esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Congestion Prediction Model
### System Model  
The model established in this study is a closed WSN without movement from one sensor network to another, and is assumed to be a mono-centric network with one sink. Packet production is predicted by including periodic data and event data together. In addition, the amount of data being passed over the network can be determined not only by considering end-to-end packet delivery, but also by predicting the number of packets by transmission path. It is assumed that the nodes can move within the WSN.  
The network modeling is shown in Figure 3. The form in which congestion occurs is the basic concept. Periodic and event data are gathered at the sink because commuting and shopping in the city are introduced. The network is divided into N sections in total. The sink is located in the middle of the network, and the N sections are located in the outermost zone [36; 37; 38; 39]. The communication radii of each sensor node are all the same, and each node is responsible for all incoming and outgoing data functions. The periodic data and event data are generated in all sections.
# Structural Optimization Design of Spaceborne Microwave Probe Antenna
## 3 Topology Optimization of the Planar Antenna Reinforcement Structure
### Topology Optimization, the Finite Element Model, and Optimization Settings  
The model of the planar antenna to the bearing part is selected, as shown in Figure 4, and established as the finite element solid model for topology optimization. Among them, the planar antenna uses tetrahedral solid elements, and the rest use hexahedral elements. The three degrees of freedom on the right side of the shaft are all constrained, and the screw connection is simulated with rigid elements. The green part of the planar antenna in the figure is the designable region of the planar antenna and the rest is the non-designable region. The relative density of each element in the designable domain forms the column vector of \(x\) in Equation (1).  
The design variable is the cell pseudo-density of all cells in the designable region, and the constraint is to retain the reinforcing portion as 20-40% of the volume of the designable region. Fixed constraints are applied by placing the right side of the shaft in the above figure at the bolts. The topology optimization objective is the maximum of the first-order frequency, i.e., the maximum of the dynamic stiffness. The minimum of the member size is equal to 5 mm and the maximum of the member size is equal to 17 mm.
# The Role of Powered Surgical Instruments in Ear Surgery: An Acoustical Blessing or a Curse?
#### 2.2.1 Noised-Induced Hearing Loss  
Noiced-induced hearing loss can, as alluded to above, be caused by either chronic, accumulative, and gradual exposure, or an acute, one-time event. The chronic, accumulative, and gradual exposure is generally defined in terms of daily exposure over years. Specifically, the National Institute for Occupational Safety and Health (NIOSH) has set down the recommended exposure limit (REL) as 85 decibels, A-weighted (dBA) for an 8-h time-weighted average (TWA) [3] while the acute, one-time event is generally set at 140 dB or higher [4]. However, any noise-induced hearing loss caused by powered surgical instruments falls into an undefined category between these two defined categories. While the noise levels tend to fall within chronic category, surgery time is only measured in minutes up to a couple of hours on a single day, rather than accumulated hours over multiple days. Moreover, while the noise levels generated by powered surgical instruments fall below noise levels defined as dangerous for acute one-time events, the fact that these instruments are used directly within the anatomy of the ear needs to be factored in.  
The connection between possible noise-induced hearing loss and powered surgical instruments used during ear surgery has long been a source of concern and study in the case of surgical drills [5; 6; 7; 8], as well as a recent target of research in the case of ultrasonic devices [9].
## 2 Compression, Entropy and Dictionaries
When we use data compression to communicate, the Sender and the Receiver use the same (static) dictionary and eventually, at the beginning of the communication, the Sender needs to send the dictionary to the Receiver. For example, in vector quantization algorithms the compressor builds up a dictionary on a training set of images and it uses this dictionary to compress the new data. The decompressor needs the same dictionary to decompress; therefore the compressor shall transmit this dictionary to the decompressor. The cost of making this dictionary available depends on its dimensions. Generally this cost is not considered in the analysis of a static dictionary compression method by using the argument that the constant cost can be amortized over time by compressing a large amount of data. In real life situations this is not always true. Frequently we have as compression target only small or medium size files, that represent computer programs, images, text data, music, videos, _etc._ For example it is often necessary to send only a few computer programs or a few images from a source to a destination, possibly via a telephone line by using a modem or via a fast internet connection. In these cases the cost of sending the dictionary might have an important impact on the total cost of the communication, making static compression methods unpractical. Dynamic dictionary methods build up the Sender and the Receiver dictionary at run time, by starting with empty dictionaries and by building up the dictionaries in terms of the already compressed data. If the files we want to compress are small, the empty dictionary might initially bias the compression process: it is not equally effective to compress data by using a dictionary that contains only the alphabet symbols instead of a dictionary that is complete. It would be obviously more effective to start up the compression/decompression process with an appropriate, fully loaded, dictionary instead of an empty one. Mainstream methods that are based on dictionaries are almost always dynamic dictionary methods: this adaptive technique is flexible and can be easily used in practical situations. All the optimality proofs for this technique use asymptotic arguments and do not take into account the (theoretically constant) cost of filling up the dictionary.
# Millimeter Wave Attenuation Due to Wind and Heavy Rain in a Tropical Region
## 3 Theory and Algorithms
### Inclination Attenuation Due to Wind
#### 3.3.2 Direct Dynamic Pole Angle Measurements via Accelerometer  
The dynamic pole inclination angle can be directly measured using the setup in Figure 1. The measured accelerometer data consist of 3-axis accelerations at the lower and upper positions on the pole. The sampling period for the acceleration data was 10 ms. The accelerations were high-pass filtered at the frequency below the natural vibration frequency of the pole to remove the gravity and the initial inclination effects. Let the filtered accelerations of the lower and upper sensors be \(a_{x}^{l}\), \(a_{y}^{l}\), \(a_{z}^{l}\) and \(a_{x}^{u}\), \(a_{y}^{u}\), \(a_{z}^{u}\), respectively. The double integration of accelerations yields the time-varying displacement of both pole positions,  
\[\mathbf{s}^{q}=\iint\left(a_{x}^{q}\mathbf{x}+a_{y}^{q}\mathbf{y}+a_{z}^{q} \mathbf{z}\right)\,d\tau d\tau^{\prime} \tag{17}\]  
where \(q=l,\;u\) denotes the upper and lower sensor positions. The pole vector is \(\mathbf{p}=\mathbf{s}^{u}-\mathbf{s}^{l}\), as shown in Figure 6. The inclination angle magnitude is calculated from the dot product using  
\[\theta=\cos^{-1}\left[(\mathbf{p}\cdot\mathbf{p}_{r})/h_{d}^{2}\right] \tag{18}\]  
where \(\mathbf{p}_{r}\) is the optimal pole position and \(h_{d}\) is the height difference of the two accelerometers. Alternatively, the directional 3D inclination angle can be calculated using the cross product  
\[\theta=\sin^{-1}\left[\|\mathbf{p}\times\mathbf{p}_{r}\|/h_{d}^{2}\right] \mathbf{\overset{\wedge}{n}} \tag{19}\]  
where \(\mathbf{\overset{\wedge}{n}}\) is the unit vector in the direction of \(\mathbf{p}\times\mathbf{p}_{r}\). Comparing to Section 3.3.1, the angle obtained in Equations (18) or (19) should be equal to the sum of initial axis misalignment, and static and dynamic angles due to wind, as in Equation (14).
## 3 Results and Discussion
### Thermal Conductivity  
Because of the cellular microstructure of foamed concrete, its insulation properties are very important. In this investigation, the thermal conductivity of foamed concrete was measured using a Hot Disk. Because of the high sensitivity of thermal conductivity tests to the moisture content of the concrete, the concrete samples were oven-dried at 105 \({}^{\circ}\)C until a constant mass was reached. The thermal conductivity measurements were carried out after cooling to room temperature at zero humidity conditions. Cubical samples with 100 mm edge sizes were used, with the measurements performed on all sides. Two samples were needed for each measurement, with the Hot Disk sensor being fixed between samples. The mean value of the six values obtained by the Hot Disk was calculated and used in this investigation. Figure 4 shows the experimental results of the thermal conductivity tests of foamed concrete. It is immediately obvious that the thermal properties of foamed concrete are not affected by the presence of a low temperature temperature. The thermal conductivity tests of foamed concrete are not affected by the presence of a low temperature temperature.  
concrete were mainly dependent on density. The control mix (mix C) had a thermal conductivity of about 0.136 W/(m\(\cdot\)K), which increased to about 0.14 W/(m\(\cdot\)K) for mix C/FA (with fly ash) due to the increase in dry density. Conversely, mix C/FA/FL50 had the lowest thermal conductivity of about 0.113 W/(m.K); this can also be attributed to its decreased dry density, as compared to the other mixes. The composition of the mix or the filler type did not have a significant influence on thermal conductivity. Thermal conductivity increased with density and vice versa. These experimental results do not indicate a clear influence of fine aggregate addition on the thermal conductivity of foamed concrete. The incorporation of fine lightweight aggregate reduced the dry density, and as a consequence the thermal conductivity of the foamed concrete fell. Other researchers have also found a correlation between thermal conductivity and pore size distribution [38; 39]. The connectivity of the pores, in addition to their size, can affect the thermal conductivity of concrete significantly. However, in this research this influence was not clear, perhaps due to the very high porosity of the ultra-lightweight concrete produced.
# Conductivity Enhancement of Graphene and Graphene Derivatives by Silver Nanoparticles
## 1 Introduction
Graphene nanosheets doped with Ag nanoparticles have been proven to be a promising material due to their potential applications in many fields, including preparing a hybrid ink and thus taking full advantage of these two materials [7; 20; 21; 22; 23]. Recently, a hybrid nanocomposite derived from the combination of silver nanowires and \(f\)-rGO has been formed for the development of stable gravure printing inks with enhanced conductivity, which is promising for electronic applications [24]. It is reasonably supposed that Ag nanoparticles that stand between graphene nanosheets decrease the intersheet resistance, while silver nanowires can furthermore form extra pathways for charge movement. However, the mechanism of the effect of the metal structure on the improved conductivity of metal-graphene is still unclear. On the other hand, due to the weak bonding between silver and graphene surfaces, it is not always successful in obtaining homogeneous Ag-graphene hybrid inks. Therefore, it is still a challenge to develop new graphene-based/metal hybrid inks for printing conductive patterns and to explore the structure-effect of metal nanoparticles on the conductivity of graphene-based patterns. In this article, the in-situ formation and direct deposition of AgNPs on two different substrates--pristine FLG and \(f\)-rGO--with several Ag percentages are described. The use of two different forms of graphene--the hydrophobic and mainly aromatic FLG nanosheets and the hydrophilic and less aromatic \(f\)-rGO--offers the opportunity to shed light, for the first time, on the role of the carbon substrate in the morphology and the properties of the final hybrid. The two different types of hybrids that were formed were compared as regards dispersibility in water and electrical conductivity as a function of the Ag percentage. Furthermore, a representative \(f\)-rGO/ AgNPs sample was used for the formulation of a gravure water-based conductive ink. The latter was achieved without any addition of rheological modifiers, other common additives, or organic alcohols, improving the sustainability of the printing process. The electrical properties of the printed patterns were tested using two different kinds of paper. Emphasis was given to the formulation of gravure ink because gravure as a printing method has played a part in the growth of the global economy over the last eightty years. Nowadays, in emerging economies such as those of Asia and Africa,gravure is considered the most effective and popular for industrial applications compared to other printing methods [25]. Additionally, gravure offers the ability to print a variable ink film thickness at high speeds and for high-volume production. Simultaneously, gravure produces high-quality patterns using a variety of substrates and the same set-up that prints conventional printing inks. Finally, its offer to the circular economy is mainly due to the reusability of the printing cylinders and also due to the potential for recovery of metals (Cu or Cr) during plating processes [25].
## 4 Self-Healing Approaches  
Self-healing approaches are the strategies and techniques to detect, diagnose, and resolve CPS problems automatically without human intervention. These approaches are commonly used in complex systems such as software applications, networks, and hardware systems to ensure that they recover from failures and continue to operate without disruption. Table 2 lists the most common machine-learning tools for cyber-physical self-healing systems. The approaches involve:  
* Redundancy: Redundancy involves having duplicate components or systems that can take over if the primary system fails. For example, if one node fails in a computer cluster, another node can take over and continue processing the request;
* Automated recovery: Automated recovery involves setting up automated processes to detect and resolve problems. For example, a computerised process can restart a server or move its workload to another server if it goes down;
* Predictive maintenance: Predictive maintenance involves using sensors and data analytics to predict when a system is likely to fail and proactively take action to prevent the failure from occurring. For example, an aircraft engine can be monitored for signs of wear and tear, and maintenance can be scheduled before failure occurs;
* Machine learning: Machine learning involves using algorithms to analyse data and learn patterns that can be used to detect and resolve problems. For example, machine algorithms can analyse network traffic and detect anomalies that may indicate security breaches;
* Fault tolerance: Fault tolerance involves designing systems that can continue to operate even if one or more components fail. For example, a database cluster can be designed  
Figure 6: Anomaly detection for radio access. to replicate data across multiple nodes. If one node fails, then other nodes can continue providing data access. It is important to consider training datasets, which are the bedrock of implementing ML self-healing functions in cyber-physical systems. The self-healing algorithms use data to identify errors, analyse their causes, and take remedial actions. Self-healing algorithms utilise the system's derived data to improve the reliability of the system and fulfil the self-healing functionality, data such as the following:  
* Log data: This contains information about the system events, such as error messages and other data that can be used to diagnose problems;
* Performance metrics: This includes data derived from CPU utilisation, memory usage, network latency, and input/output disk;
* Configuration data: Includes data related to the system's configuration changes and parameters;
* Environment data: Identifies the issues relating to environmental conditions, such as overheating and excessive humidity. * User behaviour data: These data identify patterns in the system's user behaviours, such as the response times or the frequency of errors.
# Mapping Surface Flow Velocity of Glaciers at Regional Scale Using a Multiple Sensors Approach
## 3 Results
### Seasonal Variations in Glacier Surface Velocity  
We found significant changes between summer and winter of glacier surface velocities (Figure 3B) along the main trunk of Fox, Franz Joseph, Hooker and Tasman glaciers. Differences between summer and winter speeds reach up to 150-200 m/year. We are confident that these values are significative as we find the median value in ice-free zones is \(-\)1 \(\pm\) 16 and 1.6 \(\pm\) 18 m/year for the x and y component, respectively (inset graphs in Figure 3B). Additionally, we plotted a 2-year time series in a fast flowing zone of Fox Glacier (noted P1 in Figure 3B,C), a glacier where seasonal field surface velocity have been measured in previous studies [(25; 31)]. The strong seasonal signal is clearly highlighted in the time series: the maximum glacier surface velocity is recorded during the wettest period of the year [(32)] in late November 2017 with a velocity of 560 m/year and the minimum is measured in June 2018 with 280 m/year, resulting in a decrease in the surface velocity by 45%. Our 2-year record is consistent with previous studies [(31)] that used GPSs to measure a 26% (vs. 25% for this study) decrease between  
Figure 3: **(A)** Glacier surface velocity mosaic of the Tasman Glacier area in meters per year averaged over 2016–2018 quantified from Sentinel-2 images, colorbar is coded on a logarithmic scale going from brown (slow) to red (fast). Inset shows a zoom on two tributaries of Murchinson glacier; **(B)** Difference between March-July and October-December glacier surface velocities is color coded from blue to red. The inset graph shows the statistics and distribution of displacements on stable ground; **(C)** Time series of glacier surface velocity from Sentinel-2 (green), Landsat 8 (blue) and Venus (pink) satellites between 2017 and 2018 of the upper part of Fox glacier indicated by a green dot labelled P1 in **(B)**.  
January-February and June-July. Note that the Venus satellite also captured a short acceleration in ice velocity in February 2018, where the ice velocity quickly increased from 400 m/year to 480 m/year. Such changes in glacier flow might be explained by changes in subglacial hydrology between the spring/summer and fall/winter seasons that modify the basal slip [33; 34]. Modifications in conditions at the base of the glacier are often due to changes in runoff that varies with the melt season but also with the amount of precipitation that can trigger important changes in the glacier flow velocity up to 44% [31; 33].
## Materials and Methods
### Cell Viability  
To assess a potential cytotoxic effect of _H. procumbens_ extracts on FLSs at different concentrations and time points, an MTS (3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethyloxyphenyl)-2-(4-sulfophenyl)-2\(H\)-tetrazolium)-based colorimetric assay was performed (Promega Corporation, Madison, WI, USA). Briefly, \(8\times 10^{3}\) cells per well were seeded in a 96-well plate. The day after seeding, cells were starved overnight in reduced serum medium, in order to align cell cycle progression. Cells were then left untreated (CTL) or treated with _H. procumbens_ extracts for 24, 48, and 72 h. After each time point, 100 \(\upmu\)L MTS solution was added to the wells. Spectrophotometric absorbance was directly measured at 492 nm after 3 h incubation.
## 1 Introduction
In tomatoes (_Solanum Lyopersicum_), for example, a sesqui-TPS cluster of six genes consists of two pseudogenes and characterised genes with divergent catalytic active sites [35]. In rice (_Oryza_ spp. ), the evolution of _TPS_ gene clusters involved in insect defence shows species-specific cluster differences that go through a combination of duplication and subsequent functionalisation events. These gene clusters, furthermore, show species-specific expressions and emission patterns in response to insect infestations [36]. A 690-kb region of PN40024 chr18 contains 44/152 _VviTPS_-like loci, 20 encoding for putative sesquiterpene synthases (_VviTPS01-19_ and -30). Eleven of these genes have been functionally characterised [7; 20]. The annotation of the _VviTPS_ families for CH, CS, and CR, however, revealed that there are potential structural differences in the genome space for the chr.18 cluster [27]. To date, the relatedness of the _VviTPS_ paralogues in this cluster has not been explored. Of particular interest is _VviTPS10_, a gene model linked to two distinct enzyme functions with the VvGwaBer nucleotide sequence being concordant to the gene model [7]. A genotype-specific homologue, VviMATPS10, however, showed various nonsynonymous mutations that resulted in a novel function for the cultivar Muscat of Alexandria [20]. The extent of these sequence differences presented two possibilities: _VviTPS10_ has two alleles with unique functions or a highly conserved paralogue inferring the second function. _VviTPS04_ was previously identified as a putative homologue or allelic variant of _VviTPS10_ using the PN40024 reference genome [1; 7; 20]. In this study, a cluster of chromosome (chr.) 18 genes, which includes _VviTPS10_ and -04, were analysed in order to explore the possible allelic and/or duplication differences. The annotated _VviTPS_ landscape of three phased diploid genomes [27] was utilised in combination with the publicly available whole-genome resequencing (WGRS) data [37] to identify the structural differences in the selected genotypes. The candidate gene regions homologous to _VviTPS10_ were subsequently isolated from multiple _V. vinifera_ genotypes and functionally characterised by means of _Agrobacteriumum_-mediated transient gene expression in _Nicotiana benthamiana_, followed by a volatile terpene analysis using headspace solid-phase microextraction (HS-SPME) and gas chromatography-mass spectrometry (GC-MS). The active sites of isolated paralogues were also compared to identify amino acid residues that could result in possible genotype-specific enzyme functions.
# Privacy-Preserved Image Protection Supporting Different Access Rights
## 2 Related Works
### RSA Public-Key Cryptography
#### 2.1.1 Key Generation  
We assume that the user \(U_{1}\) generates a public key and a private key. The keys for the RSA algorithm are generated as follows.  
1. \(U_{1}\) randomly chooses two large prime numbers \(p_{1}\) and \(q_{1}\) and computes \(n_{1}=p_{1}\times q_{1}\);
2. \(U_{1}\) chooses an integer \(e_{1}\) as a public key such that \(\gcd(e_{1},\ \varphi(n_{1}))=1\) and \(\varphi(n_{1})=(p_{1}-1)\times(q_{1}-1)\);
3. \(U_{1}\) computes the private key \(d_{1}\) such that \(d_{1}\) satisfies \(d_{1}\times e_{1}\equiv 1\ (\mathrm{mod}\ \varphi(n_{1}))\);
4. \(U_{1}\) publishes the public key (\(e_{1}\), \(n_{1}\)) and keeps the private key \(d_{1}\) secret.  
The difficulty of solving factoring problem can be defined as follows. When \(n_{1}\) is the product of two large prime numbers \(p_{1}\) and \(q_{1}\) only \(n_{1}\) is known, it is hard to factor \(n_{1}\) to find \(p_{1}\) and \(q_{1}\).