esa-sceva/satcom-chunk-collection huggingface dataset:
# Advances in Deep Space Probe Navigation
## 1 Introduction  
Deep space exploration, generally referring to the space exploration activities targeting the Moon and more distant extraterrestrial celestial bodies, stands as a critical indicator of a nation's comprehensive capabilities and technological prowess. Conducting deep space exploration is beneficial for breaking through the space technology necessary for human survival and development and expanding human activity space. Within this ambitious endeavor, effective navigation serves as the cornerstone for ensuring mission success, enabling spacecraft to traverse the vast and unpredictable cosmic frontier. As humanity's reach into the cosmos continues to expand--from lunar to interplanetary voyages and beyond--the demand for real-time, high-precision, and highly reliable navigation solutions has grown exponentially.  
In this Special Issue, we have collected original research and review papers that reflect the latest advancements in the navigation theories and methods for deep space probes.
## 5 Network Management Architecture of the Proposed Topology
### Security Management  
Due to the large number of sensor nodes and the broadcast nature of wireless communication, it is usually desirable for BS to broadcast commands and data to sensor nodes. The authenticity of such commands and data is critical for the normal operation of sensor networks. If convinced to accept forged or modified commands or data, sensor nodes may perform unnecessary or incorrect operations and cannot fulfil the intended purposes of the network. Thus, in hostile environments (e.g., battlefield, antiterrorists operations), it is necessary to enable sensor nodes to authenticate broadcast messages received from BSs.  
A protocol that can be adopted in the proposed logical topology is SecCOSEN, which has been proposed for authentication and establishing secret keys in WSNs for multiple chain oriented logical topology. SecCOSEN uses partial key pre-distribution and symmetric cryptography techniques. While one version of the SecCOSEN protocol uses shared partial keys in a sensor chain, the other version uses private partial keys. Both versions of SecCOSEN show high resilience to different security attacks. The protocol outperforms other random key pre-distribution protocols as it requires less space, has lower communication overheads, and offers very high session key candidates.
###### Abstract  
There is a lack of studies evaluating the metabolic contribution of non-_Saccharomyces_ yeasts in early fermentation phases. This study aimed to investigate the volatile aroma profiles produced by various non-_Saccharomyces_ yeasts just before sequential inoculation with _Saccharomyces cerevisiae_ to provide an insight into the particular effects they induce at this stage. The grape must of Malvazija istarska was inoculated with monocultures of _Torulaspora delbrueckii_, _Metschnikowia_ pulcherrima_, _Pichia kluyveri_, _Lachancea thermotolerans_, and _Schizosaccharomyces pombe_, alongside a _S. cerevisiae_ control. Eighty volatile compounds were quantified via headspace solid-phase microextraction and gas chromatography-mass spectrometry, and the data were statistically elaborated. Volatile profiles of non-_Saccharomyces_ yeasts differed significantly from the _S. cerevisiae_ control. Most treatments caused increases in finalool and \(\beta\)-damascenone, decreases in higher alcohols and fatty acids, and improved synthesis of odoriferous esters. _Torulaspora delbrueckii_ and _M. pulcherrima_ produced compounds not commonly found in _S. cerevisiae_ fermented wines. Multivariate statistical analysis linked the investigated yeasts to specific, particularly abundant compounds. Future studies should explore to what degree these contributions persist after sequential inoculation with _S. cerevisiae_ in diverse grape must matrices.  
non-_Saccharomyces_ yeasts; sequential inoculation; volatile aroma compounds; esters; GC/MS; Malvazija istarska wine 2022  
1
# Local Community Detection in Dynamic Graphs Using Personalized Centrality +
## 2 Background
### Measures of Community Quality  
Since there is no universal definition of a community, there are several metrics that exist to evaluate the quality of a community. Several of these metrics focus on calculating how tightly knit a community is in terms of comparing the number of inter-community edges to the number of intra-community edges. Let \(k_{in}^{C}\) denote the number of intra-community edges for community \(C\); that is, the number of edges \((i,j)\) with both endpoints vertex \(i\) and \(j\) inside the community. Similarly, let \(k_{out}^{C}\) denote the number of inter-community edges, or the number of edges \((i,j)\) where vertex \(i\) is in the community and vertex \(j\) is outside the community. Conductance (\(\phi\)) is a popular measure for measuring the \"fitness\" of a community by measuring the community cut, or the inter-community edges. Conductance is calculated as  
\[\phi(C)=\frac{k_{out}^{C}}{min(2k_{in}^{C}+k_{out}^{C},2k_{in}^{VC}+k_{out}^{VC })}\]  
in [4]. When optimizing a community with respect to conductance, we seek to minimize conductance scores. A lower conductance score indicates a more tightly knit community. Another popular metric for evaluating the quality of communities is to calculate a modified ratio of intra- to inter-community edges, or a normalized edge cut (\(f\)) [5] as  
\[f(C)=\frac{2k_{in}^{C}+1}{2k_{in}^{C}+k_{out}^{C}}.\]  
Here, a larger value of the normalized edge cut indicates a more tightly knit community, so methods that optimize for the value of the normalized edge cut of a community seek to maximize \(f(C)\). Modularity (\(Q\)) compares the number of intra-community edges to the expected number under a random null model and is calculated as  
\[Q(C)=k_{in}^{C}-\frac{(2k_{in}^{C}+k_{out}^{C})^{2}}{4|E|}\]in [6]. Again, larger values of modularity indicate higher quality communities so algorithms optimizing for modularity seek to maximize values of modularity. Several other metrics were used in recent DIMACS (Center for Discrete Mathematics and Theoretical Computer Science) challenges: the intra-cluster density is defined as \(k_{in}/(\frac{|C|}{2})\) and converge of a community is calculated as \(k_{in}/|E|\). For a more detailed list of metrics to measure community quality, see [7].
# Adaptive Beamformer Combined with Phase Coherence Weighting Applied to Ultrafast Ultrasound
## 2 Materials and Methods
### Experimental Methods and Evaluation Metrics
#### 2.4.3 Contrast  
Image contrast \(C\) was evaluated as follows [26]:  
\[C=\frac{|\mu_{b}-\mu_{l}|}{(\mu_{b}+\mu_{l})/2}, \tag{17}\]  
where \(\mu_{b}\) and \(\mu_{l}\) are mean gray levels in background and lesion, respectively. In the present study, an anechoic cyst phantom was adopted as the lesion, and a diffuse scattering medium was adopted as the background.  
Figure 1: Illustration of weighting procedure using Gaussian Phase Coherence Factor (gPCF) estimated (**a**) before and (**b**) after applying adaptive beamformer weights to outputs of sub-aperture beamformers.
###### Abstract  
The intelligent terminals deployed in hydropower IoT can quickly sense the status of hydropower equipment, thus improving the efficiency of system control and operation. However, communication security between the base station and intelligent terminals challenges the IoT hydropower plant. In this paper, we propose a UAV-assisted covert communication system (CCS), where a UAV acts as the base station to provide communication service to ground terminals monitored by malicious users. To improve access effectiveness, we adopt non-orthogonal multiple access (NOMA) for intelligent terminals to access the hydropower IoT. Since two devices can synchronously access the communication system with the NOMA scheme, we select one terminal to receive covert messages and the other to interfere with the malicious users to detect confidential communications. To maximize the covert rate, we formulate the optimization problem that jointly optimizes the transmit power, the altitude of the UAV, and trajectory under the constraints of covertness and the finite length of the transmission message block. Additionally, we transform the optimization problem into a geometric planning one, which is solved by a developed sequential geometric planning (SGP) approximation algorithm. Simulation results show the proposed algorithm can improve the covert rate compared to the traditional methods.  
information security; hydro-power Internet of Things; covert communication; UAV +
Footnote †: journal: Advances in Neural Information Processing Systems
# Intelligent Detection of Rebar Size and Position Using Improved DeeplabV3+
## 2 Methodology  
### DeeplabV3+  
To achieve high-precision segmentation and recognition of the rebars, a model with excellent performance in the field of semantic segmentation was selected as the basic algorithm of present study. The Deeplab series was developed on the basis of a fully convolutional network (FCN) [29], which combines deep convolutional networks and conditional random fields (CRFs) and uses Atrous convolution to effectively enlarge the field of view. In 2018, DeeplabV3+, the latest model in the Deeplab series introduced by the Google team, achieved an outstanding result of up to an 87.8% mIoU on the PASCAL VOC-2012 dataset, with segmentation results that far outperformed other models. Therefore, DeeplabV3+ was chosen as the base algorithm. The network structure is shown in Figure 1.  
The encoder part of the network consists of a backbone and an atrous spatial pyramid pooling (ASPP). A residual mechanism was used to connect separable convolutions, which was divided into three flow steps: entry flow, middle flow, and exit flow. The input image passed the backbone network to extract one high-level feature map and one low-level feature map. The high-semantic-information feature map was passed to the ASPP  
Figure 1: Structure of DeeplabV3+.  
module, which was divided into five parallel operations: normal convolution (1 \(\times\) 1), three atrous convolution groups with different rates, and image pooling. This approach expanded the convolution field, performed multi-scale feature fusion to reduce the effects of inconsistent input scales, and finally performed a 1 \(\times\) 1 convolution operation before passing to the decoder.  
In the decoder section, the ASPP-processed feature maps were bilinearly unsampled by a factor of 4 and were then concatenated with the low-semantic-information feature maps output by the backbone network using a 1 \(\times\) 1 convolution to the adjust channel. After a tandem operation, the 3 \(\times\) 3 convolution kernel and bilinear upsampling by a factor of 4 were applied to obtain the final segmentation map.  
While this model has achieved excellent results on the PASCAL VOC-2012 dataset, its original design was not specifically tailored for rebar images, and its performance in rebar image recognition is not ideal. In order to better adapt the model to the task of rebar image segmentation, improvements and optimizations were needed.
# A Survey on Congestion Control for RPL-Based Wireless Sensor Networks
## 4 Congestion Control Schemes for RPL
### Schemes Forwarding Packets via a Single Parent
#### 4.1.8 MAC-Aware Routing Metrics  
[PERSON] et al. propose two new metrics exploiting the information from the MAC layer in order to estimate reliability more accurately and to enhance network lifetime via load-balancing while satisfying reliability constraints ([PERSON] et al., 2017; [PERSON] et al., 2018). One is R-metric which represents the end-to-end reliability between a node and the sink. R-metric extends ETX at the MAC layer considering packet losses due to MAC contention. On the basis that the reliability of each link depends on the busy channel probability, the bad channel probability (LQI), and the MAC parameters, R-metric is calculated based on the probability that a packet is successfully transmitted over each link on a path, within a maximum number of backoffs and retransmissions at the MAC layer. Node \(i\) selects a parent \(j\) satisfying  
\[\max_{j\in\Gamma_{i}}R_{i,j}\cdot R(j) \tag{10}\]  
where \(R_{i,j}\) is the reliability of link \((i,j)\), \(R(j)\) the end-to-end reliability between node \(j\) and the root, and \(\Gamma_{i}\) the set of candidate receivers, i.e., the nodes that can progress packets towards the root. R-metric calculation starts from the root, progressing from parents to children in the DODAG. An advantage of R-metric over ETX is faster estimation.  
The other metric, Q-metric, is used for load balancing in the network. Node \(i\) selects the forwarding parent by solving the following optimization problem:  
\[\begin{split}\min_{j\in\Gamma_{i}}P_{i}Q_{j}+P_{r}\Big{(}Q_{j}- \lambda_{j}\Big{)}\\ s.t.\;\;R_{i,j}\cdot R(j)\geq R_{min}\end{split} \tag{11}\]  
where \(P_{t},\;P_{r},\;Q_{j},\;\lambda_{j},\;R_{min}\) are the transmission power consumption, the receiving power consumption, the traffic forwarded by node \(j\), the traffic generated by node \(j\), and the required reliability of the given application, respectively. Thus, node \(i\) needs to obtain information about its own traffic and parent candidates' traffic through DIO messages.  
Performance was evaluated via Cooja simulation for the network topologies with seven nodes and 18 nodes. The unslotted IEEE 802.15.4 MAC was used at the data link layer ([PERSON] et al., 2017). Simulation results show that R-metric and Q-metric achieve better end-to-end reliability than a back-pressure algorithm ([PERSON] et al., 2017) which uses a weighted ETX cost including the queue differential between the transmitter and receiver. In terms of balanced energy consumption, Q-metric showed better performance than R-metric and the back-pressure scheme.
## 3 Sliding Mode Speed Controller  
### Advanced Reaching Law  
The dynamic and chattering performance of SMC are associated with the design of the reaching law. The system stability can be guaranteed by Lyapunov function if condition (4) is satisfied.  
\[s\cdot\dot{s}<0 \tag{4}\]  
The classical reaching law consisting of isokinetic and pure exponential approach terms can be expressed as follows:  
\[\frac{\mathrm{d}s}{\mathrm{d}t}=-\varepsilon\mathrm{sgn}(s)-ks,\ \varepsilon>0,k>0 \tag{5}\]  
where \(s\) is sliding mode surface, \(\varepsilon\) and \(k\) are the switching gain and linear gain, respectively. However, the classical reaching law still suffers from the dilemma of approaching speed and chattering. Thus, an advanced reaching law is designed to accelerate the approaching speed and reduce the chattering simultaneously, as expressed as follows:  
\[\begin{array}{l}\dot{s}=\frac{\mathrm{d}s}{\mathrm{d}t}=-f(x,s)\mathrm{ tanh}(s)-\eta|x|s\\ f(x,s)=\frac{\lambda}{\alpha+(1-\alpha+\frac{1}{|x|})e^{-\beta|x|}}\end{array} \tag{6}\]  
where saturation function \(\mathrm{tanh}(s)=(e^{s}-e^{-s})/(e^{s}+e^{-s})\), \(x\) represents the system state quantity, \(\lambda>0\), \(\eta>0\), \(0<\alpha<1\), \(\beta>0\).  
It can be seen from the proposed reaching law that the variable gain_f_(_x_,_s_) converges to \(\lambda/\alpha\) when the system state quantity is far away from the sliding mode surface. Therefore, the approaching speed is faster than classical reaching law under the since \(\lambda/\alpha\) is larger than \(\lambda\). The introduction of \(|x|\) in the exponential gain also can obtain less convergence time during the initial approach phase. The variable gain_f_(_x_,_s_) converges to \(\lambda|x|/(|x|+1)\) when the motion trajectory approaches the sliding mode surface and the system approaches the origin under the action of the variable gain term. The switching band becomes smaller with the decrease in the state quantity \(x\), thereby suppressing the chattering. Moreover, \(\mathrm{tanh}(s)\) with a smoother curve is introduced to replace the step function \(\mathrm{sgn}(s)\) for chattering reduction. In summary, the advanced reaching law can achieve faster approaching speed and lower chattering correspondingly.  
To analyze the stability of the reaching law, the Lyapunov function is chosen as \(V=s^{2}/2\), and we find  
\[\dot{V}=s\dot{s}=-f(x,s)\mathrm{tanh}(s)-\eta|x|s^{2} \tag{7}\]  
Due to _f_(_x_,_s_) > 0, \(\mathrm{tanh}(s)\geq 0\), \(\eta|x|s^{2}>0\), \(\dot{V}\leq 0\) can be satisfied, which meets the stability condition.
# All About Audio Equalization: Solutions and Frontiers
## 5 Other Equalization Filter Designs
### Linear Phase IIR Filters  
It is well-known that IIR filters generally are not linear phase. However, several approaches exist for linear phase IIR filter design. [PERSON] and [PERSON]'s real-time linear phase IIR filters [115] used a modification of a well-known time reversal technique. [PERSON] and [PERSON] described a variation on this approach that yields higher performance (more stopband loss, less passband ripple and/or narrower transition band) [116]. [PERSON] _et al._ described performance issues in [PERSON] and [PERSON]'s original design; a sinusoidal variation in the group delay, and a harmonic distortion with sinusoidal input [117]. To alleviate these issues, they reduce the filter's overall processing delay by using shorter sections with truncated impulse response [117]. [PERSON] proposed an efficient arbitrary sample rate converter using zero phase IIR filtering, which later led to a patented signal interpolator [118]. Another approach to linear phase IIR filtering was proposed in [119]. To perform time reversal methods in real time, the signal is divided into blocks of samples. The impulse response of the time-reversed IIR filter must be truncated, but only after it has decayed sufficiently. The cumulative energy of the impulse response can be used for determining an appropriate impulse response length [120].  
A basic linear phase IIR method is shown in Figure 14. The input signal passes through a filter \(H(z)\), then the order of the samples is reversed, and then this procedure is repeated on the output, i.e, \(H(z)\) applied, then time reversal. In the time reversal operation, the input signal \(x[n]\) becomes \(x[-n]\). The corresponding z-domain transformation can be written as  
\[X(z)\to X(z^{-1}). \tag{56}\]  
The intermediate transfer functions in Figure 14 can be written as \(Y_{1}(z)=H(z)X(z)\), \(Y_{2}(z)=H(z^{-1})X(z^{-1})\), and \(Y_{3}(z)=H(z)H(z^{-1})X(z^{-1})\). Thus, the transfer function for the linear phase IIR filter given in Figure 14 is  
\[Y(z)=H(z^{-1})H(z)X(z)=|H(z)|^{2}X(z), \tag{57}\]  
which corresponds to zero phase filtering by the magnitude response of \(H(z)\) twice.
# Analytical Evaluation of Laser Cleaning Effectiveness in the Context of Contemporary Muralism
## 3 Results  
### Characterization of Painting Material  
An initial material characterization was conducted using micro-ATR-FTIR analysis to confirm the chemical composition of the paints. The spectra are presented in Figure 2.  
Spectra of Montana 94 spray paints (AlkY and AlkB) showed typical absorption peaks of alkyld resins at approximately 1730 (v(C=O)), 1600, 1580 and 1488 (v(C=C)), 1250 (v(C-O) of COO ester groups), 1120, 1086 and 1040 cm\({}^{-1}\) (v(C-O)). Many absorption peaks of pigment PY74 were identified in AlkY (1673, 1594, 1554, 1516, 1461, 1337, 1224, 1202, 1178, 1086, 954, 916, 859, 801, 778 cm\({}^{-1}\)) and a few peaks of pigment PB15 were identified in AlkB (1335, 1164, 1089 cm\({}^{-1}\)) [29; 30].  
As expected, the Flame Orange paints (AcrY and AcrB) showed an acrylic binder, evident from peaks at 1727 (v(C=O)), 1486 (\(\delta\)(CH\({}_{2}\)) of CH\({}_{2}\) groups), 1450 (\(\delta\)as(CH\({}_{2}\)) of  
Figure 2: Micro–ATR–FTIR spectra of the paints used to prepare the mock-ups listed in Table 2.  
CH\({}_{3}\) groups), 1239, 1143 and 1063 (v(C-O)), 966 (\(\rho\)CH\({}_{3}\)), and 845 cm\({}^{-1}\) (acrylate side chain vibration) [29]. The yellow spray showed peaks at 1674, 1518, 1463, 1335, 1225, 1086, 916, 859, and 801 cm\({}^{-1}\), indicating the presence of organic pigment PY74. In the blue spray AcrB, peaks at 1507, 1335, 1120, 1090, and 900 cm\({}^{-1}\) were assigned to pigment PB15.  
For the fluorescent blue spray (AcrFB), in addition to the typical peaks of the acrylic binder (1728, 1486, 1455, 1241, 1148, 1066, 842 cm\({}^{-1}\)), the spectrum revealed the presence of another polymer, a poly(aryl sulfonamide), having characteristic peaks at 3367 and 3280 cm\({}^{-1}\) for the N-H stretching, 1323 cm\({}^{-1}\) for the antisymmetric vibrations of the sulphonyl group, and 1557 and 812 cm\({}^{-1}\) for the vibrations of the aromatic rings [30]. This polymer serves as a carrier for the fluorescence pigment.  
In Alpha Acrilmat paints (StyY), the styrene-acrylic binder was identified, with contributions from the acrylic component (1729, 1160, 760 cm\({}^{-1}\)) and styrene (3061, 3028, 1601, 1584 cm\({}^{-1}\)). No typical yellow pigments were identified, but peaks at 1015 cm\({}^{-1}\) (talc absorption) and at 1048 and 872 cm\({}^{-1}\) (related to calcium carbonate) indicated the presence of fillers in the paint.
# Hybrid Colloids Made with Polymers
## 6 (Bio)Polymer Binding onto Preformed Surfactant Aggregates  
The process is a surface nucleation and depends on (bio)polymer content and on the surface charge density of aggregates, which are natural, or synthetic, vesicles. The latter are by far preferred by experimentalists for many practical reasons. First, the stoichiometry is well defined. Second, the area on which proteins bind is considerably large [37]. Third, the surface charge density of synthetic vesicles can be modulated at will [38]; this holds true in synthetic cat-anionic vesicles, which can be negatively/positively charged [39; 40]. These entities are built up by mixing oppositely charged surfactants in proper amounts, provided the mixture is not stoichiometric; in the latter eventuality, charge neutralization implies precipitation. The molecular surfactant concentration in cat-anionic dispersions is much lower than the one pertinent to the corresponding micellar solutions. _N.B._ The term _CAT-AN_s is an acronym for mixtures made of cationic plus anionic species. Possible surfactants binding to proteins, which could be a secondary reaction, is not significant. Such considerations come from statements of [PERSON] [41] and [PERSON] [42]. Binding induces noticeable changes in the size of vesicles and large modifications in the net surface charge density of the adducts. Let us mention, as an example, _BSA_ (bovine serum albumin) binding onto positively charged vesicles. The process is strongly _pH_-sensitive, because of charge modulation of the protein. For pH lower than the protein isoelectric point, _BSA_ is positively charged, and no interactions take place. _BSA_ has an isoelectric point close to 4.5 and is negatively charged above that threshold. If negatively charged  
Figure 3: Surface tension, \(\gamma\) (in mN m\({}^{-1}\)), vs. log \(m\) plot for an anionic surfactant in H\({}_{2}\)O—1.0 wt% _PEO_ pseudo-solvent (nominal PEO mass = 20 kD), at 25.0 \({}^{\circ}\)C. The lower inflection point is termed _cac_ (critical association concentration), the higher one is the _cmc_* (critical micellar concentration in presence of polymer).  
_CAT-AN_ vesicles interact with positively charged proteins, the reverse behavior holds true. In all cases, the width of the interaction region and the adducts sizes that do form largely increase compared to bare vesicles; see, for instance, Figure 4. Such vesicular dispersions find extensive use in biopolymer transfection technologies, as demonstrated by in vitro and in vivo studies.  
As shown above, systems made up of polymers and surfactants largely differ each from the other; the preparation sequence controls the formation and properties of adducts that do form. Not only the stoichiometry but, also, the intermediate pathways are relevant in the fate of the objects that are formed. Not much is known about the possibilities offered by forming vesicles onto polymer surfactant systems, which could be a good demonstration of the strict similarities existing among the two subclasses mentioned above.