esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Failure Modes Assessment  
The results of the mode of failure analysis are shown in Table 3. Chi-square test showed there was a significant association between the groups and mode of failure (\(p=0.003\)). Cohesive failure was the most prevalent failure mode among all groups. Examples of modes of failure are shown in Figure 2.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & **N** & **Mean** & **Std. Deviation** & \(p\)**-Value** & \multicolumn{3}{c}{**95\% Confidence Interval for Mean**} & \multicolumn{3}{c}{**Multiple Comparison Test (Tukey)**} & \multirow{2}{*}{**MCT \({}^{*}\)**} \\ \cline{4-9} Group 1 & 25 & 9.322 & 1.629 & & 8.649 & 9.994 & 1 & & & a \\ Group 2 & 25 & 10.893 & 1.762 & & 9.857 & 11.310 & 0.090 & 1 & & ab \\ Group 3 & 25 & 9.915 & 2.247 & & 9.898 & 10.843 & 0.593 & 0.681 & 1 & & a \\ Group 4 & 25 & 11.406 & 1.831 & & 10.650 & 12.162 & 0.001 & 0.414 & 0.031 & 1 & b \\ \hline \end{tabular} \({}^{*}\) MCT: multiple comparison test where b significantly different from a, but not from ab.  
\end{table}
Table 2: Comparing the groups by the average of Push-out bond strength at Maximum Load.  
Figure 2: Images obtained by digital microscopy at 50\(\times\) magnification for analysis of the mode of failure after push-out test. **(A–C)** Adhesive mode of failure; no sealer presents on the canal wall. **(D–F)** Cohesive mode of failure; sealer is located circumferentially on the canal wall. **(G–I)** Mixed mode of failure; sealer is present partially on the canal wall.
## 3 Materials and Methods  
This study is based on a bed-by-bed survey of field data that consisted of bed-by-bed surveying ten detailed lithological sections covering the entire succession of the Errachidia-Boudnib-Erfoud basin. These sections, arranged from east to west, are as follows: Tazzouguerte, Errachidia, Tadighouste, Goulmima, Taddart N'Oumira, Tinghir, Foum El Kouss, Imiter, Tazoughmite, and Anounizme (Figure 1). These sections were chosen according to their location in the basin, in a way that covers the majority of the basin (Table 1). The approach consisted mainly in describing the lithology, the fossil content, sedimentary structures, and the geometry of the beds. Petrographic analysis was also carried out on sections made from carbonate rocks for paleontological and sedimentological purposes. Limestone microfacies were described following the classification of [38].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Sections** & **Coordinates of the Sections** \\ \hline Tazzouguerte & \(32^{\circ}5^{\prime}28.33^{\prime\prime}\) N; \(3^{\circ}46^{\prime}16.80^{\prime\prime}\) W \\ [PERSON] & \(31^{\circ}58^{\prime}59.2^{\prime\prime}\) N; \(4^{\circ}33^{\prime\prime}09.8^{\prime\prime}\) W \\ [PERSON] & \(31^{\circ}50^{\prime}30.88^{\prime\prime}\) N; \(4^{\circ}54^{\prime}57.85^{\prime\prime}\) W \\ [PERSON] & \(31^{\circ}42^{\prime}06.7^{\prime\prime}\) N; \(4^{\circ}57^{\prime}16.3^{\prime\prime}\) W \\ [PERSON] & \(31^{\circ}37^{\prime}11.4^{\prime\prime}\) N; \(5^{\prime}7^{\prime\prime}22.67^{\prime\prime}\) W \\ Tinghir & \(31^{\circ}29^{\prime}04.6^{\prime\prime}\) N; \(5^{\circ}32^{\prime}5.9^{\prime\prime}\) W \\ Foum El Kouss & \(31^{\circ}26^{\prime}05.2^{\prime\prime}\) N; \(5^{\circ}38^{\prime}40.5^{\prime\prime}\) W \\ Imiter & \(31^{\circ}22^{\prime}32.44^{\prime\prime}\) N; \(5^{\circ}46^{\prime}34.25^{\prime\prime}\) W \\ Tazoughmite & \(31^{\circ}20^{\prime}31.43^{\prime\prime}\) N; \(5^{\circ}49^{\prime}3.21^{\prime\prime}\) W \\ Anounizme & \(31^{\circ}20^{\prime}21.7^{\prime\prime}\) N; \(5^{\circ}50^{\prime}14.1^{\prime\prime}\) W \\ \hline \hline \end{tabular}
\end{table}
Table 1: Geographical coordinates of the sections.  
The studied sections were correlated nearly along an E-W cross-section between the Tazzouguerte region in the east and the Anouizme region in the west (Figure 1). Only six main sections (Tazzouguerte, Errachidia, Goulmima, Foum El Kouss, Imiter, and Anounizme) will be presented and detailed in this paper. Tadighouse, Taddart N'Oumira, Tinghir, and Tazoughmite sections are used complementarily for correlation purposes at the basin scale for a better understanding of lateral and vertical facies changes. Other sections in the Ouarzazate and Agadir basins, logged by [17; 39], are used to follow the lateral evolution of the deposits towards the west. The lithologic features and colors used in all logs are shown in Figure 2.
# Primary User Localization Algorithm Based on Compressive Sensing in Cognitive Radio Networks
## 2 System Model  
### Spectrum Sensing Model  
Cognitive radio networks emerged as a static network paradigm to address the following problems: exhaustion of the scarce wireless spectrum and underutilization of the licensed spectrum in some areas. In CRNs, wireless nodes change their parameters to communicate efficiently, avoiding interference with PUs or SUs. This alteration of parameters is based on monitoring the radio environment, such as the radio frequency spectrum, user behavior, and network state [1].  
Figure 1 shows the spectrum sensing model in CRNs. During the process of sensing spectrum hole, SUs first scan the goal frequency band using energy detection, then send sensing results to the information processing center (IPC) (which can be a data fusion center or a cognitive node) through control channel, then IPC makes a decision on whether PU exists in detecting bands, thus realizing cooperative spectrum sensing [15; 16] (CSS). In an untrusted network environment, spectrum sensing in CRNs not only needs to detect the signal within the detecting band, but also should further confirm the identity of the signal to decide whether the signal is from legal PUs or MUs.  
Without loss of generality, we use the primary user activity model in [17], where a theoretical model for the joint optimization of spectrum sensing and transmission cycles is proposed. In this model, the primary user traffic can be modeled as a two state birth-death process with death rate \(\alpha\) and birth rate \(\beta\). An ON (Busy) state represents the period used by primary users and an OFF (Idle) state represents the unused period. Since each user arrival is independent, each transition follows the Poisson arrival process. Thus, the length of ON and OFF periods are exponentially distributed, and primary user activity can be modeled as exponentially distributed inter-arrivals. More specific details about the PU activity model are elaborated in [17], which is beyond the scope of this paper.  
With regard to the impact of nodes' mobility, in most wireless scenarios, the mobility of nodes may change the coordinates of the entire network, all nodes need to recalculate the position which consume more energy. Although the nodes in the network change their position due to the movement, the network coordinate system does not change. Mobile nodes only need to calculate their own position, and will not affect the location information of other nodes. So we consider the correlation coefficients between the source signal and SUs of the localization algorithm in this paper.
## 3 Results and Discussion  
### Microstructure and Components  
Figure 1 shows the morphologies of the original and alkali-modified flax fibers. It is apparent that the alkali-modified flax fiber surface (Figure 1b) is cleaner as compared to the original flax fiber (Figure 1a) because the alkali has removed carbohydrate substances from the fiber. EDS results show that the main components of flax fiber are carbon and oxygen, and alkali treatment did not change the composition of the flax fiber.  
Figure 2a,b show the microstructure of the cementitious composites after 28 days of curing for F0.5 and MF0.5. It is apparent that the flax fiber produces some pores in the matrix, and the incorporation of flax fibers into cement hydrates creates multi-phase boundaries and multiform interfaces.  
Figure 1: SEM/energy dispersive spectroscopy (EDS) image of (**a**) original flax fiber and (**b**) alkali modified flax fiber at 2000 \(\times\).  
Comparisons of Figure 2a,b illustrate that alkali modified flax fibers adhered better to the matrix as compared to the untreated fibers. Untreated flax fibers showed many impurities on the fiber surface (Figure 1a) while the cellulose of alkali modified flax fibers was directly exposed, improving the anchoring force of the flax fiber in the cement matrix and thus improving the fiber-matrix adhesion. Alkali treatment changed the chemical composition of the fiber by removing amorphous constituents (pectin, hemicelluloses, etc.), thereby increasing fiber crystallinity and roughness [14]. Consequently, alkali treated flax fiber enhances the adhesion and compatibility between the fiber and cement-based matrix. This compatibility, leading to a homogeneous distribution of the reinforcing fibers, is the predominant factor influencing the mechanical properties of the composites [15]. Cement hydration products are more apparent on the alkali modified flax fiber (Figure 3b) as compared to the untreated fiber (Figure 3b). These results indicate that alkali treatment improved the adhesion between the flax fibers and cementitious matrix.  
Figure 3: SEM/EDS images of flax fiber surface in (**a**) F0.5 at about 500 \(\times\) and (**b**) MF0.5 at 2000 \(\times\).  
Figure 2: SEM image of cement paste with 0.5 wt %. (**a**) Original flax fiber (F0.5) and (**b**) alkali modified flax fiber (MF0.5) at 1000 \(\times\).  
Figure 4 shows SEM images of the fracture surface of the cured F0.5 and MF0.5 specimens. It is apparent that some fibers are embedded in the cement hydrate with the opposite ends free. Untreated flax fibers were pulled off during the mechanical test (Figure 4a), and the traces left by pulled out alkali-treated flax fibers (Figure 4b) suggest that the flax fibers contributed to the cement paste damage process.
# Model-Based Assessment of Energy Efficiency in Industrial Pump Systems: A Case Study Approach
## 2 Case Study on Water-Cooling System: Operation, Data Collection, and Efficiency Modelling
### Description of Water-Cooling System Operation  
The schematic representation of the central WCS is shown in Figure 1, while Figure 2 shows the physical arrangement of the pumps in the machine room. VSDP1 and VSDP2 (look for position (1) in Figure 1) function alternately, extracting cold water (3) from a cold-water manifold and distributing it to various consumers. The desired temperature of the water supplied to the consumers is 7 \({}^{\circ}\)C (design value). After it has been heated to approx. 13 \({}^{\circ}\)C by the consumers (2), it circulates back into the WCS.  
The returning water is initially gathered in a water tank before being channelled into the warm-water manifold. Depending on the outside temperature, the system uses one of two cooling methods to bring the water temperature back to the desired 7 \({}^{\circ}\)C. In warmer weather, an active inner cooling system is used (5), comprising a water-cooled chiller which operates based on a water-to-water heat pump, a fan operated cooling tower, an associated storage tank, and a network frequency-driven pump (NFDP3). The water of the internal cooling circuit (5) and the water of the main cooling system (4) do not mix.  
The system's demand for chilled water is strongly influenced by outside temperatures, with peak usage occurring during the summer months. When the colder season arrives and temperatures drop, the demand for cooling water decreases considerably. For example, when the outside temperature drops below 5 \({}^{\circ}\)C, the amount of chilled water required can fall to just 20% of the peak value in summer. Under these conditions, the process water can be cooled with the cold outside air, making the active internal cooling system based on the water-to-water heat pump (5) superfluous. To accomplish this, the system diverts the water from the water-cooled chiller into a passive heat exchanger (6). During this process, the NFDP1 pump is switched off and the associated valves are closed, while at the same time the NFDP2 pump is started and the required crossover valves are opened. Maximum possible utilisation of cold outside air for cooling the process water is the most energy-efficient method and should be preferred whenever feasible. This solution inherently reduces the power consumption of the WCS. The approach we propose to optimise the efficiency of the VSDP is a further measure to reduce consumption, which allows us to further reduce CO\({}_{2}\) emissions and thus contributes to reducing the environmental impact of the system under consideration.
_Applied_  
_sciences_  
_Article_  
**Effects of Shot-Peening and Stress Ratio on**  
**the Fatigue Crack Propagation of**  
**AL 7475-T7351 Specimens**  
**[PERSON] 1, [PERSON] 1,*\({}^{\text{\textregistered}}\), [PERSON]. [PERSON] 1\({}^{\text{\textregistered}}\), [PERSON]. [PERSON] 1 and [PERSON] 1,2**  
Received: 23 January 2018; Accepted: 28 February 2018; Published: 5 March 2018  
**Featured Application: 7475-T7351 aluminum alloy are widely used for structural components in aerospace applications.**  
**Abstract:** Shot peening is an attractive technique for fatigue enhanced performance of metallic components, because it increases fatigue crack initiation life prevention and retards early crack growth. Engineering design based on fatigue crack propagation predictions applying the principles of fracture mechanics is commonly used in aluminum structures for aerospace engineering. The main purpose of present work was to analyze the effect of shot peening on the fatigue crack propagation of the 7475 aluminum alloy, under both constant amplitude loading and periodical overload blocks. The tests were performed on 4 and 8 mm thickness specimens with stress ratios of 0.05 and 0.4. The analysis of the shot-peened surface showed a small increase of the micro-hardness values due to the plastic deformations imposed by shot peening. The surface peening beneficial effect on fatigue crack growth is very limited; its main effect is more noticeable near the threshold. The specimen's thickness only has marginal influence on the crack propagation, in opposite to the stress ratio. Periodic overload blocks of 300 cycles promotes a reduction of the fatigue crack growth rate for both intervals of 7500 and 15,000 cycles.  
**Keywords:** aeronautical aluminum alloys; fatigue crack propagation; overloads; shot peening; Paris law
## 4 Discussion and Conclusions
In the previous study with Japanese mosaic speech [14], the intelligibility was near-perfect (>95%) for native-Japanese listeners. However, in the present study with English mosaic speech, the native-English group reached intelligibility scores of only around 75% on average. It is possible that the complexity of the English speech sounds, as discussed in the Introduction, also affected intelligibility. Therefore, we aim to further investigate this issue by generating mosaic speech in various other languages too, in order to see how intelligibility of mosaic speech varies with language type. It also would be possible to address the intelligibility of English mosaic speech by comparing our present data with those from objective, automated speech recognition systems [43]. Furthermore, a second reason for the relatively low intelligibility scores of the native-English group needs to be addressed too. Different from the previous study with Japanese mosaic speech, we here used words and not sentences. Identifying a word in isolation is not as easy as identifying a word in a sentence context [44]. It is thus necessary to investigate what is specific for the 40-ms window. **Author Contributions:** Conceptualization, S., and Y.N. ; methodology, S. and Y.N. ; software, S. and Y.N. ; validation, S., Y.N. and G.B.R. ; formal analysis, S., Y.N. ; investigation, S., Y.N. ; resources, S. and Y.N. ; data curation, S., Y.N. and G.B.R. ; writing--original draft preparation, S. and G.B.R. ; writing--review and editing, S., Y.N., K.U. and G.B.R. ; supervision, Y.N. and G.B.R. ; project administration, Y.N., K.U. and G.B.R. All authors have read and agreed to the published version of the manuscript. **Funding:** This study and the APC were funded by JSPS KAKENHI Grant Numbers JP17H06197 and JP19H00630. **Acknowledgments:** We wish to thank [PERSON], [PERSON], [PERSON], and [PERSON] for coding the mosaic speech program, [PERSON] for technical assistance, [PERSON], [PERSON] and [PERSON] for their support in data collection, and [PERSON] for her comments on an initial version of the manuscript. **Conflicts of Interest:** The authors declare no conflict of interest.
###### Abstract  
The tilt tri-rotor unmanned aerial vehicle (UAV) has three flight modes: the hover mode, the transition mode, and the fixed-wing mode. Controller design in the hover mode is the premise of realizing stable flight of this kind of UAV. Due to the particular structure with odd rotors and strong nonlinearity, the modeling and control of the tilt tri-rotor UAV remain an active and ongoing research topic. To overcome these problems and achieve stable flight control, this paper proposes a sliding mode-based nonlinear control scheme for the hovering flight of a tilt tri-rotor UAV, consisting of position control, attitude control, and control allocation. First, the mathematical model of the UAV is given by using the Newton-Euler formulation. Second, a cascade flight controller consisting of the position controller and the attitude controller is developed based on sliding mode control (SMC). For the position controller, an auxiliary dynamic system composed of the hyperbolic tangent functions is introduced to the SMC approach for constraining the output magnitude of the thrust and the reference attitudes. Besides, a disturbance observer is applied to the attitude controller to alleviate the chattering and improve robustness. Furthermore, according to the structural characteristics of the tilt tri-rotor UAV, a control allocation algorithm is developed to map the virtual control quantities calculated by the cascade flight controller to the actual actuators. Simulations are conducted to verify the robustness against the external disturbances and parameter variations, and the performance comparisons with two other control schemes are also given. Finally, the experiment is also carried out to validate the performance of the proposed control scheme.  
h  
hovering flight control; control allocation; sliding mode control; auxiliary dynamic
## 4 Results and Analysis
### Comparison
#### 4.1.2 Comparison with Image Classification Methods  
Over the past decade, numerous deep network architectures have emerged for image classification, significantly enhancing classification accuracy and fidelity for generic images. These architectures have not only advanced the field of image classification but have also been widely adopted as backbones or directly applied in various vision tasks to expedite their respective objectives. Recent works in malware classification, such as those  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline [PERSON] et al. [16] & 96.04 & 0.9096 & 0.9239 & 0.9595 \\ [PERSON] et al. [17] & 94.55 & 0.8777 & 0.8957 & 0.9469 \\ [PERSON] et al. [44] & 93.47 & 0.8682 & 0.8851 & 0.9351 \\ [PERSON] et al. [43] & 94.44 & 0.8926 & 0.9048 & 0.9438 \\ [PERSON] et al. [14] & 96.04 & 0.9151 & 0.9253 & 0.9608 \\ [PERSON] et al. [15] & 94.65 & 0.8990 & 0.9168 & 0.9459 \\ [PERSON] et al. [12] & 96.15 & 0.9256 & 0.9408 & 0.9618 \\ [PERSON] et al. [11] & 97.01 & 0.9341 & 0.9397 & 0.9708 \\ [PERSON] et al. [13] & 95.29 & 0.9075 & 0.9209 & 0.9528 \\ [PERSON] et al. [48] & 94.33 & 0.8719 & 0.8855 & 0.9448 \\ [PERSON] et al. [3] & 98.18 & 0.9678 & 0.9724 & 0.9823 \\ [PERSON] et al. [69] & 98.10 & 0.9616 & 0.9633 & 0.9796 \\ \hline
**DAMN (Proposed)** & **99.36** & **0.9892** & **0.9879** & **0.9924** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison with malware image classification methods. The proposed method outperforms the existing method in evaluation metrics with a straightforward learning strategy.  
by [PERSON] et al. [11], [PERSON] et al. [3], among others, have also begun leveraging SOTA image classification models. While these methods have individually investigated a few SOTA network architectures, a comprehensive evaluation of these image classification models is still necessary.  
This study aims to thoroughly evaluate existing SOTA image classification models to assess their efficacy in malware classification tasks. To adapt these models for malware classification, we modified their final layer to output probabilities for the 25 malware classes present in our dataset. Additionally, we leveraged pre-trained weights from ImageNet to initialize the models, aiming to achieve optimal performance. Table 3 summarizes the performance of various SOTA image classification methods, providing insights into their effectiveness when applied to malware classification.  
This comprehensive evaluation illuminates the performance of different SOTA models and facilitates a better understanding of their impact on malware classification tasks, ultimately contributing to advancements in cybersecurity research.  
As Table 3 illustrates, the proposed method substantially outperforms the image classification methods in all evaluation metrics. Also, it is worth noting that several SOTA image classification methods, like VGG-19, Squeezenet, mobile net-v2, mobile net-v3, densenet121, etc., can outperform the existing malware classification methods with a marginal score.
# A New Approach to Border Irregularity Assessment with Application in Skin Pathology
## 2 Material and Methods
### Determining Normals to the Epidermis Border  
Determining normals to the epidermis border is a preliminary step towards identifying the epidermis \"outer\" edge-- it is necessary to distinguish between the \"outer\" direction (the same as the normal direction) and the \"inner\" one.  
To find normals to the epidermis border we firstly trace the border \(L_{b}\) in the clock-wise direction using Moore-Neighbor tracing algorithm modified by [PERSON]'s stopping criteria [39] to get a sequence of border points \(P_{1},\ldots,P_{n}\). Then, to determine the normal \(\vec{n}_{i}\) at the \(i\)th border point \(P_{i}\) we compute the slope of the vector \(P_{i-d}P_{i+d}\) using the four-quadrant inverse tangent, and add a \(\pi/2\) constant to obtain a vector orthogonal to \(P_{i-d}P_{i+d}\) and pointing \"outside\" the epidermis (Figure 8). Since the border is a closed shape, if \(i-d<0\) we take the point \(P_{n+i-d}\) and if \(i+d>n\) we take the point \(P_{i+d-n}\). To make results more robust to slight local changes in border's curvature, we set the point distance to \(d=4\).