esa-sceva/satcom-chunk-collection huggingface dataset:
# Influence of Pile Foundation Stiffness on Column Design in One-Story Reinforced Concrete Frames
## 2 Consideration of Subsoil Stiffness in the Design of Reinforced Concrete  
Hall Columns  
In order to determine the foundation rotation in the static calculations of the frame, a fictitious column support model can be taken as the column support with a scheme as in Figure 1b and with a length \(L\) and a stiffness \(EI\) chosen so that its flexural stiffness \(3 EI/L\) satisfies the relation in the following formula:  
\[\frac{3 EI}{L}=I_{F}C_{z}. \tag{1}\]  
where  
\(I_{F}\)-moment of inertia of the foundation base field;  
\(C_{z}\)-coefficient of elasticity of the subsoil.  
The coefficient \(C_{z}\) is not a soil material constant, as it depends not only on the physical properties of the soil but also on the dimensions of the foundation. Using the works of [PERSON] [16] and [PERSON] [17] on soil as a homogeneous elastic half-space with characteristics defined by the modulus \(E_{0}\) and the Poisson's ratio \(\
u_{0}\), the coefficient \(C_{z}\) can be calculated from the following formula:  
\[C_{z}=\frac{\pi E_{0}b^{\prime}l^{2}}{4I_{F}\big{(}1-\
u_{0}^{2}\big{)}}, \tag{2}\]  
where \(2l\) is the width of the foundation and \(b^{\prime}\) is its length (Figure 1).  
Figure 1: (**a**) Foundation on resilient ground; (**b**) pile foundation; (**c**) how ground influence is modeled in static calculations of foundations.  
Given that  
\[I_{F}=\frac{b^{\prime}(2l)^{3}}{12}=\frac{2}{3}b^{\prime}l^{3} \tag{3}\]  
the formula is as follows:  
\[C_{z}=\frac{3\pi}{8l}\frac{E_{0}}{\left(1-v_{0}^{2}\right)} \tag{4}\]  
The angle of rotation \(\varphi\) at the support of the fictitious bar, which is induced by the moment \(M\), can be calculated from the following formula:  
\[\varphi=\frac{ML}{3 EI} \tag{5}\]  
In the case of pile foundations, the rotation angle \(\varphi\) does not depend on the stiffness of the soil in a simple, direct manner. This is due to the specifics of using pile foundations and the factors on which the mechanical response of the piles comprising the foundation depends. Generally speaking, pile foundations are used in the presence of an unfavorable subsoil when shallow foundations do not ensure that the required limit state conditions are met for an economically justified structural solution. The calculation model in these situations requires the adoption of a geotechnical profile where the subsoil is divided into layers with appropriately selected parameters. The value of the rotation angle \(\varphi\) is obtained by calculating the settlement of the individual piles in the foundation and taking into account their arrangement. Then, using Equation (5), the following relationship is obtained:  
\[\frac{M}{\varphi}=\frac{3 EI}{L}=I_{F}C_{z} \tag{6}\]  
which allows for the creation of a column support model by assuming a fictitious bar of length \(L\) and stiffness \(EI\). The main problem in this case will be the choice of an appropriate method for calculating the settlement of single and in-group piles.
# Applications of Probabilistic Forecasting in Smart Grids: A Review
## 1 Introduction
### Motivation and Contribution
On the other hand, the main contribution of [10] was to review the papers seeking to probabilistically forecast energy prices. This paper is useful for strategic stakeholders that need to make decisions with uncertain prices and participate in the market accordingly. Finally, ref. [11] provided a short review of the parametric and non-parametric probabilistic forecasting models for electricity loads, wind and solar productions. Table 1 compares the existing similar review papers with ours. The first column indicates the reference and the next four columns specify the uncertain parameters for which the probabilistic forecasting models were reviewed. The last column is devoted to the main contribution of the paper that will be discussed later. To the best of the authors' knowledge, there is no comprehensive review paper proposing a comprehensive framework for smart grid decision-makers and stakeholders based on the probabilistic forecasting of all uncertain inputs (wind, PV, price, load, etc.). In addition, the applications of probabilistic forecasts and the decision-making pathway were not fully assessed in the previous literature. The decision-making process should have three main \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \begin{tabular}{c} **Ref.** \\ **Ref.** \\ \end{tabular} & \begin{tabular}{c} **Load Probabilistic** \\ **Forecast** \\ \end{tabular} & \begin{tabular}{c} **Solar Probabilistic** \\ **Forecast** \\ \end{tabular} & \begin{tabular}{c} **Wind Probabilistic** \\ **Forecast** \\ \end{tabular} & \
\begin{tabular}{c} **Price Probabilistic** \\ **Forecast** \\ \end{tabular} &
\begin{tabular}{c} **Pathway towards Decision** \\ **Making under Uncertainties** \\ \end{tabular} \\ \hline \hline
[4] & & & ✓ & & \\ \hline
[5] & & & ✓ & & \\ \hline
[6] & & ✓ & & & \\ \hline
[7] & & ✓ & & & \\ \hline
[8] & ✓ & ✓ & & & \\ \hline
[9] & ✓ & & & & \\ \hline
[10] & & & & ✓ & \\ \hline
[11] & ✓ & ✓ & ✓ & & \\ \hline This paper & ✓ & ✓ & ✓ & ✓ & ✓ \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of this paper with the literature reviewing probabilistic forecasting models and applications in smart grids and power systems. stages, including forecasting, scenario generation, and developing optimization problems. Thus, this paper is proposed to resolve this issue as follows:  
1. It aims to introduce a roadmap and a pathway towards uncertain decision making in a smart grid environment. The roadmap includes probabilistic forecasting models of uncertain parameters, scenario generation based on probabilistic forecasts, and solving stochastic, robust, and chance-constrained optimization problems according to the results of the previous steps. 2. It tries to guide upcoming similar works by introducing the smart grid's needs in the future. In this regard, probabilistic forecasting models should be developed for a wide range of uncertain parameters and not be limited to loads, prices, and renewable generations.
## 2 Mathematical Modeling of VFRM
These periodic variations significantly impact the dq0-axis voltage equations, introducing inter-axis coupling effects. These variations are significant for designing the current control strategy. Considering \(\psi_{\mathrm{d}}\) and \(\psi_{\mathrm{q}}\) as the mathematical formulations of stator flux along the d-axis and q-axis, respectively, the flux within the dq-axis can be represented as follows:  
\[\left\{ \begin{array}{l}\psi_{\mathrm{d}}=L_{s}i_{\mathrm{d}}+L_{s}i_{0}\\ \psi_{\mathrm{q}}=-L_{s}i_{\mathrm{q}}\end{array} \right. \tag{2}\]  
The derivation of torque in a magneto-mechanical system can be achieved through the principle of energy conversion, utilizing an abstract circuit model that encompasses inductance, resistance, and electromotive force (EMF) elements [29]. In the case of VFRMs, the formula that illustrates the electromagnetic torque at any specific instant can be articulated as follows. \[T_{e}=\frac{3}{2}N_{\mathrm{r}}\big{(}\psi_{\mathrm{d}}i_{\mathrm{q}}-\psi_{ \mathrm{q}}i_{\mathrm{d}}\big{)}+\frac{3}{8}N_{\mathrm{r}}L_{\mathrm{\phi}} \Big{(}i_{d}^{2}+i_{q}^{2}\Big{)}\sin(3\theta_{e}+2\beta) \tag{3}\]  
Here, \(N_{\mathrm{r}}\) represents the number of pole pairs in the rotor, and \(\beta\) denotes the advanced current angle (phase shift between current and flux). It is worth noting that \(\beta\) influences the phase of torque ripple. Proper control of \(\beta\) can minimize ripple by aligning the currents with the optimal torque-producing flux vector. The instantaneous torque of VFRM, as detailed in (3), encompasses both the average torque and the torque related to the third harmonic. The average torque is primarily derived from the interaction between the flux linkage along the dq-axis and the AC component, along with the zero-sequence element. It is significant to emphasize that the previously mentioned model is formulated under the assumption of disregarding self-inductance saturation.
# Ultra-Low-Power FinFETs-Based TPCA-PUF Circuit for Secure IoT Devices
## 2 Adiabatic FinFET-Based PUF
### 4-Bits Adiabatic FinFET TPCA-PUF  
To validate the proposed FinFET TPCA-PUF cell, we have designed a 4-bits cascaded adiabatic PUF, as depicted in Figure 5. Each local PUF is supplied by 4 power clocks, where each adjacent clock differs by a phase difference of 90\({}^{\circ}\). For instance, if the first cell, as shown in Figure 5, is operating in the hold phase, the next cell in the same local PUF is operating in the recovery phase. Similarly, the other two PUFs are operating in the wait phase and the evaluate phase, respectively. When all 4 outputs are sampled simultaneously, it leads to a 4-bit-length. Moreover, each local PUF cell is controlled by challenge bits, and each adjacent bit has a \(\frac{1}{4}\) delay time of one power clock cycle. This means that if one cycle of the Vpc signal is 10 ns, then the second \(Cb\) signal has a 2.5 ns delay time compared to the first \(Cb\) signal. This delay time allows the challenge bits to flip the response signals right at the middle point of the idle/wait phase of the Vpc signals, and the challenge bits are perfectly flipped adiabatically, and as result of this, the energy is significantly reduced, as reported in [33].  
Figure 4: Proposed SRAM-based FinFET TPCA-PUF circuit.  
Figure 5: Proposed 4-bits FinFET TPCA-PUF architecture.  
Monte-Carlo simulation result of the 4-bits TPCA-PUF and QUALPUF challenge-response signals are depicted in Figure 6. This result was obtained with a reference temperature of T = 27 \({}^{\circ}\)C and C\({}_{\text{L}}\) = 10 HF,/\(f_{Cb}\) = 10 MHz, and \(f_{Vpc}\) = 100 MHz, with \(\pm\)10% of Vth variation. The simulation results of response signals (Rb1-Rb4) with the given challenge bit (Rb) showed correct and stable operation for both PUF circuit topologies.
## 7 Research Challenges and Recommendations for Future Works
### Key Management and Storage  
Key management and storage have always been a major issue in LPWAN. In LPWAN, secret keys are typically stored in electrically erasable programmable ROM (EEPROM) of the nodes. The EEPROMs are highly vulnerable to various attacks such as side-channel attacks. Addressing the issue of key storage, the authors in [95] explained that if the application server that holds all the secret key is compromised, the communication between the entire nodes in the network can be compromised. In order to ensure that secured communication within the networks is achieved, adequate security measures such as proper and fast encryption, authentication measures, tamper proofing, and secure elements (SEs) are some of the methods to be considered.
# A 5G-Based VR Application for Efficient Port Management
## 3 Application of 5G + VR in Smart Ports
### Advantages  
Compared with wired broadband networks or 4G networks, smart ports with industrial 5G architecture have better support for mobility, more stable QoS, and low overhead. The equipment can be connected to the network through the 5G module, which neither of the current systems used optical fiber, video cable, and other wired connection methods that have mobility restrictions nor suffers the unstable connection provided by Wi-Fi. Compared with traditional smart terminals, it can reduce the disturbance caused by limited bandwidth, network congestion, unstable QoS, etc. 5G services provides independent network resources for the carried VR video stream and emergency data, avoiding the influence of resource preemption by other services and ensuring smooth video streaming and data transmission. The introduction of multi-access edge computing servers also saves downstream network bandwidth for users. The original 360\({}^{\circ}\) video is compressed into 1/3-1/2 of the original video at the edge of the network, saving the traffic cost for users. At the same time, the transmission scheme has low requirements for the configuration of user hardware equipment, which indirectly reduces the hardware cost and thus reduces the overhead.  
In fact, this system requires a network structure with large bandwidth to support high volume data transmission. As described in Chapter 3, a complete interaction between the VR environment and the real environment requires continuous and fast transmission of data from multiple sensors. Thus, 5G is required to ensure the efficiency and stability of the system. What distinguishes the system proposed in this paper from traditional port systems is that we have built a whole set of sensor information networks to support the various interactive information needed in both VR environment and the real world. Most current VR port systems [38]. are limited to the implementation of a certain aspect of functionality, such as safety training for operators [39]. We have built a complete set of sensor networks and system architecture that can realize the requirements of the entire terminal assembly line operations. Under the VR environment, operators are able to make decisions based on the integrated information from all aspects of sources. It can help save more time than in traditional terminals [40], from the time the goods enter the port to the time they enter the warehouse.
# Hash Indexing-Based Image Matching for 3D Reconstruction
## 3 The Proposed Method
### Descriptor Extraction  
The descriptor is a representation of the local feature point, some novel descriptors have been proposed in recent years. For example, Lowe's SIFT and its variants, AKAZE feature [11], local difference binary (LDB) [58], locally uniform comparison image descriptor (LUCID) [59], learned arrangements of three patch codes (LATCH) [60], learned invariant feature transform (LIFT) [13], and SURF [10]. Binary descriptors possess a fast computational efficiency but have low matching precision. Floating-point descriptors have  
Figure 2: Flowchart of the SURF detector. The core idea of SURF consists of two steps: the first constructs a multiple-scale space for each input image, then the second locates the feature-point position.  
high matching precision but slow speed. There is an exception, namely SURF, which is a floating-point descriptor extractor, but it has comparable computational efficiency to binary descriptor extractors such as ORB and LDB. As a result, in this paper, we also use SURF to compute feature descriptors for located feature points due to it providing the best balance between distinctiveness and computational cost.  
Figure 3 presents the pipeline for extracting feature descriptors for the located feature points via the SURF feature descriptor extractor. For the given feature point \(\mathrm{x}=(x,y)\) and corresponding image \(I\), we first compute a dominant orientation \(\theta\) for feature point \(\mathrm{x}\) by computing Haar wavelet responses in the neighborhood centered at \(\mathrm{x}\) with radius \(6s\), where \(s=1.2h/9\) and \(h\) is the size of the current filter kernel. With the dominant orientation \(\theta\), we crop the patch \(p\) with the size of \(20s\times 20s\) centered at \(\mathrm{x}\) from image \(I\), then split the patch \(p\) into \(16\) sub-regions along with the dominant orientation \(\theta\), as depicted in Figure 3c, where each sub-region has four-types of Haar wavelet responses, namely  
\[Resp_{i}=\left\{\sum_{i}dx,\sum_{i}|dx|,\sum_{i}dy,\sum_{i}|dy|\right\} \tag{5}\]  
where \(i\) denotes the index of sub-regions.  
To this end, we can obtain a floating-point feature vector with \(64\) elements.  
\[D_{x}=\left\{Resp_{i}|i=1,2,\cdots,16\right\} \tag{6}\]  
Furthermore, we can normalize \(D_{x}\) by the Frobenius normal to boost its robustness to light and scale changes, and repeated texture.
# Secure and Privacy-Aware Blockchain Design: Requirements, Challenges and Solutions
## 5 Evaluation and Discussion
### Comparative Analysis
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Privacy and** **Security Mechanisms** & **Advantages** & **Limitations** & **Attack Risks** \\ \hline Ring Signature & Flexible, Efficient; Does not depend on TTP. & Computational infeasible; Could not identify actual signer of transaction. & Attribution attack. \\ \hline Zero Knowledge Proof & Protect identity and transaction data. & Efficiency; Need high computational time; Size of proofs. & One user pretends to be another. \\ \hline Mix Network & Efficient; Difficult for an attacker to trace communication. & Require TTP as mixing server. & Active attack; Sleeper attack. \\ \hline Onion Routing & Enforce security on data. & High communication overhead. & Replay attack. \\ \hline Tor Routing & Efficient; Protect user’s identity online. & Does not ensure data security; Slow network speed. & Attack on client through internet browser and external software e.g., Flash etc \\ \hline K-Anonymity & Ensure identity privacy; Easy to understand. & It does not prevent attribute disclosure; Attacks are possible; Re-identification is possible. & Homogeneity attack; Background knowledge attack. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Advantages-limitations of privacy and security mechanisms.
# An Algorithm for Rescheduling of Trains under Planned Track Closures
## 6 Simulation Experiments
### Single Track Timetable  
Let us consider a single track case as in the previous section for closure with the following parameters \(T_{0}^{\text{C}}=50,t_{0}^{\text{CES}}=0,t_{0}^{\text{CLS}}=105,R_{0,1,2,1}^{ \text{C}}\), and four trains with timetable as follows:  
* Run \(p=0\) station visit order: \(0,1,2,0\), arrival and departure times \(t_{0,0}^{\text{DEP}}=t_{1,0}^{\text{ARR}}=0,t_{1,1}^{\text{DEP}}=5,t_{2,0}^{ \text{ARR}}=55,t_{2,0}^{\text{DEP}}=t_{0,0}^{\text{ARR}}=60,t_{2,0}^{\text{ARR }}\leq 300\).  
Figure 4: Dependence of \(\bar{Q}_{\text{\tiny{agg}}}\) (minutes) on the number of train runs for selected cases.  
Figure 5: Dependence of \(\widetilde{T}_{\text{\tiny{agg}}}\) (seconds) on the number of train runs for selected cases.  
Run \(p=1\) station visit order: \(0,1,2,0\), arrival and departure times \(t_{0,1}^{\text{DEP}}=t_{1,1}^{\text{ARR}}=50,t_{1,1}^{\text{DEP}}=55,t_{2,1}^{ \text{ARR}}=105,t_{2,1}^{\text{DEP}}=t_{0,1}^{\text{ARR}}=110,t_{2,1}^{\text{ ARR}}\leq 300\).
* Run \(p=2\) station visit order: \(0,1,2,0\), arrival and departure times \(t_{0,2}^{\text{DEP}}=t_{1,1}^{\text{ARR}}=50,t_{1,2}^{\text{DEP}}=105,t_{2,2}^ {\text{ARR}}=155,t_{2,2}^{\text{DEP}}=t_{0,2}^{\text{ARR}}=160,t_{2,2}^{\text{ ARR}}\leq 300\).
* Run \(p=3\) station visit order: \(0,2,1,0\), arrival and departure times \(t_{0,3}^{\text{DEP}}=t_{2,3}^{\text{ARR}}=155,t_{2,3}^{\text{DEP}}=160,t_{1,3}^ {\text{ARR}}=210,t_{1,3}^{\text{DEP}}=t_{0,3}^{\text{ARR}}=215,t_{2,3}^{\text{ ARR}}\leq 300\).  
The timetables of train runs before and after the closure are in Figures 6 and 7. The figures represent the connection between the time and the distance traveled between nodes (stations) 1 and 2 for different trains. Red represents the time of the closure.
## 3 Results and Discussion  
### Characterization of the Bentonite under Study  
The particle size distribution of the total sample after disaggregation shows a trimodal differential curve (Figure 2), having a main peak with its maximum at approximately 5 \(\upmu\)m, together with two secondary peaks with maxima at around 17 \(\upmu\)m and 44 \(\upmu\)m. The fineness of the bentonite studied is evident from the data in Table 2, with a d50 (the particle size below which 50% of the material is contained) of 5 \(\upmu\)m and an average particle size of 13 \(\upmu\)m.  
Figure 1: Method proposed in this study of controlled soil disaggregation from a compacted specimen: (**a**) initial compacted Wyoming bentonite specimen; (**b**) specimen cut into eight portions; (**c**) standardized shaking machine and test tube with the material inside; (**d**) passing through sieves after shaking.  
Figure 2: Particle size distribution of the Wyoming bentonite studied.  
The water content of the Wyoming bentonite as received was \(w\) = 4.5%. The results of the Atterberg limits (Table 2) show the high plasticity of the material, which would be in accordance with its fineness (Figure 2 and Table 2), mineralogy, and exchange cations since the sample is predominantly sodic montmorillonite (see Table 1). The LL around 460 is consistent with the value of 400 usually found in the literature [23]. The PL value of 43.4 is also in the same order as those reported in the literature [24]. Although the PL has a relatively high value, the large difference with respect to the LL is evidenced by a very high PI value of 416.3. This causes a PI/LL ratio above 0.9, typical of materials with exceptional plastic and toughness properties, showing a \(T_{\text{max}}\) \(>\) 70 kJ/m\({}^{3}\) when values for ordinary clays are usually around 20-30 kJ/m\({}^{3}\)[20]. Therefore, as expected, the material studied is classified as CH according to both USCS [17; 18] and Moreno-Maroto and Alonso-Azcarate [20] criteria, with a \"clay\" type texture.  
The high toughness mentioned above is also demonstrated in the _flow index_ of the LL test, the value of the slope obtained in the flow line after plotting the water content (\(w\)) versus the number of blows (\(N_{\text{B}}\)) required to close the groove. According to [PERSON] [25], soils with high toughness (especially those rich in very active minerals, such as montmorillonite) have lower flow index values than most \"normal\" soils. In the case of the Wyoming bentonite studied, the flow line (Ln \(w\) = Ln 487.21 \(-\) 0.018-Ln \(N_{\text{B}}\); coefficient of determination R\({}^{2}\) = 0.911) presented a flow index of \(-\)0.018, much lower than the average values reported in the literature based on the statistical study of a large number of soil samples. Thus, the ASTM D 4318-17e1 standard [16] considers that the most probable flow index is \(-\)0.121, while Spanish standard UNE 103103:1994 [26], based on the determinations of [PERSON] [27], gave a value of \(-\)0.117. Therefore, the differences observed with respect to the Wyoming bentonite investigated here, together with the PI/LL ratio, demonstrate its high toughness.
## 2 Lempel-Ziv Data Compression
### Greedy versus Optimal Factorization  
The pointer encoding the factor \(f_{i}\) has a size increasing with the index \(i\). This means that the lower is the number of factors for a string of a given length, the better is the compression. The factorizations described in the previous subsections are produced by greedy algorithms. The question is whether the greedy approach is always optimal, that is, if we relax the assumption that each factor is the longest match, can we do better than greedy? The answer is negative with suffix dictionaries as for LZ1 or LZSS compression. On the other hand, the greedy approach is not optimal for LZ2 or LZW compression.  
However, the optimal approach is NP-complete [19] and the greedy algorithm approximates with an O(\(n^{\frac{1}{4}}\)) multiplicative factor the optimal solution [20].