esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background and Data for Power Monitoring  
In this research, an office building in Dalian (referred to as Building A), an office building in Jinzhou (Building B), and a commercial building in Anshan (Building C) are used as the research sample set. Table 1 shows the basic information, Figures 3 and 4 the temperature information.  
While using the above platforms, there are many problems with data quality, which are also common problems of Chinese building energy consumption platforms, and which are reflected in the following two aspects.  
(1) When problems occur in the process of data collection and transmission, they cannot be dealt with in a short period of time, resulting in missing data, data mutation, etc.  
(2) When the energy measurement of low-power terminal equipment is ignored, or when different functions of low-power terminal equipment are collected by the same meter, the power consumption of lighting sockets and air-conditioning are mixed. For example, lighting, sockets, and air-conditioning are monitored by the same branch monitoring. In Building A, the peak value of lighting socket power consumption in the cooling season is 1.5 times that of the peak value in the transition season, as shown in Figure 5, which exceeds the reasonable power consumption range of the lighting socket equipment. This indicates that the data of lighting socket power consumption is mixed with the data of air-conditioning power consumption.  
Therefore, cleaning abnormal data and separating mixed data is important for improving data quality, and it is also a crucial link for energy efficiency retrofits. There are two main dimensions to cleaning and separating abnormal data: one is for abnormal daily data, and the other is for abnormal hourly data. For daily data, the granularity of measurements is one day, and it means one day is one point. For hourly data, the granularity of measurements is one hour, and it means one hour is one point. Different methods are proposed to address the two dimensions, including the OATPM and KNN methods, and the details are in Section 4.  
Figure 4: Daily minimum air temperature for three cities. The energy consumption monitoring platforms of Buildings A, B, and C are listed in Table 2.
## 6 Discussion  
The results of this work are continuously translated into quantitative recommendations on regolith compression and optimal wall thickness for effective radiation protection and thermal insulation of a habitat on the Moon. Such engineering approach lays the foundation for architectural specifications to habitat construction. Future work will focus on diminishing secondary emissions and a more comprehensive thermal analysis. Different candidate locations will be analysed with respect to solar heat flux variations during the lunar day and with respect to local lunar albedo for highlands and mare terrains. However, it appears clearly that compressed highlands regolith will act as a good thermal insulator, leading to very limited heat losses out of the habitat during lunar day and night cycles.  
Looking at two radiation scenarios, namely GCR and SPE, generates an understanding of the different regolith utilisations required for a holistic radiation protection. Due to the differences in the nature of the long- and short-term effects, the BFO doses and effective dose equivalents are not cross-comparable. However, the authors aim to determine a strategy for regolith utilisation in a habitat for long-duration missions, for instance explaining how a specific SPE shelter area is required in a habitat, where the rest of it would cover the protection from GCR.  
Using the female phantom model contributes to filling gender gaps in radiation protection research. However, the utilisation of more recent phantoms as well as the male sex models would complete this work further. Gender differences can be discussed in terms of dose absorption per organ, for BFO in particular, as well as the effective dose equivalent. The effects of radiation, both long and short term, such as carcinogenesis and vision impairment could be paired with specific organ doses and sex-disaggregated data analysed on the matter of gender differences. Such work may contribute to the formulation of engineering specifications for habitat construction, if for example the female phantom is chosen as the standard model for radiation protection on exploration type missions.  
The utilisation of spherical geometries for all scenarios is a simplistic approach. Geometry affects the imparted doses and thus such an approximation is limited compared to real-life scenarios. However, when simulating the same radiation environments, this method allows to cross-compare the effectiveness of the used materials in their configurations. For example, the choice of a single geometry allows cross-comparing the cases of an astronaut performing an EVA on the surface of the Moon and their crewmate staying inside the habitat. It demonstrates the relative difference in radiation protection resulting from the change of shielding materials without the effect of geometry. However, future work will define and use geometries that better approximate the different real-life scenarios of a long-term mission on the lunar surface.
# Nero: A Deterministic Leaderless Consensus Algorithm for DAG-Based Cryptocurrencies
## 3 The Nero Algorithm
### Eventual State Machine Replication  
In this section, we adapt the Nero consensus algorithm to achieve a variant of state machine replication suited for DAG-based systems called eventual state machine replication. Concretely, we change the definition of replica coordination of [15] to all non-faulty replicas _eventually_ receive and process the same sequence of requests.  
This property can be decomposed into two parts, Agreement and Eventual Order: Agreement requires all (non-faulty) replicas to receive all requests, and Eventual Order requires that the order of received requests is eventually the same at all replicas.  
In this section, we propose a way to eventually order the transactions while maintaining the properties of the consensus algorithm. We do this by modifying the contents of the messages sent (in this case, the transactions), with the client adding a timestamp to it, and modifying the valid() predicate accordingly. In addition to the previous validation conditions, the node now has to also validate the timestamp of the transaction according to its own local timestamp when it receives it in order to discard invalid timestamps. Concretely, a timestamp t of a transaction is considered valid if \(l-d<t<l+d\), where \(l\) is the local timestamp and \(d\) is the value to account for the local timestamps desynchronization of the different nodes and the delay of broadcasting a message through the network. The local timestamps can be synchronized using a decentralized protocol such as Network Time Protocol (NTP) [26] or Network Time Security (NTS) [27].  
This is not meant to provide an accurate timestamp of when a given transaction happened but only provide a global ordering of the ledger and a common sense of time to the nodes of the network. There is a probability of valid transactions being discarded when the network is not on GST, but they can always be resubmitted with a new timestamp. In order to minimize this, a node can vote on a transaction with an \"invalid\" timestamp (in its view) if at least \(f+1\) nodes voted on that transaction, meaning that at least one correct process received the transaction in a timely manner.  
The ordering of the messages is then performed by its timestamp first and, if there is more than one message, with the same timestamp, by the hash of the message using a hash with the properties of [28]: compression, one way, weak collision resistance and strong collision resistance.  
Proof.: Assuming the agreement and termination properties of the underlying consensus algorithm, all correct processes will eventually decide on the same set of values. In the case that the values all have different timestamps, the order is naturally done. In the case that different values have the same timestamp, the hash of those values assures a unique deterministic order because any value is hashable (compression property), and each hash is unique in practice (weak collision resistance property).
## 3 Materials and Methods
### Method Overview  
The presented method uses a sequence of 3D scans of the human body in motion and translates this information into a computer animation of a virtual character. In the measurement dataset used, each measurement frame contains data in the form of triangle mesh; however, in the proposed process, data in the form of 3D point clouds can be used, as well.  
The proposed method consists of three stages:  
* Pose tracking based on the displacement of individual segments of an input scan, using a variant of the Iterative Closest Point method (hereafter referred to as tracking);
* Mapping of the scan shape in a sequence, using shape maps (hereafter referred to as mapping);
* Morphing (registration) of a uniform template mesh to the generated shape maps (hereafter called morphing).  
The input data for the algorithm are as follows:  
* A sequence of 3D scans of human body in motion, in the form of either meshes or point clouds;
* An animation skeleton pose for the first frame;
* A template mesh skinned to the animation skeleton for the first frame (we used automatic weights generated by the Heat Bone Weighting method [49] implemented in Blender).  
The general outline of the proposed method is as follows (Figure 3): the tracking algorithm, based on two measurement frames--(t) and (t + 1)--and the skeleton for frame (t) calculates the  
Figure 1: Scans from input sequence “jumping jacks”. Every fifth frame of first 100 scans was used to increase readability. Numbers denote frame numbers.  
Figure 2: Scans from input sequence “punching”. Every fifth frame of first 100 scans was used to increase readability. Numbers denote frame numbers.  
segmentation of scan (t) for the skeleton and the pose for frame (t + 1). The scan segmentation in frame (t), together with the scan, is then used by the mapping and morphing algorithms to transfer the shape to the template mesh. After obtaining the shape maps for all the segments in a given frame, the template mesh is posed according to the skeleton in frame (t) and morphed based on the shape maps, resulting in the final triangle mesh for frame (t). This process is repeated for subsequent pairs of frames (t + 1, t + 2) (note that tracking produced the skeleton for frame (t + 1)), until the end of the measurement sequence is reached. After processing the entire scanning sequence, a series of triangle meshes with the same topology as the template mesh together with a series of skeleton poses is obtained as the output of the algorithm. The shapes of the meshes reflect the scan geometry in individual frames, while the skeleton matches the body pose change in subsequent frames of the sequence.
###### Abstract  
Vegetation indices (VI) describe vegetation structure and functioning but they are affected by illumination conditions (IC). Moreover, the fact that the effect of the IC on VI can be stronger than other biophysical or seasonal processes is under debate. Using Google Earth Engine and the latest Landsat Surface Reflectance level 1 data, we evaluated the temporal patterns of IC and two VI, the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI) in a mountainous tropical forest during the years 1984-2017. We evaluated IC and VI at different times, their relationship with the topography and the correlations between them. We show that IC is useful for understanding the patterns of variation between VI and IC at the pixel level using Landsat sensors. Our findings confirmed a strong correlation between EVI and IC and less between NDVI and IC. We found a significant increase in IC, EVI, and NDVI throughout time due to an improvement in the position of all Landsat sensors. Our results reinforce the need to consider IC to interpret VI over long periods using Landsat data in order to increase the precision of monitoring VI in irregular topography.  
illumination condition; Landsat; Google Earth Engine; EVI; NDVI; topographic effects 29 November 2020; 2020; Published: 8 January 2020
# Dynamic Information Flow Tracking: Taxonomy, Challenges, and Opportunities
## 3 Hardware-Assisted Dynamic Information Flow Tracking
### Micro-Architecture-Level DIFT Design  
#### 3.2.1 Dynamic Information Flow Tracking on SoC  
[PERSON] et al. in [19] proposed an IP wrapper to protect access to IPs from software. The DIFT operations are implemented as hardware logic in the IP wrapper to check for malicious access. The tag is issued with accompanying with the memory access. They provide three types of DIFT logic in the IP wrapper: (i) configuration logic for checking the data in the configuration register; (ii) load logic for checking the input data; and (iii) storage logic for generating the tag for output data. [PERSON] et al. in [20] enforced DIFT operation on a bus and IP through a whole design. In this work, every IP was assigned a wrapper to receive the tag from the bus. They extended the bus interconnect to acquire the tag from normal memory. The data and tag were stored separately in normal memory. A two-level table for tag management was adopted. The first-level tag was used to indicate whether the current page was tagged. The second level tag indicated word-granularity in page.
# Resource Management in Cloud Radio Access Network: Conventional and New Approaches
## 3 Resource Management Techniques in C-RAN
### Radio Resource Management Techniques
#### 3.1.5 Lessons Learned  
The minimization of the number of BBUs required to handle traffic loads with a reduced number of RRHs resulted in an improved network capacity as well as less overhead in fronthaul links of the network. It also reduced the inter-cell interference. For improving the energy efficiency of a CRAN, RRHs need to be selectively turned on/off such that they cause less overhead in the transport network and reduce the number of active BBUs within the BBU Pool. However, the RRH switching method results in load balancing among all the RRHs and minimum power consumption of the system. Thereby, a stable scenario can be achieved for addressing unexpected future traffic demands effectively.  
Power consumption is maximum during the peak traffic load because of the usage of a larger number of BBUs, which results in more energy consumption. Therefore, it is more effective to consider the dynamic traffic load for maintaining the QoS requirements as well as energy efficiency, while designing algorithms for minimizing power consumption.
## 3 Proposed Approach
### Network Architecture  
#### 3.2.1 Steganography Position  
In contrast to conventional image steganography methods that conceal information within RGB or grey image, our approach in this paper involves hiding secret information within the Y channel of YCrCb images. Some RGB-based steganography techniques tend to distort the colour of the stego images. This distortion arises from the fact that RGB images consist of three channels (\(R\), \(G\), \(B\)), encompassing semantic, luminance, and colour information. When concealing secret information, the distribution of this information is inevitably disrupted, resulting in colour accuracy issues.  
\[\left\{\begin{aligned} & Y=0.299\;*\;R+0.587\;*\;G+0.114\;*\;B\\ & Cr=0.5\;*\;(R-Y)\;*\;0.713+128\\ & Cb=0.5\;*\;(B-Y)\;*\;0.564+128\\ \end{aligned}\right. \tag{1}\]  
\[\left\{\begin{aligned} & R=Y+1.403\;*\;(Cr-128)\\ & G=Y-0.714\;*\;(Cr-128)-0.344\;*\;(Cb-128)\\ & B=Y+1.773\;*\;(Cb-128)\\ \end{aligned}\right. \tag{2}\]  
As depicted in Figure 2, within the YCrCb encoding format, the \(Cr\) and \(Cb\) channels encapsulate both partial semantic and complete colour information of the image. Meanwhile, the \(Y\) channel contains only partial semantic information and luminance information. By concealing information solely in the \(Y\) channel, we mitigated the problem of colour  
Figure 1: The process in which the cover image and secret image undergo the forward hiding network, resulting in the generated stego image and associated loss information. Subsequently, the stego image, along with auxiliary variables produced from stego image, is input into the backward reconstructing network. This network reveals the cover image and secret image. The revealed secret image, alongside the original secret image, undergoes assessment by the discriminator for differentiation. Through iterative adversarial training, the quality of the reconstructed secret image is refined.  
distortion during embedding. The conversion between YCrCb and RGB coding formats is described by Equations (1) and (2). Unlike [PERSON] et al. [23], who only hides grey image in the \(Y\) channel, our approach allows for hiding colour image in \(Y\) channel, significantly enhancing steganographic capacity.
## 3 Methodology: Requirements Analysis, Selection of Buildings, and Technologies
### Deriving Technologies and Instrumentation Variants
#### 3.3.2 Possible Solutions and Alternatives  
The consideration of solution possibilities can be framed around two aspects: the communication architecture and the actual technologies and protocols. Naturally, the choice of the first aspect limits the available options in the second, and vice versa.  
For the first part, it is necessary to define a proper communication backbone architecture at the automation layer, which defines where and how many IoT gateways are used. Possible solutions range from totally decentralized to totally centralized variants. The most decentralized variant is the use of \"IoT-ready\" devices only, so that every device is able to communicate to some cloud infrastructure directly. An intermediate approach consists of clustering the communication to the cloud infrastructure by connecting several field devices to one IoT gateway. Such an approach lies on the continuum between the two extremes, depending on the the amount of devices connected to the used IoT gateways, which then communicate with the cloud infrastructure. At the totally centralized extreme, only one central IoT gateway is used to bridge all the field devices to the cloud infrastructure. To the best of the authors' knowledge, the only available technology for such an approach in practice is LoRaWAN [41].  
For the other aspect, it is important that the choice of technologies and protocols accommodates as many of the above-stated requirements as possible, if not all. For an overview, we again refer to Table 1.
## 3 Problem Formulation  
### Introduction to the Problem  
As mentioned above, assistance ratios \(\alpha_{T}\) and \(\alpha_{R}\) are commonly constant for commercial use. However, based on a preliminary study, it was found that DC motors with a fixed ratio cannot provide proper assistance in different environments because the friction term varies as the road surface changes. For example, the rolling resistance on sand is much larger than that on an indoor surface. Thus, if the assistance ratios of the cart are specified properly for indoor use, the user will feel that he or she needs extra force to operate the cart while walking on sand. In contrast, when the ratios are suitable for sand, the DC motor will offer excessive force while moving indoors. Consequently, the road roughness should be carefully dealt with for the daily use of a power-assisted cart.  
During assisted walking, the operational force corresponds to the relative movement between the cart and the user. As the velocity of the user always fluctuates periodically during stable walking, the cart will also move in a similar manner. Figure 4 indicates the time histories of the operational forces during walking with the cart, which does not provide any assistance like a traditional walker. The subject (male, 28 years old, height 183 cm, and weight 75 kg) walked on an indoor surface, as is shown later. The data, including the operational forces and angular velocities, were recorded and filtered via a zero-phase 10 th order low-pass filter with a cut-off frequency of 10 Hz. The power spectral densities for the calculated resultant operational force \(F_{\text{Human}}\) and translational velocity \(v\) were estimated by using MATLAB function Periodogram, as shown in Figure 5. The same dominant frequency for \(F_{\text{Human}}\) and \(v\), which is related to the step time, was observed, thus the movement of the cart was governed by the user whose body accelerated and decelerated alternately during stable walking [32].  
Human-machine interaction also affects the movements of the user and the walking assistant during assisted walking. When a user walks steadily with the power-assisted cart, not only the operational forces but also the motor output exert an influence on the cart. The user must expend extra force to pull back the handles for slowing down the cart  
Figure 4: Operational forces for one subject with \(\alpha=0\) in 15 s on the indoor surface.  
when its velocity becomes larger than the user. At this moment, the deceleration process is again amplified by \(\alpha\), but the cart decelerates more than necessary when \(\alpha\) is very large. Thus, in the next acceleration process, the uncoordinated movement between the cart and the user will also lead to a larger operational force. Finally, the overall operational force is increased, which brings about inefficiency and discomfort. On the other hand, with a small \(\alpha\), the motor output slightly reduces the burden on the user. In conclusion, we hypothesize that there is an assistance ratio \(\alpha\) that is most suitable for each surface; the hypothesis was verified, as is covered later on.
# Towards Mobile Federated Learning with Unreliable Participants and Selective Aggregation
## 5 Results and Discussion
### Increasing the Complexity of the Dataset  
The use of the weighted average aggregation led to an overall improvement in training. However, for CIFAR10, both the weighted average and the arithmetic average achieved a similar maximum accuracy. Therefore, we performed a final test on a larger and more complex dataset--CIFAR 100 [30]--to further evaluate the performance of our FL solution.  
This dataset has the same number of training and test images as CIFAR10. The main difference is in the number of image classes. While CIFAR10 considers 10 image classes, CIFAR100 considers 100 image classes.  
In Table 6, we present the results obtained by repeating the tests with eight clients with an unbalanced dataset for both the arithmetic average and the weighted average aggregation methods.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Elapsed Rounds** & **Weighted Average Accuracy** & **Arithmetic Average Accuracy** \\ \hline
10 & 28\% & 8\% \\ \hline
30 & 42\% & 17\% \\ \hline
50 & 74\% & 42\% \\ \hline
100 & 90\% & 89\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results obtained with cross-validation for 8 devices.  
Figure 7: Division of the CIFAR10 dataset for the cross-validation tests.  
Note that for the same rounds of training with this dataset, only a maximum accuracy of 63% was achieved. This was to be expected since the dataset had a much higher complexity. [PERSON] et al. (2018) showed that with the CIFAR100 dataset, the **VGG19** CNN achieved a maximum accuracy of 70.3% after 100 training rounds, which was obtained with a balanced dataset and a centralized machine learning method with access to all training data. In our case, since we performed distributed training with unreliable clients (due to the lack of dataset balance), we would need a much larger number of rounds to converge closer to that maximum accuracy.  
These results support the use of aggregation methods that benefit those clients who contribute more to training.