esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Advantages and Objectives of Clustering
Mechanisms of energy hole avoidance, _i.e._, energy consumption balancing, can be classified into three groups: node deployment, load balancing, as well as energy mapping and assigning [53]. Especially, uneven clustering is one of the methods of load balancing. In this method, a smaller cluster radius near the sink and a larger cluster radius away from the sink are defined respectively, so the energy consumption of processing data in inter-cluster is less for cluster with smaller radius, and thus more energy can be used to relay data from remote nodes [54]. On the other hand, it is not easy to analyze the optimization of cluster radius theoretically [55]. _Maximizing of the Network Lifetime_: Network lifetime is an inevitable consideration in WSNs, because sensor nodes are constrained in power supply, processing capability and transmission bandwidth, especially for applications of harsh environments. Usually it is indispensable to minimize the energy consumption for intra-cluster communication by CHs which are richer in resources than ONs. Besides, sensor nodes that are close to most of the sensor nodes in the clusters should be prone to be CHs. Additionally, the aim of energy-aware idea is to select those routes that are expected to prolong the network lifetime in inter-cluster communications, and the routes composed of nodes with higher energy resources should be preferred. _Quality of Service_: The network applications and the functionalities of WSNs prompt the requirement of quality of service (QoS). Usually, effective sample, less delay and temporary precision are required. It is difficult for all the routing protocols to satisfy all the requirements of QoS, because some demands may breach one or more protocol principles. Existing clustering routing approaches in WSNs mainly focus on increasing energy efficient rather than QoS support. QoS metrics must be taken into account in many real-time applications, such as battle-target tracking, emergent-event monitoring, and _etc._
## 3 Thin-Film Materials and Their Applications  
### Inorganic Thin-Film Materials  
Inorganic dissolvable thin-films have been explored including monocrystalline/polycrystalline/amorphous silicon (mono/poly/a-Si), ZnO, MgO, and indium-gallium-zinc oxide (a-IGZO) as semiconductors [4; 4; 72; 73; 74]; Mg, Zn, tungsten (W), molybdenum (Mo), and Fe as conductive materials [5]; and MgO, SiO\({}_{2}\), and silicon nitride (SiN\({}_{\rm x}\)) for dielectric materials [4; 16; 75]. In water or simulated body fluids (Hanks' solution), the dissolution of them involves hydrolysis to produce corresponding oxides and/or hydroxides which are water-soluble. Therefore, they are selected to use in resorbable, degradable, and compatible applications. Furthermore, great efforts have been devoted to exploring new inorganic materials such as perovskite for environmental applications, especially halide perovskite [76; 77; 78; 33; 79; 80; 81]. Some researches indicate that halide perovskite materials can also be utilized for various applications in terms of environmental protection, for instance, environmental-friendly sensors, disposable consumer devices, and green electronics [77; 80; 81; 82; 78]. The products we use today are more environmental-friendly and efficient, while the method of obtaining energy is definitely less harmful. In this section, environmental-friendly applications of the various inorganic thin-film materials are reviewed.  
Figure 1: **(a)** Printing schematic diagram: (I) printing a base elastomer layer, (II) spray printing liquid metal materials, (III) activating selectively the electrical path, and (IV) printing of an encapsulation elastomer. Reproduced with permission from [69], Copyright WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim, 2017. **(b)** Scheme of the key process for transfer printing a transient device from a temporary substrate (such as Si) to a degradable substrate (such as poly lacttico-glycolic acid (PLGA)). Reproduced with permission from [9], Copyright WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim, 2014.
## 4 Simulation Experiments
### Testing Evaluation  
We tested the methods in an environment different from the training environment. The test environment contained abundant elements, including columns, square columns, walls, etc. The test scenario was a long corridor (in Figure 10), where the agent needed to go from one end to the other and for more than 20 m.  
Figure 8: Comparison of three methods in the first stage. (**a**) The reward of methods. (**b**) The step-Q of methods.  
Figure 9: Comparison of three methods in the second stage. (**a**) The reward of methods. (**b**) The step-Q of methods.  
We tested the success rates of three comparison methods in the test environment. The final weights of the three policies in training were tested 1000 times for the path planning process. The success rate table is in Table 2.  
It can be seen that the success rate of the original DDPG algorithm was only 27.49%, and its obstacle avoidance ability in such a long-distance environment with dense obstacles was limited. SO-DRL with EGO-Planner optimization is better than SO-DRL with A* algorithm optimization and both SO-DRL methods have excellent path planning and obstacle avoidance effects. It can be seen that SO-DRL outperformed the other two frameworks in generalization performance in different environments.  
We randomly selected four sets of velocity in the test to demonstrate the control process of our method on the agent in Figure 11.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Method** & **Success Rate** & **Episode** & **Time Consuming** \\ \hline Original DDPG & 27.49\% & 1000 & 89 min \\ SO-DRL(A*) & 87.23\% & 1000 & 84 min \\ SO-DRL(EGO) & 96.64\% & 1000 & 83 min \\ \hline \hline \end{tabular}
\end{table}
Table 2: The success rate of the three methods tested in different scenarios.  
Figure 11: _Cont._  
Figure 10: The test environment was a narrow corridor with an area of 24 m \(\times\) 3 m, with rich obstacle elements and high-difficulty obstacle avoidance requirements.
## 2 Effect of Floating DC Bus on System Stability
### Validation of Methodological Approach  
Dynamic transients are useful for validating both approaches with regards to the large-signal stability, i.e., BA and RAS. To this aim, BA and RAS for the hard lower bound case (i.e., working point \(v_{0}=0.8\) p.u. and \(i_{0}=1.25\) p.u.), together with the dynamic transients after the perturbation, are depicted in the \(v\)-\(i\) state plane of Figure 6. As can easily be seen, the BA (blue line) can correctly assess system stability. As expected, the transient starting from outside the basin at \(v(t_{0})=0.56\) p.u. (black point) diverges, whereas the red trace of the transient starting inside the basin at \(v(t_{0})=0.59\) p.u. (red point) converges toward the stable working point. Conversely, the RAS (green line) covers only part  
Figure 4: Voltage transients due to a voltage impulse perturbation at \(t=0.2\) s.  
Figure 5: Current transients due to a voltage impulse perturbation at \(t=0.2\) s.  
of the actual BA. This is expected, as the RAS is based only on a sufficient condition. Therefore, it is impossible to predict the system stability through Lyapunov analysis for the transient starting from \(v(t_{0})=0.59\) p.u. (red point).  
However, although the RAS can only partially analyze the large-signal behavior, it can constitute a valuable method for designing a stability preserving criterion for floating-bus DC microgrids. Indeed, the determination of the RAS is possible after the identification of the voltage limit above which the Lyapunov first derivative (Equation (2)) is negative.  
\[\overset{\bullet}{\textbf{\Psi}}(v)=\frac{1}{lc}\bigg{\{}v+rp\frac{1}{v}-e \bigg{\}}\cdot\bigg{\{}-\frac{r}{l}(v-v_{0})-\frac{p}{c}\frac{1}{v}+\frac{p}{cv _{0}}\bigg{\}}. \tag{2}\]  
For the microgrid under study, such a limit is expressed by Equation (3).  
\[\overset{\bullet}{\textbf{\Psi}}(v)\leq 0\Leftrightarrow v\geq\frac{pl}{rcv_{0}} :=v_{\text{min}}. \tag{3}\]  
By assuming the system parameters used in Reference [33]--CPL power \(p=1\) p.u. and working point \(v_{0}=0.8\) p.u.--the voltage limit \(v_{\text{min}}=0.6055\) p.u. can be determined (represented by the \"\(\times\)\" in Figure 6). Actually, \(v_{\text{min}}\) and the basin's voltage limit (yellow triangle located at \(v=0.5768\) p.u.) are very close, having a difference smaller than 0.03 p.u. Considering this gap negligible, the voltage limit \(v_{\text{min}}\) can act as an effective, yet still conservative, margin for the large-signal stability in the presence of the class of perturbations envisaged in Section 2.4. Moreover, it has to be noted that Equation (3) is a simple equation that can be evaluated immediately, thus making the voltage limit assessment very easy.
# Overview on Different Types of Solar Cells: An Update
## 3 Perovskite Solar Cells
### The Meaning of Perovskite
As a result, the rapid and effective separation of electrons and holes are observed through the ETL and HTL, respectively [127]. Due to the minimum number of interfaces, planar heterojunction structures help to understand the operating mechanisms of light absorption and electron-hole separation precisely and optimize the structure for the development of highly efficient laminated perovskite photovoltaic cells. Besides the two abovementioned structures, perovskite solar cells without HTL have become an interesting research project due to their advantages of easy and simple device fabrication and high stability [128]. These perovskite materials served the purpose of both light absorbers and hole transport channels. Using device simulation, [PERSON] and [PERSON] showed that the built-in electric field was not affected  
Figure 5: (**a**) 3D and (**b**) 2D schematic diagrams and (**c**) the schematic diagram of a perovskite unit cell [116]. if the work function of metal electrodes was close to the valence band maximum of the light-absorbing materials for the case of HTL-free perovskite solar cells [129]. Usually, the perovskite layer is spin-coated on the substrate via either a single-step or two-step process. In the single-step process, perovskite precursor (a mixture of CH\({}_{3}\)NH\({}_{3}\)I and PbI\({}_{2}\) dissolved in DMF:DMSO) is spin-coated onto the FTO substrate in one step and kept at 100\({}^{0}\) C to achieve a uniform perovskite film. In recent times, an anti-solvent method is effective, in which the perovskite precursor solution is prepared in the polar solvent and quenched during the process of spin coating by a non-polar solvent. It is often observed that precise control over the volume of quenching solvent and timing is very much essential to achieve uniform morphology and optimal device performance. In the two-step process, lead iodide (PbI\({}_{2}\)) was the first to be spin-coated and then transferred into a vessel where 1 molar CH\({}_{3}\)NH\({}_{3}\)I was dissolved in isopropanol. Other than these two frequently used methods, OIHPs can also be deposited via a 'vacuum-assisted solution process' (VASP) in which perovskite films are coated and annealed in a chamber filled with the organic component vapor [130].
## 6 Pre-Posterior Analysis  
Based on the decision tree presented in Figure 1, a pre-posterior analysis of the cost effectiveness of chloride content measurement was performed. For the analysis, values of the uncertainties of events were chosen based on the previous chapters, as presented in Table 3.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Variable:** & **Value [–]** \\ \hline \(P_{dep}\) & 0.2, 0.4, 0.6, 0.8 \\ \(P\left(D_{clock}\mid D_{dep}\right)\) & 0.80 \\ \(P\left(D_{dep}\mid T_{no}\right)\) & 0.10 \\ \(P\left(D_{no\text{-}dep}\mid T_{no}\right)\) & 0.90 \\ \(P\left(D_{ddp}\mid T_{yes}\right)\) & 0.90 \\ \(P\left(D_{no\text{-}dep}\mid T_{yes}\right)\) & 0.10 \\ \(P_{rep}\) & 0.875 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Values of the uncertainty in the decision tree used for the analysis (see Table 1).  
The calculation was performed for different probabilities of depassivation, \(P_{dep}\), namely \(P_{dep}\) = 0.2, \(P_{dep}\) = 0.4, \(P_{dep}\) = 0.6, and \(P_{dep}\) = 0.8. These probabilities were chosen to represent different concrete mixtures, since a general case was analyzed. The probability of damage occurring, given that the depassivation was indicated, \(P\) (\(D_{crack}\) \(|\)\(D_{dep}\)), was taken as 80%. Since the critical chloride level, \(C_{crit}\), is not an exact number, \(P\) (\(D_{crack}\) \(|\)\(D_{dep}\)) covers possible situations where the chloride level is higher than the critical but does not cause the depassivation, or situations where depassivation occurs but does not cause any damages. The probability of successful repair was set as \(P_{rep}\) = 0.875, as described in the chapter \"repair methods\". For the purpose of the analysis, the actual chloride level was assumed to approximately correspond to the critical chloride level. This assumption led to the probability of correct measurement (i.e., probability of detection) being considered with an accuracy of 90% (previously elaborated in the chapter \"measuring techniques\").  
From the presented uncertainties, it was possible to calculate the ratio of the maximum cost of measurement, \(C_{meas,max}\), which is still cost effective, as dependent on the cost of proactive and reactive repairs. The calculated \(C_{meas,max}\) are herein presented graphically through charts in Figure 2, and through Equations (10) and (11). Figure 2. shows the graphs for the ratio of the cost of proactive, \(C_{rep,p}\), and reactive repair, \(C_{rep,r}\), and calculated maximum still cost-effective cost of measurement, \(C_{meas,max}\), for four different probabilities of depassivation. In this way, one can easily graphically extract the value of the maximum cost-effective cost of measurement, \(C_{meas,max}\), and translate it to a real cost, in a real decision-making situation.
## 2 Related Work  
Consumer electronics, commercial aviation, and medical devices are adjacent industries that have many hardware devices containing loadable firmware components and have the task to track and verify the installation of the software updates across their fleets. In the NHTSA report [6], the authors present a literature review of the state-of-the-art of software updates in the industries related to the automotive industry and conclude that there are mainly two common existing defense mechanisms: trusted content distribution networks and digitally signed software updates.  
The Internet of Things (IoT) devices have similar resource constraints (e.g., energy, computation, and storage capacity) to the automotive ECUs [7; 8]. The Trusted Computing Group (TCG) has released a reference report [9] describing how secure software and firmware update for embedded systems can be performed using trusted computing technologies. The Internet Engineering Task Force (IETF) Software Updates for Internet of Things (SUIT) working group is actively working on specifying a software update architecture for IoT devices [10].  
Numerous mechanisms have been considered, standardized, and adopted by the automotive industry for software updates over the air in the vehicle. Uptane [11] and other proprietary frameworks (e.g., [12]) have been introduced to manage the software update process. In addition, different security mechanisms have been used to secure the software updates by means of hashing mechanisms [13; 14] as well as symmetric and asymmetric cryptographic mechanisms [15; 16]. SOTA approaches have been extended by using compression [17] and delta flashing [18] to reduce the network utilization and bandwidth used for software updates. Blockchain-based approaches [19; 20] have also been considered for performing software updates for vehicles to guarantee the authenticity and integrity of new updates. The implementation of blockchain for automotive software updates is promising for the new electrical/electronic (E/E) architecture; however, it requires considerable efforts for OEMs as well as high resource consumption at the ECU level. We categorize these SOTA solutions in the automotive industry based on the security mechanisms used by the researchers and summarize them in Table 1. In a typical new-generation vehicle architecture, the topology of an ECU has several software entities (known as software clusters). Software updates of such clusters need to be applied across a distributed system of automotive ECUs that are designed and serviced by different suppliers. Hence, OEMs have to shift from the traditional software update approaches toward a more flexible and scalable approach required for such software-defined architecture.
###### Abstract  
Safety stock (SS) is an appropriate tactic to deal with demand and supply uncertainty with the aim of preventing inventory shortages. In the literature, previous work on SS estimation assumes that the forecast error distributions (FED) are independent and identically distributed (i.i.d) following the normal distribution. In order to assess violations of this assumption, there are many solution methods in the recent literature that include the following: (1) Consider the FED as other distribution models, such as gamma distribution or log-normal distribution, etc. (2) Use the Generalized Auto-Regressive Conditional Heteroskedasticity (GARCH) model to consider the Heteroskedasticity phenomena, (3) Use the extreme value theory (EVT) to take into consideration the occurrence of extreme demands, etc. However, the performance of these methods is not guaranteed because there is an absence of comparative studies. Indeed, the estimation of SS is based on the approximation of quantiles of the FED. Such quantiles are related to the cycle service levels (CSL) that are important to achieve company goals. Accordingly, the aim of this research is to propose two combined empirical methods to determine the SS in a more robust fashion and compare them with traditional methods under different supply chain parameters. The first combined method, named Filtered Historical Simulation (FHS), consists of combining the GARCH model with the simulation method. The second combination named Conditional Extreme Value Theory (CEVT) is the GARCH model with EVT. To validate these proposed combined methods, the SS is also estimated using traditional methods, such as simple exponential smoothing (SES), simulation, and kernel density estimation (KDE). The methodology is illustrated with both simulation data and real case study data for different lead times. For the FED, two cases are studied: lognormal distribution and gamma distribution. The results show the superiority of the two proposed combination methods with respect to the tick loss function (TLF) for the different CSL targets and for shorter and longer lead times. Results are confirmed using the ANOVA test.  
safety stock estimation; forecast errors distribution; heteroscedasticity; extreme demands; GARCH model; simulation; EVT; CEVT; FHS; tick loss function; inventory investment; backorders
# Modeling and Reasoning of Contexts in Smart Spaces Based on Stochastic Analysis of Sensor Data +
## 4 Principles of Context Reasoning
### Context Graph Building Steps
#### 4.2.2 Defining Contexts  
We observe that a meaningful state space is sufficiently distant from other meaningful state spaces but could be close to other relevant, yet non-meaningful, state spaces. To find out which state spaces are meaningful, all spaces are partitioned into \(\mathbf{k}\) clusters, in which each state space belongs to the cluster with the nearest mean. Therefore, the universal set of state spaces \(\mathbf{S}_{\mathbf{i}}=\{\mathbf{S}_{\mathbf{i}},\ \ldots,\ \mathbf{S}_{\mathbf{i}},\ \ldots,\mathbf{S}_{\xi}\}\) is divided into \(\big{\{}\hat{\mathbf{S}}_{\mathbf{i}},\ \ldots,\ \hat{\mathbf{S}}_{\mathbf{i}},\ \ldots,\hat{\mathbf{S}}_{\xi}\}\), where \(\mathbf{S}_{\mathbf{i}}\) is a state space and \(\hat{\mathbf{S}}_{\mathbf{i}}\) is a cluster of state spaces. Each cluster \(\hat{\mathbf{S}}_{\mathbf{i}}\) minimizes the sum of distances between the within-state space and the mean, according to the following formula:  
\[\operatorname*{argmin}_{\mathbf{S}_{\mathbf{i}}}\sum_{i=1}^{k}\sum_{\mathbf{S}_{\mathbf{i}}\in \hat{\mathbf{S}}_{\mathbf{i}}}\|\mathbf{S}_{\mathbf{i}}-\mathbf{\mu}_{\mathbf{i}}\|^{2}, \tag{6}\]  
where \(\mathbf{S}_{\mathbf{i}}\) means a state space in cluster \(\hat{\mathbf{S}}_{\mathbf{i}}\). After \(\mathbf{S}_{\mathbf{i}}\) is classified into \(k\) clusters, cluster centroids are considered meaningful state spaces and are candidates for contexts. Note the number of contexts obtained from the previous step. Figure 4 illustrates an example of how contexts are clustered and chosen from a set of state spaces.  
Figure 4: State spaces are clustered to capture contexts where the number of sensors \(\mathbf{\omega}\) is 3. In this example, the number context \(\mathbf{k}\) is 7. The centroid of each cluster is defined as context and highlighted by red spots.
# Automatic Estimation of Dynamic Lever Arms for a Position and Orientation System
## 4 Simulation Analysis
### Error Distribution Simulation Results  
The error distribution simulation tests are devoted to determining which error sources primarily affect the estimation accuracies of specific parameters. For sake of simplicity, the associated error sources in full-order model are classified into eight categories, as shown in Table 3, with the same RMS values as Table 2. The noteworthy parameters are chosen as the norm of lever arm \(\mathbf{L}_{OI}^{b}\), with RMS error denoted as \(\sigma\Big{|}\mathbf{L}_{OI}^{b}\Big{|}\); the norm of lever arm \(\mathbf{L}_{OG}^{b_{c}}\) with RMS error denoted as \(\sigma\Big{|}\mathbf{L}_{OG}^{b_{c}}\Big{|}\); and the norm of positioning error with RMS error denoted as \(\sigma|\delta\mathbf{p}|\). Under the Trajectory 1, the effects of each category on the estimation accuracies are illustrated in Figure 8.  
Figure 7: The positioning results of SINS/GPS navigation with lever arm manual measuring.  
As shown in Figure 8, the initial errors and process noises have little effect on the positioning and lever arm estimation accuracy, while the final positioning accuracy are mainly determined by the CDGPS error, consistent with the previous analysis. In addition, the time-asynchrony error and ISP encoder error have important influences on the final accuracy, and thus should be treated seriously. We note that the results of Figure 8 are based on input error values shown in Table 2. According to the linear error model, if the input errors expand \(n\) times, their effects will also increase \(n\) times.