esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Image steganalysis methods aim at detecting whether there exist hidden messages in images. Deep learning (DL) models have been proposed to enhance steganography detection. These models occupy a large amount of memory and, for this reason, should be optimized when the scenario involves resource-limited devices and systems. This work addresses different deep learning model optimization strategies, namely model pruning, quantization and weight clustering, applied to a deep learning model that presents competitive accuracy results in image steganalysis and belongs to the family of DL models with smaller memory requirements. The results show that the use of optimization schemes can lead to similar or even better accuracy compared to the original model (without the use of optimization schemes), while requiring less memory to store the model. Different scenarios are simulated for each optimization technique, and, finally, quantization is combined with pruning. For dynamic range quantization (DRQ), we achieve models that can save approximately 72% of storage. For FP16 quantization, we obtain better accuracy results and a model with approximately 50% less memory consumption. By applying weight clustering, we also achieve compressed models that can save more than 72% of storage space and lead to better accuracy for some scenarios. Using the combination of pruning and quantization, smaller models in terms of memory requirements are obtained.  
steganalysis; deep neural networks; pruning; quantization; weight clustering Article
## 3 Results
### Driving Force Analysis for Internunual GPP Dynamics
#### 3.3.4 Greening Effect and CO\({}_{2}\) Fertilization Dominated GPP Enhancement  
The annual average CO\({}_{2}\) concentration at the MLO site increased approximately linearly from 2001 to 2016 at an average rate of 2.1 ppm yr\({}^{-1}\) (Figure 8a). Correspondingly, the rising ambient CO\({}_{2}\) concentration boosted the European GPP at a rate of increase of nearly 16.9 Tg C yr\({}^{-2}\) (\(p<0.001\); \"CO\({}_{2}\)\"), accounting for 32.8% of the total interannual increasing GPP trend. Accounting for the CO\({}_{2}\) fertilization effect alone, GPP increased by 8.6% per 100 ppm (\"CO\({}_{2}\)\", Figure 8a); if all the factors were included, GPP increased by 27.8% per 100 ppm (\"All\"; Figure 8a). By calculating cumulative GPP anomalies using different simulation scenarios, we identified the LAI and CO\({}_{2}\) as the key factors dominating the GPP dynamics from 2001 to 2016. Although the climate change and interannual V\({}_{\rm cmax}\) change effect on GPP is not significant (Figure 8b), they cause interannual fluctuations in GPP (Figure 8a).  
Land surface greening and elevated CO\({}_{2}\) had significant positive effects of 35.5 Pg C yr\({}^{-2}\) and 16.9 Pg C yr\({}^{-2}\), respectively (Figure 8b). Climate change exerted minor (statistically insignificant) impacts on the GPP increase (+1.2 Pg C yr\({}^{-2}\)) but played important roles in driving the interannual GPP dynamics (Figure 8a). In contrast, LULCC had a small but significant negative effect (\(-\)1.3 Pg C yr\({}^{-2}\); \(p<0.001\)) for the long-term GPP trend. Although the interannual V\({}_{\rm cmax}\) change effect on the GPP trend was minor (Figure 8b), it played a minor role in driving the annual dynamics (Figure 8a). Among all the factors, the LAI contributed most significantly to the GPP interannual trend, followed by CO\({}_{2}\), VPD, air temperature, and LULCC (Figure 8b).  
Figure 7: Impacts of interannual changes in V\({}_{\rm cmax}\) on GPP. (**a**) Interannual dynamics of mean V\({}_{\rm cmax}\). (**b**) Effects of interannual V\({}_{\rm cmax}\) change on GPP constrained by the “All” and “PC1” simulations.
# Optimal Configurations of Mueller Polarimeter for Gaussian-Poisson Mixed Noise
## 2 Materials and Methods
### Effect of Linear Retarders on Polarization States
#### 2.2.2 Constant Retardance and Variable Fast Axis Angle  
Waveplates are one of the most common devices to change the polarization state and have constant retardance. Half-wave plate and quarter-wave plate are two common types of waveplates. The half-wave plate is usually used to shift the polarization direction of linearly polarized light, and the quarter-wave plate can be used to produce elliptical polarization or circular polarization. Besides, waveplates can be very easily fixed to rotating motors to change their fast axis angle. Different from the variable retarders, the retardance of a waveplate is constant, but the fast axis angle is variable.  
Similarly, we can calculate how the polarization state changed by the Mueller matrix of a retarder after a light beam traveled through it and plot the trajectory on the Poincare sphere. As observed in Figure 3, we select a pair of orthogonal linearly polarized light as  
Figure 2: The trajectory of several different incident light passes through a retarder with varying retardance. (**a**) Linear polarization, (**b**) Circular polarization, and (**c**) Elliptical polarization. Notice that all the trajectories appear as straight lines in the top view of the Poincaré sphere, and the lines’ directions are not changed with the incident light changing.  
incident light and four waveplates with sequentially increasing retardance to demonstrate the changing of polarization states on the Poincare sphere.  
If the incident polarized light is linearly polarized, the polarization state of the outgoing light will form an 8-shaped curve on the Poincare sphere with the rotation of the waveplate. In addition, its projection in the \(S_{1}S_{2}\) plane is a circle whose diameter is determined by retardance. When the waveplate is a half-wave plate, the two segments of the 8-shaped curve coincide at the equator.
## 1 Introduction
However, the substrate accelerated microbial shifts were observed in the high-strength phase with a significant decrease in _Syntrophaceae_ of up to 26.9% [18]. _Syntrophobacter_ was identified as the most abundant and consistent bacterial partner in syntrophic propionate conversion, regardless of the concentration of propionate, while among methanogen partners, _Methanoculleus_ was specifically enriched at the inhibitory levels of propionate, likely due to the ability to function under unfavorable environmental conditions [19]. Although the amount of butyrate produced during organic waste decomposition can be 5-10 times higher than that of propionate [9], the effect of butyrate on the AD process attracts considerably less attention. In a mesophilic upflow anaerobic sludge blanket reactor fed with high-butyrate (up to 6 g/L or 68 mM) containing palm oil mill effluent, butyrate was \(\beta\)-oxidized to acetate and hydrogen by obligate proton reducers in syntrophic association with H\({}_{2}\) utilizing methanogens [20]. The conversion of acetate to methane appeared to be a rate-limiting step but a key microbial community was not identified. Better butyrate-tolerated microbial consortia were domesticated and enriched through adding butyric acid, ranging from 0.2 to 4.4 g/(L-d) (i.e., up to 55 mM in the influent) in the mesophilic continuously stirred reactor [21]. The syntrophic consortia mainly included butyrate-oxidizing bacteria (_Syntrophomonas_), acetate-oxidizing bacteria (_Synergistaceae_ and _Mesotoga_), interspecies H\({}_{2}\) transfer promoted bacteria (_DMER64_) and methanogen (_Methanosarcina_). No inhibition of the microbial community was observed [21]. It is well-known that the thermophilic AD system has higher biochemical reaction rates but is more prone to destabilization as a result of the VFA build-up [22; 23; 24]. Moreover, it would be interesting to study the effect of much higher concentrations of butyrate on the shifts in the microbial community in order to better evaluate patterns of adaptation to inhibition. Such findings could be useful to improve degradation efficiencies, and the obtained enriched culture could be used to recover soured (specifically, butyrate-accumulated) digesters by bioaugmentation. The goals of the present work were: (1) the gradual adaptation of the thermophilic methanogenic community to extremely high (up to 170 mM) butyrate concentrations; (2) to study the change in the thermophilic microbial community in response to extremely high concentrations of butyrate; and (3) to evaluate the feasibility of bioaugmentation using an enriched syntrophic consortium to stabilize the overloaded AD system.
## 2 CNNs and Attack Scenarios
### Attack Scenarios in the \(\mathcal{R}\) Domain  
Let \(\mathcal{C}\) be a trained CNN, \(c_{a}\) be a category among the \(\ell\) possible categories, and \(\mathcal{A}\) a clean image in the \(\mathcal{R}\) domain, classified by \(\mathcal{C}\) as belonging to \(c_{a}\). Let \(\tau_{a}\) be its \(c_{a}\)-label value. Based on these initial conditions, we describe two attack scenarios (the _target scenario_ and the _untarget scenario_) aiming at creating an adversarial image \(\mathcal{D}\in\mathcal{R}\) accordingly.  
Whatever the scenario, one requires that \(\mathcal{D}\) remains so close to \(\mathcal{A}\), that a human would not notice any difference between \(\mathcal{A}\) and \(\mathcal{D}\). This is done in practice by fixing the value of the parameter \(\epsilon\), which controls (or restricts) the global maximum amplitude allowed for the modifications of each pixel value of \(\mathcal{A}\) to construct an adversarial image \(\mathcal{D}\). Note that, for a given attack scenario, the value set to \(\epsilon\) usually depends on the concrete performed attack, more specifically on the \(L_{p}\) distance used in the attack to assess the human perception between an original and an adversarial image.  
The \((c_{a},c_{t})\)_target scenario_ performed on \(\mathcal{A}\) requires first to select a category \(c_{t}\
eq c_{a}\). The attack then aims at constructing an image \(\mathcal{D}\) that is either a _good enough adversarial image_ or a \(\tau\)_-strong adversarial image.  
A _good enough adversarial image_ is an adversarial image that \(\mathcal{C}\) classifies as belonging to the target category \(c_{t}\), without any requirement on the \(c_{t}\)-label value \(\tau_{t}\) beyond being strictly dominant among all label values. A \(\tau\)-_strong adversarial image_ is an adversarial image that \(\mathcal{C}\) not only classifies as belonging to the target category \(c_{t}\), but for which its \(c_{t}\)-label value \(\tau_{t}\geq\tau\) for some threshold value \(\tau\in[0,1]\) fixed _a priori_.  
In the _untarget scenario_ performed on \(\mathcal{A}\), the attack aims at constructing an image \(\mathcal{D}\) that \(\mathcal{C}\) classifies in any category \(c\
eq c_{a}\).  
One writes \(atk_{\mathcal{R},\mathcal{C}}^{scenario}\) to denote the specific attack _atk_ performed to deceive \(\mathcal{C}\) in the \(\mathcal{R}\) domain according to the selected _scenario_, and \(\mathcal{D}=atk_{\mathcal{R},\mathcal{C}}^{scenario}(\mathcal{A})\) an adversarial image obtained by running successfully this attack on the clean image \(\mathcal{A}\). Note that one usually considers only the first adversarial image obtained by a successful run of an attack, so that \(\mathcal{D}\) is uniquely defined.  
Finally, one writes \(\mathcal{C}(\mathcal{D})=(c,\tau_{c})\) the classification of the adversarial image obtained. Note that \((c,\tau_{c})=(c_{t},\tau_{t})\) in the case of the target scenario.
## 4 Results and Discussions
### Received Power  
Figure 5 shows the empirical Cumulative Distribution Functions (CDFs) of the power received by the PD in the UM scenario for values of \(N\) between 40 and 200, which is computed based on the Equation (41). Furthermore, in each graph, we insert the power received distribution in the UM scenario. We evaluate the received power by keeping the \(z\) coordinate of the PD with a value of 1.8 m because it is a typical position value of communication devices in these scenarios. Also, \(P_{t}\) is set to 5 W, to provide constant illumination in the tunnel.  
Compared with the CDF and distribution of the received power obtained in the work presented in [12] (see Figure 5f), the maximum and minimum values of the received power obtained in this study for all the values of \(N\) are smaller. This finding implies that the derived hemispherical dust particle distribution model presents a greater similarity with a real UM scenario since the negative effect of scattering on the received power is more noticeable. In fact, if we compare the CDFs and distributions of received power for the chosen values of \(N\), we can notice that if we increase the value of \(N\), the effect on the decrease of the received power is greater, even though the power distribution remains quite irregular for all values of \(N\).  
\begin{table}
\begin{tabular}{c c} \hline
**N** & **CIR Maximum Value** \\ \hline
20 & \(3.53\times 10^{-9}\) \\
40 & \(3.45\times 10^{-9}\) \\
80 & \(3.18\times 10^{-9}\) \\
120 & \(2.83\times 10^{-9}\) \\
160 & \(2.55\times 10^{-9}\) \\
200 & \(2.23\times 10^{-9}\) \\ \hline \end{tabular}
\end{table}
Table 2: Maximum CIR values for each value of N.  
Figure 4: CIR of the scattering component with different values of \(N\) in the evaluated UM scenario.
# MPEG and DA-AD Resilient DCT-Based Video Watermarking Using Adaptive Frame Selection
## 3 Proposed Method
### Extraction of the Embedded Watermark  
The extraction process of the embedded watermark pattern was carried out as follows. The DCT coefficients of a possibly corrupted watermarked frame \(F^{*}\) were computed, and vector \(V^{*}\) was extracted from the DCT coefficients in the pre-selected position during the embedding step. Next, the cross-correlation between the watermarks from the predefined sets \(\mathbb{X}\) and \(\mathbb{Y}\) and vector \(V^{*}\) were calculated and adopted as a measure of the presence of the watermark. The correlation \(z\) was defined as follows:  
\[z=\frac{X\cdot V^{*}}{N}=\frac{1}{N}\sum_{i=1}^{N}x_{i}\cdot v^{*}_{i}. \tag{3}\]  
The presence of watermark \(X\) was determined through a comparison between the threshold \(T\) and the calculated correlation \(z\). Here, the threshold \(T\) was computed using  
\[T=\frac{1}{20\cdot N}\sum_{i=1}^{N}|v^{*}_{i}|. \tag{4}\]  
To minimize the false-positive rate, the threshold value from the equation was obtained experimentally based on the evaluation results. The presence of each watermark among the watermark pattern sets \(\mathbb{X}\) and \(\mathbb{Y}\) in the target frame \(F^{*}\) was decided through thresholding. Accordingly, the presence of watermarks in all frames was checked. Subsequently, each detected watermark was matched to the proper 8 bits of data information, and all 16 bits of data were recovered. If there are more or fewer than two extracted watermarks, the data cannot be accurately recovered.
## 1 Actinobacteria and Their Primary Metabolism as Basis for Secondary Metabolism Precursor Supply
### Primary Metabolism in Actinobacteria: Phosphate Metabolism
#### 1.3.2 Regulation of Phosphate Metabolism  
Phosphate control in Actinobacteria has been studied in _Streptomyces_, in which it is mediated by the two-component system (TCS) PhoR-PhoP; its orthologs have been found in most Actinobacteria [53; 54]. It belongs to class IIIA of TCSs with the PhoR protein acting as a sensor kinase [53; 55]. PhoP belongs to the OmpR family of DNA-binding response regulators. The PhoR protein kinase can self-phosphorulate under phosphate depletion conditions. The phosphate group is then transferred to a receiver aspartate in PhoP. This causes a structural rearrangement and activation of the DNA-binding domain. The phosphorylated PhoP can activate or repress the expression of genes in the _Pho_ regulon [23]. PhoR-PhoP has been shown to control the primary metabolism and secondary metabolism of _S. coelicolor_, _S. lividans_, _S. tsukubaensis_, _S. natalensis_, _S. avermitilis_ and some others [22; 24; 25; 52; 56] (Figure 1).  
Phospholipids (e.g., phosphatidylserine, phosphatidylcholineylglycerol, phosphatidylcholine and phosphatidylcholine) are present in all living organisms. They are abundant in some habitats as part of decaying plants and animals [48]. Glycerol phosphodiesters are synthesized by the deacylation of phospholipids. They keep a phosphate at carbon-3 of the glycerol backbone. The phosphodiesers can be hydrolyzed by glycerophosphodiester phosphodiesterase (GDPDs), which cleave the ester bond, leading to the release of glycerol-3-phosphate. Soil dwelling actinobacteria like _Streptomyces_ are able to utilize the phosphate from glycerophosphodiesters. In _S. coelicolor_, seven putative _gdpg_ genes have been characterized. Three of these genes are named _glpQ1_ through _glpQ3_, encoding secreted GDPDs, and the other four genes encode intracellular uncharacterized phosphodiesterase. A gene orthologous to the _glpQ_ genes of _S. coelicolor_ can be found in the genomes of _S. avermitilis_, _S. clavuligerus_ and _S. venezuelae_, and encode proteins with 67 to 80% amino acid identity. The PhoP regulator has been shown to regulate _glpQ1/glpQ2_ in _Streptomyces_, which encode glycerophosphodiesterases [48; 56; 57].
## 4 Factors Affecting Pesticide Degradation
### Concentrations of Chlorine Dioxide and Ozone  
An appropriate increase in the concentration of chlorine dioxide will promote the progression of the reaction, thus increasing the degradation rate of pesticides. In the study of chlorine dioxide degradation of sulfonylurea herbicides nicosulfuron and thifensulfuron in water, when the concentration of chlorine dioxide was changed from 5 mg/L to 10 mg/L, the degradation rate of nicosulfuron was changed from 52.55% to 64.17%, and the degradation rate of thifensulfuron was changed from 34.38% to 46.75% [67]. However, in water treatment, excessive chlorine dioxide delivery can produce large quantities of chlorite and chlorate [68]. These chlorites and chlorates can have an impact on human health. [53] A high concentration of chlorine dioxide has bleaching properties and is commonly used for paper bleaching [69]. In the process of food application, it will affect the sensory changes in food [70; 71].  
The concentration of ozone promotes the reaction of ozone directly with pesticides, and at the same time, it also increases the concentration of -OH produced by ozone, which promotes the reaction of -OH with pesticides and increases the degradation rate of pesticides. In the study of pyrethoid insecticide permethrin, benzoylurea insecticide chlorofluazuron, and organochlorine fungicide chlorothalonil on Chinese white cabbage, it was found that the degradation rate of these three pesticides increased with the concentration of ozone [72]. Similarly, in the degradation of the triazole fungicide difenoconazole and the substituted urea herbicide liunuron on treated carrots, the degradation rate of the pesticides increased with the concentration of ozone [73]. However, in the food industry, excessive ozone concentrations can cause some negative effects on the quality of produce, such as morphological changes, spoilage, discoloration, weight loss, and unpleasant odors [74].  
In the reaction system with pesticides, as the concentration of chlorine dioxide and ozone increases, it increases the rate of reaction with pesticides and accelerates the reaction process. Under the same conditions, the degradation rate of pesticides increases with increasing concentrations of chlorine dioxide and ozone. However, excessive concentrations of chlorine dioxide and ozone also have adverse effects. Excessive concentrations of chlorine dioxide and ozone in treating pesticides on the surface of fruits and vegetables can affect the quality of fruits and vegetables [75]. In addition, excessive chlorine dioxide can produce by-products such as chlorite and chlorate when treating pesticides in water [76].
# Observing APOD with the AuScope VLBI Array
## 2 Experiments  
The presented experiments aim at deriving group delay observables from direct observations of the APOD satellite with the geodetic VLBI antennas of the AuScope VLBI array. Following initial APOD tracking tests in April and July 2016 [29] the discussed series of experiments was carried out in November 2016. A complete list is shown in Table 1. In total, we observed 8 sessions applying the experiment design described in Section 3.
## 3 Results  
### Study Selection  
As shown in Figure 1, a search across five databases yielded 3389 relevant studies. After removing duplicates, 958 studies were excluded, and 2431 studies were assessed by reviewing titles and abstracts, leading to the exclusion of 2286 articles. Following a full-text evaluation of the remaining 145 articles, 98 studies were excluded primarily for the following reasons: (1) unclear data (\(n=6\)); (2) participants not diagnosed with HF (\(n=14\)); (3) non-exercise interventions (\(n=3\)); (4) absence of a control group (\(n=14\)); (5) inappropriate exercise protocols (\(n=17\)); (6) lack of outcome measures (\(n=4\)); and (7) conference articles (\(n=40\)). Ultimately, 47 studies [34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67; 68; 69; 70; 71; 72; 73; 74; 75; 76; 77; 78; 79; 80] were included in the meta-analysis.  
Figure 1: PRISMA flowchart of study selection.
# Towards a Highly Available Model for Processing Service Requests Based on Distributed Hash Tables
## 1 Introduction
### State-of-the-Art
#### 1.1.1 Distributing Systems Using Highly Available Data  
Nowadays, there is a high demand to _process_ large amounts of data, coming both from the industry (domain led by companies such as Google, Facebook, Microsoft, Amazon, etc.) and from science (e.g., the CERN cluster), this is why, any popular distributed service, which aims to serve its clients impeccably, should also focus on how to improve the process, in any type of external condition, the entire large amount of data, related to the large set of its clients.  
Classically, DHTs have been invented and used only in order to help storing highly available data [1; 18; 19]. Recently, there is a trend to use them for other purposes, too, such as to improve Blockchain models, e.g., the Lightchain [2], or to offer an alternative to the classical DNS lookup system [3]. Since we _have not identified_ another model to address and solve exactly the request _processing availability_ problem, by means of DHTs, despite DHTs being one of the most appropriate systems to offer availability, this novel approach and the main contribution of this paper is to go even further when using DHTs and to _offer highly available service request processing_. For this purpose, the naturally good dispersion of the DHT's hash function, which should be applied to each client's name, is of much help. Note that to process client's requests by application servers is a much harder task than the data mining Map-Reduce techniques used in Spark [20] or Hadoop [21].