esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparison between the Biofilm Desorption Abilities of T4 and MS2 Colliphages
## 1 Introduction  
Biofilms are spatial collections of various species of microorganisms that adhere to each other and grow onto the surface where they form. Biofilms can form in natural as well as artificial settings [1; 2; 3; 4]. Each biofilm displays unique chemotaxis, cell adhesion, and a dynamic three-dimensional shape. Although pure culture biofilms that contain all genetically identical cells could form on biotic and abiotic surfaces, they rarely exist in ecosystems. Biofilms display beneficial or harmful effects based on the location of their formation.  
The formation of a biofilm within a living tissue is usually detrimental, whereas some biofilms, such as anodic biofilm that form on abiotic surfaces are essential in industry [2; 3; 4]. Once a biofilm is established on a surface, it evolves in a manner that makes it mechanically stable [1]. This mechanical stability of biofilms is a direct result of their cell density and encasement in complex extracellular polymeric substances (EPS). EPS is secreted primarily by the microorganisms that make up the biofilm. EPS is held together by several weak chemical interactions between polysaccharides, lipids, cell adhesion molecules, and toxic substances within the biofilms [1].  
The enzymes secreted by the antibiotic resistant bacteria in certain biofilms impart antibiotic resistance to the entire microbial community within the biofilm [5]. Antimicrobial agents are generally effective in disrupting pure culture biofilms; however, the more biodiversity and stability the biofilm has, the more difficult it is to disrupt [6; 7; 8]. Several natural and artificially synthesized agents possess an ability to disrupt biofilms [9]. The use of bacteriophages as a safer and effective anti-biofilm agent is an emerging alternative method to control biofilm growth. Additionally, bacteriophages display a narrow host range and cell-type specificity.  
Even though the combination of phages or application of phages along with chemicals effectively disrupt/detach biofilms [10], a comparison of the biofilm desorption propertiesof DNA and RNA phages has received limited attention. In this study, we treated natural biofilms individually with equivalent viral loads of T4 DNA coliphage and MS2 RNA coliphage. Our results indicate similar biofilm desorption upon MS2 phage treatment compared to the biofilm desorption displayed by T4 phage.
## 3 Routing Issues and Challenges in WBSNs  
Design and development of efficient routing protocols for WBSNs is a challenging job due to their unique requirements and specific characteristics [18]. In the following sections, we discuss the routing issues and challenges of WBSNs.
## Materials and Methods
### Characterization of the CDW  
Three granulometry fractions, coarse (4.75-1.18 mm), medium (1.18-0.6 mm) and fine (<0.6 mm) of the CDW were characterized by multiple assays in order to determine their granulometric composition. The Brazilian standard NBR NM 248 [31] considers the mean retained percentage and the mean accumulated retained percentage in each sieve. [PERSON]'s assay, as described by Brazilian standard NBR 9776 [32], determines specific mass. Brazilian standard NBR 6467 [33] describes an assay that determines the swelling of CDW, which is described as the variation of apparent volume caused by the absorption of free water by the aggregate grains altering their mass. X-ray fluorescence (XRF) identifies the chemical elements present in CDW composition. Loss on ignition was also measured, which is related to the amount of volatile organics and the dehydration of the cement contained in the CDW. Visual analysis of macrographic images was also conducted in order to verify the constituents of CDW such as bricks, concrete, wood, and glass, among others. X-ray fluorescence was performed using a PANalytical-MiniPal 4 equipment (PANalytical B.V., Almelo, The Netherlands).  
The proposed methodology for CDW aggregate swelling determination is based on the successive addition of water in order to obtain a homogeneous mixture between the aggregate sample and a moisture content of 0.5% and 12%, by determining the unitary mass at each moisture content. The ratio between wet and dry volumes for the same unitary mass is expressed by Equation (1).  
\[\frac{V_{h}}{V_{s}}=\frac{\delta_{s}}{\delta_{h}}\times\frac{(100+h)}{100} \tag{1}\]  
where, \(V_{h}\) is the aggregate's volume at \(h\%\) moisture, \(V_{s}\) is the aggregate's volume after being oven dried, \(\delta_{h}\) is the aggregate's unitary mass after being oven dried, and \(h\) is the aggregate's moisture (%).
## 3 Results and Discussion
### Time-Averaged Results
#### 3.1.3 Velocity and Turbulence Fluctuation's Profiles  
To examine the distribution of the oscillating jet in more detail, time-averaged velocity components and turbulence fluctuations were extracted along a line that was 1D\({}_{\rm h}\) away from the exit of the fluidic oscillator (Figure 10). The non-dimensional u-component and the velocity magnitude had a small spreading angle in the central region for Re = 2680, and the majority of the jet momentum was in the streamwise direction, along the \(y\)-axis. As Re increased, these values were reduced quickly to less than half of the peak value. In contrast, for the v-component profiles, there was a reduction of the peak values. Thus, for higher Re numbers, a pair of peaks was created, and the values of the peak almost increased with Re. The turbulence fluctuation's profiles were also similar to the velocity magnitude profiles.  
The profiles of non-dimensional velocity and turbulence fluctuation for fully confined geometry are shown in Figure 11 along the line y = 1D\({}_{\rm h}\). The jet momentum did not have peaks on the lateral sides, unlike the free external domain (Figure 10), and did not switch from the left position to the right position in the external domain in the cavity. For the lowest Reynolds number, Re = 2680, a steady  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**\(\Gamma\)\(\times\)\(10^{-3}\)**} & \multicolumn{2}{c}{**DU\({}_{\rm j}\)/T**} \\ \hline  & **Free External Domain** & **Fully Confined** & **Free External Domain** & **Fully Confined** \\ \hline Re = 2680 & 1.815 & 13.19 & 3.467 & 0.214 \\ \hline Re = 6440 & 2.031 & 13.28 & 3.339 & 0.510 \\ \hline Re = 10,730 & 2.751 & 13.90 & 4.109 & 0.813 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Circulation (\(\Gamma\)) and DU\({}_{\rm j}\)/\(\Gamma\) over the area with positive Q-criterion (Q \(>\) 0).  
Figure 10: Time-averaged velocity components and fluctuations along y/D\({}_{\rm h}\) = 1 for the free external domain.  
and continuous jet was generated, and as the Re increased, the behavior of the jet was changed, and it attached to the left wall. For instance, at Re = 6440, the peak value of the non-dimensional velocity magnitude was at x/D\({}_{\rm h}\) = 1. For Re = 10,730, a jet with a single peak was obvious, which showed a streamwise and continuous jet instead of a V-shaped pattern.
# Application of Deep Neural Networks in Recognition of Selected Types of Objects in Digital Images
## 2 Results of Research
### Description of Selected Convolutional Network Structures  
#### 2.1.1 LeNet-5  
LeNet-5 is a classic convolutional neural network architecture, proposed by [PERSON] in 1998. It was created mainly for recognizing handwritten digits, which has been used in banking for automatic check reading [27].  
LeNet-5 consists of seven layers, which include convolutional layers, subsampling layers, and fully connected layers. The specific structure shown in Figure 1 is as follows:  
* Two convolutional layers: the first one processes the input image of 32 \(\times\) 32 pixels, using 6 filters of size 5 \(\times\) 5 pixels. The second convolutional layer uses 16 filters.
* Two subsampling layers: they reduce the resolution of feature maps using max-pooling operations.
* Three fully connected layers: these layers function similarly to the classical multilayer perceptron layers and are used for final classification [27].  
LeNet-5 was chosen for this study for several reasons. First of all, it is a relatively low-computational-cost architecture, which makes it suitable for preliminary experiments and as a starting point for more advanced models. Furthermore, LeNet-5 is characterized by acceptable accuracy in classification tasks, which is crucial when computational resources are limited.
## 6 The Design and Application of MESs
### Benthic and Sedimentary MFCs for Power and Biofuel Production  
Reimers and Tender have been at the forefront of benthic MFC (BMFC) development [13; 14; 31]. [PERSON] pioneered the development of benthic unattended generators (BUGs), which generate electricity from the microbial consumption of organic matter in aquatic sediments [10; 13; 14]. BMFCs are also known as sediment MFCs (SMFC) and their layout consists of an anode embedded into the sediment with the cathode suspended in the overlying water column [1]. Electrons from the oxidation of organics are transferred to the anode and pass onto the cathode where dissolved oxygen is reduced to water [1; 117]. The abundance of organics in marine and other aquatic environments guarantees a continuous but low power output. This is advantageous when powering remote sensors and weather equipment, as there is no need to constantly replace batteries. In fact, [PERSON] et al [118] estimated that it would take 22 years to consume all of the organics in 1 L of wet sediment (6% organic matter), producing an average continuous current of 0.3 mA, making SMFCs a promising alternative to battery-powered marine sensors [1]. In areas of low organic matter, SMFCs can be positioned in areas with high organic flux such as beneath fish farms, near wastewater treatment outlets and other nutrient-rich environments [2]. The use of BMFCs as an in situ restoration solution for the degradation of polycyclic aromatic hydrocarbons (PAH) has been demonstrated using sediments from riverbeds with up to 50% removal of some PAHs over a 60-day period [119]. BMFCs present an ideal alternative power source compared to replaceable batteries for marine sensors such as magnetometers and acoustic sensors, with these systems having been operated for up to 6 months purely on BMFC-provided power [120; 121].
## 2 Materials and Methods
### Spectral Modeling of LED Channels  
The LED emitters are characterized by multiple parameters, specifically the peak wavelength \(\lambda_{p}\), FWHM, and optical power \(P_{\text{opt}}\). In this research, the availability of components with various \(\lambda_{p}\) was monitored. In some cases, emitters with the same \(\lambda_{p}\) but various FWHMs may be available. Thus, at the beginning of the research, the manufacturers' data of high-power LEDs were used to design the SPDs mimicking the illuminant A. The spectral radiant power \(P(\lambda)\) of the chromatic channel was simulated with two models: the Gaussian model (Equation (1)) and the Lorentz model (Equation (2)) [34]:  
\[SPD_{G}=P(\lambda)=P_{max}\cdot\frac{exp\left(\frac{-4 ln(2)\left(\lambda- \lambda_{p}\right)^{2}}{FWHM^{2}}\right)}{FWHM\cdot\sqrt{\frac{\pi}{4 ln(2)}}}, \tag{1}\]  
\[SPD_{L}=P(\lambda)=P_{max}\cdot\frac{2}{\pi}\cdot\frac{FWHM}{4\left(\lambda- \lambda_{p}\right)^{2}+FWHM^{2}}, \tag{2}\]  
where \(P_{max}\) is the maximum radiant density, \(\lambda\) is the wavelength, \(\lambda_{p}\) is the peak wavelength in the LED spectrum for which the radiant power is the maximum, and _FWHM_ is the full width at half maximum in nm. More advanced functions approximating the spectral distribution of LEDs [34] require prior spectroradiometric measurements [32]. At this stage of the research, it is assumed to rely only on the manufacturers' data.
# Design of an Autonomous Cooperative Drone Swarm for Inspections of Safety Critical Infrastructure
## 6 Communication Services  
The UAS is modeled as a three-tiered design consisting of a cloud services-based back-end, the drone swarm, and a network facilitating communication between the former two. The UAS offers a communication solution where connectivity and operable services are provided end-to-end. The supporting network architecture comprises subnetworks composed of (1) Drone-to-ground communication (D2G) supporting the communication between individual drones and the ground infrastructure; (2) Drone-to-drone communication (D2D) enabling swarming functions to be provided; and (3) Drone-to-cloud communication (D2C) that allows the exchange of information between information serves.  
The requirements for the different types of communication determine the possible communication technology to use for the different channels. The Drone-to-Ground (D2G) communication targets support of telemetry and Command & Control (C2) over long distances, potentially up to several kilometers. For this communication channel, the data throughput need is relaxed to tens of Kbps [28]. However, resiliency and low latency (\(<\)50 ms one-way-delay) are required. Drone-to-Drone (D2D) communication is needed for drones to form networks and exchange necessary information for cooperation such as motion path policies [30]. The supported range may be reduced to less than 200 m as drones are moving together operating autonomously as a swarm. This offers the possibility to achieve higher data rates on the order of tens of Mbps. Drone-to-Cloud (D2C) communication is dependent on the internet connectivity provided in the inspection area and its characteristics. Mobile networks with 5G are suitable candidates to provide this access with effective data rates in rural areas of up to 100 Mbps [36]. Table 2 motivates the technology choices for different types of communication and their compliance to key requirements.  
\begin{table}
\begin{tabular}{l l l} \hline
**Communication Type** & **Key Requirements** & **Technology Choice** \\ \hline \multirow{3}{*}{Drone-to-Ground (D2G)} & Long-range (\(>\)1 km) & LoRa was chosen due to its \\  & Low data rate (\(<\)20 Kbps) & range and resistance to \\  & Low latency (\(<\)50 ms) & interference. \\ \hline \multirow{3}{*}{Drone-to-Drone (D2D)} & Medium range (up to 200 m) & IEEE 802.11s WiFi mesh \\  & High data rate (\(>\)10 Mbps) & provides a good trade-off \\ \cline{1-1}  & Medium latency (\(<\)200 ms) & between range and achievable \\ \hline \multirow{3}{*}{Drones-to-Cloud (D2C)} & Internet access & IP forwarding over and \\ \cline{1-1}  & High data rate (\(>\)10 Mbps) & internet-connected GCS or via \\ \cline{1-1}  & Tolerant to high latency & public 4G/5G mobile. \\ \hline \end{tabular}
\end{table}
Table 2: Selected communication technologies for the UAS design [37].
###### Abstract  
This paper introduces a new simple moment-generating function (MGF) design modelling method to conclude an optimum filter to maximize the Q-factor and increase the link communication span. This approach mitigates the pulse temporal dispersion, particularly the underwater wireless optical communication (UWOC) systems. Hence, some form of equalizing filter design is highly desirable. The model solution environment includes a Double Gamma Function (DGF) water channel impulse response, intersymbol interference (ISI), stochastic Poisson process, and additive Gaussian thermal noise (AGTN). The optimal filters exhibit temporal profiles comparable to those derived by published works based on complex Chernoff Bound (CB) and Modified Chernoff Bound (MCB) methods. The results show the impact of the optimum filter at a signal level and optical receiver level utilizing Eye-Diagrams and BER vs. Q-Factor, respectively. The computation involves four different UWOC propagation channel models for Coastal and Harbor waters. One of the main conclusions indicates that the optimum filter manages the temporal dispersion due to the ISI impairment correctly. Also, the proposed optimum filter reduces eye-opening and the corresponding Q-Factor by less than 15% for a five-times increase in pulse width for the same transmitted optical power level.  
MGF; UWOC; receiver filter design; eye diagram; Q-Factor; BER  
3
## 3 Data Routing Approaches in WSNs
### Delay-Aware Approaches
#### 3.2.2 Soft Delay-Aware Approaches
The other factor required to estimate the latency involved in delivering data along a path from a node \(n_{i}\) to the sink via the neighbor node \(n_{j}\), is the expected number of hops in that path, since the expected waiting time will be encountered at every hop along the path. Finally, the expected latency from node \(n_{i}\)to the sink node, when node \(n_{i}\)sends data to neighbor node \(n_{j}\), \(L_{expected}^{total}\) (\(j\)), can be calculated at node \(n_{i}\)as follows:  
\[L_{expected}^{total}\ (j)=\ L_{expected}^{one-hop}\ (j)\ \times\ H_{expected}^{total}\ (j)\]  
where \(L_{expected}^{one-hop}\ (j)\) and \(H_{expected}^{total}\ (j)\) respectively are the expected latency per hop and the expected number of hops to the sink. Lastly, node \(n_{i}\) chooses its ER-neighbor which has the smallest expected latency to the sink node, as the node to which to send data. This routing scheme calculates the expected latency of each ER-neighbor more accurately by using not only duty-cycle and geographic information about its one-hop neighbors but the same information about its two-hop neighbors. Since each one-hop neighbor also has information about its own one-hop neighbors, the node naturally can use the information about its two-hop neighbors.
## 2 Literature Review
Consequently, our article focuses on dictionary and sparse-representation-based classification methods. Class-specific discriminative dictionary learning approaches produced the best classification accuracies, but heavy computation was involved during the training and classification of a test sample. Joint and discriminative learning of the dictionary and the classifier gained significant attention from the researchers. Traditional dictionary and classifier learning techniques could not adaptively optimize the model parameters, particularly sparsity and the size of the dictionary and the classifier. Nonparametric Bayesian approaches coupled with beta-Bernoulli processes became very famous for adaptively learning the parameters and inducing discrimination [24; 25; 44]. However, the emphasis and the success in controlling the accuracy through the discrimination-inducing power of beta-Bernoulli processes ignored an important aspect of learning the same representation weights for training examples and their corresponding labels. The sparse weights for the representation of the test sample and the corresponding label play a pivotal role in the classification process. The optimization of the same sparse weights over the dictionary and the classifier is intuitively an effective approach to enhance classification performance. The literature review is summarized in Table 1.  
\begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline  & **Authors** & **Method(s)** & **Datasets** & **Accuracy (\%)** & **Train vs. Test Examples (\%)—Other Conditions** \\ \hline \multirow{4}{*}{**Datasets**} & \multirow{4}{*}{[PERSON]} & \multirow{4}{*}{Classification and clustering via dictionary learning with structured incoherence and shared features} & MNIST & 97.00 & Size of dictionaries = 800 \\  & & & USPS & 98.00 & Size of the dictionaries = 80 \\  & & & ISOLET & 98.50 & \\  & & & [PERSON] dataset & 99.60 & \\ \cline{2-6}  & \multirow{4}{*}{[PERSON] et al.} & \multirow{4}{*}{Robust sparse coding (RSC) by modeling the sparse coding as a sparsity-constrained robust regression problem} & Extended Yale B & 99.40 & 50/50—Acc. decreases with low-dimension features \\  & & & & AR & 96.00 & 50/50—Acc. decreases with low-dimension features \\  & & & Multi-PIE & 97.80 & 35/65—Acc. with smile expression \\ \cline{2-6}  & \multirow{4}{*}{[PERSON] et al. [32]} & Supervised dictionary learning (SDL) with generative training (SDL-G) and with discriminative learning (SDL-D) along with linear (L) and bilinear (BL) decision functions & MNIST & 98.96 & 86/14—with SDL-D L USPS & 96.46 & 78/22—with SDL-D L USPS & \multirow{4}{*}{[PERSON] dataset} & \multirow{4}{*
# Biomass Refined: 99% of Organic Carbon in Soils
## 3 Results and Discussion
### Soil SOC and Biota Summary  
Summary of the global SOC status prior to the current paper is updated herein, new values are shown below for the global SOC and Biota Biomass calculation (values in braces are often or generally included within SOC values, thus they should not be counted twice):  
**Mineral soil** (\(\sim\)1500 Gt C + 20-30% glomalin, 26% for saprock, \(\times\) 4 =)--10,000 Gt SOC. **Permafrost** (\(>\)4200 Gt C plus glomalin that may add 25-50%?)-->5000 Gt SOC. **Peat** (total 3000 + 3000 lignite = 6000 Gt); just peat alone to depth--3000 Gt p-SOC. **Sediment Carbon** in Lakes & Rivers (820 + \(\sim\)1640 Gt =)---2500 Gt SeOC. **Above-ground Vegetation**-->1100, or [PERSON] et al. above-ground value--1272 Gt C. **Roots**, often overlooked or undervalued, range: 916-1128 Gt C, median --1022 Gt C. **Litter/logs**, included with SOC, reported separately, usually ignored--600 Gt I-SOC. **Biocrust and Phytomenon** rarely included, mostly ignored--10-20 Gt b-SOC. **DOC** sometimes included in superficial SOC, possibly added for depth--52 Gt DOC? **SIC and DIC** (4600-7400 Gt C?); 2300 + 1400, possibly doubled--7400 Gt SIC/DIC. **Fungi** typically included in total SOC (raised from 12-15 to \(>\)300-3600 Gt f-SOC). **Bacteria and Similar Microbes** usually included in SOC (raised from 8 to \(\sim\)210 Gt C). **Earthworms** almost always removed and excluded from soil samples, now---4 Gt C. **In total \(\sim\)10,000 mineral, \(>\)5000 Permafrost, 3000 Peat, 2500 SeOC, 600 litter, 15 biocrust, and 52 DOC = \(>\)21,167 Gt SOC that is slightly above initial 20,000 Fermi guess and closer to the CO\({}_{2}\) NOE drawdown median value in range 5000-40,000 of 23,000 Gt C. If inorganic 7400 SIC/DIC are added, then a total approaches \(\sim\)30,000 Gt as is alluded to in the Abstract. This is about ten times the SOC plus SIC total of 3000 Gt shown in Figure 5. Additionally, 2400 in above- and below-ground vegetation plus 5000-10,000 Gt fossil fuels, give a total (21,169 + 2400 + 5000-10,000 =) \(\sim\)31,000 Gt terrestrial biomass carbon. This corresponds well to the NOE drawdown as estimated above of \(\sim\)30,000 Gt C. (Q.E.D.). Table 2 highlights the most important review changes from prior \"best guess\" values.