esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
The experimental results of this paper show that the proposed system can serve as a very inexpensive, commercially available and easy to calibrate and operate point to point backhaul MMW wireless communication link for indoor and outdoor application. The suggested quasi-optical system can produce a strong signal with low losses depending on the distance between the transmitter and receiver, and the diameters of the dishes. The system can increase the product signal by improving the alignment of the dishes and improving the coating of the aluminum tapes to form a smoother surface. The proposed system can also be used as an inexpensive alternative for in-space satellite communications.  
Figure 4: The voltage values of the Arduino transmitted bits in TTL levels (top, yellow signal), the raw detected signal received at the GDD detector of size 295 mV pick to pick’ (bottom, blue signal) and the reconstructed signal of the transmitted bits in the receiver side at the comparator output in TTL levels (middle, green signal).  
Figure 5: The bit error rate performance for free space communication systems under various modulation method as a function of single to noise ration.
## 1 Introduction
To experimentally assess the impact of robotic exoskeleton usage on construction productivity, this study focuses on three representative construction activities: sack carrying [53], masonry bricklaying [54], and scaffolding [55; 56]. Each activity was performed under both low-intensity and high-intensity conditions, comparing productivity with and without a robotic exoskeleton. For example, in bag carrying tasks, the study compared transport on level ground (low-intensity) versus on an inclined surface (high-intensity). For masonry work, this study contrasted situations where bricks are positioned at hand-level (low-intensity) versus on the ground requiring repeated bending (high-intensity). For scaffolding work, this study examined clamp installation (low-intensity) versus frame installation (high-intensity). This study makes three primary contributions to evaluating the practical effectiveness of wearable robotic technology. First, it verifies whether robotic exoskeletons can increase work output within the same timeframe, empirically demonstrating that wearable robots can contribute to productivity improvement beyond merely reducing fatigue. Second, using IMU data, it analyzes how robotic exoskeleton usage affects workers' balanced maintenance and movement stability, showing that wearable robots can enhance physical stability [57; 58; 59; 60; 61]. Third, it compares the differential effects of robotic exoskeleton according to work intensity (low-intensity vs. high-intensity), assessing whether wearable robots are particularly effective for higher-intensity tasks. Through these contributions, this study aims to empirically demonstrate that wearable robotic technology can serve as a significant tool for enhancing actual construction productivity rather than merely functioning as a supplementary assistive device. Furthermore, the findings are expected to serve as foundational data for examining the applicability of wearable robots across various industries beyond construction, including logistics and manufacturing, where high-intensity physical work is required.
## 2 Materials and Methods
### Environmental Data  
Photosynthetic photon flux density (PPFD), air temperature and precipitation (input variables for simulation, see Section 2.5) are acquired in 49 m height on the eddy covariance tower. All these variables are sampled at a frequency of 0.05 Hz, aggregated on a 10 min basis and stored by a data logger (Campbell Scientific, Shepshed, UK). Soil water content at 40 cm was derived as an average from measurements in two spatially separated profiles, using horizontally placed sensors (see Table 1). Measured soil water content of the first simulation day (1 January 2015) was used to initialize the soil water content in the simulation (see Section 2.5.2 and Appendix A).  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Location: Hohes Holz (DE-HoH)** & **52°08′ N 11°22′ E** \\ \hline  & Inventory data & \\  & Biomass (tree carbon content) (Mg C ha\({}^{-1}\)) & 145 \\  & Basal area (m\({}^{2}\) ha\({}^{-1}\)) & 28.25 \\ Inventory 2018 & Mean stem diameter (dbh) (cm) & 32.4 \\  & Average stand height (crown tops) (m) & 23.5 \\  & Stand density (ha\({}^{-1}\)) & 260 \\  & Stand Age (a) * & 91 \\ \hline  & Climate data & \\ Climatological means & Mean daytime temperature (°C) & 10.4 \\
2015–2017 & Mean PPFD (\(\mu\)mol m\({}^{-2}\)s\({}^{-1}\)) & 559.9 \\  & Annual precipitation sum (mm a\({}^{-1}\)) & 516.8 \\ \hline  & Eddy Covariance estimates & \\ Carbon flux means & _GPP_ annual mean (Mg C ha\({}^{-1}\) a\({}^{-1}\)) & 19.4 \\
2015–2017 & _R\({}_{coo}\)_ annual mean (Mg C ha\({}^{-1}\) a\({}^{-1}\)) & 15.9 \\  & _NEP_ annual mean (Mg C ha\({}^{-1}\) a\({}^{-1}\)) & 3.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: General information about the study site Hohes Holz (DE-HoH) in Central Germany. Presented are forest inventory data from 2018 for the 1-ha intensive research area, climate data from 2015–2017 and carbon fluxes derived from EC measurements. Daytime was defined as the time when photosynthetic photon flux density (PPFD) > 20 \(\mu\)mol m\({}^{-2}\) s\({}^{-1}\) (night time was excluded). Climatic mean values were calculated as averages over 30 min measurements per day for 2015–2017. Eddy covariance mean values are the annual mean value of the daily sum of 30 min measurements for the years 2015–2017. * stand age according to forest management documentation.  
Soil properties were derived in several sampling campaigns and spatially distributed soil pits in the surrounding of the EC tower; averages from these for silt, clay and sand are 80%, 9% and 11%, respectively. According to [PERSON] (1993) [31], the soil is therefore defined as silt loam soil (see Appendix C).
## 3 Results and Discussion
### Protein Oxidation  
Carbonyl content showed the same behavior observed for MDA levels considering the storage period and the differences among all treatments (\(p<0.05\); Table 2).  
Protein oxidation follows a similar pathway to lipid oxidation generating a free-radical chain reaction through abstraction of a hydrogen atom from a protein, mainly from functional groups into the side chains of amino acids [33; 34]. Depending on the oxidized amino acid, there is the formation of carbonyl residues (e.g., oxidation of lysine, arginine and proline), cross-linking and sulfur-containing residues (e.g., oxidation of cysteine and methionine), which result in adverse effects on color and texture of meat products [5; 33]. In this way, the increase of carbonyl content occurs naturally during refrigerated storage of fish species, which is well known in trout fillets [35; 36] and agrees with our findings.  
The differences between treatments may be explained by the same reasons as those mentioned for lipid oxidation in association with the fact that protein and lipid oxidation are concomitant reactions [8; 28]. MDA from lipid oxidation can bind to amino acid residues into myoglobin and to some sites into myofibrillar proteins exposing it to the oxidizing environment and making it more susceptible to protein oxidation [29; 37]. On the other hand, protein oxidation releases large amounts of free iron, which are pro-oxidant agents for lipid oxidation [28; 37]. In the present study, a positive correlation was observed between protein and lipid oxidation (\(r=0.986\), \(p=0.000\)), which reinforces our findings.  
Although protein oxidation is recognized as a good indicator of fish quality, the isolated effect of UV-C radiation and, mainly of the O\({}_{2}\) scavenger on carbonyl content of fish species is scarce in the literature. Similar to our findings, UV-C doses at 0.10 and 0.30 J/cm\({}^{2}\) increased carbonyl levels in a dose-dependent manner in tilapia fillets stored at 4 \({}^{\circ}\)C [9; 13]. Likewise, O\({}_{2}\) scavenger decreased the formation of carbonyl groups in tilapia fillets stored under aerobic refrigerated conditions for 23 days [9]. Furthermore, protein oxidation induced by two UV-C doses (0.10 and 0.30 J/cm\({}^{2}\)) was equally suppressed by O\({}_{2}\) scavenger during refrigerated storage of tilapia fillets [9], which has lower oxidative potential than the target fish species of this study [2; 3; 13].
## 3 Tapped Delay Line Model
### A2G TDL Model  
As one of the most popular implementations of the WSSUS channel, the TDL model is adopted to simulate the A2G propagation channel, where the complex amplitude of individual channel taps varies over time [24]. The TDL channel model is a classical \"block fading\" model for efficient simulations in communication applications, like the COST-207 model. Thus, the CIR can be represented as \[h(t,\tau)=\sum_{i=1}^{M}c_{i}(t)\delta(\tau-\tau_{i}), \tag{3}\]  
where \(M\) is number of taps (to distinguish from the true number of paths \(N(t)\), we use \(M\) to denote the number of taps, which is a constant value within a given scenario), \(c_{i}(t)\) is the complex coefficient of the \(i\)th tap depending on time, and \(\tau_{i}\) is the delay of the \(i\)th tap. For a WSSUS channel, wide-sense stationary implies that large scale characteristics of the channel are rather constant over the considered time duration and the complex coefficient of the individual tap is generated independently. Figure 3 shows a simple representation of the TDL model. The number of taps depends on the dedicate system bandwidth and maximum propagation delay. The time variation of the complex channel tap is described by short-term fading process and Doppler spectrum [29]. In the following, we describe the model characteristics of a certain number of taps, such as the delay, amplitude distribution, K-factor and Doppler spectrum of individual taps, and the statistical channel model.
## 3 Results and Discussion
### Reliability Analysis  
The cumulative heat consumption is a meter feature, which can be treated as an equivalent of operating time. In article [22], we showed that the probability of failures follows a Weibull distribution. With the \(k\) parameter close to one, it is simply the exponential distribution:  
\[f(t)=\lambda e^{(-\lambda t)} \tag{1}\]  
It is characterized by the constant intensity of failures, i.e., \(\lambda=3.08*10^{-5}\). This means that failures occur as external random events and do not depend on usage time. Instead, they appear randomly with the fixed intensity. Such a feature is called a \"memoryless\" of the exponential distribution and implies that if we know that in moment \(x\) the element was fit for use, thus, counting from that moment, the fitness time had the same distribution as a distribution of a new element [34; 35; 36].  
It can be stated that during these ten years, the intensity of failures of the heat meter was weakly dependent on the usage time. It is a fundamental assumption since it enables the prediction of failures, which are independent of the history of operation. We did not have to possess information on how and when the equipment was used; if we know its state variables, it is entirely sufficient. The above conclusion, as well as correlation matrix suggested the selection of algorithms of machine learning that perform well in classification problems with non-linear separable classes.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Producer** & **Meter \#** & **Failure \#** & **\% of Failure** \\ \hline
1 & 2555 & 290 & 11.35\% \\
2 & 118 & 114 & 96.61\% \\
3 & 1674 & 281 & 16.79\% \\
4 & 671 & 303 & 45.16\% \\
5 & 713 & 187 & 26.23\% \\
6 & 823 & 345 & 41.92\% \\
7 & 1708 & 438 & 25.64\% \\
8 & 1494 & 603 & 40.36\% \\
9 & 1832 & 443 & 24.18\% \\
10 & 2026 & 332 & 16.39\% \\
11 & 1853 & 600 & 32.38\% \\
12 & 2440 & 234 & 9.59\% \\
13 & 3348 & 1859 & 55.53\% \\
14 & 3506 & 655 & 18.68\% \\
15 & 3977 & 2059 & 51.77\% \\
16 & 23,511 & 3042 & 12.94\% \\ \hline \end{tabular}
\end{table}
Table 3: Failures by producer in numbers.
# CS-FL: Cross-Zone Secure Federated Learning with Blockchain and a Credibility Mechanism
## 5 Experiments and Analysis
### Results and Analysis
#### 5.2.1 Defenses Against Local Poisons  
Figure 5 shows the model accuracy under different percentages of DPA nodes. CS-FL demonstrates superior ability in identifying and excluding malicious or low-quality ENs with significantly poor local data quality during FL training on two datasets. This is attributed to the innovative introduction of a pre-training phase in CS-FL, wherein a lightweight probe task effectively detects and eliminates such nodes. Similarly, Oort also possesses good assessment of the data quality on ENs, thereby exhibiting a comparable resistance to DPA attacks as CS-FL. However, FedAvg and FedCS suffer from a decrease in accuracy rate due to the lack of corresponding defense mechanisms.  
Figure 6 shows the model accuracy under different percentages of MPA nodes. Due to the SCI Incentive Mechanism of CS-FL, FL system can conduct posterior analysis on abnormal model parameters from malicious ENs and impose corresponding penalties on their SCI values, thus being less affected by MPA attacks. However, Oort only includes an evaluation phase for the utility of local data. As a result, its defense effectiveness rapidly declines in MPA scenarios as the number of MPA nodes increases.
# SASLedger: A Secured, Accelerated Scalable Storage Solution for Distributed Ledger Systems
## 3 Background
### Hyperledger Fabric  
#### 3.1.1 A Modular Pluggable Blockchain System  
Hyperledger Fabric is a popular open-sourced permissioned blockchain system. In a permissionless blockchain system, the fundamental components (e.g., consensus algorithm and block generation rules) must be pre-determined before the system starts to provide services, since each node in a blockchain system must obey the same rules. If a change is needed after the blockchain system begins to run, the so-called \"fork\" [37] operation is required. On the contrary, components in HLF are modular and pluggable. Provided the stakeholders reach a consensus, changes to the system can be easily realized. The nodes in HLF networks are divided by their different functions. The nodes that are responsible for saving blocks and answering query requests are called peers, some of the peers are also obtained to carry out the calculations defined in the transactions, these peers are called endorsers. The peers in the network always belong to different organizations: each organization represents one or several stakeholders in the real world. The nodes that are responsible for ordering the transactions and packing them into blocks are called orderers, it is recommended that each organization deploys one orderer to represent the organization. The orderers provide ordering services together follow certain consensus algorithms such as Raft [38] or PBFT [39]. The orderers are referred to as Ordering Service Nodes (OSN). Figure 1 illustrates the structure of a typical HLF network.
## 3 Methodology
### Instantiating Bounded and Unbounded Spaces  
#### 3.2.1 Challenges in Automating Clipping Conversion  
Depending on the type of clippings and the type of half spaces involved in a clipping entity, there are four subtypes of clipping, i.e., (a) one-clipping with bounded half space, (b) one-clipping with unbounded half space, (c) multi-clipping with bounded half space, and (d) multi-clipping with unbounded half space. Given the fact that it is already possible to automatically convert one-clippings and multi-clippings with bounded half space [24], the main challenge of automating the conversion comes from multi-clipping with unbounded half space (see Table 1), mainly due to the fact that it is impossible to represent an infinite space (unbounded half space) using B-Rep that can only enclose a finite space.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{2}{c}{**Automatic Conversion?**} \\ \cline{2-3}  & **Bounded Half Space** & **Unbounded Half Space** \\ \hline One-clipping & Yes & Yes \\ Multi-clipping & Yes & No \\ \hline \hline \end{tabular}
\end{table}
Table 1: Types of clippings and types of half spaces.  
Figure 5: Algorithm for processing clipping entity.  
To solve this problem, this study suggested assigning an imagined boundary to unbounded half space to create a spatially sufficiently large B-Rep. Then, the challenge was simplified to determine the size of the imagined boundary. This was solved by analyzing a number of IFC models to obtain the maximum dimension of building components. A B-Rep was considered to be sufficiently large in space as long as it was larger than all building components in any dimension (\(x\)-axis, \(y\)-axis, and \(z\)-axis).
# Review on BCI Virtual Rehabilitation and Remote Technology Based on EEG for Assistive Devices
## 5 Feature Extraction
### Feature Extraction Techniques for BCI According to the EEG Stimulation Source  
The EEG analysis for BCI implementation adds new criteria to perform the feature extraction process. Mainly, these criteria are based on neuronal activity detection considering either internal or external brain stimulation.  
If internal stimulation is analyzed, the (spontaneous) oscillatory activity is driving the feature extraction. In spontaneous systems, no external stimulation is required. Hence, BCI produces controlled actions based on the user mental activity.  
Common spatial patterns (CSPs) is a filtering method to extract features from spontaneous activity based on multi-channel EEG information [46]. This is an algorithm that generates characteristics for classification [42]. The CSP generates weights for linear mapping where the variance (power) is optimized. The extracted power (by means of FFT, wavelet, etc.) is used as a feature.  
In evoked systems, external stimulation is required, such as visual, auditory, or sensory stimulation. Therefore, feature extraction is enforced by the type and intensity of the external sources.  
Mainly, the feature extraction methods when EEG is enforced by evoked potentials are event driven techniques. Additionally, there exist the event-related potentials that can be obtained by averaging EEG signals over a specific event type. Complmentarily, event-related synchronization (ERS) and event-related desynchronization (ERD) are common methods for detecting power changes in EEG information (promoter and inhibitor). The first is the increase in the activity in a particular band, and the second evaluates its decrease. These changes can be triggered by motor imagery, motor activity, and stimulation of the senses [14].  
ERD/ERS produced by motor imagery cannot be classified in the evoked potentials. From [47] MI signals are of spontaneous type because they are generated based on a user's imagination of performing a task.
# Predicting User's Measurements without Manual Measuring: A Case on Sports Garment Applications
## 4 Discussion
### Relevance for Other Sectors  
In comparison to regular apparel that would be bought online, the stretchy characteristics of sports clothing and the fully customized production per client is the key difference. It is impossible to return a clothing item without having to reproduce this item at added cost. For regular online retail, the added benefit of optimizing the selection procedure could possibly save massive amounts of delivery trucks and returned goods. However, some adaptions will be needed, since the measurements used in this experiment might not at all be relevant for normal confection garments.  
The proposed methods and analysis would also fit other applications where tight fitting is required, such as pressure clothing used to treat skin burns. These pieces of clothing are made to size using special equipment to avoid having seams on the fabric. At the moment, this is an iterative process to adjust, but it is undesirable to remove the clothing too often in the first moments of treatment, because of the fragile skin during initial treatment. Having a method of predicting the measurements and shape of the person and required patterns could help to reduce manufacturing time and start treatment quicker after the burns without having to perform physical measurements on the patient.
# Jet Noise in Airframe Integration and Shielding
###### Abstract  
This paper reviews and presents new results on the effect of airframe integration and shielding on jet noise. Available experimental data on integration effects are analyzed. The available options for the computation of jet noise are discussed, and a practical numerical approach for the present topic is recommended. Here, it is demonstrated how a hybrid large eddy simulation--unsteady Reynolds-averaged Navier-Stokes approach can be implemented to simulate the effect of shielding on radiated jet noise. This approach provides results consistent with the experiment and suggests a framework for studying more complex geometries involving airframe integration effects.  
a 2020 10 51 [FOOTNOreflection of engine noise. In general, the investigation of airframe propulsion aeroacoustics includes both reducing the noise sources that arise from integration, as well as employing the installation itself as an approach to reduce noise of the airframe or propulsion system. For example, current aircraft designs may employ complex geometry exhaust systems to maximize efficiency or reduce noise. The subsonic jet engines have benefited from the large bypass ratio design to reduce noise emission. However, a great part of the research on supersonic aircraft has been focused on minimizing sonic boom. The design criteria stemmed from these efforts have led to embedded engine designs, which often suggests exhaust of jet over aft-airframe surfaces. We review here the effect of propulsion-airframe interactions on jet noise for both conventional aircraft designs, as well as some novel configurations. Installation effects of conventional designs include the wing behaving as a reflective surface, the wing or flap interacting with the exhaust flow, the pylon causing a large fan blockage resulting in an asymmetric flow, or a combination of these effects.