esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Time-Dependent Reliability Analysis of an Aqueduct Based on Dual Power Transformation
### Proposed Fourth-Moment Method Based on Dual Power Transformation  
3.2.1. Transformed Performance Function Based on Dual Power Transformation  
Without loss of generality, the performance function \(G(\textbf{X})\) in structural reliability analysis can be expressed as  
\[G(\textbf{X})= R(\textbf{X})-S(\textbf{X}) \tag{10}\]  
where \(R(\textbf{X})\) and \(S(\textbf{X})\) are the structural resistance and load effect, respectively. In engineering applications, the resistance \(R(\textbf{X})\) and load effect \(S(\textbf{X})\) are generally positive. If \(R(\textbf{X})>0\) and \(S(\textbf{X})>0\), then the performance function \(G(\textbf{X})\) of Equation (10) may be equivalent to  
\[g(\textbf{X})= \frac{R(\textbf{X})}{S(\textbf{X})}-1=\lambda(\textbf{X})-1 \tag{11}\]  
where \(\lambda(\textbf{X})=\frac{R(\textbf{X})}{S(\textbf{X})}\) is also a positive function of X. This reshaping of the function does not change the limit state surface, that is, the failure domain where \(G(\textbf{X})\ \leq 0\) can be mapped perfectly to the domain where \(g(\textbf{X})\ \leq 0\).  
The dual power transformation can be expressed as [43]  
\[y^{(\lambda)}=\left\{\begin{array}{ll}\frac{y^{\lambda}-y^{-\lambda}}{2 \lambda},\lambda\
eq 0\\ \log y\qquad,\lambda=0\end{array}\right. \tag{12}\]  
Based on the dual power transformation, the performance function of Equation (10) can be equivalently expressed as  
\[\widetilde{g}(\textbf{X})=\left\{\begin{array}{ll}\frac{\left(\lambda( \textbf{X})^{\theta}-\lambda(\textbf{X})^{-\theta}\right)/2q}{q}\,\ q\
eq 0\\ \ln[\lambda(\textbf{X})]\qquad\qquad\qquad,q=0\end{array}\right. \tag{13}\]  
where \(\widetilde{g}(\textbf{X})\) is the transformed performance function, and \(q\) is a parameter of the dual power transformation, which can be any real number. The reliability problem described by the transformed performance function in Equation (13) is the same as that described by the original performance function in Equation (10), that is, the failure domain in which \(G(\textbf{X})\ \leq 0\) can be perfectly mapped to the domain in which \(\widetilde{g}(\textbf{X})\leq 0\).  
3.2.2. Determination of the Parameters of Dual Power Transformation  
The Jarque-Bera (JB) test for normality is one of the most popular goodness-of-fit tests [44]. The JB test statistic is given by  
\[JB=\frac{n}{6}(\text{SK})^{2}+\frac{n}{24}(\text{KU}-3)^{2} \tag{14}\]where  
\[\text{SK}= \frac{\sum_{i=1}^{n}\left(\widetilde{g}_{i}-\widetilde{\overline{g}} \right)^{3}}{\left(\sqrt{\sum_{i=1}^{n}\left(\widetilde{g}_{i}-\widetilde{ \overline{g}}\right)^{2}}\right)^{3}}\text{and KU}= \frac{\sum_{i=1}^{n}\left(\widetilde{g}_{i}-\widetilde{\overline{g}}\right)^{4 }}{\sqrt{\sum_{i=1}^{n}\left(\widetilde{g}_{i}-\widetilde{\overline{g}} \right)^{2}}}.\]  
Then, the transformation parameter is determined by minimizing JB, which can be equivalently expressed as  
\[\left\{\begin{array}{l}\text{Find}:q\\ \text{Minimize}:JB\end{array}\right. \tag{15}\]
# High-Throughput Line Buffer Microarchitecture for Arbitrary Sized Streaming Image Processing
## 4 Logic Implementation of D-SWIM
### Logic of Controller
#### 4.2.1 Buffer-Write Logic  
In the D-SWIM design, the length of input block (InBlk) is larger than the width of LB. Thus, the buffer-write logic extends the InBlk signal to the same width of LB in a certain pattern, and the BRAM byte-wise write enable signal (WrMask) is generated concurrently. As Figure 10 shows, two stages exist in the signal generation. In the first stage, place-holding bytes (cross marked in Figure 10) are padded at the beginning and the end of the input block. By doing so, the padded block has the same width as the LB. The number of place-holders at the start of the block is equal to MEMoffset in the instruction word. Thus, the PHPad hardware in this stage is controlled by the corresponding register decoded from the instruction. In the second stage, the pixels from the first stage are rearranged by a circular right-shift (CRS) operator. The shift distance is an integer multiple of the BRAM width, and it is given by MEMstart in the instruction that ensures the pixel-block storage starts from the proper BRAM in the LB. Note that the pattern of place-holder padding is fixed for blocks in the entire image line, but the shift distance in the second stage changes for every block. Thus, the CRS hardware in the second stage is controlled by the MEMstart and a run-time Counter which provides the input block index of one image line. Along with the WrData signal, the WrMask signal is generated in a similar manner. After the two-stage processing, a set of binary flags are generated, where 0 corresponds to the positions of the place-holders in WrData and 1 indicates that the BRAM byte position will be overwritten by the value in WrData.  
In particular, when the input block exceeds the end of the image line, the logic stores the _remainder pixels_ belonging to the next image line to the beginning of the next LB concurrently, where specific  
Figure 9: The D-SWIM buffer is composed of LBs and Controller. Each BRAM in the LB is equipped with an Address Counter to manage the write address. It performs address incrementation or reset according to the signal on the controller bus. The Addr MUX allows the write addresses to be broadcasted during a block write operation of a specific LB as the read addresses of the other LBs for block loading.  
logics are set to process these remainder pixels. Because the number of remainder pixels is provided by the REMAIN section in the instruction, the MUX in the logic separates remain pixels (red pixels in Figure 10) and pads place-holders in the tail as the WrData signal of the next LB, while the WrMask signal with \(N_{remain}\) ones at the beginning of the binary set is generated. Subsequently, the WrData and WrMask signals from the circuits for general pixels and remainder pixels are concatenated as the output bus of the Controller. Other buffer-write related signals, WrEn, AddrInc, and AddrRst, were generated concurrently by the Counter and specific logics.
## 5 Simulation Results  
### LSTM Model Traffic Prediction  
The dataset used in this paper is the data traffic of 4G LTE network SBSs on Kaggle ([[https://www.kaggle.com/datasets/habibmrad1983/lte-data-traffic?select=train.csv](https://www.kaggle.com/datasets/habibmrad1983/lte-data-traffic?select=train.csv)]([https://www.kaggle.com/datasets/habibmrad1983/lte-data-traffic?select=train.csv](https://www.kaggle.com/datasets/habibmrad1983/lte-data-traffic?select=train.csv)) accessed on 15 November 2022). The dataset contains data traffic information for 57 SBSs for one year (from 23 October 2017 to 22 October 2018), where the data traffic is the total data capacity served by the SBS to all users in one hour. Therefore, each SBS contains 8733 data, and we used a total of 168 data from the most recent week (16 October 2018 to 22 October 2018) as the test set and 8565 data from the rest of the time (from 23 October 2017 to 15 October 2018) as the training set to train the model and compare the autoregressive integrated moving average model (ARIMA).  
In predicting SBSs traffic using the LSTM network, we used the SBS traffic of the first five consecutive time series in time period \(t\) to predict the SBS traffic. \(\mathbf{u}_{m}(t)\) denotes the input to the LSTM network, it can be expressed as  
\[\mathbf{u}_{m}(t)=[h_{m}(t-5),h_{m}(t-4),\cdots,h_{m}(t-1)] \tag{34}\]where \(h_{m}(t)\) is the real traffic of SBS \(m\) in time period \(t\). There are two hidden layers with nodes of 64 and 8, and an output layer that performs single-value prediction to obtain the SBS predicted traffic, \(\widetilde{h}_{m}(t)\). The training prediction error is defined as follows  
\[\theta=\frac{\sum_{t}\left|h_{m}(t)-\widetilde{h}_{m}(t)\right|^{2}}{\sum_{t} \left|h_{m}(t)\right|^{2}} \tag{35}\]  
Figures 6 and 7 compare the results of the LSTM model and ARIMA model for the traffic prediction of Cell_000111 SBS and Cell_000113 SBS, respectively. The traffic prediction values of both prediction models have a high degree of fit with the actual values, and the overall trend remains basically consistent with the actual values. Figures 8 and 9 compare the prediction errors of the LSTM model and ARIMA model for Cell_000111 SBS and Cell_000113 SBS, respectively, and here we use the absolute error to represent the prediction error. Compared with the ARIMA model, the prediction error of the LSTM model is much lower, mainly because of the larger time-varying variability of the SBS traffic. In general, the ARIMA model has better prediction for data with little variation trend and that is more stable overall, such as the exchange rate or the price of a certain stock with more stable price, while the LSTM model has better prediction for more volatile data. Table 2 compares the RMSE and MAE of the two models, and also shows that the LSTM model has better performance in SBS traffic prediction.
## 3 Review of the Scheme of [PERSON] et al. [19]
### Blockchain-Enabled Handover Authentication  
When \(V_{i}\) transitions to \(RSU_{k}\) from \(RSU_{j}\), a mutual authentication process between \(V_{i}\) and \(RSU_{k}\) occurs. The authentication phase consists of the following steps:  
1. \(V_{i}\) generates a random value \(c_{i}\), calculates \(M_{5}=h(RID_{i}||TID_{i}||ID_{k}||T_{3})\oplus c_{i}\), and computes \(M_{6}=h(c_{i}||RID_{i}||TID_{i})\). It then transmits these values \((TID_{i},M_{5},M_{6},T_{3})\) to \(RSU_{k}\).
2. Upon receipt, \(RSU_{k}\) retrieves the corresponding transaction \((TID_{i},n_{j},ID_{j},\)\(Sig_{j}(h(TID_{i}||RID_{i}||n_{j})))\) from the blockchain. The RSU verifies the signature, computes \(RID_{i}^{*}=TID_{i}\oplus h(n_{i}||ID_{j}||k)\), and checks if \(h(TID_{i}||RID_{i}^{*}||n_{j})\stackrel{{?}}{{=}}h(TID_{i}||RID_{i }||n_{j})\).
3. If the values are consistent, \(RSU_{k}\) calculates \(c_{i}=h(RID_{i}||TID_{i}||ID_{k}||T_{3})\oplus M_{5}\) and checks if \(M_{6}\stackrel{{?}}{{=}}h(c_{i}||RID_{i}||TID_{i})\). Upon successful verification, \(RSU_{k}\) generates random \(c_{k}\) and \(n_{k}\), then computes \(TID_{new}=RID_{i}\oplus h(n_{k}||ID_{k}||k)\)\(M_{7}=TID_{new}\oplus h(c_{i}||RID_{i})\), \(M_{8}=c_{k}\oplus h(c_{i}||TID_{new}||RID_{i})\), \(SK_{ik}=h(c_{k}||c_{i}||TID_{new}||RID_{i})\), and \(M_{9}=h(SK_{ik}||T_{4})\). These values \((M_{7},M_{8},M_{9},T_{4})\) are then sent to \(V_{i}\).
* \(V_{i}\) upon receiving this information, calculates \(TID_{new}=M_{7}\oplus h(c_{i}||RID_{i})\), \(c_{k}=M_{8}\oplus h(c_{i}||TID_{new}||RID_{i})\), and \(SK_{ik}=h(c_{k}||c_{i}||TID_{new}||RID_{i})\). It verifies \(M_{9}\stackrel{{?}}{{=}}h(SK_{ik}||T_{4})\). The successful completion of these steps confirms mutual authentication between \(V_{i}\) and \(RSU_{k}\). Additionally, \(RSU_{k}\) appends \((TID_{new},n_{k},ID_{k},Sig_{k}(h(TID_{new}||RID_{i}||n_{k})))\) to the blockchain.
# What Is an Open IoT Platform?
## 1 Introduction and Motivation
Another study focuses in surveying IoT platforms for massive sensing and actuation, and discusses some of the platforms and mainly ideas about how they work, including strengths and weaknesses [25]. A survey of IoT cloud platforms is also provided [21], while identifying suitable middleware platforms to manage things and applications with a reliable solution [22]. In two other studies, three open-source platforms and one proprietary are compared while discussing concepts, similarities and differences [23; 24]. Despite several efforts contributing to open platforms having been done, there is no consensus about what characterizes an open IoT platform. To the best of our knowledge, no comprehensive study on research related to open IoT platforms exists addressing what makes them \"open\". Thus, the goal of our study is to characterize open IoT platforms by using a Systematic Mapping Study, and to gain new knowledge of what makes the IoT platform open. Below we highlight the contributions of our study:  
1. We provide a comprehensive overview of the \"open IoT platforms\" to the research community via a systematic mapping study. Openness as trend has significantly increased during recent years. One factor of this trend is the emerging interest of research in the use of openness in the IoT domain. 2. We identified 46 IoT platforms as \"used\", \"indicated\" and \"proposed\" from 221 analyzed papers. We highlighted the seven most used open IoT platforms. We note that NodeMCU and ThingSpeak are widely adopted platforms among the research community with the biggest share, followed by FIWARE, Mobius, Kaa, OpenIoT, and ThingsBoard. We map all the identified open IoT platforms into six openness types: _open-source, open standards, open APIs, open data, open layer_ and _not specified_ with emphasis to understand why the IoT platforms are known as \"open\". We also note that some papers express the same open IoT platform with different openness types, while the most dominant openness type labeled among the analyzed studies is \"open-source\". 4. Finally, we propose a new perspective on how to define openness in the context of IoT platforms by providing several insights from the different stakeholder viewpoints, which interprets the differences among all the papers' different expressions of the openness of IoT platforms. The rest of the paper is organized as follows. In Section 2, we discuss our research method. We present the obtained results and analysis from the selected studies in Section 3. Thereafter, we discuss the results of our review (Section 4), followed by some limitations highlights (Section 5) and finally we conclude the study (Section 6).
## 1 Methods for the Analysis of Protein Fluorescence Spectra  
### Protein fluorescence  
Proteins are the most extensively studied naturally fluorescent molecules. There are three emitting residues in proteins: tryptophan (Trp), tyrosine (Tyr) and phenylalanine (Phe). The illumination of proteins at wavelengths 295-305 nm allows for the selective excitation of mostly Trp residues. The fluorescence parameters of tryptophan residues, in contrast to tyrosine and phenylalanine residues, are sensitive to the environment. The main reason for this is the large redistribution of electron density in the asymmetric indole ring of the Trp residue (Figure 2) after the excitation of photons, while practically no redistribution occurs in Tyr and Phe symmetric rings [1-10]. Quantum-mechanical studies showed that much electron density is lost from the Ne\({}_{1}\) and C\(\gamma\) atoms and is deposited at the C\({}_{6}\), C\(\zeta_{2}\), and C\(\delta_{2}\) atoms of the indole ring during excitation (10\({}^{-15}\) sec) in the main fluorescent state \({}^{1}\)L\({}_{\rm a}\) [11-12]. This leads to a large increase in the indole dipole moment in the excited \({}^{1}\)L\({}_{\rm a}\) state compared with the ground state (by up to \(>\)10 D in water) and creates a local non-equilibrium in the surrounding Trp environment [3,5,11,13-15]. Depending on the structural properties of the environment of tryptophan residues in proteins, various interactions between atoms of the indole ring and protein atoms, and/or water molecules, could occur during the lifetime of the excited state (pico- and nano-seconds). These interactions affect the fluorescence properties, including the position of maximum of the fluorescence spectrum, which we will discuss here.  
Figure 1: Examples of protein fluorescence spectra of tryptophan residues located in different environments of protein molecule. The position of maximum of fluorescence spectra can vary in range of 305 to 355 nm.
# Experiments and Design of an Anti-Disaster Support System for Apple Orchards
## 4 Design of the Anti-Disaster Support System  
### Design Method  
Orchard systems have been developed to increase productivity, not for the safety of the trees. [PERSON] and [PERSON] [13] suggested a dynamic simulation model based on the complex biological and economic relationships in an apple orchard system. In this paper, an orchard system for the safety of trees from disasters was designed. The design of an anti-disaster support system for apple trees follows the allowable stress design method [14]. The design method is based on elasticity analysis to apply a safety rate in consideration of various variables such as the applied load or uncertainty of the materials. The anti-disaster support system for apple trees was designed so that the resistance moment of the apple tree and support pipe would be greater than the required moment generated by wind load. When the safety rate of allowable stress design is set to 1.67, the design can be expressed as Equation (1):  
\[\Sigma M_{f}<\Sigma M_{a}+\frac{1}{\Omega}\Sigma M_{y} \tag{1}\]  
where \(M_{f}\) is the required moment due to wind load; \(M_{a}\) is the resisting moment of the apple tree; \(M_{y}\) is the yield moment of the support pipe; \(\Omega\) is the safety rate (equal to 1.67); and \(1/\Omega\) is the reduction coefficient (equal to 0.6).
## 3 Model and Algorithm Framework
### The Algorithm Framework  
Before presenting the algorithm for DSS-Fle, we first study the following three special DSS-Fle variants:  
* Delivery service routing with the fixed window (**DSR-Fix**). We first consider the DSR-Fix variant where orders have the fixed drop-off window and there is no order sharing in routing plan. In terms of the fixed drop-off window, we mean that each order \(r_{j}\) has a fixed drop-off time window, i.e., \(t_{j}^{e}=t_{j}^{l}=t_{j}\), and each order can only be finished at its fixed window \(t_{j}\). In terms of without order sharing, we mean that orders are served in sequence and each courier can only serve one order at a time.
* Delivery service routing with the flexible windows (**DSR-Fle**). In DSR-Fle, each order has a flexible drop-off time window \([t_{j}^{e},\ t_{j}^{l}]\), but they are still cannot be shared by a courier in the same service time.
* Delivery service sharing with the fixed window (**DSS-Fix**). In DSS-Fix, multiple orders can be shared and served at a time, but each order \(r_{j}\) has the fixed drop-off time window \(t_{j}\).  
Inspired by the efficiency of a shareability network (proposed in [6; 18]) on addressing DSR-Fix, we propose an approximation algorithm and a time efficient heuristic algorithm for DSR-Fle and DSS-Fix, respectively. Combining DSR-Fle and DSS-Fix algorithms together, we finally propose the DSS-Fle algorithm, which is efficient on both computation complexity and order service rate. The algorithm framework for DSS-Fle is outlined in Figure 1.
# Resilience Assessment for the Northern Sea Route Based on a Fuzzy Bayesian Network
## 2 Proposed Resilience Measurement Methodology
### Fuzzy Theory
#### 2.1.2 Weight Determination for the Expert Capacity  
With different professional positions, experience levels, education levels, and so on, the experts employed gave various judgements regarding the issues in the present study; therefore, it is necessary to weigh individual expert capacities to value the judgements made by high-level experts more than those from low-level experts. Before evaluating the expert capacity, indicators representing the expert capacities should be designed. Then, a score rating for each indicator should be developed to quantify various capacities. Next, pairwise comparison matrices can be established based on expert information. These matrices can be further processed as follows until the weight of an individual expert is calculated [49].  
With respect to fuzzy numbers in pairwise comparison matrices, the geometric mean technique is applied to obtain the synthetic pairwise comparison matrix, \(\widetilde{B}~{}=~{}\left[\widetilde{b}_{ij}\right]\), as follows:  
\[\widetilde{b}_{ij}~{}=~{}\left(\widetilde{a^{(1)}}_{ij}\otimes\widetilde{a^{ (2)}}_{ij}\otimes\cdots\otimes\widetilde{a^{(k)}}_{ij}\right)^{\frac{1}{4}} \tag{8}\]  
where \(\widetilde{A^{(k)}}~{}=~{}\left[\widetilde{a^{(k)}}_{ij}\right]\) is the pairwise comparison matrix of the Kthindicator for the expert capability evaluation.  
The fuzzy weights of the criteria for each expert can be calculated by the following equation:  
\[\widetilde{r}_{i}~{}=~{}\left(\widetilde{b}_{i1}\otimes\widetilde{b}_{i2} \otimes\cdots\otimes\widetilde{b}_{in}\right)^{\frac{1}{4}} \tag{9}\]  
where the fuzzy weight of the \(i\)thexpert is indicated by \(\widetilde{r}_{i}\).  
The fuzzy weights for each criterion are defined as follows:  
\[\widetilde{w}_{i}~{}=~{}\widetilde{r}_{i}\otimes\left(\widetilde{r}_{1}\oplus \widetilde{r}_{2}\oplus\cdots\oplus\widetilde{r}_{n}\right)^{-1} \tag{10}\]  
where \(\widetilde{w}_{i}(lw_{i},mw_{i},uw_{i})\) denotes the fuzzy weight of the \(i\)thcriterion, for which \(lw_{i}\), \(mw_{i}\), \(uw_{i}\) indicate the lower, middle, and upper values of the fuzzy weights of the \(i\)thcriterion, respectively.  
The weight of each expert is computed by employing the center of area technique, as follows:  
\[P(E_{i})~{}=~{}\left(\frac{1}{3}\right)\left[(uw_{i}-lw_{i})+(mw_{i}-lw_{i}) \right]+lw_{i} \tag{11}\]
# In Silico Modeling the Impact of Cartilage Stiffness on Bone Tissue Stress
## 2 Materials and Methods
### Mechanical Properties of Model Components  
The bone tissue was assumed as a perfectly elastic continuum. The Young's modulus for bone components was set to 7.3 GPa; the Poisson's coefficient was set to 0.3 [17]. It was assumed that the critical stress of bone tissue is equal to 140 MPa [18].  
The cartilage was modeled as a hyperelastic continuum. The Poisson's ratio \(v\) for the cartilage and meniscus was set to 0.4995. The Mooney-Rivlin material model was applied.  
The Mooney-Rivlin strain energy density function is expressed as a two-constant formulation [19]:  
\[w=C_{1}(I_{1}-3)+C_{2}(I_{2}-3)+\frac{1}{2}K(I_{3}-1)^{2}, \tag{1}\]  
where \(C_{1}\) and \(C_{2}\) are the first and second material constants, respectively, related to the response of distortion, and \(K\) is the material constant referred to the volumetric response. \(I_{1}\), \(I_{2}\), and \(I_{3}\) are the reduced invariants of the Cauchy-Green deformation tensor and can be determined in terms of principal stretch ratios.  
The material constant \(K\) can be expressed as:  
\[K=\frac{6(C_{01}+C_{10})}{3(1-2v)}; \tag{2}\]  
The deterioration of cartilages is modeled by varying the values of elastic constants. The more deteriorated cartilage is reflected with higher stiffness; the average level of deterioration is determined by a twice lower values of the elastic constants, while the healthiest cartilage is assigned the lowest stiffness. The range of cartilage elastic constants was reported by [20] and was involved in the study. The \(C_{1}\) and \(C_{2}\) values of femur and tibia are presented in Table 1.  
Figure 3: (**a**) Section view of the knee model; (**b**) Tabecular structure of femur; (**c**) Tabecular structure of tibia.  
The meniscus was modeled as perfectly elastic nonlinear material. In order to determine its mechanical behavior, the stress-strain curve offered in [21] was involved in the study (Figure 4). The presented curve reflects the 3 rd stage of OA.