esa-sceva/satcom-chunk-collection huggingface dataset:
# Labeling Expert: A New Multi-Network Anomaly Detection Architecture Based on LNN-RLSTM
## 6 The RLSTM Model  
The multi-neural network model is a fusion of supervised and unsupervised learning algorithms. The keys of this model are labeling neural network and LSTM-based RLSTM neural network. Therefore, the detailed structure of the labeling neural network will be introduced, LSTM neural network, and the LNN-RLSTM model outlined. Long short-term memory (LSTM) is a special form of recurrent neural network first proposed by [PERSON] [4] in 1997. In the gradient back-propagation phase, traditional recurrent neural networks suffer from gradient disappearance and gradient explosionwhen the gradient signal is multiplied by many times. LSTM neural networks overcome the long-term dependence and solve the problem of too small an impact factor due to the large time span by cell variables. These features of LSTM neural networks are just suitable for user-aware classes that possess time-series properties and the anomaly detection of KPI data in the network. To simulate the RLSTM, a graph of the operational structure of a single neuron is constructed in Figure 10. \(C^{(t-1)}\) is the input to the memory unit, \(C^{(t)}\) is the memory unit state, \(\widetilde{C}^{(t)}\) is the updated memory unit state, \(O^{(t)}\) is the value of the output gate at moment \(t,\ W_{f},\ W_{c},\ W_{u},W_{r},\ W_{o}\) is the weight matrix and \(b_{f},\ b_{c},\ b_{u},\ b_{r},\ b_{o}\) is the deviation vector. The key to LSTM networks is to add or remove information using memory unit states, which are regulated by structures called \"update gates\" and \"forget gates\". In fact, the traditional LSTM neural network is improved by adding a \"correlation gate\", which adds the correlation of the before and after time series as a weighting factor in the calculation of \(\widetilde{C}^{(t)}\). The gates act like filters, allowing optional information to pass through. They consist of a sigmoid neural layer with output values between 0 and 1. The formulas inside these gates are not indicated in Figure 1, but they will be given in detail later in the procedure. The point-state multiplication operations \(\oplus\) and \(\ominus\) are used to denote the algebraic operations referred to in the RLSTM internals. From Figure 10, an RLSTM has four gates to control the information, which are: update gate, forget gate, correlation gate and output gate. Through the RLSTM (Algorithm 3) neural network, the time-series information will be transmitted, filtered, combined and finally output a whole message to the next memory storage unit. The detailed steps of transmission are shown below. **Step 1: Calculate update gate to store new useful information**  
\[U_{t}=\sigma\Big{(}W_{u}\Big{[}a^{(t-1)},x^{(t)}\Big{]}+b_{u}\Big{)}\]  
**Step 2: Calculate forgetting gate to forget the redundant old information**  
\[f_{t}=\sigma\Big{(}W_{f}\Big{[}a^{(t-1)},x^{(t)}\Big{]}+b_{f}\Big{)}\]  
Figure 10: Structural diagram of RLSTM.
# Adaptive Packet Coding for Reliable Underwater Acoustic Communications
## 6 Proposed Two-Step DP Approach
### The Cost of the \(i\)-th Transmission  
For the \(i\)-th transmission, the system state for the \((k+1)\)-th block is \(\mathbf{c}^{(k+1)}_{i}=(q^{(k)}_{i-1})\), \(h^{(k)}_{i-1}\), \(q^{(k+1)}_{i-1}\), \(h^{(k)}_{i-1}\), \(B^{(k+1)}_{i})\). The expected cost incurred from the \(i\)-th transmission of the \((k+1)\)-th block to termination is given as Equation (51).  
\[I^{\prime}_{i}(\mathbf{c}^{(k+1)}_{i})=\left\{\begin{array}{cl}0,&\text{ if }q^{(k)}_{i-1}\geq W,q^{(k+1)}_{i-1}\geq W\\ J_{i}(h^{(k+1)}_{i-1},q^{(k+1)}_{i-1}),&\text{ if }q^{(k)}_{i-1}\geq W,q^{(k+1)}_{i-1}<W\\ \mathbb{E}\big{[}J_{i+1}(h^{(k)}_{i},q^{(k)}_{i})|(h^{(k)}_{i-1},q^{(k)}_{i-1}) \big{]},&\text{ if }q^{(k)}_{i-1}<W,q^{(k+1)}_{i-1}\geq W\\ \min_{N^{(k+1)}_{i}}\left\{\gamma^{i}(T^{(k+1)}_{i})+\gamma\mathbb{E}\big{[}J ^{\prime}_{i+1}(\mathbf{c}^{(k)}_{i+1})|\mathbf{c}^{(k+1)}_{i}\big{]}\right\}&\text{ if }q^{(k)}_{i-1}<W,q^{(k+1)}_{i-1}<W\end{array}\right. \tag{51}\]  
The conditional expectation in (51) is given as  
\[\mathbb{E}\Big{[}J^{\prime}_{i+1}(\mathbf{c}^{(k)}_{i+1})|\mathbf{c}^{(k+1)}_{i}\Big{]} =\sum_{s^{(k)}_{i}=0}^{N^{(k)}_{i}}\sum_{l=0}^{L}I^{\prime}_{i+1}(\mathbf{c}^{(k)} _{i+1})P_{s}(s^{(k)}_{i},h^{(k)}_{i},N^{(k)}_{i})P(h^{(k)}_{i}=\delta_{i}|h^{( k)}_{i-1}) \tag{52}\]  
It is interesting that (52) does not seem to have much to do with \(N^{(k+1)}_{i}\). The transition probability mainly depends on the channel state \(h^{(k)}_{i-1}\) and the transmitted packets of \(N^{(k)}_{i}\). However, \(N^{(k+1)}_{i}\) affects \(I^{\prime}_{i+1}(\mathbf{c}^{(k)}_{i+1})\) by limiting the range of \(N^{(k)}_{i+1}\) in (52).  
For the \(k\)-th block, the system state is \(\mathbf{c}^{(k)}_{i}=(q^{(k)}_{i-1}\), \(h^{(k)}_{i-1}\), \(q^{(k+1)}_{i-2}\), \(h^{(k)}_{i-2}\), \(B^{(k)}_{i})\). The expected cost incurred from the \(i\)-th transmission to termination is given by (53). Similarly, we have  
\[I^{\prime}_{i}(\mathbf{c}^{(k)}_{i})=\left\{\begin{array}{cl}0,&\text{ if }q^{(k)}_{i-1}\geq W,q^{(k+1)}_{i-2}\geq W\\ J_{i}(h^{(k)}_{i-1},q^{(k)}_{i-1}),&\text{ if }q^{(k)}_{i-1}<W,q^{(k+1)}_{i-2}\geq W\\ \mathbb{E}\big{[}J_{i}(h^{(k+1)}_{i-1},q^{(k+1)}_{i-1})|(h^{(k+1)}_{i-2},q^{(k +1)}_{i-2})\big{]},&\text{ if }q^{(k)}_{i-1}\geq W,q^{(k+1)}_{i-2}<W\\ \min_{N^{(k)}_{i}}\left\{\gamma^{i}\mathbb{E}\Big{[}T^{(k)}_{i}|\mathbf{c}^{(k+1) }_{i}\Big{]}+\mathbb{E}\big{[}J^{\prime}_{i}(\mathbf{c}^{(k)}_{i})|\mathbf{c}^{(k+1)} _{i}\big{]}\right\}&\text{ if }q^{(k)}_{i-1}<W,q^{(k+1)}_{i-2}<W\end{array}\right. \tag{53}\]  
\[\mathbb{E}\Big{[}J^{\prime}_{i}(\mathbf{c}^{(k+1)}_{i})|\mathbf{c}^{(k)}_{i}\Big{]}= \sum_{s^{(k+1)}_{i-1}=0}^{N^{(k+1)}_{i-1}}\sum_{l=0}^{L}I^{\prime}_{i}(\mathbf{c}^{(k +1)}_{i})P_{s}(s^{(k+1)}_{i-1},h^{(k+1)}_{i-1},N^{(k+1)}_{i-1})P(h^{(k+1)}_{i-1} =\delta_{i}|h^{(k+1)}_{i-2}) \tag{54}\]  
\[N^{(k)}_{i}\text{ also has no influence on transfer probability and affects }I^{\prime}_{i}(\mathbf{c}^{(k+1)}_{i})\text{ by limiting the range of }N^{(k+1)}_{i}\text{.}\]
## 3 Problem Explanation  
### Assumptions and System Model  
We consider multi-hop transmissions in a purely ad-hoc mode scenario, in which a single channel is used to communicate the clustered mobile hops (i.e., nodes). A unique ID is given to each node within a cluster, depending on its MAC address. Each node delivers its packets depending on the routing protocols by operating in the ad-hoc mode. Figure 1 depicts a simple scenario of V2V communication in multi-hop environment. The authors in [26] addressed that, in smart driving applications, a carrier sense multiple access with collision avoidance (CSMA/CA)-dependent medium access control protocol is unable to control the increased message density. Therefore, a likely solution to solve this issue is a TDMA-based MAC.  
In the proposed scenario, we assume a four-lane vehicular environment to provide location-based information to each node by considering an IEEE 802.11p standard radio device with global positioning system (GPS) installation. Every node contributes its information to their one-hop neighbors about its current position, direction, lane, and speed. By this, we enforce that each safety message produced by a hop inside a cluster should be delivered triumphant to every surrounding clusters in the direction of message travel until the fourth hop (\(h_{c}\)).
## 5 Weighted Least-Squares Approach to State Estimation
### Interpretation of the Filters and Smoothers
It therefore follows that the contemporaneous-form filter in Equations (88) and (89) is equivalent to the filter in Equations (25) and (26) and that the future-form filter in Equations (92) and (93) is equivalent to the filter in Equations (41) and (42), although these equivalences are far from obvious; however, they can be verified either through their application to numerical examples, or--preferably--by explicit algebraic manipulation. As an example of the latter approach, it is shown in the Appendix A how Equation (88) can be transformed into Equation (25). The transformation for the corresponding expressions for the covariance matrix \(\mathsf{P}_{t|t}\) could probably be performed in a similar way; the same remark also applies to the expressions for the conditional mean vector and covariance matrix of the future-form filter. Finally, both the contemporaneous-form smoother algorithm in Equations (90) and (91) and the future-form smoother algorithm in Equations (94) and (95) represent generalisations of the standard RTS smoother algorithm.
## 3 Results and Discussion  
### Master Curves Result Analysis  
#### 3.1.1 Master Curves before Aging  
The G* master curves and \(\delta\) master curves of the HCRMB with different amounts of sulfur are shown in Figure 1. Based on Figure 1a, the ranking of G* master curves in the high frequency region (low temperature region) is 40 TB_0.25 ul\(<\) 40 TB_0.4 Sul \(<\) 20 TB_0.25 ul \(<\) 20 TB_0.45 ul \(<\) 70%, and the ranking of G* master curves in low frequency region (high temperature region) is 40 TB_0.25 ul \(<\) 40 TB_0.45 ul \(<\) 20 TB_0.25 ul \(<\) 70% \(<\)20 TB_0.45 ul. The above results show in the high frequency region (low temperature region), the G* master curves of 70% are higher than that of the HCRMB with different amounts of sulfur, and increasing the sulfur content can improve the elasticity of HCRMB. This may be due to the moderate depolymerization of the macromolecular structure in the CR in RMB during the TB process preparation process, which results in partial plasticity and viscosity, but HCRMB also loses some of the elasticity of the CR at low temperature environments, and the more CR is doped in the HCRMB, the more elasticity it loses, resulting in a lower G* value for 40 TB_0.45 ul than for 20 TB_0.45 ul [29]. Moreover, according to Figure 1b, the ranking of \(\delta\) master curves in the high frequency region (low temperature region) is 70%\(<\) 20 TB_0.45 ul \(<\) 20 TB_0.25 ul \(<\) 40 TB_0.45 ul \(<\) 40 TB_0.25 ul. The ranking of \(\delta\) master curves in low frequency region (high temperature region) is 40 TB_0.45 ul\(<\) 40 TB_0.25 ul \(<\) 20 TB_0.45 ul \(<\) 20 TB_0.25 ul \(<\) 70%. This also indicates that the elasticity of HCRMB at low temperatures is enhanced by the incorporation of sulfur. The cause of this result lies in the ability of sulfur to make the HCRMB form a cross-linked network between the CR, which can improve the elasticity of the HCRMB [30].
_Applied_  
_sciences_  
Exercise Training of Secreted Protein Acidic and Rich in Cysteine (_Sparc_) KO Mice Suggests That Exercise-Induced Muscle Phenotype Changes  
Are SPARC-Dependent  
**[PERSON] 1,2**(r)**, **[PERSON] 1,2**, **[PERSON] 2** and **[PERSON] 1,2,*(r)**  
1 Department of Molecular Medicine, Faculty of Medicine, Laval University, Quebec, QC G1V 0A6, Canada; [EMAIL_ADDRESS] (A.G.); [EMAIL_ADDRESS] (A.M.)  
2 Functional Genomics Laboratory, Endocrinology and Nephrology Axis, CHU de Quebec-Universite Laval Research Center, Quebec, QC G1V 4G2, Canada; [EMAIL_ADDRESS]  
* Correspondence: [EMAIL_ADDRESS]; Tel.: +1-418-654-2296; Fax: +1-418-654-2761  
Received: 1 December 2020; Accepted: 15 December 2020; Published: 20 December 2020  
Featured Application: This work highlights secreted protein acidic and rich in cysteine (SPARC) and its pathways as pharmacological targets/tools for conditions and diseases in which muscle properties enhancement would provide therapeutic benefits.  
**Abstract:** We previously identified secreted protein acidic and rich in cysteine (_Sparc_) as an exercise-induced gene in young and elderly individuals. Via this animal experiment, we aim to identify selected implications of SPARC mainly within the muscle in the contexts of exercise. Mice were divided into eight groups based on three variables (age, genotype and exercise): Old (O) or young (Y) \(\times\)_Sparc_ knock-out (KO) or wild-type (WT) \(\times\) sedentary (Sed) or exercise (Ex). The exercised groups were trained for 12 weeks at the lactate threshold (LT) speed (including 4 weeks of adaptation period) and all mice were sacrificed afterwards. Body and selected tissues were weighed, and lactate levels in different conditions measured. Expression of skeletal muscle (SM) collagen type I alpha 1 chain (COL1A1) and mitochondally encoded cytochrome c oxidase I (MT-CO1) in addition to SM strength (grip power) were also measured. Ageing increased the body and white adipose tissue (WAT) weights but decreased SM weight percentage (to body weight) and MT-CO1 expression (in WT). Exercise increased SM COL1A1 in WT mice and MT-CO1 expression, as well as weight percentage of the tibialis anterior muscle, and decreased WAT weight (trend). Compared to WT mice, _Sparc_ KO mice had lower body, muscle and WAT weights, with a decrease in SM MT-CO1 and COL1A1 expression with no genotype effect on lactate levels in all our blood lactate measures. _Sparc_ KO effects on body composition, adiposity and metabolic patterns are toward a reduced WAT and body weight, but with a negative metabolic and functional phenotype of SM. Whereas such negative effects on SM are worsened with ageing, they are relatively improved by exercise. Importantly, our data suggest that the exercise-induced changes in the SM phenotype, in terms of increased performance (metabolic, strength and development), including lactate-induced changes, are SPARC-dependent.  
**Keywords:** secreted protein acidic and rich in cysteine (_Sparc_); exercise; muscle performance; metabolic phenotype; lactate; ageing
## 3 Results and Discussion
### Analysis of the Dopant Concentration Distribution  
In this work, the dopant diffusivity and concentration were investigated in relation to the process conditions using a zig-zag pattern and a beam overlap ratio of 50%. In this case, the temperatures at three specific points were measured to determine the diffusivity and concentration profile at each corresponding locations within the scanned area. The first temperature (1337 \({}^{\circ}\)C) was taken at the end of the first track, the second temperature (1433 \({}^{\circ}\)C) was taken from the peak point (hot spot) of the temperature profile shown in Figure 3b, and the third temperature (1100 \({}^{\circ}\)C) was taken from the peak point of the temperature profile for point 1 (beam overlap area) shown in Figure 5b. In order to ascertain the diffusivity at the corresponding locations, an Arrhenius plot of the phosphorus diffusivity was obtained using Equation (10), as shown in Figure 6. Further, also in Figure 6, the diffusivity of the dopant is shown to increase with an increase in the temperature. Based on the Arrhenius plot, the diffusivity were determined for the temperatures of 1100 \({}^{\circ}\)C, 1337 \({}^{\circ}\)C and 1433 \({}^{\circ}\)C. These are 1.83\(\times\)10\({}^{-10}\) cm\({}^{2}\)/s for 1100 \({}^{\circ}\)C, 5.46\(\times\)10\({}^{-9}\) cm\({}^{2}\)/s for 1337 \({}^{\circ}\)C and 1.53\(\times\)10\({}^{-8}\) cm\({}^{2}\)/s for 1433 \({}^{\circ}\)C.  
Figure 5: Temperature distribution along the depth direction: (**a**) test 1 and (**b**) test 2.  
Based on these diffusivities, the phosphorus concentration distribution was determined using Equation (9), as shown in Figure 7. From Figure 7, phosphorus diffusion in the depth direction increases with the temperature. The concentration profile for the 1100 \({}^{\circ}\)C (beam overlapped region) condition is nearly identical to that of the as-implanted case, which indicates that phosphorus diffusion is very limited in the beam overlap area. However, at a hot spot (1433 \({}^{\circ}\)C) with a temperature above the melting point (1414 \({}^{\circ}\)C) of silicon, phosphorus diffusion in the depth direction is significantly increased.  
Assuming that the junction depth is a depth at which the concentration of the dopant is equal to \(1\times 10^{18}\) cm\({}^{-3}\), the junction depths obtained for the temperatures of 1100 \({}^{\circ}\)C, 1337 \({}^{\circ}\)C and 1433 \({}^{\circ}\)C are around 84 nm, 146 nm, and 208 nm, respectively. For the as-implanted case, the diffusion length obtained in the temperature range of 1100-1433 \({}^{\circ}\)C is between 4 and 128 nm. For the hot spot, the diffusion length increased by nearly thirty times compared to that of the overlapped area. This major difference in the diffusion length of the dopant can lead to critical problems within a semiconductor device. Accordingly, the most appropriate process condition should be selected to improve the temperature uniformity and minimize the effects of the hot spot.  
Figure 6: Arrhenius plot of phosphorus diffusivity.  
Figure 7: Phosphorus concentration distributions obtained at different temperature conditions.
# A Comprehensive Review on Network Protocol Design for Autonomic Internet of Things
## 3 Review of Protocol Designs
### Protocol Designs for Clustering
#### (1) LEACH-TLCH  
Some studies have offered improvement to the LEACH protocol. [PERSON] et al. in [17] proposed LEACH Two Level Cluster Head (LEACH-TLCH) protocol to balance the energy consumption. In each round, there are two CHs selected. If the first CH has insufficient energy or its distance to the base station is too far away, it can be replaced by the second CH. Through a simulation, the LEACH-TLCH is proved to have improved energy efficiency and longer network lifetime than the original LEACH.
# An Application of a LPWAN for Upgrading Proximal Soil Sensing Systems
## 3 Hardware Design of the AHS
### Ultralow-Power MCU System  
MCUs typically use CMOS technology, and their power consumption mainly includes static power consumption and dynamic power consumption. Static power consumption mainly consists of the energy consumed by transistors, which is almost constant, most of the time. Dynamic power consumption includes switching power consumption, short-circuit power consumption, and burr power consumption. In general, especially when working at a high frequency, dynamic power consumption plays a major role, which can be approximately expressed as the following Equation (1) [16]:  
\[\mathrm{P}=C_{L}\times V_{DD}^{2}\times f \tag{1}\]  
where \(C_{L}\) is the load capacitance, \(V_{DD}\) is the supply voltage, and \(f\) is the clock frequency. The total power consumption is the sum of the static power consumption and dynamic power consumption. Therefore, to reduce the total power consumption, we can reduce the size of the MCU chip or the number of transistors; reducing the MCU supply voltage can reduce power consumption at the square level and reduce the clock frequency to just meet the application needs. In addition, a reasonable choice of working mode, such as entering sleep mode after working at full speed for a very short time, can also greatly save energy [17; 18; 19].  
In this paper, the ultralow-power MCU adopted the MSP430 series, which was specially designed for battery-powered devices in field environments [20]. It adopted a low-power supply voltage of 1.8-3.6 V. When operating under the clock condition of 1 MHz, the power consumption in active mode was only approximately 280 uA, in standby mode it was approximately 1.6 uA, and the minimum power consumption in RAM hold mode was only 0.1 uA. In addition, the MSP430 integrated rich on-chip resources and had multiple interrupt sources, which could be arbitrarily nested and used in a flexible and convenient manner. When the system was in a low-power state, the wake-up interrupt took only 5 us. The minimum ultralow-power MCU system of the AHS is shown in Figure 3.
# SPN-Based Dynamic Risk Modeling of Fire Incidents in a Smart City
## 3 Modeling Methodology and Analysis
### Modeling Analysis  
The reachability set is a key step in verifying the SPN model. It refers to the set of all possible states that can be reached by triggering various transitions starting from the initial state, ensuring a comprehensive check on all possible states of the system and simultaneously eliminating the risk of deadlock. By implementing the reachability algorithm, all the test item places that tokens can reach are determined, and a fault test Dependency Matrix (DM) is established to describe the reachability relationships among them, a process which reveals the ways in which faults propagate in the system and the detection capabilities of tests for faults.  
After judging the feasibility of the model, further performance analysis on the SPN is carried out. Referring to the emergency rescue of a certain urban natural fire event, \(\lambda 0\), \(\lambda 1\), \(\lambda 2\),..., \(\lambda 14\) in the SPN model are set. Set Mi as P(Mi) and bring it into the state equation. Calculate the average probability distribution, place, and transition utilization rates when the system reaches a steady state according to Formula (1), as shown in Tables 1, 3 and 6.  
After completing the calculation of the state probability distribution of the model, an analysis of the results reveals that specific place fire information (p\({}_{2}\)), expert decision information (p\({}_{4}\)), emergency plan upgrade information (p\({}_{5}\)), and rescue feedback information (p\({}_{15}\)) have relatively high utilization probabilities, resulting in the accumulation of information during the rescue process. The transitions analyze alarm information (t\({}_{1}\)), rescue personnel in place (t\({}_{8}\)), open emergency communication (t\({}_{9}\)), road condition isolation completed (t\({}_{10}\)), and allocate rescue materials (t\({}_{11}\)) play a crucial role in accident investigation, assessment, and post-disaster handling, influencing the efficiency of emergency rescue. There are two key turning points in the occurrence of an accident, namely the information processing after the fire breaks out (at analyze alarm information, t\({}_{1}\)) and the moment of expert decision making (at expert decision, t\({}_{4}\)). By changing the average rates of the above two turning points and analyzing their impact on the entire accident development process, it can be seen that, as the onsite crowd's reporting rate increases, the probability of a fire starting (PM\({}_{2}\)) decreases rapidly. Reporting an alarm can prevent accidents to a large extent. The reporting rate is directly proportional to the time required for firefighters to arrive at the scene.
## 2 Background
In recent research, ensemble-based attack detection methods that combine multiple machine- and deep-learning-based detectors have been proposed [18; 19; 20; 21]. These studies use multiple detectors to overcome the weaknesses of the machine and deep learning, such as a limited amount of training data, an imbalance between normal and abnormal samples, and the selection of optimal hyperparameter settings. In the simple strategy, the results are ensembled based on majority voting [18] and veto voting [18; 19]. The veto voting determines the \"malware\" regardless of the number of votes, even if one vote claims the file is malware. The literature [20; 21] makes the final malware detection based on the predicted class probabilities from multiple detectors. To ensemble the results from multiple detectors, [PERSON] et al. [20] attempted weighted voting predictions based on the prediction accuracy of each detector. [PERSON] and [PERSON] [21] used a soft voting strategy to ensemble the multiple detection results. The soft voting strategy is one of the majority voting strategies that averages the class probabilities from multiple detectors and obtains the answer according to the maximum average class probability. This strategy can be useful in an environment where each detector can give the class probabilities of all categories. Although these papers mentioned weighted majority voting, they do not clarify how weights are calculated.