esa-sceva/satcom-chunk-collection huggingface dataset:
# Solar Concentration for Wastewaters Remediation: A Review of Materials and Technologies
## 2 Solar Collector Systems Employed for Wastewater Treatment
### Concentrating Systems
#### 2.1.1 Parabolic Trough Collectors (PTC)  
Early solar photoreceptor designs for photochemical applications were mounted on parabolic-through-concentrators (PTCs), which were adapted from installations employed for thermal energy generation. PTCs can be defined as parabolic reflective surfaces that concentrate the sun's radiation on a focal line, where the wastewater flows in a tubular reactor [33]. PTCs are constructed by bending a sheet of reflective or highly polished material, generally reflective silver or polished aluminum, into a parabolic shape. Figure 1 shows the schematic drawing of a PTC and an image of a constructed collector [34]. To increase the efficiency of direct solar radiation collection, the platform can work with one or two motor tracking systems, thereby keeping the aperture plane perpendicular to the incident radiation [13]. PTC applications are different depending on aperture areas, and can be divided in two fields according to temperature range [35]: the first for temperatures in the range between 100 and 250 \({}^{\circ}\)C and the second between 300 and 400 \({}^{\circ}\)C [36; 37; 38]. Examples include solar water heating [39], desalination [40; 41], and water disinfection [42].  
Considering the applications of PTC to wastewater purification, early examples consisted of solar thermal parabolic-trough collectors where wastewaters flowed in a borosilicate glass tube placed on the focal line [44; 21]. Pyrex or Duran borosilicate glasses were chosen because of the necessity to have low levels of iron impurities. A tubular shape for the photoreactor configuration is considered optimal for sustaining the pressure and flow levels required by circulation systems [19].  
Figure 1: Schematic drawing of a parabolic through collector [43].
# Benefits of Probiotics--Biodetoxification
## 2. Probiotics--Biodetoxification of Food
### The Role of Probiotics in Food Biodetoxification
#### 2.2.1 Mycotoxins  
Various strains of probiotics have been proven in vitro to annihilate common food mycotoxins (aflatoxin, ochratoxin, zearalenone, fumonisins) [28]. [PERSON] et al. mention that many species of bacteria can degrade mycotoxins, including lactic acid bacteria [29]. For example, _Lactobacillus acidophilus_ is effective for OTA, aflatoxin B\({}_{1}\) (AFB\({}_{1}\)), and aflatoxin M\({}_{1}\) (AFM\({}_{1}\)) biocontrol; _Bifidobacterium animals_ is useful for patulin control [30]. The degradation process depends on chosen factors, such as the pH, the microorganism species, and the incubation time as well as the concentration of the bacterial cells. [PERSON] et al. reported that many bacteria can degrade more than one mycotoxin [31]. Moreover, _Pseudomonas fluorescens_ strain 3 JW1 is able not only to degrade AFB\({}_{1}\) but also to inhibit the AFB\({}_{1}\) production of _Apergillus flavus_. It reduces the amount of AFB\({}_{1}\) produced by _Apergillus_ in peanut medium and peanut kernels [32]. The efficacy of probiotics in removing mycotoxins is high. [PERSON] et al. reported that _Bacillus licheniformis_ BL010 reduced the content of aflatoxin B\({}_{1}\) by 97.3% [33]. Probiotics can also inhibit mycotoxin production. In the study by [PERSON] et., al. _Lactobacillus brevis_ and _Lactobacillus paraassei_ showed inhibition of AFB\({}_{1}\) production by up to 96% [34]. In biodetoxification, paraprobiotics were shown to be even more effective than viable cells. In the study by [PERSON] et al. heat-treated lactic acid bacteria were more effective in removing AFB\({}_{1}\) and trichothece-2 (up to 62 and 52%, respectively) [35]. This was explained by the increased toxin-binding ability of the membrane of non-viable cells.
# Analyzing Data Reduction Techniques: An Experimental Perspective
## 8 Experimental Evaluation
### Lossy Data Reduction Algorithms  
In this subsection, we explain the implementation of the algorithms and provide an analysis of the results. We also present a detailed analysis of our findings through tabulated metrics.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Algorithm** & **Use Case** & **Application** \\ \hline \multirow{4}{*}{\begin{tabular}{} \end{tabular} } & Data storage optimization, version control systems & Delta Encoding is commonly used in scenarios where data changes over time, such as in version control systems like Git. Instead of storing entire files, only the differences (delta) between versions are stored. It is also used in data compression techniques to reduce redundancy. Applications include minimizing storage requirements for historical versions of files, efficient data transfer over network protocols, and optimizing database storage for incremental backups. \\ \hline \multirow{4}{*}{\
\begin{tabular}{} \end{tabular} } & \multirow{4}{*}{
\begin{tabular}{} \end{tabular} } & BZip2 is used to compress files and data streams. It provides a high compression ratio and is particularly effective for compressing text files, XML, and large datasets. Applications include compressing software distributions for faster downloads, reducing storage requirements for archival data, and optimizing data transmission over limited bandwidth networks. It is often used in conjunction with formats such as TAR to create compressed archives (tar.bz2 files). \\ \hline \hline \end{tabular}
\end{table}
Table 5: Lossless data reduction key use cases and applications.
## 3 Performance Attributes and Scenarios
### Attribute Values  
The attribute values calculated or collected from published data which were used for the MCDM analysis (refer to Table 2) were the same for the non-recycling scenario (refer to Table 3). The GPR values of the gasifiers are between 0.75 Nm\({}^{3}\)/kg and 3.6 Nm\({}^{3}\)/kg of feedstock. Dual fluidized beds have minimum performance due to the requirement to achieve a high syngas quality and low tar content and hence operate at lower temperatures and have a longer residence time for pyrolysis of the fuel [28]. Bubbling beds have a maximum gas output rate due to exposure of more surface area of the particles with the air with a maximum value of 3.6 Nm\({}^{3}\)/kg [29]. Downdraft and cross-draft gasifiers have an acceptable performance around 2 Nm\({}^{3}\)/kg [30]. Carbon conversion efficiency depends on the type of gasifier, feedstock characteristics, operating conditions such as temperature, pressure, airflow rate, and gasifier design [19; 31]. Carbon conversion efficiency is determined from the carbon to oxygen ratio in the gasification process, and syngas with a higher level of hydrogen and less carbon monoxide is produced [32]. Updraft gasifiers have the highest tar content because the pyrolysis process takes place when the gases come in contact with the high-temperature fuel as it flows in the upward direction [33]. Cold gas efficiencies are inversely proportional to the gasification temperature, and since static beds have higher temperatures, their corresponding cold gas efficiencies are lower [34]. The installation cost of fluidized bed gasifiers is higher due to the air supply systems, gas cleaning systems, and fuel preparation systems used to meet the specifications of FB gasifiers. The higher product quality of the syngas and reduced nitrogen content make dual bed fluidization the best option in terms of the payback period [35]. Environmental performance includes emissions like carbon dioxide during the combustion reactions and passage across the bed without conversion to carbon monoxide. Methane emissions are possible during the storage of waste before gasification [36]. Normally, fixed-bed gasifiers have acceptability to higher sizes of feedstock compared with fluidized bed gasifiers. The same requirements are specified in terms of moisture content also [28; 37].
# Singulation of Objects in Cluttered Environment Using Dynamic Estimation of Physical Properties
## 6 Quantitative Analysis  
In this section, the real and virtual world results are compared to evaluate how exactly the physical properties are estimated. We measured the distance among all objects after singulation in the virtual world and real world. The quantitative data is shown in Figure 17. Figure 17a-c denote the comparison of real/virtual world's distance for the case of two objects and three objects singulation, respectively. Similarly, the distance comparison of real/virtual world for the case of four objects is shown in Figure 17d,e. There is a small error, which is expected because we are using the average value of estimated parameters, and ground truth of the friction coefficient varies even on one surface [13]. The error is comparatively grater for the case of general shape objects. This is because, for circular shaped objects, the contact direction is always toward the center. It is confirmed that the distance between any two objects is greater than \(d_{\text{min}}=90\) mm and that the condition given in Equation (35) is also satisfied in all cases. Conclusively, the proposed scattering algorithm was found to be effective to analyze and control the scattering behavior of objects being singulated by collision with a robot manipulator. The Supplementary Material (entitle Singulation of objects in virtual and real world) is a video clip that demonstrates the whole procedure of training the physical properties of objects and scattering experiments.  
Figure 16: Scattering experiments for singulation of four objects by considering general shapes: (a) before collision; (b) after collision. Manipulator velocity: 0.5 m/s.  
Figure 17: The quantitative analysis between real world and virtual world. A comparison between real/virtual world distances among all objects. (a) Two-object singulation; (b) three-object singulation by considering the circular shape only; (c) three-object singulation by considering the general shape of objects; (d) four-object singulation considering the circular shape only; (e) four-object singulation considering the general shape of objects.
## 3 Mathematical Modeling of Precipitation Effects
### Link-Budget Equation  
Accordingly, the optical power \(P_{r,u,v}\) received on a pixel \((u,v)\) in the Flash LiDAR's FPA can be calculated based on a number of elements: a system element, a geometric beam propagation element, and an attenuation element that is influenced by both the target and the atmospheric conditions in the optical channel. Given certain assumptions, these elements can be considered as multiplicative functions [13; 16]:  
\[P_{r,u,v}=\underbrace{C_{A}P_{0,u,v}\tau_{h}}_{system}\underbrace{\frac{1}{L _{u,v}^{2}}}_{\begin{subarray}{c}\text{$\underset{\text{$  
While the geometrical term describes the measurement distance \(L_{u,v}\)-dependent quadratic power density decrease, the attenuation term divides into the target response function \(H_{\text{T}u,v}(L_{u,v},\beta)\) (see also Figure 1, object space) and the response function of the optical channel \(T^{2}(L_{u,v},\alpha)\) (see also Figure 1, measurement noise).  
The latter describes the two-way signal attenuation \(T(L_{u,v},\alpha)\) by particle scattering and absorption effects. Both are considered by the attenuation coefficient \(\alpha\), integrated over \(L_{u,v}\):  
\[T(L_{u,v},\alpha)=e^{-\int_{l=0}^{L_{u,v}}a(l)dl} \tag{2}\]  
\(\alpha\) can again be calculated based on the extinction efficiency \(Q_{\text{ext}}\), representing the particle diameter \(D\)-dependent scattering mechanism [13; 16; 32]. Hence, \(Q_{\text{ext}}\) is integrated over the particle size distribution in a unit volume \(N(D)\):  
\[\alpha=\frac{\pi}{4}\int_{D=0}^{\infty}D^{2}Q_{\text{ext}}(D)N(D)dD \tag{3}\]  
\(H_{\text{T}u,v}(L_{u,v},\beta)\), on the other hand, depends on the target nature which is described by the backscattering coefficient \(\beta\). Depending on the reflection mechanism, this might simply be expressed by a reflection coefficient in terms of an ideally diffuse target surface, or a bidirectional reflectance distribution function (BRDF) in case a surface with a more complex reflection behavior is considered [16]. The overall power, backscattered by a particle distribution \(N(D)\), can be calculated by integrating over the respective backscattering efficiency \(Q_{\text{b}}(D)\) instead of \(Q_{\text{ext}}\), analogous to \(\alpha\)[13; 16; 32].
# Demystifying MLOps and Presenting a Recipe for the Selection of Open-Source Tools
## 5 Tooling in MLOps
### Tool Comparison
#### 5.2.1 Mlflow  
MLflow [59] is a Machine Learning Platform that can be used for partial automation for small- to medium-sized data science teams. The tool has four parts--Tracking, Model, Projects, and registry--targeting a specific phase of the ML lifecycle. The feature of User management is missing from the tool. It is also not fully customizable (it cannot group experiments under one architecture).
# Artificial Intelligence Applications in Smart Healthcare: A Survey
## 5 Challenges and Existing Solutions
### Underserved and Remote Areas with Limited Connectivity  
#### 5.10.1 Challenges  
In healthcare, deploying artificial intelligence (AI) in resource-limited and remote areas presents significant technical challenges. Firstly, accessing or utilizing AI applications running on cloud infrastructure becomes difficult due to unreliable or unavailable internet connectivity. Without stable internet connections, AI systems cannot meet their high bandwidth requirements, leading to unmet demands for real-time analytics. Additionally, data collection and uploading processes are affected by unstable networks, severely restricting the learning and updating capabilities of AI systems in these regions [162]. For example, portable ultrasound devices used in remote areas, such as those from GE Healthcare, rely on offline AI for image analysis. These devices can provide diagnostic services without a network connection by storing results locally and uploading them once connectivity is restored. This capability allows doctors to make immediate diagnoses in areas with unstable connections [163]. Therefore, addressing these challenges requires innovative solutions to adapt AI technologies to resource-constrained environments. For instance, some healthcare institutions in remote areas have begun deploying offline AI models to tackle connectivity issues. These models can run on local devices, reducing reliance on internet connections and ensuring efficient diagnostic services in resource-limited settings.
# Fixed Wireless Access in Flexible Environment: Problem Definition and Feasibility Check
## 4 Feasibility Check
Step 2C. If there is a feasible solution, then \(pCP_{\text{P}}\) is marked as a candidate connection point to set up a FWA connection to serve the PL. The solution found by the SDN controller of the existing network combined with the radio link between \(pCP_{\text{P}}\) and PL will meet the connectivity requirements of all service traffic e2e. Step 2D. If there is no feasible solution, then for \(pCP_{\text{P}}\) the algorithm stores what requirements were not met: for which service traffic, at which time interval, and what requirement (throughput and/or availability and/or latency). Step 3. At this point the feasibility of all \(pCP_{\text{P}}\) is checked. If multiple candidate connection points are found, then CSP may select one based on:  
* cost parameters (not considered in this algorithm),
* the marketability of unused additional capacities at the PL,
* flexibility (e.g., selecting a connection point such that, after introducing the PL load, the minimum of the unused capacities will be maximal), or
* a further search algorithm to check if a lower cost solution with multiple links exists. The single radio link solution provides a cost limit for this search. If there is no feasible solution, check failed requirements. If a common latency failure occurs for all potential connection points, then no feasible solution exists (even with multiple radio links). If a throughput requirement fails, using multiple radio links to different \(pCP_{\text{P}}\)--assigning different service traffics to different radio links--is advisable. Similarly, failed availability requirements can be addressed with multiple radio links. However, the availability of multiple radio links is a more complex calculation, see the following note. The search algorithm for multiple radio links is beyond the scope of this paper. The complexity of dual-link solutions arises from the abundance of technical options, including common path (e.g., dual-band, dual- or multiple-channels, dual-polarization radios or combinations thereof); radio links over different paths; and hybrid solutions combining radio with FSO links (again, either via common or different routes). Note that the combined availability of two radio links cannot be simply calculated from the individual availabilities. If the angle between two radio links is small, the probability of intense rain affecting both is much higher than if the angle is near 180 degrees.
# The Development of Key Technologies in Applications of Vessels Connected to the Internet
## 5 Challenges and Prospects  
### Challenges of the IoV System  
#### 5.1.1 Safety Mechanism  
Compared with the traditional networks, due to the changes of ship trajectory and the unstable topology structure, the network of IoV is a dynamic, temporary network [PERSON] (2017). In the effective range of communication, the nodes of IoV networks keep a connection. When the distance of nodes goes out of scope, the connection will break immediately [PERSON] (2017). In addition, because of the ad hoc technology among the vessels, the wireless networking structure between the shore and ships as well as the wired network structure between the data sensing equipment and the databases, the networks of IoV are in multi-network integration conditions. Therefore, the IoV system has more security issues than the traditional networks.  
The IoV has the features of centerless and self-organization so it cannot use the safety mechanism of traditional networks directly Xu (2017). Meanwhile the sensing nodes of the IoV system are deployed in an environment that needs nobody to see or control. As a result, the IoV will be under threat  
Figure 12: The schematic diagram of the electronic freight.  
from information leakage, information tampering, congestion attack and flooding attack. The attacker could obtain the information data from the sensing nodes and break into the networks of the IoV system. The other challenges of the IoV system with regards to the safety mechanism are ensuring the authentication technology based on the user identity security, the establishment of security keys' transmissions, the security of data encryption and the users' privacy protection [76].
## 3 Four-Port MIMO Antenna
### MIMO Antenna Design  
Figure (a)a shows the four-port MIMO antenna for ultra-wideband and high gain applications. The proposed MIMO antenna has an overall size of \(\rm{M_{Y}}\times\rm{M_{X}}\times\rm{H}=27\) mm \(\times\) 27 mm \(\times\) 1.52 mm and is designed over same material as the single element. Each unit element of the MIMO antenna has the same dimension as the single element given in Figure 1. The gap between two adjacent elements M1 = 4 mm and between two opposite elements is \(\rm{M_{2}}=10.2\) mm. Moreover, the hardware prototype is fabricated to verify the simulated results, as depicted in Figure (b)b.  
Figure 10: (**a**) Configuration of proposed MIMO antenna; (**b**) measurement setup for S-parameters and far-field parameters.  
Figure 9: Simulated radiation efficiency of proposed antenna.
# Altered Mechano-Electrochemical Behavior of Articular Cartilage in Populations with Obesity
## 3 Results and Discussion
### Alterations of Cartilage Tissue in People with Obesity  
#### 3.2.1 Displacement and Water Flux  
Under normal weight (BMI = 22.262 kg/m\({}^{2}\)), the model displays a maximum surface displacement of \(-\)1.38 \(\times\) 10\({}^{-4}\) m (Figure 5a.1) after 0.44 s of simulated time, significantly lower than that observed in cases of obesity (BMI = 33.978 kg/m\({}^{2}\)), \(u_{z}\) = \(-\)2.4 \(\times\) 10\({}^{-4}\) m (Figure 5b.1). This reduction in tissue deformation results in a lower outgoing water flux from the cartilage sample. Thus, simulated cartilage of people with a normal BMI generates a maximum value of J\({}^{\mathrm{w}}\) = 1.31 \(\times\) 10\({}^{-8}\) m\({}^{3}\)/s (Figure 5a.2,) whereas in people with obesity, the water flux is increased to 1.58 \(\times\) 10\({}^{-8}\) m\({}^{3}\)/s (Figure 5b.2).  
Biologically, this increase in the outgoing water flux produces pathological dehydration, commonly associated with the joints of obese people and with the majority of cartilage pathologies such as osteoarthritis. These results are consistent with the study of [PERSON] et al. [11] who found a reduction in cartilage water content in people with obesity. Similarly, they suggested this aspect as an essential promoting agent of cartilage osteoarthritis.  
Figure 4: Fixed Charge Density (FCD) distribution along the thickness of a cartilage sample with 8% sample deformation due to compressive loads.
# Vision-Language Models for Zero-Shot Classification of Remote Sensing Images
## 4 Experimental Results
### Comparison to Existing Solutions  
In this section, we compare our results to the existing zero-shot solutions proposed for RS scenes. Examples of such methods include those based on stacked autoencoders and GANs. Table 5 shows the results for different unseen ratios for Merced, AID, and NWPU datasets. It is worth recalling that while existing solutions use the seen classes for learning, in our case, we do not use them at all. Instead, we simply classify the unseen classes using VLMs. The results provided in the tables clearly confirm the great capabilities of VLMs in performing zero-shot classification. VLMs significantly surpass the existing models and establish new state-of-the-art results for all three datasets. In addition, we observe that the larger the VLM, the better the obtained results. Indeed, these results show that larger VLMs learn more complex relationships between images and text pairs. This improved generalization ability is essential for zero-shot classification, as it allows VLMs to perform well on classes they have not seen during training. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & (A) & & & \\ \hline Method & \(16/5\) & \(13/8\) & \(10/11\) & \(7/14\) & \(0/21\) \\ \hline SSE [20] & \(35.59\pm 5.90\) & \(23.42\pm 3.81\) & \(17.07\pm 3.56\) & \(10.82\pm 2.10\) & —— \\ \hline DMaP [21] & \(48.92\pm 8.71\) & \(30.91\pm 4.77\) & \(22.99\pm 4.81\) & \(17.3\pm 3.04\) & —— \\ \hline SAE [22] & \(49.5\pm 8.42\) & \(32.71\pm 6.49\) & \(24.04\pm 4.36\) & \(18.63\pm 2.76\) & —— \\ \hline ZSL-LP [15] & \(49.01\pm 8.85\) & \(31.26\pm 5.09\) & \(23.28\pm 4.13\) & \(17.55\pm 2.9\) & —— \\ \hline VSOP [23] & \(46.48\pm 7.83\) & \(29.81\pm 4.56\) & \(21.97\pm 4.11\) & \(16.14\pm 2.59\) & —— \\ \hline f-CLSWGAN [24] & \(56.97\pm 11.06\) & \(36.47\pm 6.28\) & \(27.89\pm 4.99\) & \(19.34\pm 3.96\) & —— \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Zero-shot classification accuracies with different unseen ratios. Unlike SOTA models, we use VLMs for classifying unseen classes without training on the classes considered as seen. (A) Merced, (B) AID, and (C) NWPU datasets.