esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Uncertainty Analysis  
As mentioned in the Introduction section, the reduction of skin friction drag appears to be in the order of several percent (e.g., using a riblet surface). Therefore, it is vital to quantify the uncertainty inherent in the measurement of physical properties of interest, such as \(U\)-velocity distribution and in the postprocessing of the measured raw datasets. Table 1 summarizes the possible uncertainty sources inherent in both measurement and postprocessing. Each measured value of flight data including atmospheric air data and pitot pressure data was averaged for a duration of 1 min, and those associated errors were considered to be \(1\sigma\) for the averaging duration as mentioned in the previous section. The RSM error obtained from the interpolation described in the previous section was the governing error source. The cumulative error was approximately 4.1%, which corresponds to \(\varepsilon\) in Equation (9). Thus, the boundary layer profile based on \(U\)-velocity obtained from pitot-rake measurement under flight conditions was interpolated by applying response surface methodology within an uncertainty of 4.1%.  
Figure 11: (**a**) Average difference for \(U\)-velocity compared between reference case and compared case plotted against \(s^{+}\); and (**b**) histogram of average error in \(U\)-velocity.
# Failure-Robot Path Complementation for Robot Swarm Mission Planning
## 3 Design and Principle of Path Programming
### Workflow of Multi-Vehicle Path Programming
#### 3.3.3 Establishing the Initial Solution  
The formation of the initial solution was processed through three steps. In step 1, the nearest distance method was used to generate a rough plan for single path programming. First, the distance array \(c_{ij}\) was established, and the zeroth row of \(c_{ij}=\infty\). Then, the point in which the row of \(c_{ij}\) had the minimum value was set as the 1 st point, and the value of this row of \(c_{ij}\) was set at infinity. Then, the same procedure was repeated to search and to set the 2 nd point, the 3 rd point, etc. The initial solution was repeated until all the points were searched. In step 2, TS and 2-opt were used to optimize the single path. In step 3, the single path was divided into multiple sub-paths, which were as short as possible, and the maximum distance limit was set; these multiple sub-paths formed the initial solution for the next phase.
# A Two-Port Dual-Band Dual-Circularly-Polarized Dielectric Resonator Antenna
## 4 Conclusions  
A C-band two-port DBDCP DRA was designed, fabricated, and measured. The TE\({}_{111}\) and TE\({}_{113}\) modes of the designed DRA were utilized to realize dual-band operation and to achieve broadside radiation. The DRA was fed by two 50-\(\Omega\) microstrip feedlines through a common cross slot to separate its two modes into two pairs of near-degenerate orthogonal modes to achieve DBDCP performance. The experimental results verified that the proposed two-port DRA has a RHCP radiation in the LB (3.58-4.43 GHz) and a LHCP radiation in the UB (5.14-6.03 GHz). The measured gains of the two CP bands were in the ranges of  
Figure 14: Measured and simulated normalized radiation patterns of the proposed two-port DBDCP DRA at (**a**) 4.0 GHz, fed by P1 and (**b**) 5.5 GHz, fed by P2 (Solid lines: measurement. Dotted lines: simulation).  
4.89-5.64 dBic and 5.81-8.13 dBic. Therefore, the proposed two-port DRA with DBDCP performance is a promising candidate for use in SATCOM systems.  
Conceptualization, T.V.T., S.T.-V. and K.C.H.; methodology, T.V.T. and S.T.-V.; software, T.V.T. and S.T.-V.; validation, T.V.T., S.T.-V. and K.C.H.; formal analysis, T.V.T. and S.T.-V.; investigation, T.V.T. and S.T.-V.; resources, T.V.T., K.-YL. and Y.Y.; data curation, T.V.T., S.T.-V., K.-YL. and Y.Y.; writing--original draft preparation, T.V.T.; writing--review and editing, T.V.T., S.T.-V. and K.C.H.; visualization, T.V.T.; supervision, K.C.H.; project administration, K.C.H. All authors have read and agreed to the published version of the manuscript.  
Not applicable.  
All data have been included in study.  
The authors declare no conflicts of interest.
## 3 Results and Discussion
### Rail Seat Forces
Hence, rail seat forces for adjacent bearers are considerably high in comparison to the reference case. Relative increase for adjacent bearers escalates with the number of unsupported bearers. It ranges from 19% to 80% for longest adjacent bearers, and from 10% to 135% for shortest adjacent bearers. It is also noteworthy that in the case of three unsupported bearer, one of the unsupported bearer is the shortest bearer (bearer no. 61) that is used for transition from turnout to normal track section. As can be seen from the figure, shortest unsupported bearer carries nearly no force due to independency from the diverging route when three unsupported bearer is considered. Figure 15, plotted for the shortest bearer when it is unsupported, carries typical properties of normal track section. The unsupported bearers are clearly visible in the figure. The magnitude of the force transferred via fastening system ranges from 0.53 to 1.24. The shortest bearer provides less track support, causing a larger displacement of track and reaction forces distributed over the bearers. It also should be noted that the longest bearer is also in the vicinity of shortest bearer, having higher track stiffness. Consequently, load distributions are more uneven at this section, causing that shortest bearer carries relatively higher forces, when maximum rail seat forces are considered. In all cases, the latter adjacent bearers carry much of the loads compared to other adjacent bearer, which is different from the case of unsupported bearers at normal track section. Relative increase range from 22% to 198% associated with vehicle velocity and the number of unsupported bearers. When considering the results for a particular time (Figure 15a), this behavior is vice versa. Former adjacent bearer is subjected to higher forces as the rear wheel also affects it.
# Multiple-Mode Orthogonal Time Frequency Space with Index Modulation
Then, a signal detection algorithm based on distance was designed at the receiver. After that, the SE, EE, and computational complexity of the receiver signal detection algorithm of the MM-OTFS-IM system were analyzed. Finally, the BER performance of the MM-OTFS-IM system was compared with that of the OTFS, OTFS-IM, and OTFS-DM-IM systems in the time-varying channel to verify the superiority of the MM-OTFS-IM system.
# Buzzing through Data: Advancing Bee Species Identification with Machine Learning
## 2 Materials and Methods  
### Bee Species Determination Using Machine Learning  
Bees, crucial pollinators within the Apoidea superfamily, are distinguished by their vast diversity, with over 20,000 identified species. The taxonomic hierarchy within the Apidae family organizes its vast diversity across multiple levels, encompassing sub-family, tribe, genus, species, and subspecies, providing a structured framework for understanding the intricate relationships and characteristics of each member within this family (Figure S1). They are characterized by their members' roles in pollination and the various social structures observed in the different species. Specifically, the study primarily concentrates on the Apidae family, which encompasses a diverse range of honeybees and stingless bees, although it is not exclusively restricted to these species.  
The research considers the determination of any one species within the Apidae family by leveraging ML to mimic human learning through data and algorithms. Although the term \"species classification\" frequently appears in the machine learning literature, \"species determination\" is used throughout this manuscript. This terminology was chosen to clearly distinguish this work from taxonomic classification, which organizes species into hierarchical groups based on their evolutionary relationships and morphological characteristics. Using \"species determination\", the manuscript clarifies that the objective is to identify distinct bee species using machine learning models, avoiding any potential confusion for readers with backgrounds in biological sciences. The ML field encompasses both shallow learning (SL) and deep learning (DL) approaches. Shallow learning, characterized by simpler models with fewer processing layers, includes techniques such as Linear Regression (LR), Decision Trees (DT), and Support Vector Machines (SVM), which whilst less structurally complex, are only capable of handling simpler data patterns [23]. Deep learning, in contrast, involves sophisticated neural networks with multiple layers, enabling the handling of more complex, high-dimensional data. Prominent examples of deep learning include convolutional neural networks (CNNs) and recurrent neural networks (RNNs), which are widely used in advanced applications such as image and speech recognition [24; 25; 26]. A consideration of both SL and DL allows for an encompassingsearch application on the topic, catering to a broad spectrum of complexities found in real-world scenarios.
###### Abstract  
As high-speed big-data communications impose new requirements on storage latency, low-density parity-check (LDPC) codes have become a widely used technology in flash-memory channels. However, the iterative LDPC decoding algorithm faces a high decoding latency problem due to its mechanism based on iterative message transmission. Motivated by the unbalanced bit reliability of codeword, this paper proposes two technologies, i.e., serial entropy feature-based layered normalized min-sum (S-EFB-LNMS) decoding and parallel entropy feature-based layered normalized min-sum (P-EFB-LNMS) decoding. First, we construct an entropy feature vector that reflects the real-time bit reliability of the codeword. Then, the reliability of the output information of the layered processing unit (LPU) is evaluated by analyzing the similarity between the check matrix and the entropy feature vector. Based on this evaluation, we can dynamically allocate and schedule LPUs during the decoding iteration process, thereby optimizing the entire decoding process. Experimental results show that these techniques can significantly reduce decoding latency.  
entropy feature; LNMS LDPC decoding; cosine similarity; flash-memory channel Article Design of Low-Latency Layered Normalized Minimum Sum Low-Density Parity-Check Decoding Based on Entropy Feature for NAND Flash-Memory Channel. Entropy 2024, 26, 781. [[https://doi.org/10.3390/c26090781](https://doi.org/10.3390/c26090781)]([https://doi.org/10.3390/c26090781](https://doi.org/10.3390/c26090781))  
1  
[PERSON], H. Design of Low-Latency Layered Normalized Minimum Sum Low-Density Parity-Check Decoding Based on Entropy Feature for NAND Flash-Memory Channel. Entropy 2024, 26, 781. [[https://doi.org/10.3390/c26090781](https://doi.org/10.3390/c26090781)]([https://doi.org/10.3390/c26090781](https://doi.org/10.3390/c26090781))  
1
# Indoor Autonomous Vehicle Navigation--A Feasibility Study Based on Infrared Technology
## 1 Introduction  
Positioning is a key technology that enables the development of indoor autonomous vehicles. There are various kinds of useful approaches, such as radio frequency identification (RFID) [1; 2; 3], ZigBee, WiFi [4; 5; 6; 7], have been proved in many research studies over the past years. Recently, a robotics company (Marvelmind Robotics: Sunnyvale, CA, USA) has even launched an \"Indoor GPS\" with \(\pm 2\) cm precision based on stationary ultrasonic beacons united by radio interface in a license-free band [8]. Indeed, most of the problems of positioning could be solved by any of the above methods. However, this paper verifies a cost-effective method utilizing infrared technology. Not only can the positioning of the test vehicle be accomplished by making use of the mature encoding technology of the infrared signal, the path planning has also been demonstrated by use of the _invisible route_ (described latter). Eventually, based on infrared technology, a simple method for the navigation of an indoor autonomous vehicle has been presented in this article.
# Low-Thrust Transfer to Quasi-Synchronous Martian Elliptic Orbit via Nonlinear Feedback Control
## 3 Nonlinear Orbit Control
### Operational Orbit  
The desired orbit is defined by five orbit elements: semimajor axis, eccentricity, inclination, argument of perigee, and RAAN. The main effect of the zonal harmonic \(J_{2}\) is the average linear time-variation of the RAAN, given by Equation (16). This means that desired RAAN \(\Omega_{d}\) of the operational orbit is  
\[\Omega_{d}=\Omega(t_{0})+\dot{\Omega}(a_{d},e_{d},i_{d})(t-t_{0}) \tag{21}\]  
where \(\dot{\Omega}(a_{d},e_{d},i_{d})\) is given by Equation (16) and subscript \(d\) refers to the desired (nominal) value of the respective orbit element. Moreover, as the critical inclination is selected, the \(J_{2}\) perturbation yields no average change in the argument of periapse. Finally, the \(J_{2}\) zonal harmonic generates no average variation of \(a\), \(e\), and \(i\). In short, the desired orbit elements are  
\[a_{d}=13,799\ \text{km}\ \ \ e_{d}=0.698\ \ \ i_{d}=63.4\ \text{deg}\ \ \ \Omega_{d}(t_{0})=0\ \text{deg}\ \ \ \omega_{d}=-90\ \text{deg}\]  
where for the RAAN, only the initial value is reported as it is time-varying.  
The desired insertion conditions are expressed in terms of equinoctial elements. Because \(p_{d}:=a_{d}\big{(}1-e_{d}^{2}\big{)}\), one gets  
\[x_{1}-p_{d}=0 \tag{22}\]  
To get the correct eccentricity and argument of periapse, using the definitions of \(l\) and \(m\), the following relation is enforced:  
\[\left[x_{2}-e_{d}\cos(\Omega_{d}+\omega_{d})\right]^{2}+\left[x_{3}-e_{d}\sin (\Omega_{d}+\omega_{d})\right]^{2}=0 \tag{23}\]  
leading to  
\[{x_{2}}^{2}+{x_{3}}^{2}-2e_{d}\{x_{2}\cos(\Omega_{d}+\omega_{d})+x_{3}\sin( \Omega_{d}+\omega_{d})\}+e_{d}^{2}=0 \tag{24}\]  
The remaining conditions deal with the orbital plane orientation. The instantaneous direction of the spacecraft's angular momentum, \(\hat{h}\), is required to be aligned with the desired one, \(\hat{h}_{d}\). The unit vector \(\hat{h}\) can be expressed in terms of \(\Omega\) and \(i\), i.e., \(\hat{h}=\hat{c}_{1}\sin\Omega\sin i-\hat{c}_{2}\cos\Omega\sin i+\hat{c}_{3}\cos i\), leading to  
\[\hat{h}\cdot\hat{h}_{d}=1\Rightarrow\sin\Omega\sin i\sin\Omega_{d}\sin i_{d} +\cos\Omega\sin i\cos\Omega_{d}\sin i_{d}+\cos i\cos i_{d}=1 \tag{25}\]  
After some steps, Equation (25) can be rearranged as  
\[2x_{5}\sin\Omega_{d}\sin i_{d}+2x_{4}\cos\Omega_{d}\sin i_{d}+\left(1-x_{4}^ {2}-x_{5}^{2}\right)\cos i_{d}-\left(1+x_{4}^{2}+x_{5}^{2}\right)=0 \tag{26}\]  
The final conditions (22), (24), and (26) can be incorporated as the three components \(\{\psi_{1},\psi_{2},\psi_{3}\}\) of the vector \(\psi\) and finally written as  
\[\psi(z,t)=\mathbf{0} \tag{27}\]The three (scalar) insertion conditions, written in the vector form (27), define the _target set_ of the problem. It is worth noticing that \(\mathbf{\psi}\) is continuous and has continuous partial derivatives. Moreover, it is time varying due to \(\Omega_{d}(t)\).
## 7 Effects of Roasting on Lipid Content
### Almonds  
[PERSON] et al. (2016) examined the influence of various roasting conditions (150, 180, and 200 \({}^{\circ}\)C for 5, 10, and 20 min) on the fatty acid profile of almonds. The most pronounced changes were observed in almonds roasted at 200 \({}^{\circ}\)C for 20 min. Specifically, significant decreases were observed in palmitic acid (16:0), linoleic acid (18:2), and oleic acid (18:1). Consequently, the total saturated and unsaturated fatty acid content decreased [79].  
According to [PERSON] et al. (2017), roasting almonds at 110 \({}^{\circ}\)C for 16 min did not significantly alter their fatty acid composition. Analysis of raw almonds revealed 7.2%saturated fatty acids, 69.3% MUFAs, and 23.4% linoleic acid. Roasted almonds exhibited a similar fatty acid profile, with 7.6% saturated fatty acids, 68.3% MUFAs, and 23.9% linoleic acid [80].  
In their study, [PERSON] et al. (2015) reported that oleic acid constituted 66.8% of the FAMEs in raw almonds and 66.1% in almonds roasted at 170 \({}^{\circ}\)C for 15 min. The linoleic acid content increased slightly from 24.5% in raw almonds to 24.9% in roasted almonds. The palmitic acid content also showed a minor increase, from 5.6% in raw almonds to 5.7% in roasted almonds [88].
# Industry 4.0 and World Class Manufacturing Integration: 100 Technologies for a WCM-I4.0 Matrix
## 7 Discussion: I4.0 and WCM Synergy
### Industry 4.0 Impact on Safety Pillar  
The goal of Safety is zero injuries, zero medication and risk reduction at its root. WCM's approach to safety is based on the principle that any minor or missed injury is a potentially serious injury; serious injuries must also be eliminated. In this sense, I4.0 technologies can offer a significant contribution in several ways:  
* Streamlining the main bottom-up processes: the registry of AU, UC and NM can be carried out directly by the operator in the workplace and instantly displayed by safety managers, line managers, supervisors and other operators, ensuring greater transparency of information and shorter response times from management;
* Ensuring the proper use of Personal Protective Equipment (PPE): Visual Camera and nonuse alarm systems can reduce accidents due to the lack of use of PPE;
* Reducing the risks for pedestrians: forklifts and people can be equipped with sensors that signal their presence on interactive maps; a forced braking system can be activated in case the distance between forklift and pedestrian becomes dangerous and the speed does not decrease;
* Introducing new countermeasures: AI allows an automatic stop of the machine as soon as UC occurs, which can also be learned over time thanks to Machine Learning techniques; augmented reality enables the operator to real-time monitoring process parameters in order to recognize UC and prevent UA or, for example, identifying the weight of a load; collaborative robotics and exoskeletons reduce unergonomic operations and help the operator to carry out operations by strongly reducing the associated risk factors.
## 2 Material and Methods
### Gas Chromatography-Olfactometry-Mass Spectrometry  
Aliquots of samples (2 \(\upmu\)L) or SPME needles were injected manually in splitless mode. The effluent of the capillary column was split evenly (1:1) between a sniffing port (200 \({}^{\circ}\)C) and an MS detector (MSD). The GC-MS system employed was the Agilent 7890B-5975B (Agilent, Santa Clara, CA, USA) equipped with the Agilent DB-5 MS and DB-WAX (30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m) capillary-type chromatographic columns. The GC-O-MS experiments were conducted primarily using the DB-5 MS column and the DB-WAX column was used to assist in identification. The GC settings were as below. For the DB-5 MS column, the oven temperature was programmed to increase from 50 \({}^{\circ}\)C (5 min hold) to 230 \({}^{\circ}\)C (2 min hold) at a rate of 5 \({}^{\circ}\)C/min, then to 280 \({}^{\circ}\)C (1 min hold) at 15 \({}^{\circ}\)C/min, and 50 \({}^{\circ}\)C (5 min hold) to 230 \({}^{\circ}\)C (30 min hold) at a rate of 5 \({}^{\circ}\)C/min for the DB-WAX column. Mass spectrometer conditions were as follows: ionization mode, electron impact (EI); ionization energy, 70 eV; ion source temperature, 230 \({}^{\circ}\)C; quadrupole temperature, 150 \({}^{\circ}\)C; and quadrupole mass spectrometer scan range, 30-350 atomic mass units (amu).
# Defense against Adversarial Attacks in Image Recognition Based on Multilayer Filters
## 4 Experimental Results  
### Experimental Setup  
In the in-depth exploration of adversarial attacks in experimental research, we have established a prerequisite assumption: that the attacker is completely unaware of the existence of the filtering layer. In addition, this study also reproduced six typical adversarial attack patterns, including FGSM, BIM, PGD, CW, AutoAttack, and VNIFGSM. In the selection of attack samples, we adopted a rigorous approach to ensure that all generated test samples can effectively lure black box classifiers into making false positives. Table 1 shows in detail the number of test samples used in different attack scenarios.  
To ensure that the disturbance is visually imperceptible, all attack factors are set to \(\epsilon=8/255\). The selection of this value aims to maintain the integrity of image information to the greatest extent possible while introducing minimal disturbances that are difficult to detect with the naked eye. Subsequently, we used six different attack methods to generate a series of adversarial samples for all test samples in the dataset. On this basis, we used a trained black box classifier to classify these adversarial samples. Finally, we will gather all adversarial samples that have been misclassified into one dataset. This dataset will serve as the test sample for our subsequent experiments.  
To evaluate the performance of classifiers in the face of adversarial attacks, we usually use test datasets that have not participated in training to generate adversarial samples. The difficulty level of generating adversarial samples varies among different datasets. The MNIST dataset focuses on handwritten digit recognition, with smaller image sizes and relatively single categories. In this context, the accuracy of the black box classifier can reach up to 98.17%. Therefore, for the MNIST dataset, when using adversarial attack methods such as FGSM and BIM, the number of generated adversarial samples is relatively small. In contrast, the CIFAR100 dataset contains larger and more diverse types of image data. On this dataset, the accuracy of the black box classifier reached 70.25%. Due to the relatively limited performance of the classifier, in this case, applying a small degree of disturbance to the image may induce misjudgment by the classifier. Therefore, on the CIFAR100 dataset, using attack methods such as FGSM and BIM can more easily generate a large number of adversarial samples.
## 1 Introduction
### Determination of the Overvoltage That Is Transmitted to the Tertiary Winding  
The MT winding, because it is connected to earth at one end, acts as a screen between the IT windings and the tertiary winding (T). Consequently, the overvoltage that is transmitted to T is the product of the inductive transfer between the MT and T windings (see Figure 5) [20,23,36].  
\[M^{2}=L_{MT}\cdot L_{T} \tag{33}\]  
\[\underline{u}_{MT}=(R_{MT}+j\cdot\omega\cdot L_{MT})\cdot\underline{i}_{1}-j \cdot\omega\cdot M\cdot\underline{i}_{2} \tag{34}\]  
\[\underline{u}_{T}=-(R_{T}+j\cdot\omega\cdot L_{T})\cdot\underline{i}_{2}-j \cdot\omega\cdot M\cdot\underline{i}_{1} \tag{35}\]  
but \(\underline{i}_{2}=0\); it then follows that  
\[\underline{u}_{MT}=(R_{MT}+j\cdot\omega\cdot L_{MT})\cdot\underline{i}_{1} \tag{36}\]  
\[\underline{u}_{T}=j\cdot\omega\cdot M\cdot\underline{i}_{1},\ \text{therefore}\ \underline{i}_{1}=\frac{\underline{u}_{T}}{j\cdot\omega\cdot M} \tag{37}\]\[\underline{u}_{\text{MT}}=(R_{\text{MT}}+j\!\cdot\!\omega\!\cdot\!L_{\text{MT}}) \cdot\frac{\underline{u}_{T}}{j\!\cdot\!\omega\!\cdot\!M} \tag{38}\]  
\[\underline{u}_{\text{MT}}=(R_{\text{MT}}+j\!\cdot\!\omega\!\cdot\!L_{\text{T}}) \cdot\underline{i}_{1} \tag{39}\]  
\[\underline{u}_{\text{T}}=\underline{u}_{\text{MT}}\cdot\frac{j\!\cdot\!\omega \!\cdot\!M}{R_{\text{MT}}+j\!\cdot\!\omega\!\cdot\!L_{\text{MT}}} \tag{40}\]  
because \(R_{\text{MT}}<<\omega\!\cdot\!L_{\text{MT}}\).  
\[u_{T}(t)=u_{\text{MT}}(t)\cdot\frac{\sqrt{L_{\text{MT}}\!\cdot\!L_{\text{T}}} }{L_{\text{MT}}}=u_{\text{MT}}(t)\cdot\sqrt{\frac{L_{\text{T}}}{L_{\text{MT} }}}\approx u_{\text{MT}}(t)\cdot\frac{w_{\text{T}}}{w_{\text{MT}}} \tag{41}\]  
where \(w_{\text{T}}\) and \(w_{\text{MT}}\) are the number of turns of the tertiary and secondary windings (MT).