esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials Selection
### Binder Properties
#### 2.2.2 Recycled Glass Powder (GP)  
In this study, recycled glass powder (GP) was collected from household waste in Quebec, Canada. The ESEM image of the GP (Figure 2) shows irregular, flaky, and angular particle shapes less than 25 \(\upmu\)m. These observations shows that GP has an intrinsic pore structure with a distribution that is not uniform.  
Some early experiments were conducted to investigate the behaviour of asphalt mixtures modified with PCMs at high temperatures. The PCM, at a modification rate of 5%, was directly mixed with the asphalt binder (PG58-28) at a temperature of 150 \({}^{\circ}\)C. It was observed that the mixtures containing PCM had a brownish colour and demonstrated visible segregation of the aggregates (Figure 3). Even though all the mix design requirements were met during the mixing and compaction procedures, the specimen collapsed immediately after compaction, as shown in Figure 4.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Index** & **Conditions** (\({}^{\circ}\)**C**) & **Unit** & **Results** & **Requirements** & **Test Method** \\ \hline Specific gravity & At 25 & & 1.03 & - & \\ Brookfield viscosity & At 135 & Pa-s & 0.9 & 3.0 max & AASTHO T316 \\ Flash point & - & \({}^{\circ}\)C & \(\geq\)230 & 230 min & AASHTO T 48 \\ G\({}^{\star}\)/sin(8) & At 58 & kPa & 3.64 & 1.0 min & AASTHO T315 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Properties of the PG70-28.  
Figure 1: Physical properties of PCMs (PureTemp LLC.).  
It was concluded that the asphalt binder had been affected by the addition of PCMs. The modified binder apparently lost some of the cohesiveness properties needed to hold the sample together after the compaction. Similar findings were reported by [7] that the direct blending approach of PCMs to asphalt binders led to leakage issues of the asphalt binder.  
We have therefore investigated the use of glass powder to tackle these challenges. Two main assumptions were behind this choice. The first is that the GP particles will absorb and retain the PCM, which reduces the risk of leakage of the PCM. The second assumption is that the mastic formed by mixing the GP with the asphalt binder will have increased stiffness due to the high stiffness of the glass and increased stability due to the amorphous morphology and frictional properties of the GP particles. Further experiments were conducted by mixing the GP with the PCM at ambient temperature to create a GPCM paste, after which the GPCM paste was added to the asphalt binder at 150 \({}^{\circ}\)C. It was concluded that the high-temperature stability of the binder at high temperatures was improved, and the presence of GP compensated for the loss of stiffness caused by the PCM.  
Figure 3: Asphalt mix containing 5%PCM and 0%PCM.  
Figure 2: Physical properties of PCMs (PureTemp LLC.).
## 5 Conclusions  
The application of digital volume correlation to X-ray images provides an efficient method for the characterization of full strain field in heterogeneous rocks during long term creep tests. This method can be extended to complex environmental conditions with variations of temperature and moisture content.  
The creep deformation of the COx claystone has been investigated under uniaxial compression condition by using the proposed measuring method and a specially designed device. Both the full strain fields and averaged global strains have been computed for the tested samples. In particular, the effects of structural anisotropy have been investigated by considering three different loading orientations.  
It is found that due to the material heterogeneities, especially the presence of hard mineral inclusions, the distributions of both stress-induced and creep strains are strongly non-uniform inside the tested samples. There are strong strain concentration zones around the hard inclusions. Even under uniaxial compression loading, local compressive radial strains can be observed. At the mesoscopic scale, the motion of bedding planes plays an important role in the deformation and failure processes of the COx claystone. The progressive compaction of bedding planes leads to large instantaneous and time-dependent axial strains. The frictional sliding along the bedding planes enhances the growth of strain localization and controls the failure for inclined loading orientations. The strain localization process is strongly affected by the loading orientation with respect to the bedding planes.  
However, the effects of materials heterogeneities such as mineral inclusions and pores are complex. Further in-depth analyses are still needed for getting a quantitative characterization of those effects.  
Conceptualization, J.T., J.S. and S.X.; laboratory tests, H.S., J.H. and T.R.; results analysis, H.S., J.H., T.R., S.X. and J.S.; writing, H.S., J.H., T.R., S.X., J.S., J.T. and G.L. All authors have read and agreed to the published version of the manuscript.  
Figure 10: Distributions of accumulated radial strain under two different stress levels: (**a,d**): slice-680 of parallel sample (\(\parallel\)); (**b,e**) slice-505 of inclined sample (\(\parallel\)); (**c,f**) slice-500 of perpendicular sample (\(\perp\)).  
**Funding:** The present study was jointly supported by Andra, the I-site project of University of Lille and the ISIS4D X-Ray CT platform. This platform has been funded by the International Campus on Safety and Inter-modality in Transportation (CISIT), the Hauts-de-France Region, the European Community and the National Center for Scientific Research (CNRS).  
**Acknowledgments:** Special thanks are addressed to [PERSON] and [PERSON] for their invaluable assistance to the design of experimental device and preparation of samples.  
**Conflicts of Interest:** The authors declare no conflict of interest.
Looking over the latest rectangular waveguide bandpass filters including the reports above, as they follow the design technique of half-wavelength cavities, effective size reduction is nowhere to be found. One might suggest application of metamaterial filter designs seen from microstrip- and CPW-lines like in [8; 9] to waveguide components, but this turns out to be not possible because of differences in geometries and modes. Motivation arises for setting up a metamaterial resonator which is much shorter than the typical cavity and geometrically appropriate for the waveguide. This paper proposes a novel design method of realizing the compact WG BPF of a high order for satellite communication system devising and using the waveguide metamaterial resonator. First, the zeroth-order resonator is formed on the cross-section of the standard waveguide, compliant with the composite right/left-handed (CRLH) circuit model. The geometrical parameters are of unit of 100 \(\upmu\)m to avoid the problems of fabrication tolerance, whose lower precision makes this design much tougher than others using the unit of 10 \(\upmu\)m with more expensive facilities. Second, the ZOR as a building block for the equivalent network of the eighth-order BPF is substituted for conventional cavities. Third, the resonators are cascaded through short waveguide sections as coupling elements to generate the passband, and the stopbands have high attenuation. Fourth, the WG BPF of ZORs and coupling elements is physically prototyped by the CNC milling technique, which is driving the industry to transform into 3D printing as in [10; 11; 12]. Fifth, the metallic WG filter is adopted to the feeding component of the horn antenna in the wireless communication system to attain channel selectivity. Sixth, the performances of the individual components and the filter-integrated antenna are investigated by way of real experiments. The suggested method is evaluated by circuit modeling, full-wave EM simulation and measurement. Good agreement between the theoretical and measured results is unveiled from the procedural steps. As noted in the required specifications on the WG BPF, the passband has about \(\leq\)1 dB and \(\leq\)\(-\)15 dB as insertion loss and reflection coefficient, the stopband has 40 dB of noise suppression. Regarding size reduction, the proposed PBF of the metallic waveguide is 3.5 \(\lambda_{g}\) for the eighth-order filtering, much smaller than the conventional waveguide filter of the same order. This effect will be obvious for much higher-order filters and MUXes.
## 1 Introduction  
The human middle ear, including the eardrum or the tympan membrane (TM), transmits sound energy from the environment to the inner ear for hearing [1; 2]. The mechanism of this energy transmission through the middle ear is intrinsically multifaceted and is affected by the TM's complex geometry (shape and thickness), spatially varied mechanical properties and microstructure (radial and circumferential fibers), as well as the load from the ossicular chain and inner ear [3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15]. Middle-ear pathologies such as TM perforation, otitis media with effusion, tosclerosis, and ossicular discontinuity can result in mild to severe conductive hearing loss. Currently, in otology and audiology clinics, there is no perfect objective diagnostic tool to differentiate middle-ear diseases, and the gold standard for the diagnosis of ossicular disorders is surgical exploration with an elevation of the eardrum to visualize the ossicular chain.  
Over the past decade, various non-invasive optical methods, for example, holographic interferometric methods, were developed to quantify TM dynamics such as excitation-induced displacements, and physical characteristics such as shape and thickness [11; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26]. However, a study on the live subject remains challenging due to the natural noise, such as motions due to respiration, heartbeat, muscle tremor, etc. and high sensitivity of the optical tools. Researchers utilized a high-speed digital holographic method to minimize these undesirable effects and made quantitative measurements on live subjects possible [27; 28; 29; 30; 24].  
The high-speed digital holographic system we developed measures the TM's shape and transient acoustic-induced displacement nearly instantaneously (<200 ms) with a spatial shape resolution of 50-200 um and a displacement resolution of 15 nm at an acquisition rate of 67,200 Hz [28; 30; 31; 32]. This high-speed system takes into account the holographic-sensitivity vector variations due to the sample's shape and optical configuration to derive the true surface normal motion of the TM.  
To investigate if such a high-speed digital holographic (HDH) system can be used as a diagnostic tool for middle-ear pathologies, this paper describes a study on the shape and displacement measurements on post-mortem temporal bones with different experimentally introduced middle-ear pathologies. We compared the full-field-of-view shape measurements and the time and frequency domain analysis of displacements under various middle-ear conditions (open and closed middle-ear cavity, two levels of fluid injection into the middle-ear cavity, tapes immobilization, and incudo-stapedial (IS) joint interruption) to identify TM motion patterns, and data trends associated with each middle-ear condition.
# Polar Codes and a M-ary Modulation-Based OFDM-PLC System
###### Abstract
A polar code encoding scheme with low complexity is used to suppress the impulse noise and improve transmission performance. Furthermore, high-order 16 QAM or 16 APSK modulations and soft demodulation techniques are introduced in the OFDM-PLC system, which shows improved performance and are suitable for PLC channels, thereby enhancing transmission efficiency. The remainder of this paper is organized as follows. Section 2 introduces the encoding and decoding principles of polar codes. Section 3 describes the OFDM-PLC system model based on polar codes, including the structural diagram, multipath channel model, and noise model. Section 4 introduces the modulation and demodulation principles of higher-order QAM and APSK, and applies them to the OFDM-PLC system. Section 5 presents the simulation and performance evaluation of the system. Finally, conclusions are drawn in Section 6.
# CNN Based Image Classification of Malicious UAVs
## 2 Materials and Methods
### Model Theoretical Background  
The deep-learning CNN image classification models employed as part of this study are compared and summarized in Table 1. These are all well-known and high performing image classification models. Some of them are part of families, e.g., there are Resnet-18, Resnet-34, Resnet-50 and Resnet-101 models. For this investigation, we generally employed the least complex model in a family (e.g., Resnet-18 in the case of Resnet) because it has the fewest number of parameters to train and therefore is less likely to be overfit when using a dataset that is not particularly large.  
Figure 4: Effect of Different Elevations \(h\) on Size, View Angle, and Image Background (UAV: DJI, Phantom 4, 10\(\times\) Zoom, \(d\) = 10 m, images reduced to 256 \(\times\) 256 resolution).  
Figure 3: Effect of Different Camera Zoom Levels On Size and Image Background (UAV: DJI, Mavic, \(h\) = 5 m, \(d\) = 10 m, images reduced to 256 \(\times\) 256 resolution).  
As discussed in the Introduction, we are primarily interested in Resnet-18 because it is relatively lightweight and has such a good reputation in a large variety of image classification tasks. Resnet-18 is a Convolutional Neural Network model which has 18 convolutional and/or fully connected layers in its architecture [15] as illustrated in Figure 5. To understand the structure of a typical convolution layer, consider the convolutional layer with designation \"3 \(\times\) 3, Conv, 128,/2\". This uses 128 filters with window size 3 \(\times\) 3 and a stride of 2. The curved arrows represent skip connections which provide some protection against overfitting.  
AlexNet was proposed by [PERSON] [16] in 2012 and is a deep and wide CNN model. This was considered as a significant step in the field of machine learning and computer vision for visual recognition and classification. The AlexNet architecture has 3 convolution layers and 2 fully connected layers. The recognition accuracy was found to be better against all traditional machine learning and computer vision approaches.  
VGG-16 is a Convolutional Neural Network (CNN) model which was proposed by [PERSON] and [PERSON] [17]. The use of uniform 3 \(\times\) 3 filters is the standout feature of the VGG technique which reduces the number of weight parameters when compared to a 7 \(\times\) 7 filters.  
MobileNetv2 is a CNN that is based on an inverted residual structure whereby the residual connections are between the thin bottleneck layers [18]. As a source of non-linearity, the intermediate layer utilizes the lightweight depth wise convolutions to filter features. The architecture has a fully convolution layer with 32 filters and 19 residual bottleneck layers.
# Underestimation of Dry Matter of Anaerobic Media with High Bcarbonate Concentration
## 3 Results and Discussion
### Bicarbonate Interference
#### 3.2.1 Pure Bicarbonate Solutions
The loss of 4% during freeze-drying is probably due to an experimental error, linked to the powdery side of the freeze-dried solution and, therefore, to weighing difficulties. When the dry freeze-dried solid is later heated to 105 \({}^{\circ}\)C, under standard conditions, 35% of the bicarbonate decomposes, further confirming the thermo-dependent nature of the bicarbonate decomposition. These tests were conducted with pure bicarbonate solutions in water, so four questions arise: firstly, whether this decomposition is observed in real anaerobic digestion effluents; secondly, to what extent is this decomposition of the bicarbonate and, therefore, this underestimation of DM detrimental; thirdly, how does this underestimation affect the measurement of OM; and, fourthly, at which temperature the decomposition begin?
# Analyzing Zone-Based Registration under 2-Step Paging in Mobile Communication Network
## 1 Introduction
This paper proceeds as follows: Section 2 introduces the zone-based registration and 2-step paging and suggests an inter-cell mobility model in 1 ZR for comparison with 2 ZR. In Section 3, the total signaling cost of 1 ZR under 2-step paging is analyzed using the Markov chain model. Numerical results on the performance of 1 ZR are obtained and compared with 2 ZR in Section 4. Finally, Section 5 provides the conclusions of this paper.
# Wireless Remote Control for Underwater Vehicles
## 4 Scenario Description and Simulation Setup
### Acoustic Modem Simulator  
The Subnero WNC and the EvoLogics S2C HS acoustic modems were simulated by using two instances of the DESERT acoustic physical layer, called from here onwards MF PHY and HF PHY, respectively. Both PHYs were half duplex, i.e., they can either receive or transmit, and a mean-power interference model was employed. We implement the empirical underwater sound propagation and  
Figure 5: Variation of the attenuation coefficient along the water depth (**a**) and optical receiving area with the control station modem fixed at a depth of 60.5 m and aligned to the x axis by varying the ROV modem installation, namely, in the back of the ROV with the modem aligned to the x-axis (**b**), in the top of the ROV pointing to the surface (**c**), and in the bottom of the ROV pointing to the seabed (**d**).  
noise models described in [10], with a spreading coefficient \(g\) equal to 1.75 in the spreading loss component, shipping activity \(s\) equal to 1, and speed of wind \(w\) equal to 5 m/s. The speed of sound underwater was assumed to be constant and equal to 1500 m/s.  
The MF PHY layer's source level was 168 dB re \(\upmu\)Pa\({}^{2}\) at 1 m: this value was used to match in the simulator the maximum range of 3.5 km declared from the modern manufacturer because, in the simulations, we did not consider transducer inefficiencies and multipath (the maximum source level of the actual modem was 185 dB re \(\upmu\)Pa\({}^{2}\) at 1 m). The transmission rate was set to 4 kbps, the carrier frequency was 24 kHz, and the bandwidth was 12 kHz. Actually, a higher rate can be achieved, as a maximum bit rate of 15 kbps can be reached in good conditions: we used a lower bit rate because we considered horizontal transmissions, that often require adaptation of the bit rate to a lower value and the need for FEC and networking headers. In addition, in our design, the MF PHY was also used for LBL positioning, that usually comes at the cost of a lower communication rate, that motivates our bit rate choice.  
The HF PHY layer's source level was 156 dB re \(\upmu\)Pa\({}^{2}\) at 1 m: also in this case, this value was used to match in the simulator a maximum range of 500 m (the maximum source level of the actual modem was 177 dB re \(\upmu\)Pa\({}^{2}\) at 1 m). The net transmission rate was 30 kbps (a higher rate can be achieved, as a maximum bit rate of 62.5 kbps can be reached in good conditions, from which the bits needed for FEC and networking headers need to be removed), the carrier frequency was 150 kHz, and the bandwidth was 60 kHz.  
Both PHYs used a carrier-sense MAC layer (CSMA).
## 3 Task Assignment Algorithm Based on Extended CNP
### Distributed Multi-Constraint Dynamic Task Assignment Algorithm
At the time, the set of all bidders participating in the \(Task_{k}\) assignment is  
\[V_{b,k}=\left\{V_{m}\Big{|}w_{jm}=w_{mj}=1,j\
eq m\;;B_{mk}>B_{jk}\right\} \cup\left\{V_{j}\right\}\]  
If tenderee \(V_{j}\) does not participate in the bidding, i.e., \(B_{jk}=\varnothing\),  
\[V_{b,k}=\left\{V_{m}\Big{|}w_{jm}=w_{mj}=1,j\
eq m\;;B_{mk}>0\right\}\]  
To sum up, after preselection of negotiation threshold, all bidders participating in the assignment for \(Task_{k}\) can be represented as  
\[V_{b,k}=\left\{ \begin{array}{l}\left\{V_{m}\Big{|}w_{jm}=w_{mj}=1,j\
eq m;B _{mk}>0\right\},B_{jk}=\varnothing\\ \left\{V_{m}\Big{|}w_{jm}=w_{mj}=1,j\
eq m;B_{mk}>B_{jk}\right\}\cup\left\{V_ {j}\right\},B_{jk}\
eq\varnothing\end{array} \right. \tag{12}\]  
Consider the cooperative constraints of subtask \(Task_{k}\), assume \(Task_{k}\) requires the participation of \(N_{p,k}\) UAVs, and quickly sort the bidding value set \(\left\{B_{jk}\right\}\). The greedy algorithm is used to select the \(N_{p,k}\) largest bidding values in the set to form the winning bidding value set (reward set) \(B_{k}\)  
\[B_{k}=\left\{B_{(1),k},\ B_{(2),k},\ \ldots,\ B_{(N_{p},k),k}\right\} \tag{13}\]  
Figure 4: The determination mechanism on cooperators. where \(B_{(x),k}\) represents the \(x\)-\(th\) order element of \(\{B_{mk}\}\) in descending order. The winner set \(Winner_{k}\) is correspondingly:  
\[Winner_{k}=Index(B_{(1),k},\ B_{(2),k},\ \ldots,\ B_{(Np,k),k}) \tag{14}\]  
(2) Sequential task selection mechanism  
Take the sequential task assignment process of a single follower UAV as an example. After receiving the task information, the bidder determines the executable tasks according to the negotiation threshold, UAV capability constraints, and the execution sequence and combines them to generate an alternative sequence without a time window conflict. Based on the bid winning situation, the optimal sequence is selected as the final signing one. The operation process is shown in Figure 5.  
Assume that the concurrent task set \(Task^{new}=\left\{Task^{Lev}_{1},\ldots,Task^{new}_{\textit{Ntask}}\right\}\) is received by the follower UAV \(V_{m}\). The set of tasks that \(V_{m}\) can execute and determine to participate in bidding is expressed as  
\[Task^{a,m}=\left\{Task^{a,m}_{K}|k=1,2,\ldots,N_{a,m}\right\}\subseteq Task^{ new} \tag{15}\]  
where \(N_{a,m}\) is the element number of \(Task^{a,m}\). Denote \(Comb_{n}(N_{a,m},N_{vm,n})\) as the combination which consists of random \(N_{vm,n}\) elements from set \(\left\{1,2,\ldots,N_{a,m}\right\}\), where \(N_{vm,n}\leq N_{a,m}\). **Definition 6** (Task Sequence Alternative). : _If \(\forall k\in Comb_{n}(N_{a,m},N_{vm,n}),\forall\overline{k}\in Comb_{n}(N_{a,m},N_{vm,n})-\left\{k\right\}\), there is \(TimeBar_{k}\cap TimeBar_{\overline{k}}=\varnothing\). Then, \(Comb_{n}(N_{a,m},N_{vm,n})\) is called a sequence alternative. \(TimeBar_{k}\) is the time window of subtask \(Task^{a,m}_{k}\).
# Composition and Basic Physical Properties of the Phobos Surface: A Comprehensive Review
## 6 Characteristics and Physical Properties of Phobos Regolith
### Physical Properties of Regolith  
#### 6.2.1 Grain Size  
The surface of Phobos is most likely covered with an unsorted mixture of solid rock fragments and material melted under the influence of impacts, ranging in size from micrometers to meters, with a suggested deficiency of fractions smaller than 0.3 mm [12].  
There is also an assumed difference in the maturity of Phobos' regolith between the leading and trailing hemispheres [57]. However, this difference is not apparent in higher-resolution images. This apparent homogeneity could be explained by limited image resolution or particle homogenization due to horizontal surface movements. As reported by [PERSON] et al. [58], periodic dynamic changes driven by the eccentricity of the orbit, or by seasonal variations, might contribute to this phenomenon. However, it is not ruled out that this apparent homogeneity is related to a dust layer (grain size \(\sim\)0.1 mm) that could have covered Phobos' surface [59; 60].  
Additionally, based on the surface thermal inertia (40-70 \(\mathrm{Jm^{-2}\cdot K^{-1}\cdot S^{-1/2}}\)--Viking observations; 20-40 \(\mathrm{Jm^{-2}\cdot K^{-1}\cdot S^{-1/2}}\)--Phobos 2 observations) it has been estimated that the average grain size should be smaller than 2 mm in most regions [61; 62]. [PERSON] et al. [63] assume a surface thermal inertia of 55 \(\mathrm{Jm^{-2}\cdot K^{-1}\cdot S^{-1/2}}\), which gives them the most probable grain diameter below 1 mm.