esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Related Technologies  
### 6 LoWPAN  
Supporting IPv6 on sensor nodes simplifies simultaneously the task of connecting LoWPAN devices to the Internet and the application developing process. Currently, the IEEE 802.15.4 protocol is widely accepted as the PHY and MAC layer protocol for LoWPAN networks. Nevertheless, the network layer protocol must comply with the constraints imposed by the IEEE 802.15.4 protocol and the properties of the standard IPv6 protocol do not fully match with such constraints. Low bandwidth, low-power resources and the maximum packet size of 127 bytes are the most relevant characteristics of the IEEE 802.15.4 standard, which must be dealt with in proper protocol adaptation. Moreover, the support of standard IPv6 headers over LoWPAN would result in extremely small payloads for higher layer protocols. Besides LoWPAN requirements, it is also necessary to guarantee that 6 LoWPAN is compliant with the IPv6 minimum MTU of 1280 bytes and, consequently, fragmentation and reassembly is required.  
The IETF created the 6 LoWPAN working group to define how to support IPv6 over IEEE 802.15.4 protocol. The 6 LoWPAN working group was focused on the following issues [1]: (i) to define a neighbor discovery protocol fitted for low-power networks, (ii) to describe mechanisms allowing compression of 6 LoWPAN headers in order to reduce the header overhead and (iii) to define the 6 LoWPAN routing requirements and approaches. Two RFCs were released, the RFC 4919 [1] and the RFC 4944 [30]. The first document describes the assumptions, problem statement, and goals of 6 LoWPAN. The second document proposes a 6 LoWPAN adaptation layer (between IPv6 and IEEE 802.15.4) describing the frame format for transmission of IPv6 packets, the method for defining IPv6 link-local addresses and stateless auto configured addresses, the header compression and the frame delivery process in IEEE 802.15.4 mesh networks.  
Rather than defining a single header (like IPv4), the 6 LoWPAN uses stacked headers as the original IPv6 protocol does. The 6 LoWPAN standard defines four header types: the dispatch header, the IPv6 compression header, the fragmentation header and the mesh header (by default, only the dispatch and compression headers are used). At the beginning of each header, a header type field identifies the header format.  
An illustrated in Figure 1, a typical LoWPAN [31] consists of nodes (named 6 LN or 6 LoWPAN Nodes), routers (named 6 LR or 6 LoWPAN Routers) and border routers (named 6 LBR or 6 LoWPAN Border Routers).  
Nodes (or 6 LNs) usually do sensing and actuation operations but they do not forward datagrams form other nodes to their destination nodes. Routers (or 6 LRs) are intermediate nodes that forward datagram from others nodes (or routers) to their destination nodes in the same LoWPAN and are present only in route-over topologies. Border routers (or 6 LBRs) are the interconnection devices between the LoWPAN network and others networks as, for example, the Internet. Typically, nodes and routers have energy and computational resource constraints while border routers are mainly powered with much more computational resources.
# Efficient Multi-Identity Full Homomorphic Encryption Scheme on Lattice
## 2 Preliminaries
### LWE Hardness Problem  
The security of all our structures is reduced to the LWE problem, which was first defined by [PERSON] [27] in 2005. It proved to be a non-deterministic polynomial (NP) problem with polynomial complexity.  
**Definition 5**.: _(LWE Hardness Problem [27]) Consider a positive integer \(n\), a prime \(q\), a noise distribution \(\chi\) over \(\mathbb{Z}_{q}\), and uniformly random secret key \(\mathbf{s}\in\mathbb{Z}_{q}^{n}\). An \(\left(\mathbb{Z}_{q},n,\chi\right)-\)LWE problem include accessing an unspecified challenge oracle \(\mathcal{O}\), that is, the oracle can be a noisy pseudo-random sampler \(\mathcal{O}_{s}\) with some constant random secret key \(\mathbf{s}\in\mathbb{Z}_{q}^{n}\), or it can be a truly random sampler \(\mathcal{O}_{\mathbf{s}}\). The behaviors of the two kinds of samplers are as follows._  
\(\mathcal{O}_{s}\): outputs sample of the form \((\mathbf{u}_{i},\mathbf{v}_{i})=(\mathbf{u}_{i},\langle\mathbf{u}_{i},\mathbf{s}\rangle+\mathbf{x}_{i })\in\mathbb{Z}_{q}^{n}\times\mathbb{Z}_{q}\), where \(\mathbf{s}\in\mathbb{Z}_{q}^{n}\) is a randomly uniform and invariant secret vector, \(\mathbf{u}_{i}\in\mathbb{Z}_{q}^{n}\) is a randomly uniformly selected vector, and \(\mathbf{x}_{i}\in\mathbb{Z}_{q}\) is fresh sample from \(\chi\).  
\(\mathcal{O}_{\mathbf{s}}\): outputs truly uniform random samples from \(\mathbb{Z}_{q}^{n}\times\mathbb{Z}_{q}\).  
The \(\left(\mathbb{Z}_{q},n,\chi\right)-\)LWE problem allows repeated queries to the challenge oracle \(\mathcal{O}\). For a random \(\mathbf{s}\in\mathbb{Z}_{q}^{n}\), if \(\text{LWE}-\mathbf{adv}[\mathcal{A}]=\)\(\left|\Pr\left[\mathcal{A}^{\mathcal{O}_{s}}=1\right]-\Pr\left[\mathcal{A}^{ \mathcal{O}_{s}}=1\right]\) is non-negligible, we say that algorithm \(\mathcal{A}\) can solve the \(\left(\mathbb{Z}_{q},n,\chi\right)-\)LWE problem, where \(\text{LWE}-\mathbf{adv}[\mathcal{A}]\).represents the advantage of algorithm \(\mathcal{A}\) in solving the \(\left(\mathbb{Z}_{q},n,\chi\right)-\)LWE problem.  
[PERSON] [27] showed that for some noise distributions \(\chi\), denoted \(\overline{\Psi}_{\alpha}\). The LWE problem is as difficult as the worst-case SIVP and GapSVP under quantum reduction (see also [28]).  
Definition 6: ([27]) Consider a positive integer \(n\), a real parameter \(\alpha=\alpha(n)\in(0,1)\), and a prime \(q\). Denote \(\mathcal{V}_{\alpha}\) as the normal distribution on \(\mathbb{Z}_{q}\) with the mean 0 as the Gaussian center and the standard deviation \(\frac{\alpha}{\sqrt{2n}}\), whose corresponding discrete distribution is \(\overline{\Psi}_{\alpha}\).  
Lemma 1: ([27]) Consider positive integer \(n,q\) and \(\alpha\in(0,1)\), if there is an efficient, possibly quantum, algorithm to solve the \(\left(\mathbb{Z}_{q},n,\overline{\mathcal{V}}_{\alpha}\right)-\text{LWE}\) problem for \(\alpha q>2\sqrt{n}\), then in the worst case, there is an efficient polynomial quantum algorithm to solve the SIVP and the GapSVP problems with an approximate factor of \(\widetilde{O}(n/\alpha)\).
# Joint Probabilistic Hypergraph Matching Labeled Multi-Bernoulli Filter for Rigid Target Tracking
## 5 Conclusions  
In this paper, hypergraph representation is adopted to incorporate structure information of rigid targets for better data association performance in MTT. The problem of data association is formulated as a hypergraph matching problem. To achieve better data association performance in hostile environments with clutter and missed detections, all undetected cases are enumerated following some ideas of joint probabilistic data association and the likelihood is built based on group structure rather than distance between predicted states and measurements. Then, the structure information is integrated into LMB filter by revising each single target likelihood with joint association probabilities. However, the number of association events grows exponentially with the number of targets, the proposed approach is usable in real time only for a limited number of targets. The effectiveness of our method has been demonstrated by extensive experiments. Specially, in the first scenario, when detection probability is 0.9, MOTA of JPHGM-LMB is 91.86%, there is almost three percent increase compared with HGM-LMB and eight percent increase compared with LMB. Moreover, as detection probability decreases, JPHGM-LMB always outperforms other filters. In the second scenario, when the standard deviation of measurement noise is 0.2 m, MOTA of JPHGM-LMB achieves 78.61%, however, that of HGM-LMB and LMB are 70.13% and 63.9% respectively, JPHGM-LMB achieves better tracking performance.  
The representation of hypergraph can utilize the structure information well, it can handle camera shake, because the structure of targets remain unchanged, the measurement noise will be more complicated if camera shake is considered. In order to handle more complex scenarios in practice applications, particle filter may be introduced, then the problem is how to reduce computational cost, it is our future work.  
[PERSON] conceived the idea of the study and conducted the research. [PERSON] and [PERSON] analyzed most of the data and wrote the initial draft of the paper. [PERSON] and [PERSON] contributed to finalizing this paper. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (Nos.61605242).  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# The State-of-the-Art of Sensors and Environmental Monitoring Technologies in Buildings
## 2 Sensors and Monitoring Technologies
### Humidity Sensors  
Humidity--the quantification of water vapor in air--has a direct effect on the comfort of occupants, the functioning of various electrical appliances and industrial equipment, and can strongly influence various chemical and physical processes within buildings. The continuing drive to reduce energy loss from uncontrolled air infiltration by increasing the airtightness of buildings has had the unintended consequence of increasing humidity-related issues. Therefore, it has become increasingly essential to measure humidity within indoor environments, especially in control systems where the health and safety of occupants is of paramount importance.  
Humidity sensors are designed to measure the amount of water vapor in air which is generally measured in terms of (1) relative humidity (measured as a function of temperature), or (2) dew point (measured as a function of pressure of the gas) or (3) absolute humidity (also known as PPM, and is independent of a temperature factor) [46; 47; 48]. Humidity measurement devices are generally classified into two main types: (1) capacitive and (2) resistive sensors (see Table 1). Capacitive sensors are electronic devices comprising of a dielectric material (usually a plastic or polymer) sandwiched between two metal electrodes (usually gold or platinum) and deposited on a substrate (glass, silicon or alumina). This device structure enables the formation of a 'capacitive effect' with the capacitance measured via the dielectric constant of the dielectric material when the humidity of the surrounding air changes. These sensors are very linear (can measure % RH (relative humidity) from 0%-100%), highly accurate (\(\pm\)0%-5%) and generally, have low power consumptions [48; 49; 50; 51; 52]. However, capacitive sensors require complex circuitries, regular calibration and are more expensive in comparison to other humidity sensor technologies. On the other hand, resistive sensors--electronic devices which measure electrical resistance with respect to the humidity level--tend to have a narrow measurement range (i.e., most resistive sensors are unable to measure values <5% RH) and are less accurate (\(\pm\)1%-10%). However, these sensors have lower costs on average, and have good response times (10-60 s), making them suitable for locations within buildings where very low measurements (<5% RH) are not necessarily required [48; 49; 50; 51; 52].
# Mitigation of Nonlinear Distortions for a 100 Gb/s Radio-Over-Fiber-Based WDM Network
## 1 Introduction
### Major Contributions  
It is a fact that a RoF transmission link transporting a very large number of user signals at high data rates suffers badly from the NLIs and LDs. Thus, in this paper, a novel WDM-RoF-based system is developed, aiming to enhance the performance of the overall system and to support current high capacity demands. The basic goal was to reduce NLI, LD and PAPR issues and to achieve an optimize structure. The major contributions of this paper are documented as follows:  
1. Optical transmission up to long range distances accumulates high order NLIs and LDs in a WDM-RoF network, but is still required to address the high capacity transportation demand of the future mobile networks. In this work, such effects are studied in detail with an analytical model and a solution is presented to compensate them for a high data rate WDM system.
2. System parameters like input launch power, received power and length of the transmission fiber are used to explore the effectiveness of the proposed model.
3. BER parameter is used to compute the fidelity of the proposed WDM-RoF proposed model and results are presented for various scenarios and conditions to explore the effectiveness of the proposed receiver.
4. Analytical approach is presented for the proposed WDM-RoF model and simulation work is carried out using realistic values of RF and optical components, suggesting 1 km to 10 km of transmission range. A \(-\)40 dBm to \(-\)15 dBm received power sensitivity and \(-\)20 dBm to 0 dBm input power are required to achieve multi Gb/s transmission and support high order modulation formats.  
The remainder of this work is arranged as follows. Section 2 presents the proposed system model layout, followed by the description of analytical model in Section 3. Section 4 summarizes the results and discussion of the presented system. Finally, the conclusion is presented in Section 5.
## 3 Numerical Uncertainty Analysis
### Evaluation of the Results
#### 3.2.4 Coefficient of Determination \(R^{2}\)  
The coefficient of determination \(R^{2}\) describes the proportion of variability in training data that can be explained using a model [46]. To calculate \(R^{2}\) with the noise-free training data \(x\) and the reconstructed time series \(\hat{x}\), we use the following definition:  
\[R^{2} =1-\frac{RSS}{TSS}, \tag{14}\] \[TSS =\Sigma(x_{i}-\hat{x})^{2},\] (15) \[RSS =\Sigma(x_{i}-\hat{x}_{i})^{2}, \tag{16}\]  
where TSS means the total sum of squares and RSS is the residual sum of squares. The value of \(R^{2}\) varies in the interval \([0,1]\). The better a reconstruction fits the training data, the higher \(R^{2}\) becomes. In the ideal case, \(R^{2}\) is equal to 1.  
As for the training process, the analysis of the coefficient of determination \(R^{2}\) is only applied to the reconstruction using the same initial condition \(x_{0}(0)\) as in the training process. The distributions of \(R^{2}\) in Figure 10a,b show group characteristics for different polynomial orders. The first group of both LSST and LSPL methods with \(Ord=\{1,2\}\) have similar medians and quantiles; for example with \(\epsilon_{N}=0.01\), their medians are equal to 0.9985, and the 0.75 and 0.25 quantiles are 0.9993 and 0.9971, respectively. The second group of the LSST method has two orders, \(Ord=\{3,4\}\). However, the LSPL method has three orders, \(Ord=\{3,4,5\}\), in its second group of \(R^{2}\) with a higher median and smaller distribution quantiles. The \(R^{2}\) of the LSST method in the fifth polynomial order decreases more rapidly with the increasing noise factor than the other groups of MCSs.  
For the purpose of comparing the distribution of \(R^{2}\) in detail, the boxplots as an example with \(Ord=3\) and \(\epsilon_{N}=0.005\) in Figure 11 and Table 2 indicate that LSPL has a slightly higher median value and a smaller interval between both quantiles compared to LSST. This means that the time series from the models trained by LSPL are more consistent with the noise-free true signals.
# Indoor-Outdoor Detection in Mobile Networks Using Quantum Machine Learning Approaches
## 1 Introduction
In short, the three main benefits of quantum machine learning are (interpretation based on [21]):  
* Improvements in run time: obtaining faster results;
* Learning capacity improvements: increase in the capacity of associative or content-addressable memories;
* Learning efficiency improvements: less training information or simpler models needed to produce the same results or more complex relations can be learned from the same data. The exponential speed is mainly delivered by the use of the HHL [22] algorithm as part of the solution. This algorithm solves a system of linear equations very quickly. However, it is known that the fact that the data has to be encoded into the quantum state and that the output is in a quantum state will require a lot of effort to overcome. The author of [23] warns about the assumptions made by those quantum algorithms:  
* They assume preloaded databases in quantum states, for example, using quantum RAM (QRAM);
* They assume data to be'relatively uniform', meaning no big differences in value;
* They produce a quantum state as output, meaning this has to be translated efficiently to a meaningful result. All are non-trivial to solve without losing the expected exponential gain. In summary, we can say that machine learning is important for providing location information to mobile operators and that machine learning is an important application area for quantum computing, despite its current limitations. In this paper, we connect these two worlds by proposing quantum machine learning for indoor-outdoor classification. The main research question is 'how can quantum computing, in the short term, support the demand for computing power that is needed by machine learning approaches in mobile networks?'. To the best of our knowledge, we are the first to propose the use of quantum machine learning in this area. Three approaches for quantum machine learning for this supervised machine learning problem are presented and evaluated on the current hardware. It is meant as starting point for further research. The growth of the number of qubits in the coming years will make real added value of quantum computers, quantum advantage, within reach in the next 5-10 years. In the rest of this paper, first, a short introduction to quantum computing is provided in Section 2. Next, in Section 3, the data that were used in the analysis are generated. For this, a simple theoretical propagation model is used. Based on these data, three quantum machine learning approaches are presented and executed in Section 4: a hybrid quantum variational classifier, a quantum distance-based classifier and a quantum annealing-based support vector machine. The last section contains conclusions and some ideas for further research.
## 3 System Description
### Multiple Access Scheme  
The frequency band is divided into two parts: one is for data (\(f_{{}_{data}}\) ) and the other is for control (\(f_{{}_{control}}\) ). The control channel is shared by all network nodes by means of a contention-based protocol such as MACA-U, MACA-UPT, or ROPA [23, 24]. By contrast, the data channel is exclusively allocated to nodes by means of contention-free protocol such as TDMA and CDMA. In much of the recent research, TDMA is found to be an effective way to meet intra-cluster communication requirements [5, 25]. Thus, we adopt TDMA for multiplexing the link MN\(\rightarrow\)PN, where PN is in-charge of time-slot allocation, as shown in Figure 2.  
Figure 2: Medium-access scheme.  
CDMA is employed to escape the interference between subclusters. A different orthogonal code is assigned to each subcluster by the CH [26, 27]. That is, the TDMA multiplexed data from MNs are additionally coded by the assigned orthogonal code. The links of CH\(\rightarrow\)AUV and PN\(\rightarrow\)CH are used only to exchange of the control information; therefore, they share the contention-based control channel of _f__control_. The link AUV\(\rightarrow\)sink also uses CDMA with a dedicated orthogonal code.
## 6 Conclusions  
The article considers a variant of solving the problem of increasing the accuracy of estimating radio signal samples. The proposed solution can be implemented by an optoelectronic device in which a coherent luminous flux is formed. The parameters of the generated light flux are modulated by a voltage proportional to the readings of the received radio signal. Using a modulated light flux, an interferogram is formed, the parameters of which are directly related to the parameters of this light flux, and, accordingly, to the parameters of the received radio signal. The spatial-spectral distribution of the intensity of the optical field in the interference plane is recorded by photodetectors, the signals from the outputs of which are converted into digital form. Analog-to-digital conversion of the received radio signal using an EOM, a holographic interferometer and a group of ADCs placed in the interference bands of the interferogram requires mandatory consideration of the nature of the spatial-spectral distribution of the intensity of the optical field in its plane. The performed analysis showed the relationship between the accuracy of estimating the parameters of the received radio signal and a number of design parameters of an optoelectronic device determined by the characteristics of its optical circuit elements. The choice of parameters of the optical part of the optoelectronic device \(\alpha\), \(\alpha_{0}\), \(\alpha_{1}\), \(\lambda\), \(n_{0}\), \(L_{1}\), \(F\), \(R_{1}\), justified by the results of the conducted research, allows to reduce the quantization step when measuring radio signal samples. This leads to an increase in the accuracy of measurements of these samples. The greatest variation of the gain in the accuracy of measuring the parameters of the radio signal, as the research results show, is associated with the angle \(\alpha_{1}\) and distance \(R_{1}\).  
The achieved increase in the accuracy of estimating the radio signal counts makes it possible to increase the base of the alphabet used for code manipulation, the efficiency of using the allocated frequency band for transmitting information and reduce the probability of a bit error.  
This research received no external funding.  
Figure 4: Variation of the reduction of the ratio of quantization counts of an optoelectronic device and an electronic ADC: (**a**)—in \(R_{1}\), variation \(\alpha_{1}=85^{\circ}\); (**b**)—in \(\alpha_{1}\) variation, \(R_{1}=0.01\) m.
## 4 Design Analysis
### Data Analysis  
The housekeeping (HK) data generated by Light-1 is essential for monitoring the health and status of the CubeSat. The data is collected and stored in the on-board memory within a three-level data file structure. The first level includes a folder for each subsystem: OBC, EPS, ADCS and COM, the second level is a subfolder created for the predefined variables, while the final level contains two types of files, i.e., actual data file and indexing file.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Pointing Mode** & **Power (W)** & **Energy (Wh)** \\ \hline Nadir & 5.300 & 8.170 \\ \hline Sun horizon & 10.12 & 15.60 \\ \hline Full Sun Tracking & 13.19 & 20.33 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Power and energy generated in different modes.