esa-sceva/satcom-chunk-collection huggingface dataset:
# An Energy-Efficient Fail Recovery Routing in TDMA MAC Protocol-Based Wireless Sensor Network
###### Abstract  
Conventional IoT applications rely on seamless data collection from the distributed sensor nodes of Wireless Sensor Networks. The energy supplied to the sensor node is limited and it depletes after each cycle of data collection. Therefore, data flow from the network to the base station may cease at any time due to the nodes with a dead battery. A replacement of the battery in WSNs is often challenging and requires additional efforts. To ensure the robust operation of WSNs, many fault recovery routing mechanisms have been proposed. Most of the previous fault recovery routing methods incur considerable delays in recovery and high overhead in either energy consumption or device cost. We propose an energy-efficient fail recovery routing method that is aimed to operate over a data aggregation network topology using a TDMA media access control (MAC). This paper introduces a novel fault recovery routing algorithm for TDMA-based WSNs. It finds an optimal neighbor backup parent (NBP) for each node in a way that reduces the energy consumption. The proposed method allows the NBPs to utilize the time slot of the faulty parent nodes, so it eliminates the overhead of TDMA rescheduling for NBPs. To evaluate the fault recovery performance and energy efficiency of the proposed method, we implemented it in C++ simulation program. Simulation experiments with an extensive set of network examples demonstrate that the proposed method can extend the network lifetime by 21% and reduce the energy consumption by 23% compared with the reference methods.  
Keywords:failure recovery; routing; wireless sensor network; redundant path; backup parent node; network lifetime and power consumption; active period +
Footnote †: journal: Electronics Electronics
## 3 System Model  
In this section, the network model, the channel model, and the computation model are introduced in detail and sequence.
# Numerical Analysis of the Settlement Behavior of Soft Soil Improved with Stone Columns
## 1 Introduction
There is a lack of information regarding the serviceability (settlement) performance of lightly loaded narrow footings supported by small groups of stone columns [36]. The most popular design method [37] underestimates the settlement of stone columns in some cases where soft soils are overlain by relatively stiff layers. Furthermore, [PERSON]'s design method [37] does not take into account the disturbance of the soft, sensitive clay during the stone columns' installation which may contribute to the increase of footing settlement. In this parametric study, a numerical modeling investigation is carried out to study the settlement performance of lightly loaded narrow strip footings on top of soft clay supported by a small group of stone columns. The results of the FE analysis simulations are validated against measured data from a well-characterized site. This research investigates the effect of key factors, such as column spacing, column length, presence of a crust layer, footing shape, column diameter, and column strength on the design of small groups of stone columns. Results regarding the settlement performance, ground surface settlement profile, lateral bulging, and the influence of geogrid encasement and their depths are obtained from the FE analysis. This paper focuses on the influence of key parameters on the footing behavior by evaluating the settlement performance.
# Load Models Representative of Brazilian Actual Traffic in Girder-Type Short-Span Highway Bridges
## 6 Analysis of the Vehicle-Pavement-Structure Interaction  
The first analytical models to address bridge dynamic behavior under traffic load consisted of beams subjected to moving loads [42]. Regarding the problem of the vehicle-bridge dynamic interaction considering the pavement roughness, the motion equations of the structure are derived and coupled to the vehicle motion equations ensuring equilibrium and displacement compatibility at the contact points [43]. The bridge may be represented as a simple beam in vertical bending; a 3D beam, including torsional vibration modes; a grillage system [44]; or by finite element discretization [45]. In general, vehicle models consist of mass-spring damper multi-degree of freedom systems, which are either plane or 3D [44; 45]. A simplified model represents the vehicle as a moving load plus a pre-defined forcing function as usually done in the footbridge--pedestrian interaction problem [46]. In this work, an analytical-numerical model was developed and implemented aiming to perform the analysis of the vehicle-pavement-structure dynamic interaction. A three-axis vehicle is considered to be moving at a constant velocity along the bridge with eccentricity relative to the bridge axis. The vehicle is represented by a plane system of rigid bodies--see Figure 6a--connected by springs (\(k_{vi}\) and \(k_{pi}\)) and dampers (\(c_{vi}\) and \(c_{pi}\)), and is associated to five degrees of freedom: vertical displacement (\(u_{v}\)) and rotation (\(\theta_{v}\)) of the suspended mass (\(m_{v}\)) and vertical displacements (\(u_{pi}\)) of the three non-suspended masses (\(m_{pi}\)). The bridge structure motion is considered by generalized coordinates (\(u_{e}\)) in terms of vertical bending and torsion vibration modes and corresponding frequencies associated to the stick model of Figure 3c. These dynamic characteristics of the bridge are identified using the equivalent grid model shown in Figure 3b. Displacements and internal forces are calculated by the modal superposition method. From the analytical models of vehicle and structure, the
# Cartesian Aerial Manipulator with Compliant Arm
## 5 Experimental Results  
### Cartesian Base Control  
As stated in the introduction, the Cartesian base that supports the compliant arm enhances the positioning accuracy of the end effector compared to a revolute joint manipulator, in such a way that the mapping defined by the forward kinematics given by Equation (1) is almost straightforward. In order to evaluate this feature and the low level position control of the Cartesian base described in Section 4.1, a calibration test is conducted, executing a stair sequence from \(d_{1}=-100\) to \(d_{1}=100\) (mm)and from \(d_{2}=-50\) to \(d_{2}=50\) (mm), with increments of \(\Delta d_{1}=25\) and \(\Delta d_{2}=10\) (mm), respectively. Figure 10 represents the evolution of the two variables along with the position control error. The Murata SV01 potentiometers used to measure the rotation angle and the number of turns of the micro motors provide an effective rotational angle of \(333^{\circ}\). Note that a pair of potentiometers is required to overcome the dead zone. The 12-bit resolution ADC limits the accuracy to \(0.08^{\circ}\), which corresponds to a linear displacement of \(0.014\) mm for a 21 mm \(\O\) timing pulley.  
Figure 9: Functional blocks of the Cartesian aerial manipulator controller.  
However, in practice, the accuracy is limited by the backlash of the gearbox of the micro motors (around \(2^{\circ}\)) and the clearance in the timing belt.
## 3 Results and Discussion
### Mechanical Characterization of Specimens
#### 3.1.1 Relative Humidity (RH)
This result indicates that exposure to RH leads to a loss of stiffness in the materials, contrasting with the tensile test results. Figure 9: RH exposure results for: (**a**) tension, (**b**) monotonic compression, and (**c**) residual compression (CB) tests. Finally, the residual compression of the UN-FREI and UR-FREI specimens is presented in Figure 9c. For both materials, RH exposure led to an increase in the permanent deformation of the material. For UN-FREI, the deformation increased by 18.42% and 34.63%, while for UR-FREI, it increased by 19.74% and 15.25% at 14 and 30 days of exposure, respectively. The recycled rubber composite exhibited greater deformation under prolonged periods of applied load. Regarding the cyclic shear test, data collection was limited to 30% deformation to determine the shear modulus G (Figure 10a) and the damping ratio \(\beta\) (Figure 10b), due to the unstable behavior of the UR-FREI specimens, which were damaged at higher deformations. The test was performed with a period of 5 s.  
In general, the shear modulus and damping ratio of UR-FREI were higher than those of UN-FREI, similar to the findings presented by [PERSON], [PERSON], et al. [36]. It was observed that exposure to RH for 30 days contributed to a loss in stiffness in the materials, which aligns with the tensile and compressive test results. RH exposure led to a decrease in the shear modulus of the UN-FREI and UR-FREI specimens by 23.38% and 4.17%, respectively (Figure 10a). Similarly, a reduction in damping of 2.67% for UN-FREI and 11.14% for UR-FREI was observed under the same test conditions (Figure 10b).
## 2 Results and Discussion
### ParDE-AbrB-ParE Composite
#### 2.4.1 ParDE of _G. kaustophilus_ HTA426 and _Geobacillus_ sp. ZGt-1  
The gene with the locus tag GK2354, annotated in the RefSeq genome record of strain HTA426, and the one encoded on contig 23_242 in strain ZGt-1 code for a putative ParE-toxin (Table 1). The aa sequences of these two putative toxins are identical between the two strains, except for the start aa, M, which is duplicated in strain ZGt-1 (global alignment in Figure S6a). This duplication also appears in the putative ParE toxin of _G. thermolevornans_ Gts (described below).  
We noticed there are aa sequence differences in the putative toxin between the original and RefSeq genome annotations in strain HTA426. The putative toxin annotated in the RefSeq genome record is better aligned with that of strain Gts (described below). Therefore, we considered it for our analysis (Table S1). Nevertheless, in both genome records, the putative toxin harbors the same ParE toxin domain and has been annotated as a hypothetical protein (Table S1). Contig 23_242 in strain ZGt-1 also codes for a hypothetical protein (Table S2). The current study highlighted the identity of these hypothetical proteins as putative ParE toxins (Table 2).  
The analysis also indicated that the gene with the locus tag GK2355, which is adjacent to and upstream of the putative ParE toxin-coding gene annotated in the RefSeq genome record, codes for a putative SpoVT-AbrB domain-containing antitoxin, here labeled as AbrB (I) (Table 1). GK2354 and GK2355 potentially share the operon (Table 1), and overlap by 11 nt. There are also differences in the aa sequences of this AbrB antitoxin between the original and RefSeq genome annotations, but both harbor the SpoVT-AbrB domain. For our analysis, we considered the putative antitoxin annotated in the RefSeq genome record (Table S1). Here as well, the putative antitoxin has been annotated as hypothetical in both genome records (Table S1), but we could identify it as a putative AbrB antitoxin (Table 2).  
In strain ZGt-1, the gene encoded on contig 23_243, which is adjacent to and upstream of the putative ParE-coding gene mentioned above, codes for a putative AbrB (I) (Table 1), with aa sequence that is 100% identical to that of strain HTA426 (Table S1; global alignment in Figure S6b). This antitoxin is 100% identical to a hypothetical protein (Table S2), but the current study identified it as a putative AbrB antitoxin (Table 2). The putative two genes coding for the TA pair of strain ZGt-1, as well, potentially share the operon (Table 1), and overlap by 14 nt.
# Joint Channel Estimation Algorithm Based on DFT and DWT
## Appendix B Noise Power Boundary Analysis
 \tag{119}\]  
Therefore, the conditional probability density function can be given by  
\[f(z|z\leq Z_{\max})=\left\{ \begin{array}{cc}\frac{N}{K\sigma_{\rm int}^{2}} \mathrm{e}^{-\frac{z}{\beta_{\rm int}^{2}}},&z\leq Z_{\max},\\ 0,&z>Z_{\max}.\end{array} \right. \tag{120}\]  
By substituting Equation (120) into Equation (117), we can obtain the mean noise power of \(z\leq Z_{\max}\), i.e,  
\[ \begin{split}& E(z|z\leq Z_{\max})=\int_{0}^{+\infty}zf(z|z\leq Z _{\max})\mathrm{d}z\\ &=\frac{N}{K}\int_{0}^{Z_{\max}}\frac{z}{\tilde{\sigma}_{\rm int}^{2}}- \frac{-\frac{z}{\beta_{\rm int}^{2}}}{\tilde{\sigma}_{\rm int}^{2}}\mathrm{d}z \\ &=\frac{N}{K}\bigg{(}\sigma_{\rm int}^{2}-\big{(}\tilde{\sigma}_{\rm int}^{ 2}+Z_{\max}\big{)}\mathrm{e}^{-\frac{Z_{\max}}{\tilde{\sigma}_{\rm int}^{2}}} \bigg{)}\\ &=\sigma_{\rm int}^{2}\frac{N}{K}\Big{(}\frac{K}{N}+\Big{(}1-\frac{K}{N} \Big{)}\ln\!\Big{(}1-\frac{K}{N}\Big{)}\Big{)}.\end{split}  \tag{121}\]  
Therefore, among \(N\) samples of the estimated CIR, the total noise power of the \(K\) samples with the lowest noise power can be approximated as  
\[\sum_{n\in\Omega_{K}}|w[n]|^{2}\approx K\cdot E(z|z\leq Z_{\max})=\tilde{ \sigma}_{\rm int}^{2}N\bigg{(}\frac{K}{N}+\bigg{(}1-\frac{K}{N}\bigg{)}\ln\! \bigg{(}1-\frac{K}{N}\bigg{)}\bigg{)}. \tag{122}\]  
By approximating the time-domain noise variance \(\tilde{\sigma}_{\rm int}^{2}\) for the current OFDM symbol frequency domain noise average power \(\sigma_{s-wf}^{2}\), we can obtain  
\[\sum_{n\in\Omega_{K}}|w[n]|^{2}\approx\frac{N_{\rm P}}{N}\bigg{(}\frac{K}{N}+ \bigg{(}1-\frac{K}{N}\bigg{)}\ln\!\bigg{(}1-\frac{K}{N}\bigg{)}\bigg{)}\sigma_ {s-wf}^{2}. \tag{123}\]  
By combining Equations (10), (111) and (123), the value range of \(\sum_{n\in\Theta}\!|w[n]|^{2}\) is finally obtained as  
\[\bigg{(}\frac{K}{N}+\bigg{(}1-\frac{K}{N}\bigg{)}\ln\!\bigg{(}1-\frac{K}{N} \bigg{)}\bigg{)}\frac{N_{\rm P}}{N}\sigma_{s-wf}^{2}\leq\sum_{n\in\Theta}| \tilde{w}[n]|^{2}\leq\frac{KN_{\rm P}}{N^{2}}\sigma_{s-wf}^{2}. \tag{124}\]
# A Feature-Based Cost Estimation Model for Wind Turbine Blade Spar Caps
## 4 Results and Discussion
### Resource Costs: Material, Tooling, Capital, Labor  
At annual production rates of 285 for 35 m and 272 for 75 m, material is the highest contributor to total cost. All resource costs increase as a quadratic function of spar cap mass with blade length as a proxy for mass (Figures 4 and 6). In Figure 4, material (GFRP and CFRP) and direct labor costs increase as a proportion of total cost from 35 to 75 m, while indirect labor, capital, tooling, and utilities decrease. CFRP rises from 65% to 83% compared with GFRP, which rises from 39% to 64%, dominating total cost to a much greater extent as carbon is 10 times more expensive than glass.
## 2 Materials and Methods
### Methods
#### 2.2.4 PSD Extrapolations  
The mass concentration modelling will require the entire optically relevant particle distribution to be included as the input, or the output will not be comparable with independently measured mass concentration values. The FC method was found to reliably retrieve particle diameters between -0.5-10 um [1]. This range covers a large fraction of the contribution to scattering and backscattering, but the whole optically relevant range spans from tens of nanometers to a few millimeters [22; 23]. The undetectable fraction of the particle population has to be accounted for using an approximation of the PSD to extend the range of the distribution over the whole relevant range.  
Ever since pioneering work in the '60s and '70s found that the number of particles suspended in the ocean increased continuously and monotonically towards smaller scales [24; 25], power law distributions of the type used by [PERSON] [26] for aerosols have been the most common form of approximation for natural seawater particle populations [27; 28]. The PSDs determined by the FC method for the UKCW dataset broadly conformed to this model, and consequently, following in the steps of [PERSON] et al. [5], a least squares best fit of the UKCW PSDs through power law distributions as defined by  
\[N(D)=N^{\prime}(D)dD=kD^{-\gamma}dD \tag{8}\]  
was used to extend the range of the measured PSDs (Figure 2). Here \(N(D)\) is the number concentration of particles within the size bin corresponding to diameter \(D\), \(N^{\prime}(D)\) the density function of the PSD, \(dD\) the width of the size bin, \(k\) the scaling coefficient of the PSD, and \(\gamma\) its slope. The form given in Equation (8) is necessary because the FC PSDs have bin-like nature; accordingly, the extrapolations need to be bin-like as well.  
Values for the real refractive index \(n_{r}\) in the Junge extensions must also be accounted for using some approximation of the PRID to extend the range of known refractive indices; this was done by averaging \(n_{r}\) at the extremes of the measured PSD fraction (last four bins on either side) and using these averaged values on the respective arms of the extension (Figure 2). Since the FC method does not offer any information on the imaginary part of the refractive indices, \(n_{i}\) values are unknown both in the available FC PSDs and in their extrapolations. Typical values for \(n_{i}\) were therefore adaptedfrom literature (Figure 8 of [PERSON] et al. [8]), for both organic and inorganic particles. These were then assigned to the particles according to the value of the real refractive index of each bin, both directly determined by the FC method and extrapolated.
## 1 Introduction  
In the age of global information, it is of paramount importance to provide reliable and high-quality software at a reasonable cost and duration. Because of the continuous increase in the number of IT projects, the demand for information and communication technology (ICT) specialists is steadily growing [1]. Software development companies already have difficulties in recruiting specialists with the required knowledge and experience [2]. One of the solutions to the problem of an insufficient workforce in the ICT sector may be to increase employee productivity.  
Introducing affect awareness in software development management may be one of the solutions to this problem. Recent research has already shown that, in IT projects, positive emotions increase productivity, while negative emotions can significantly reduce performance [3; 4; 5; 6; 7]. Affect-aware IT project management can help software developers stay productive as well as detect when emotions, such as frustration, reduce their performance [5]. However, to bring these ideas to life, tools to recognize the emotions of software developers while working are essential.  
The affective computing domain has provided many methods and tools for recognizing the emotions of computer users. However, it has not been verified yet whether they can be used to monitor the emotions of programmers during their daily work and to what extent. Moreover, some of them, as a result of their invasiveness or cost, are not suitable for use in a work environment and can be used only in a laboratory.  
Spontaneous emotions cannot be expected during laboratory experiments. Therefore, to study the emotional states of programmers in such an environment, they should be induced somehow. Only the induction of negative emotions was considered. Previous studies have shown that it is more efficient than in the case of positive emotions [8].  
The aim of the study is to review the available emotion recognition methods for their use in a software development environment. In addition, selected methods of stimulating emotions during programming in a laboratory environment have been evaluated. Three research questions have been formulated:  
* What methods and tools known from affective computing research can be used to recognize the emotions of programmers in a laboratory environment?
* Which of the identified methods are suitable for programming in a real working environment?
* How can the negative emotions of programmers be induced in a laboratory environment?  
The rest of the paper is organized as follows: In Section 2, methods useful in recognizing emotions of software developers are described; Section 3 describes the experiment design, and Section 4 describes its execution and results; finally, Section 5 discusses the results, and Section 6 concludes.
# Design Trends and Comparative Analysis of Lightweight Block Ciphers for IoTs
## 2 Methodology  
Literature resources in this article were collected from multiple digital databases commonly used by cryptography researchers. These include IEEE Xplore (IEEE), SpringerLink (Springer), ScienceDirect (Elsevier), Google Scholar, MDPI, IOP Publishing, KoreaScience, Wiley Online Library, and Inderscience, as shown in Figure 2. Each of these platforms hosts peer-reviewed journals or conference proceedings relevant to lightweight cryptographic design, ensuring that all surveyed ciphers are derived from academically credible and accessible sources.  
The screening process ensures that only the most relevant literature resources are included in this survey by applying the article selection criteria outlined in Table 1. Given the large volume of publications retrieved from academic databases, it is essential to filter and retain only those that contribute meaningfully to the research objectives. Key criteria for selection include the publication year, type of article, language, research domain, cryptographic primitive, block size, key size, and encryption type. These criteria help maintain alignment with the intended research scope and ensure consistency in the evaluation process. By applying these guidelines, researchers can effectively translate the collected input into a structured and focused research output.  
Figure 2: Cipher sources by databases.  
Security evaluation criteria for LWBC (Table 2) presents a comparative overview of widely recognized security evaluation criteria for lightweight block ciphers as defined by NESSIE (2000), NIST (2015), and the National Cryptographic Standard (NCS) issued by the National Cybersecurity Authority (NCA) of Saudi Arabia (2020) [4]. Each standard outlines minimum requirements for key and block sizes, as well as the types of cryptanalytic evaluations expected. While NESSIE recommends at least a 128-bit key and 64-bit block with a focus on generic and side-channel attacks, NIST allows for a slightly smaller key size (minimum 112-bit) and emphasizes resistance to fault and side-channel attacks. In contrast, the NCS's guidelines are tailored for national cybersecurity policies, requiring a minimum key size of 80 bits and a block size of at least 64 bits, while mandating robustness against differential and linear cryptanalysis, statistical validation through NIST test suites, and protection against side-channel threats. These criteria collectively serve as a benchmark to ensure the cryptographic soundness and practical security of LWBCs [5], especially for IoTs and embedded applications.
## 3 Results and Discussion
### Growth Measurements
#### 3.2.2 Carotenoid Analysis  
A HPLC analysis of carotenoids identified only lutein and \(\beta\)-carotene, with lutein measured at 0.272 mg L\({}^{-1}\) and \(\beta\)-carotene at 0.085 mg L\({}^{-1}\). Among these pigments, lutein is a key component of the macula in the human eye, whereas oxidative stress is considered a major factor contributing to age-related macular degeneration [37; 38]. Additionally, lutein has been reported to play a role in preventing atherosclerosis in cardiovascular disease due to its antioxidant properties [39; 40].