esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Testing of Passive Linear TMD for Postural Tremor Attenuation
## 3 Results
### IMU Measurements  
An IMU is added to the free end of the experimental arm. The vibrometer is also used to measure the response of the whole system consisting of the experimental arm and IMU. The total mass of the IMU is 21.0 g including the plastic cover and batteries. It leads to an increase in the total mass of the system to 263.3 g. The mass ratio corresponding to the three TMDs of Set#1 is now reduced to 15.7%. Measurements using the vibrometer are carried out before and after the addition of the three TMDs. The measurement point of the vibrometer is located on the IMU surface as shown in Figure 8a. The response of this system as obtained from the vibrometer measurements, before and after the addition of the three TMDs, is shown in Figure 8b. The peak observed after the addition of the TMDs, representing the frequency of the coupled system (experimental arm with three TMDs), is shifted from 8.60 Hz (without IMU as in Figure 7) to 8.98 Hz (with IMU as in Figure 8b). The percentage of reduction in the amplitude of the system including the IMU, before and after the addition of the TMDs of Set#1, is 69%.  
The IMU is also used to provide measurements of the acceleration and angular velocity of the system in the direction of motion, before and after the addition of the three TMDs. The results due to the three TMDs of Set#1 are shown in Figure 9. It shows that the reduction is 29% for the acceleration signal, 79% for the displacement signal, 67% for the angular velocity signal, and 82% for the angular displacement signal. The passive TMDs system causes a significant level of reduction in the amplitude of the system, excited by the PT signal, especially in the angular displacement signal which represents the flexion-extension angular displacement motion of the hand.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Number of TMDs**} & \multicolumn{2}{c}{**One TMD**} & \multicolumn{2}{c}{**Two TMD**} & \multicolumn{2}{c}{**Three TMD**} \\ \hline \multirow{2}{*}{Numerical study} & \(a\) [mm] & 74.9 & 76.8 & 75.6 & 79.1 & 77.0 & 75.9 \\  & _f\({}_{a}^{meas}\)_ [Hz] & 6.69 & 6.23 & 6.44 & 6.15 & 6.41 & 6.55 \\ \hline \multirow{3}{*}{Experimental work} & Position & '0’ & '1’ & '2’ & '1’ & ’0’ & ’2’ \\  & \(a\) [mm] & 73.0 & 79.0 & 76.0 & 79.5 & 76.0 & 75.0 \\ \cline{1-1}  & _f\({}_{a}^{meas}\)_ [Hz] & 6.68 & 6.21 & 6.41 & 6.18 & 6.41 & 6.52 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Frequencies of one, two, and three TMDs system for the optimal positions obtained from a numerical study carried out by Gebai [22] and their corresponding nearest frequencies used for the current experimental work.  
Figure 8: (**a**) IMU added to the end of the experimental arm; (**b**) PSD obtained from vibrometer measurement for the system with three TMDs (Set#1).
# Joint Energy Supply and Routing Path Selection for
## 3 A Joint Algorithm of Energy Supply and Routing Selection
### Two-Stage Energy Replenishment Strategy
Based on this premise, it should be ensured that nodes which have not been charged in the current charging schedule have at least enough energy to spend in the subsequent rounds of scheduling time \(\partial*T\). In this work, we define the charging schedule cycle \(T\) as the sum of charging duration and the maximum travel time of the mobile charger, and the travel time of the mobile charger can be computed depending on the simulated annealing algorithm. The simulated annealing algorithm is a stochastic optimization algorithm based on the Monte-Carlo iterative solution strategy. Its starting point is the similarity between the annealing process of a solid material in physics and the general combinatorial optimization problem: starting from an initial temperature, with the constant decrease of temperature parameters, the global optimal solution of the objective function is randomly searched in the solution space by combining the probability jump characteristics, that is, it can jump out of the local optimal solution with probability and eventually tend to global optimum. Here, this algorithm is applied to finding a Hamilton circuit that contains all charged nodes, and the procedure is described in Algorithm 2. As for the maximum travel time, it should be reserved for the mobile charger to visit all the nodes selected to be charged in this round, so the maximum travel time is set up for the mobile charger according to a shortest Hamilton Loop that contains the whole network. Analysis of the charging algorithm shows that in the early stage of network operation, the total energy that the WCD can output during the \(T_{c}\) time is generally more than the energy of the entire network deficiency, in this case, all nodes with relatively faster energy consumption can obtain sufficient energy, and the pointer \(k\) moves faster than the pointer \(m\), and finally the update process will be halted when the two values are equal. However, as the network continues to run, nodes consume more and more energy, and the total energy that WCD can output during \(T_{c}\) will not be enough to fill the entire network. In order to prolong the network lifetime, energy needs to be prioritized for nodes with more serious energy consumption. In our charging strategy, the fusion index \(\partial\) determines the proportion of nodes that need to be prioritized when WCD's energy is scarce, which affects the concentration of energy: the larger the \(\partial\) is, the more energy the nodes will eventually be replenished, and the more decentralized the charging energy is; the smaller the \(\partial\) is, the fewer energy supplements the nodes ultimately receive, that is, WCD replenishes other nodes after satisfying those nodes that have faster energy consumption and greater possibility of death. Thus, the charging duration and fusion index will have a significant impact on the performance of our algorithm.
# Towards NGGM: Laser Tracking Instrument for the Next Generation of Gravity Missions
## 4 Instrument Options and Next Steps
### Scale Factor Measurement System  
As mentioned in Section 2, knowledge of the absolute laser frequency was required to transfer the laser phase measurements into actual ranging measurements. In GRACE-FO, the laser ranging instrument (LRI) measurement was referenced to the microwave ranging instrument (MWI), which was stabilized to the ultra-stable oscillator (USO). The USO frequency was measured with high accuracy during a GPS-based orbit and clock error determination. The observed variations in differential LRI and MWI scales in GRACE-FO exhibited variations of approximately 0.2 ppm, corresponding to approximately 60 MHz, and likely absorbed tone errors from both of the instruments. For the NGGM, where there would be no MWI, the absolute frequency had to be derived differently, potentially down to 0.01 ppm, to meet the tightened performance requirements. Several alternatives are currently being studied, including the derivation of the scale factor through post-processing or a dedicated scale factor measurement system, such as a scale factor unit in addition to the LSU, measuring the cavity-free spectral range as described in [1], an iodine spectroscopy unit or even a (relatively low-performance) frequency comb. The TRL of the SFMS is, depending on the technology assessed as TRL2-4 and a trade-off between different options, currently ongoing.  
Figure 10: (**a**) Qualification model of the RRU of the LRI of GRACE-FO; (**b**) NGGM RRU concept with smaller beam separation and modified mounting interface.
## 6 Conclusions  
The main goal of this paper is to analyze the parameter estimator bias. It provides an overview of the reliability of Gauss-Newton method applied to UWB positioning. The linearization of the positioning functional model result in biased least-squares estimators. The Gauss-Newton method only includes the first-order Taylor expansion of distance equations. The bias comes from neglected higher order terms, which can be regarded as a systematic error. In this paper, we analyze how the second-order partial derivate affect the parameter estimator bias. The parameter estimator bias is related to the system geometry and the observation quality. It is driven by the prior observation precision, the design matrix and the amount of nonlinearity. It will be realized that the parameter estimator tends to be unbiased with a sufficiently small relative ranging error or a good positioning configuration with the lowest DOP. In outdoor satellite positioning, the ranging distances are long  
Figure 16: The bias estimate of two set of tests.  
Figure 15: The hypothesis test based on Mahalanobis distance.  
enough so that the relative residual are very small. A better positioning geometry configuration also can be chosen thanks to the large number of satellites available. This extremely large system makes the bias totally negligible. However, in the UWB indoor positioning, the ranging distances are extremely short. Meanwhile, the NLOS environment makes the error of measurements increased and biased. In addition, considering the complexity and cost of the positioning system, the geometry configuration of anchor nodes is prone to be ill-conditioned. Thus, it makes the parameter estimator bias significant, and the performance and the reliability of the positioning functional model are inevitably influenced. A judging index for the hypothesis test based on Mahalanobis distance is proposed to determine if the bias is significant. It is essentially to assess whether the linearized model is a sufficient approximation and the bias becomes potential problem for UWB positioning. If the bias is too large, the estimator needs to be corrected to endure the unbiasedness for positioning. In practical applications, an unbiased estimation of the positioning result will be expected, the bias can be calculated by the standard least-squares procedure prior to designing a positioning system. Through detecting the large bias due to the error of functional model, the positioning system can be designed the minimum bias from which the parameters are to be estimated.  
Conceptualization, C.W., J.W. and H.Y.; methodology, C.W., J.W. and H.Y.; writing--original draft, C.W.; writing--review and editing, H.Y., J.W. and T.L. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the National Key Research and Development Program of China (2016 YFC0803103).  
We are very grateful to anonymous reviewers for their constructive comments and suggestions.  
The authors declare no conflicts of interest.
###### Abstract
Moreover, the contemporary security schemes applied in laptops, personal computers, and smartphones cannot be directly adopted for IoT systems due to the resource-constrained nature of IoT sensors, devices, and networks. As reported in Table 2, IoT devices come with memory space of a few megabytes (typically, \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Target Device** & **Security Issue** & **Highlight** \\ \hline Multimedia centers and appliances & Insecure Web interface & Compromised devices were used for sending phishing emails, and texts were sent from compromised Blu-ray devices and refrigerators [11]. \\ \hline Surveillance camera & Weak credentials & Internet-connected cameras were compromised to create a botnet and perform DDoS attacks on websites [12]. \\ \hline Programmable Logic Controller (PLC) & Insecure firmware & Reprogrammed with rootkit [13] \\ \cline{2-3}  & Insecure operating system & Processed malicious commands [14] \\ \hline Webcams and smart bulbs & Account enumeration & A botnet was formed using a large number of compromised devices [15] \\ \hline Thermostat & Lack of access control methods & A thermostat was compromised to shut down the heating of a building [16] \\ \hline Vehicle & Insecure Controlled Area Network (CAN) interface & Adversaries took control over radio, dashboard, brake, and acceleration [17] \\ \hline Drug pump & Insufficient authentication and authorization & Adversaries changed the dose of the drug pump [18] \\ \hline Baby monitor & Insufficient authentication and authorization & An Internet-connected baby monitor allowed unauthorized access to its camera [19] \\ \hline Traffic sensors & Insecure firmware & Adversaries sent fake data to traffic control systems [20] \\ \hline Smart TV & Insecure communications & Adversaries eavesdropped on broadcast messages [21] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Real attacks on smart systems. 8-32 KB of RAM and 48-512 KB of ROM) and a CPU with low power consumption (corresponding to a clock frequency of 8-96 MHz), and they have to work with a low-bandwidth connection (corresponding to a low data rate of 16-250 kbps) [23; 24; 25; 26; 27]. Some of the pivotal constraints on employing conventional security solutions in IoT systems are highlighted below:  
1. IoT devices have low-powered CPUs, and most are battery powered. The cryptographic algorithms used in conventional security methods may not be executable on IoT devices, as these devices operate at a slower clock speed. 2. IoT devices have less memory and storage compared to conventional digital devices, such as smartphones and laptops. The security protocols used by conventional devices may not consider memory limitations in their design, so IoT devices may not have enough space in the RAM to load and execute the conventional security methods after loading embedded software, such as operating systems, services, and applications. 3. IoT devices communicate over low-data-rate radio interfaces. Conventional security methods may not be optimized for these lossy and low-powered communication links.
# High Throughput and Acceptance Ratio Multipath Routing Algorithm in Cognitive Wireless Mesh Network
## 7 Conclusions  
Aiming at the problem of link failure in CWMNs, this paper studied the problem of multipath routing and spectrum allocation, which aims to improve the network throughput and the wireless request acceptance ratio, and satisfy the QoS constraint. A multipath routing and spectrum allocation algorithm based on channel interference and reusability was proposed, which included the primary path construction and spectrum allocation algorithm, as well as the backup path construction and spectrum allocation algorithm. In the primary path construction and backup path construction, different link weight functions were proposed. For each new wireless request arrival, a primary path and a backup path were constructed. The simulation results showed that the MRIR could achieve the goal of improving the network throughput and wireless request acceptance ratio. The throughput and acceptance ratio of the MRIR algorithm was higher than that of the Route-Stability and Route-Closeness algorithms. The multipath routing and spectrum allocation algorithm based on power control to meet the QoS constraints is an interesting topic to explore in the future.  
This work is supported in part by the National Natural Science Foundation of China under Grant No. 61309027, 61772553, 61702562, and 61702561; the Scientific Research Fund of Hunan Provincial Education Department under No. 13B148; the China Postdoctoral Science Foundation under No. 2013M542136; and the Key Research and Development Project of Hunan Science and Technology Plan Project under No. 2016 SK2028. [PERSON] and [PERSON] reviewed the multipath routing algorithm in cognitive wireless mesh network in recent years; [PERSON] and [PERSON] classified the various algorithms, and analyzed the data; [PERSON] collected the data; [PERSON] drafted the manuscript; [PERSON] and [PERSON] revised the manuscript. All authors have read and approved the final manuscript.  
The authors declare no conflict of interest.
# Mechanical Structure Design and Experimental Study of Gamma-ray Monitor for Small Satellite Payload
## 2 Materials and Methods  
The core of the novel GRM is a sodium iodide crystal calorimeter and SiPM. According to the requirements of the detection task, the size of the sodium iodide is \(\Phi\)115 mm \(\times\)10 mm. Directly above the sodium iodide crystal is the beryllium window. The main function of the mechanical structure of the novel GRM is to maintain the external configuration of the monitor, and provide the installation space for functional modules such as calorimeter, SiPM, circuit board and independent components, as well as support and protect the internal components of the monitor.
# Software Defined Networks in Wireless Sensor Architectures
## 2 Background
### OpenFlow Architecture  
OpenFlow is an open source protocol that provides instructions to program flow tables within switches and routers. Therefore, a network administrator is able to program the traffic across the network and also control its own flows by selecting their routes based on their requirements. In this way, OpenFlow opens up several possibilities in the research community to elaborate new network protocols.  
The OpenFlow architecture is based on three main entities as is shown in Figure 2: an OpenFlow switch (data plane), an external controller (control plane) and the OpenFlow Protocol [19] that is responsible for the communication between controller and switches through a secure channel.  
On a data plane, one or more flow tables and a group table are used for the packet lookup and data forwarding within an OpenFlow switch. Each flow table is based on a group of match fields, counters and instructions. Match fields manage incoming packets that must be processed by the switch or must be sent to the controller through a secure channel in a process called matching. Moreover, in counters, records are saved regarding the number of packets that are matched. Finally, the instructions field determines the actions to be performed when a matching occurs.  
OpenFlow switches are divided into two categories attending to the ones (dedicated OpenFlow switches) that do not support processing in Layer 2 and Layer 3 traffic forwarding using traditional distributed control protocols, such as RSTP, LLP, and the ones that are used as general purpose commercial Ethernet switches and routers (OpenFlow-enabled switches). In this way, dedicated OpenFlow switches forward incoming packets to the port that the controller has assigned to them. In contrast, OpenFlow-enabled switches are normal commercial switches that are provided with the OpenFlow architecture adding flow tables, secure channel and the OpenFlow protocol.  
The packet treatment is performed in a process called matching. This process is divided into two steps: the first consists of checking the packet header with the match table of a single flow table. The second step depends on the result of the step one. If these two values (packet header and match table) are similar, the corresponding actions of the instructions field are taken. Otherwise, the OpenFlow Protocol specifies the required and optional actions that a single switch can take. Three possible instructions compose the required actions: send the packet by a given port, send the packet to the controller or drop the packet. Even these actions are obligatory, in the case where the incoming packet does not match with a match field, the switch can be configured to send it to the controller or drop it.
# PV Fault Detection Using Positive Unlabeled Learning
## 3 Results
### Evaluation Metrics  
In PU learning in general it is common to have heavily skewed datasets with the rare class generally labeled positive. With only a fraction of the rare positive class labeled, PU classification is a skewed classification problem with too few data to perform class balancing measures. Instead, the F-score (also called the f1-score) is typically used to evaluate each experiment as the accuracy and error rate metrics are misleading when the class sizes are not similar (if 99% of the data were negative and 1% positive, a model that predicts everything negative would have a 99% accuracy and be completely worthless). The F-score is the harmonic mean between the precision and recall (also known as sensitivity). The F-score can be thought of as analogous to accuracy in that it varies between zero and one, with better models being closer to one. The F-score is calculated as:  
\[Fscore=2\cdot\frac{Precision\cdot Recall}{Precision+Recall} \tag{10}\]  
where:  
\[Precision=\frac{\#\ True\ Positives}{\#\ True\ Positives+\#\ False\ Positives} \tag{11}\]  
and:  
\[Recall=\frac{\#\ True\ Positives}{\#\ True\ Positives+\#\ False\ Negatives}. \tag{12}\]  
For each fault type and \(c\) value, the MLRf algorithm was run five times and the mean value was chosen as the F-score. This was intended to reduce variance, though we found that the variance per run was minimal when \(c\) was greater than 10%. High variance with small \(c\) values is not unexpected as the random selection of the labeled samples becomes more impactful as the number of samples decrease. Tables including these mean and variance values are available in Table 11 in Appendix A.
# A Federated Generalized Linear Model for Privacy-Preserving Analysis
## 1 Introduction
### Generalized Linear Models  
The term generalized linear model (GLM) refers to a large class of models popularized by [PERSON] ([PERSON] and [PERSON], 2018). In these models, the response variable \(y_{i}\) is assumed to follow an exponential family distribution with mean \(\mu_{i}\), which is assumed to be some (often non-linear) function of \(x_{i}^{T}\beta\).  
There are three components to any GLM. First, there is the _random component_, which describes the probability distribution of the response variable \(y\). We will consider only cases in which the observations come from a distribution in the exponential family with probability density function as given by Equation (1):  
\[f(y,\theta)=exp\Bigg{\{}\frac{y\theta-b(\theta)}{a(\phi)}+c(y,\phi)\Bigg{\}} \tag{1}\]  
Here, \(\theta\) is the canonical parameter (such that \(\mathbb{E}(y)=\mu=b^{\prime}(\theta)\) and \(Var(y)=a(\phi)b^{\prime\prime}(\theta)\)). It is straightforward to show that the canonical parameter for \(y\sim N(\mu,\sigma^{2})\) is \(\theta=\mu\), and the canonical parameter for \(y\sim Bin(n,\pi)\) is \(\theta=logit(\pi)=log\Big{(}\frac{\pi}{1-\pi}\Big{)}\).  
Secondly, there is a _systematic component_, which defines how the linear combination of the explanatory variables \(x=(x_{1},x_{2},\ldots,x_{k})\) define the linear predictor Equation (2), where \(\beta\) must be estimated:  
\[\eta=x^{T}\beta \tag{2}\]  
Lastly, there is a _link function_\(g(\cdot)\), which specifies the link between the random and the systematic components (depending on how the mean function is expressed). The most commonly used link function for a normal model is \(\eta=\mu\), while for a binomial model, it is \(\eta=logit(\pi)\). Note that whenever \(\eta=g(\mu)=\theta\), we say that the model has a canonical link.