esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Mathematical Formulation  
### Elements of Elasticity, Plasticity, and Visco-Plasticity  
Cyclic plasticity theories describe material behavior by incorporating both elastic and plastic deformation using a yield criterion, a flow rule to define the increment of plastic strain in deviatoric stress space, and a hardening rule to capture evolution of the yield surface during loading. These components work together, while ensuring that the consistency condition is maintained throughout the deformation process. The total strain is obtained by integrating the elastic strain (\(\varepsilon_{c}\)), plastic strain (\(\varepsilon_{p}\)), and visco-plastic strain (\(\varepsilon_{vp}\)) as follows [41]:  
\[\varepsilon=\varepsilon_{c}+\varepsilon_{p}+\varepsilon_{vp}, \tag{1}\]  
where  
\[\varepsilon_{c}=\frac{\sigma}{2G}-\frac{\theta}{E}(\sigma\cdot I)I, \tag{2}\]  
In Equation (2), the terms \(\sigma\), \(I\), \(E\), \(G\), and \(v\) represent the applied stress, the unit tensor representing a multi-dimensional identity matrix, the elastic modulus, the shear modulus, and Poisson's ratio, respectively. These constants describe the material properties governing elastic deformation.  
The plastic strain increment is defined as [41]  
\[d\overline{\varepsilon}_{p}=\frac{1}{H_{p}}\bigg{\langle}ds\cdot\frac{( \mathbf{s}-\overline{\mathbf{a}})}{|(\mathbf{s}-\overline{\mathbf{a}})|}\bigg{\rangle}\frac{( \mathbf{s}-\overline{\mathbf{a}})}{|(\mathbf{s}-\overline{\mathbf{a}})|}, \tag{3}\]  
where \(H_{p}\) is the modulus of plasticity, and \(\overline{\mathbf{a}}\) is the backstress, which quantifies the extent of the loading state beyond the elastic domain. \(s\) is the deviatoric stress tensor and represents the part of the stress tensor that causes distortion (shape change) but not volume change. [PERSON] [41] developed the visco-plastic flow rule to define the visco-plastic strain rate as  
\[\dot{\varepsilon}_{vp}=\sqrt{\frac{3}{2}}\big{\langle}\frac{\sigma_{v}}{K} \big{\rangle}^{\eta}\ \frac{(\mathbf{s}-\overline{\mathbf{a}})}{|(\mathbf{s}-\overline{\mathbf{a}})|}, \tag{4}\]  
where the terms \(\eta\), \(\sigma_{v}\), and \(K\) in Equation (4) correspond to the viscous exponent, viscous stress, and drag stress, respectively. These term are determined from tensile stress-strain data. According to the [PERSON] criterion, yielding begins when the material transitions from elastic to plastic behavior, defined by the following condition:  
\[f\big{(}\sigma,\ \overline{\mathbf{a}},\ \sigma_{y}\big{)}=\sqrt{\frac{3}{2} \bigg{(}\sigma-\frac{1}{3}(\sigma\cdot I)I-\overline{\mathbf{a}}\bigg{)}\cdot \bigg{(}\sigma-\frac{1}{3}(\sigma\cdot I)I-\overline{\mathbf{a}}\bigg{)}}-\sigma _{y}^{0}, \tag{5}\]  
where \(\sigma_{y}^{0}\), \(\sigma_{y}\), and\(f\) are the initial yield stress, yield stress, and von Mises equivalent stress, respectively.
## 3 System Architecture
### Continuous Phase Modulation (CPM)
When the numerator of \(h_{i}\) is even, the phase states \(\psi(t;\alpha)\) are given as  
\[\left\{0,\pi\frac{K_{i}}{p},2\pi\frac{K_{i}}{p},\ldots,(p-1)\pi\frac{K_{i}}{p}\right\} \tag{5}\]  
If the numerator of \(h_{i}\) is odd, the phase states \(\psi(t;\alpha)\) are given as  
\[\left\{0,\pi\frac{K_{i}}{p},2\pi\frac{K_{i}}{p},\ldots,(2p-1)\pi\frac{K_{i}}{p}\right\} \tag{6}\]  
For example, if \(h=2/5\), there will be \(p=5\) phase states given by \(\left\{0,2\pi/5,4\pi/5,6\pi/5,8\pi/5\right\}\). Similarly, if \(h=1/4\), there will be \(2p=8\) phase states given by \(\left\{0,\pi/4,2\pi/4,3\pi/4,4\pi/4,5\pi/4,6\pi/4,7\pi/4\right\}\). In this work, we use a single-\(h\) CPM employing a fixed \(h\). The phase pulse \(q(t)\) can be understood as the time integral of a frequency pulse \(f(t)\) with an area of \(\mathbb{1}\) as shown below:  
\[\int_{0}^{LT}f(\tau)d\tau=q(LT)=\frac{1}{2} \tag{7}\]  
When the time interval is outside of \((0,LT)\), the frequency pulse is zero. The signal is referred to as a full response when \(L=1\) and a partial response when \(L>1\).
## 5 Simulation and Numeric Results
### Performance
In terms of optimal value, NSGAII and MOEA/D increased by 5.28% and 10.12%, respectively, when compared with MACO-NSGA II, while they increased by 6.18% and 11.02% respectively on the median value. Moreover, with the same number of UAVs, the energy efficiency of the three algorithms decreased significantly as the network size increased. The reason is that compared with the more received energy by SDs, the flight energy consumption of the UAV increases more, which is the denominator of Equation (13). However, more UAVs can improve energy efficiency with the same network size, which is consistent with the phenomenon in Figures 5 and 6. Especially in a large distribution, the improvement is more obvious. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Number of SDs**} & \multirow{2}{*}{**Number of UAVs**} & \multicolumn{5}{c}{**MAC-NSGA II**} & \multicolumn{5}{c}{**NSGAIII**} & \multicolumn{2}{c}{**MOEA/D**} \\ \cline{3-10}  & & **Optimal** & **Worst** & **Average** & **Median** & **Optimal** & **Median** & **Optimal** & **Median** \\ \hline \multirow{4}{*}{200} & 6 & 2.13\% & 1.08\% & 1.64\% & 1.69\% & 2.02\% & 1.59\% & 1.93\% & 1.54\% \\  & 9
## 3 Results
### Perceived Soundscape Attributes and Emotional Responses
#### 3.3.2 Identifying Emotionally Related Visual and Aural Indicators  
Correlational analysis results (Table 4) showed that visual and aural perception attributes play different roles in influencing human emotions in the three types of perceptive environments.  
Three visual indicators, aesthetics, layering and ordering, and two aural indicators, eventfulness and appropriateness, are positively correlated with people's pleasure in type A settings, while for people's state of arousal, only the order of the visual landscape and calm and appropriateness of the aural landscape show promotive influences. In type B settings, people perceived openness, and aural characteristics including calm, eventfulness and appropriateness made them feel happy. The soundscape that facilitated affective arousal also had calm and harmonious characteristics. Furthermore, visual landscapes with a good sense of layering are also useful. As for type C settings, the visual aspects of aesthetics and order and aural aspects of vibrancy, eventfulness and appropriateness have a positive impact on pleasant human emotions. The arousal dimension, however, is not correlated with any visual indicators, and only two aural attributes, calmness and appropriateness, showed positive influences.  
Comparing the setting types, the influences of visual perception attributes were evidently varied. Eventful and harmonious soundscapes were consistently found to have a positive influence on pleasant, and calm and harmonious soundscapes on arousal. Therefore, it can be concluded that visual landscapes with order and aural landscapes with appropriateness are more likely to evoke positive human emotions.
# Efficient Mobile Sink Routing in Wireless Sensor Networks Using Bipartite Graphs
## 5 Simulation  
### Simulation Scenarios  
Several simulation scenarios were run on the NS-2 simulator to examine the performance of the proposed sink mobility model. NS-2 is a discrete event-driven simulator designed to support research in communication networks. It supports wired and wireless networks and provides different protocols [33]. The Ad Hoc On-Demand Distance Vector (AODV) routing protocol conveys messages to their intended recipients. Static sensor nodes provide traffic at a steady bit rate (C.B.R.). AODV routing protocol was used because it is a reactive routing protocol. As a result, routing information to only some nodes are kept in the routing table constructed by AODV.  
On the contrary, routing information will be obtained when needed to reduce the size of the routing table maintained by sensor nodes. Thus, processing time and memory consumed are reduced [17]. C.B.R. was also used to generate constant bit-rate traffic from stationary sensor nodes to study the proposed model's performance under heavy-loaded conditions.  
In addition, the effectiveness of the proposed mobility model was examined in networks consisting of 26, 51, 76, and 101 randomly distributed nodes in a grid of 1000 \(\times\) 1000. Additionally, each network consists of N nodes numbered from 0 to n\(-\)2 representing static sensor nodes for each network size. One additional energy-rich node serves as a mobility sink in the network, marked by n\(-\)1. To further explain, the stationary sensor nodes in the network of 26 nodes are numbered from 0 to 24. A different node with the number 25 represents an energy-rich mobile sink node that will move between the stationary sensor nodes per the mobility model proposed in this work. Additionally, for each network size, the performance of the suggested model was examined for mobile sink speeds of 5, 10, 15, and 20 m/s. In Table 1, the simulation parameters are displayed.  
\begin{table}
\begin{tabular}{l l} \hline
**Parameter** & **Value** \\ \hline Simulation Time & 500 s \\ Number of Nodes & 26, 51, 76, 101 \\ Pause Time & 5 s \\ Simulation Area & 1000 \(\times\) 1000 \\ Traffic Type & C.B.R. \\ Mobile Sink Speed & 5, 10, 15, 20 m/s \\ Packet size (bytes) & 512 \\ \hline \end{tabular}
\end{table}
Table 1: Simulation Parameters.  
Figure 3: Handshake diagram of the proposed model.
# Coarsely Quantized Decoding and Construction of Polar Codes Using the Information Bottleneck Method
## 2 Prerequisites
### Polar Code Construction  
The process of determining the position of information bits in \(\mathbf{u}\) is known as code construction. Ideally, the \(K\) qualitatively best bit channels are included in the information set \(\mathcal{A}\) during the code construction phase. The quality of the bit channels can be quantified in terms of their error probability or capacity, both of which can be computed from their transition probabilities. The probability of error, \(P_{e}^{i}\), of the \(i\)th bit channel is computed as ([23], Equation (13)):  
\[P_{e}^{i}=\frac{1}{2}\sum_{\mathbf{y}_{i}}\min\left\{p(\mathbf{y}_{i}|u_{i}=0),p(\mathbf{y}_{i}|u_{i}=1)\right\}, \tag{11}\]  
while, its (symmetric) capacity is given by the mutual information \(I(U_{i};\mathbf{Y}_{i})\):  
\[I(U_{i};\mathbf{Y}_{i})=\frac{1}{2}\sum_{u_{i}}\sum_{\mathbf{y}_{i}}p(\mathbf{ y}_{i}|u_{i})\log_{2}\frac{p(\mathbf{y}_{i}|u_{i})}{p(\mathbf{y}_{i})}. \tag{12}\]  
However, exact computations according to Equations (11) or (12) are not easy in the case when the underlying channel has a continuous output alphabet, e.g., the AWGN channel. [PERSON] proposed in [13] to approximate the AWGN channel as a binary erasure channel with equivalent Bhattacharyya parameter, in which case, bounds on the error probabilities of the bit channels in terms of their Bhattacharyya parameters can be computed efficiently.  
[PERSON] and [PERSON] [22] showed that the error probability of a bit channel can be determined using density evolution since decoding at each stage \(i\) of the SC decoder can be treated as belief propagation on a cycle-free graph. However, precise implementation of the check node and variable node convolutions during the density evolution is challenging for a channel with continuous output, like the AWGN channel. Discretizing the continuous channel output using a quantizer also does not solve this problem since the output alphabet of bit channels is exponential in the codeword length \(N\). Therefore, if the codeword length \(N\) is not too small, the sizes of the conditional probability mass functions \(p(\mathbf{y}_{i}|u_{i})\) of the bit channels, required for capacity or probability of error computation, become impractically large. For example, if the 4-bit channel quantizer is used that quantizes the physical channel output into 16 bins, the output alphabet size of the bit channels of a polar code will be of the order of \(16^{8}\) even for a short codeword length of \(N=256\). The information bottleneck as well as [PERSON]'s construction methods circumvent the exponential growth of the bit channel output alphabet by introducing quantization at each level of the polarization transform during the evolution of the conditional probability densities and restrict the output alphabet size to a small finite number.
## 4 Data Foraging-Oriented Reconnaissance
### Data Foraging Graph Properties  
The properties of any given DFG \(G_{h}\) of size \(h\) are introduced. The properties help us formally define the problem of Data Foraging-Oriented Reconnaissance (DFORE) for any Hextille of radius \(h_{ex}\) and analyze our algorithm to prove it satisfies the restrictions of DFORE.  
The linear distance between the base station and the farthest region is called the depth of the DFG.  
**Property 1**.: _For any given DFG \(G_{h}\) where \(h\geq 1\), its depth \(p_{h}\) is equal to:_  
\[p_{h}=2h-1. \tag{1}\]  
To show that Property 1 a straight line is drawn from the base station of \(G_{h}\) to the farthest node of the graph and count the number of regions the lines cross are counted.  
Figure 4: Enumerating the graph.  
Figure 3: A Data Foraging Graph is created from the Hextille of Figure 2. The square represents the base station while the circles represent the regions of the Hextille. Every region has adjacent regions which are connected with edges between the circles.  
The hexagonal tilling consists of a number of hexagons bordered by other hexagons. It is necessary to know the number of hexagons for any Hextille with radius \(h_{ex}\). This allows us to know how the sampling area grows and to measure the performance of our algorithm compared to others, in terms of the number of regions they visit. The DFG will also have the same number of nodes as the Hextille.  
For any given DFG \(G_{h}\) with depth \(p_{h}\) (see Equation (1)), the number of regions \(n_{h}\) is given by the following recursion:  
\[n_{h}=(3p_{h-1}+3)+n_{h-1}\]  
The close solution for the recurrence is (see Appendix A):  
\[n_{h}=3h^{2}-3h+1 \tag{2}\]  
The focus is on the minimum number of steps to travel from the base station to another region of the DFG without having to recharge the MDF.  
Let \(e_{h}\) be the required number of steps that are necessary to reach any region from the base station. For any DFG \(G_{h}\), \(h\geq 1\), the required number of steps \(e_{h}\) is:  
\[e_{h}=2h-1. \tag{3}\]  
Since it is necessary to return to the base station, the total number of steps an MDF can make is \(2e_{h}\).  
Let \(\eta\) be the farthest region from the base within the main line. Let \(\zeta\) be the base station, which is at the root of the DFG.  
It is impossible to get from one region to all the regions of the DFG with the movement capabilities of the MDF. Only the base station can be reached from any region of the DFG with the required number of steps (see Property 3). Therefore, we are interested in defining the set of reachable regions given the remaining energy of the MDF.  
Let be two regions, \(r,r^{\prime}\in R,r\
eq r^{\prime}\), the remaining number of steps \(e_{u}\) of an MDF and the physical distance between a pair of regions \(r\) and \(r^{\prime}\) noted as \(d(r,r^{\prime})\). A region \(r^{\prime}\) is reachable if and only if there are enough steps \(\Pi\) to visit the region and return to the base station:  
\[\Pi=e_{u}-d(r,r^{\prime})-d(r^{\prime},\zeta)\geq 0 \tag{4}\]
## 5 Results and Discussion
### Results of the Control Simulation  
#### 5.2.1 Response of the Frequency Domain  
Figure 7 shows the root mean square (\(RMS\)) for the dynamic response of the frequency domain in passive and semi-active mounts. Based on the displacement transmissibility \(RMS(Z/q)\) (Figure 7a) and absolute displacement \(RMS(Z)\) (Figure 7b), within the excita  
Figure 5: Semi-active controller: (**a**) full control diagram; (**b**) damping force control.  
Figure 6: Magnetic density B on (**a**) full view and (**b**) cross-section view of mount, and magnetic intensity H on (e) full view and (**d**) cross-section view of mount.  
tion frequency range of 0-50 (Hz), the performance of semi-active mounts is much better than that of passive mounts. First, the displacement transmissibility and absolute displacement of the car using the semi-active controller are always under one unit, meaning that the displacement of the sprung mass is always lower than the displacement of the un-sprung mass. Second, the resonance at the natural frequency disappears and the displacement reduces by 83.33% at this frequency. Similarly, Figure 7c,d present the absolute velocity and the acceleration of the semi-active mount, respectively. These responses of the semi-active mount are higher than those of the passive mount. They reduce by 77% for the velocity and 70% for the acceleration at the resonance frequency.
# Open-Source MQTT-Based End-to-End IoT System for Smart City Scenarios
## 4 Proof-of-Concept
### Gateway Interface to Sensing Layer: The ESP32 Platform
#### 4.1.1 Software Implementation
Even though the BLE Special Interest Group (SIG) provides a list of short 16-bit or 32-bit UUIDs that are standardized and might be used in applications, in the considered system, we have decided to resort to custom UUIDs due to the custom and specific implementation; in this case, the full 128-bit UUID value should be used since we are not using the standard base UUID [32]. At the software level, we have defined each attribute by specifying the type, and hence service, characteristic or descriptor, then we assigned a corresponding UUID for each one, as previously defined, and the properties. In this case, the attributes should be read-only since we do not want the Android gateway to be able to overwrite them, we only want it to read their values corresponding to the sensors readings. In addition, we implemented the _Notify_ property, as defined by the BLE Core Specification. Since we want to send the readings from the sensors asynchronously and in real-time to the GATT Client to avoid the client cycling asking the server for new readings, since it is not a power-efficient solution, the Notify property allows the server to automatically notify the client whenever there is a new sensor reading, and we have a flow that is different from the usual request/response pairs. However, for each characteristic, it must be implemented as a descriptor, as can be seen in Figure 2. For the purposes of this implementation, we resorted to the Client Characteristic Configuration Descriptor (CCCD), a GATT-defined descriptor, which works similarly to a switch, enabling or disabling server-initiated updates [33]. This function allows the client to decide whether or not it wants to receive automatic asynchronous updates from the server by writing the attribute's value, a two-bit field; the client will simply use a Write Request Attribute Protocol (ATT) packet, which set the least significant bit to 1, while the server will reply with a Write Response ATT packet. When the process is successful, the server will be able to send automatic updates.
## 1 Introduction
### IOTA Constituents  
The IOTA protocol and cryptocurrency were introduced in early 2015, illustrating the use of a Directed Acyclic Graph (DAG) in lieu of a blockchain [3, 4]. As Figure 2 depicts, IOTA's ledger, \"The Tangle\", stores all the transactions that are issued in the network by actors called IOTA Full Nodes  
Figure 1: High Level Architecture.  
(FN). In a blockchain each block aggregates a large number of transactions and is connected to the previous block of usually the longest chain depending on the consensus rule. In IOTA, each transaction references two (2) other transactions in the DAG so in order to issue a transaction, an agent needs to verify two (2) previous transactions. Albeit each transaction references _directly_ 2 transactions, it also references _indirectly_ all the transactions that are referenced by those two, either _directly_ or _indirectly_. The current implementation offers a reference algorithm [5] to the FN for the choice of the two transactions but there is no enforcement by the protocol. The protocol will give incentives to the FN to choose transactions that are not referenced already, called \"Tips\", in order for the graph to grow organically. On top of that, IOTA is built using ternary logic, thus using trytes instead of bytes for its various operations (e.g., hashing) [6]. Each user has a unique string formed from 81 trytes called a seed, from which the protocol generates all the user's private and public keys.  
A transaction, as depicted in Figure 3, is the smallest unit of data in the IOTA protocol consisting of 2673 trytes (1589 bytes) and can be used to transfer both value (IOTA tokens) and data (1300 bytes). It is worth noting that as the IOTA network is enriched with new transactions, the size of the Tangle grows substantially. For this reason, the network is currently performing network synchronization or local snapshots at fixed time intervals, eliminating all zero transactions.
## 1 Introduction
[20] proposed a novel solution to the distortional buckling moment for stiffened CFS beams based on the minimum potential energy rules and using web and flange stiffeners. The presence of flange stiffeners enhanced the bearing capacity and increased the distortional buckling moment [21]. The presence of slots affected both the shear rigidity and distortional buckling load and the web stiffeners improved the flexural behavior [22]. Following the literature, perforated CFS beams subjected to different bending scenarios should be able to deal with different buckling modes [23]. There is almost no simple way to address this significant concern. Hence, this study proposed a simple and low-cost reinforcing system for CFS uprights consisting of a bolt, nut and spacer. A series of four-point bending tests were performed on the specific stiffened perforated beams to measure the buckling-resistant moment of an upright section about its major and minor axes and to evaluate its improvement by proposed reinforcement. A total of eighteen monotonic tests were conducted to determine both the flexural capacity and failure modes of the reinforced sections, which consisted of uprights with 1.6 mm thickness strengthened by bolt and spacer systems. Finite element (FE) analysis was also performed by ABAQUS program and was verified by the experimental results. The comparison of the parametric study and test study showed both outstanding accuracy for the experimental results, and the proficiency of the proposed reinforcing system.