{(u) }(\zeta)\right]=\\ \\ \sum_{l=0}^{\mathcal{N}-1}\mathrm{E}\left[f_{z}^{(u)}(l)\bigg{|}\mathbf{y}_{u },\widehat{\alpha}(u,\zeta),\widehat{\Omega}(u,\zeta),\widehat{\mathbf{h}}^{( u)}(\zeta)\right]\exp(j2\pi ml/\mathcal{N}),\end{array} \tag{20}\]  
where  
\[\begin{array}{c}\mathrm{E}\left[f_{z}^{(u)}(l)\bigg{|}\mathbf{y}_{u}, \widehat{\alpha}(u,\zeta),\widehat{\Omega}(u,\zeta),\widehat{\mathbf{h}}^{(u) }(\zeta)\right]=\sum_{m=0}^{\langle\Phi(u)\rangle-1}\\ \\ \mathrm{E}\left[d_{z}^{(u)}(m)\bigg{|}\mathbf{y}_{u},\widehat{\alpha}(u,\zeta),\widehat{\Omega}(u,\zeta),\widehat{\mathbf{h}}^{(u)}(\zeta)\right]\exp(-j2 \pi ml/\langle\Phi(u)\rangle),\end{array} \tag{21}\]  
and  
\[\begin{array}{c}\mathrm{E}\left[d_{z}^{(u)}(m)\bigg{|}\mathbf{y}_{u}, \widehat{\alpha}(u,\zeta),\widehat{\Omega}(u,\zeta),\widehat{\mathbf{h}}^{(u) }(\zeta)\right]=\sum_{d_{z}^{(u)}(m)\in\Omega(u)}\\ \\ \widehat{d}_{z}^{(u)}(m)\mathrm{Pr}\left(\widehat{d}_{z}^{(u)}(m)\bigg{|} \mathbf{y}_{u},\widehat{\alpha}(u,\zeta),\widehat{\Omega}(u,\zeta),\widehat{ \mathbf{h}}^{(u)}(\zeta)\right).\end{array} \tag{22}\]  
As (22) states, the central concern is to evaluate the a posteriori probability of \(\mathrm{Pr}(\widehat{d}_{z}^{(u)}(m)|\mathbf{y}_{u},\widehat{\alpha}(u,\zeta ),\widehat{\mathbf{h}}^{(u)}(\zeta))\). Thankfully, the decoders of contemporary error-correcting codes are able to determine this probability while they are operating in an iterative manner [47; 52]. We take advantage of this likelihood to aid the suggested identification and estimation approach without adding unnecessary complexity to the decoding procedure. Figure 2 depicts the architectural block diagram of the intended approach.
## 3 Hypothesis Development and Research Model
### Technology Acceptance Factors and Psychological Experience Factor for Users
#### 3.2.2 Perceived Usefulness  
Perceived usefulness captures the extent to which consumers perceive themselves as effective and efficient in searching for and accessing the necessary information to evaluate and make purchasing decisions regarding cultural heritage products via AR [44]. AR technology can adeptly educate consumers on how to use a product displayed virtually, offering them a direct or near-direct experience with the product, thus enhancing their understanding of popular products. AR technology affects the perceived benefits of cultural heritage products for consumers, which enhances consumer engagement and psychological incentives and ultimately creates a willingness to consistently use AR applications and pay a premium [45].
# Peer-to-Peer Ultra-Wideband Localization for Hands-Free Control of a Human-Guided Smart Stroller
## 2 Methodology
### Control Method  
The control method and human-robot interaction are described in this section. We implemented a hands-free interface method to allow the parents to control the stroller's movement simply using their body movement. The control method is shown in Figure 3a, \(D\) denotes the destination of the stroller, which is located in the line of the \(HR\) with a length of \(l\) from \(H\). The distance error \(d-l\) and angle error \(\alpha\) are used to control the stroller. The stroller continuously attempts to move directly in front of the tracked person's current position and reach point \(D\) to maintain the distance \(l\) from the user. The user needs to perform distance-shift behavior to rotate the stroller towards the opposite direction like virtually pushing the stroller as shown in Figure 3b. Based on the estimated human position \(\hat{X_{h}}(x_{h},\hat{y_{h}})\) and stroller state \((x_{r},y_{r},\theta_{r})\), the estimated length of \(HR\)\(\hat{d}\) and angle from the stroller's centerline to \(RH\)\(\hat{a}\) could be calculated by  
\[\left\{\begin{array}{l}d_{t}=\sqrt{\left(x_{r}-\hat{x_{h}}\right)^{2}+\left( y_{r}-\hat{y_{h}}\right)^{2}}\\ \alpha_{t}=\operatorname{atan}2\left(y_{r}-\hat{y_{h}},x_{r}-\hat{x_{h}}\right) -\theta_{r}\\ \hat{d}=\sqrt{\left(d_{t}\cdot\sin\alpha_{t}\right)^{2}+\left(d_{t}\cdot\cos \alpha_{t}-L\right)^{2}}\\ \hat{a}=\operatorname{atan}2\left(d_{t}\cdot\sin\alpha_{t},d_{t}\cdot\cos \alpha_{t}-L\right)\end{array}\right. \tag{3}\]  
The linear velocity \(v\) and angular velocity \(w\) are controlled by Equations (4) and (5) separately.  
\[\left\{\begin{array}{l}v=k_{p}^{d}\cdot e_{d}+k_{d}^{d}\cdot\dot{e_{d}}&|e_{ d}|>\;th_{d}\\ v=0&|e_{d}|\leq\;th_{d}\end{array}\right. \tag{4}\]  
\[\left\{\begin{array}{l}w=k_{p}^{*}\cdot e_{\alpha}+k_{d}^{d}\cdot\dot{e_{ \alpha}}&|e_{a}|>\;th_{\alpha}\\ w=0&|e_{a}|\leq\;th_{\alpha}\end{array}\right. \tag{5}\]  
where \(e_{d}=\hat{d}-l\), \(e_{\alpha}=\hat{a}\), \([-th_{d},\;th_{d}]\), and \([-th_{\alpha},\;th_{\alpha}]\) are the velocity dead zone, which is used to reduce the stroller's sensitivity when the human is in still standing in place or making slight movements, such as turning one's body to talk to someone nearby.