esa-sceva/satcom-chunk-collection huggingface dataset:
# Two-Level Congestion Control Mechanism (2 LCCM) for Information-Centric Networking
## 2 Related Work
The process of forwarding this congestion signal hop-by-hop also continuously adjusts the ratio of forwarding to each forwarding plane through the router. MFTP [27] is a new transport protocol for the MF architecture that uses a backpressure congestion control mechanism similar to BCON. However, the difference is that MFTP's hop-by-hop congestion control is applied to two adjacent routers and is based on a sender-driven transmission mode, where the upstream sender sends data and requests acknowledgement from the receiver, the receiver replies with an acknowledgement message and receives a window notification, the sender adjusts the sending window according to the receiving window, and the congestion status is fed back to the data source hop-by-hop. R2T [28] is a variant of MFTP that refines MFTP retransmission by chunk to retransmission by packet. Instead of using packet loss due to timeout to determine network congestion, R2T considers that a longer RTT means that a packet has experienced a longer queuing time, thus indicating a higher network load or light congestion. In contrast, if a packet passes through a router in a congested state, meaning that the transmission path of the packet has experienced a bottleneck bandwidth link, then the link is considered to be experiencing heavy congestion. This congestion signal will be notified to the receiver explicitly through the source backpressure algorithm of the intermediate router. Considering that the content in ICN is partitioned into chunks first, and is then further partitioned into smaller packets, a hop-by-hop congestion control similar to CCN has been proposed in the ICTP [16] protocol at the first level, and the TCP Reno [29] algorithm has been adopted at the second level. It is a receiver-driven transport protocol for the CONET [30] architecture, which also inspired the 2 LCCM proposed in this paper. The difference is that our proposal adopts a replica selection or switching method at the first level and a receiver-driven congestion control algorithm at the second level. Moreover, the two-level congestion control mechanism proposed in this paper can be switched to execute or take effect, which means that the replica location can be switched when heavy congestion happens, unlike existing mechanisms that still insist on adjusting the transmission rate in this situation, thus allowing better utilization of the multi-replica feature.
## 3 Analysis of Pumped Storage Transformation
### Transformation Methods  
Under the premise of meeting the transformation conditions, the pumped storage transformation of cascade small hydropower stations can be divided into two main methods based on the regulation capacity of existing reservoirs: \"integrated combination\" and \"upper reservoir combination\". The appropriate transformation method should be selected according to the existing reservoir type.  
When two adjacent cascades, upper and lower reservoirs, have regulation capacity, the \"integrated combination\" can be used for transformation. In this method, the hydropower station's upper and lower regulation capacity simultaneously serve as the upper and lower reservoirs for pumped storage, requiring only the addition of pumped storage units to complete the transformation. This approach fully utilizes existing reservoirs, with the hydraulic conditions of the upper and lower reservoirs and transmission equipment already in place. It only increases the unit's capacity, and the project investment rises less.  
When only the upper reservoir has the regulation capacity, but the downstream has the conditions for constructing the lower reservoir, the \"upper reservoir combination\" can be used for transformation. In general, this method uses the upper reservoir of the hydropower station as the upper reservoir of pumped storage, and it is necessary to build a new lower reservoir after the upper reservoir dam and add pumped storage units to complete the transformation. However, in reality, this method can determine whether the hydropower reservoir is the upper reservoir or the lower reservoir of the pumping reservoir according to the actual situation, and the engineering flexibility is higher.
## 3 Problem Statement  
A single cost model needs to evaluate deployment with all used variables for data transmissions, such as time and energy consumption. It must determine the local to task execution among MEC, fog, or cloud environments. This is a multiple-objective optimization problem. Therefore, finding a minimal cost to all these system variables is an NP-hard problem.  
The solution must consider the energy consumption, such as the energy to send tasks, energy consumption to data download, energy for the task processing, energy cost with CPU idle, and battery level. It also requires estimating the execution time to variables, such as time spent on task transmission, wait time in queues, task runtime, and time spent on downloads. Simultaneously, the system must choose one among three distinct environments to produce the best performance considering energy optimization.  
Thus, the computation to solve these issues is hard to model. Deciding between three different environments is another complex task due to needing to produce task distributions with adequate performance and energy saving. We propose a dynamic solution in real-time for each task using an integer linear programming (ILP) optimization to achieve this challenge.
## 3 Results and Discussion
### Whole Blood Clotting Assay  
In order to evaluate the blood clotting capacity of the electrospun membranes in vitro, the whole blood clotting experiment for 5 and 10 min was carried out. After incubation with the calcified whole blood and addition of deionized water, the red blood cells (RBCs) not trapped or unstable in clots were ruptured along with the release of hemoglobin. Hence, the concentration of free RBCs can be reflected by the absorbance at 540 nm [44]. Afterwards, BCI was estimated to characterize the inverse side of the degree of clot formation quantitatively. As illustrated in Figure 6a, with medical gauze as the reference, the BCIs of all electrospun membranes at 5 and 10 min were significantly decreased (\(p<0.001\)) which indicated a better blood clotting capability. Meanwhile, the crosslinking treatment of collagen also had an influence on their haemostatic property (\(p<0.01\)), whereas no difference was observed between the crosslinked samples when it was crosslinked to 30.8% or more. Simultaneously, compared with the non-crosslinked collagen, clot formation and the darkness of non-trapped clots dramatically increased in all crosslinked samples (Figure 6b). It is well known that collagen can induce platelet adhesion and aggregation to promote the coagulation process. Therefore, for the electrospun collagen nanofibrous membranes, a stable enough physical support should be provided to promote platelet adhesion and clotting factors binding, culminating in rapid clot formation [45; 46]. For the non-crosslinked sample, the integrity of its structure would be partially broken followed by unstable support for the clot formation. This is why the BCI value of the non-crosslinked sample exhibited a significant difference from all the crosslinked samples. In addition, a decrease in BCI values along with the progression of time was observed for all samples except for the non-crosslinked one. This phenomenon was also due to its instability in aqueous solution. Whole blood clotting results indicated the crosslinking treatment enhanced the blood clotting capability of the electrospun collagen fibers appreciably.  
Figure 5: FTIR spectra of collagen/PEO nanofibrous membranes non-crosslinked and crosslinked for 18 h (**a**) and DSC thermogram of raw collagen foam, collagen/PEO nanofibrous membranes non-crosslinked and crosslinked for 18 h (**b**).  
Figure 6: (**a**) Results of whole blood clotting formation on the medical gauze, non-crosslinked and GA crosslinked electrospun membranes. The clotting formation capacity is expressed as the blood clotting index (BCI), which reflects the free RBCs not trapped in clots. (**b**) Macroscopic view of whole blood clot formation on various crosslinked electrospun membranes.
## 3 Defining Physics of Failure for Power Electronics
### The Test Plan
#### 3.3.1 Defining Test Standards  
The test overview details for each test can usually be found in one of the many standards that exist for the testing of electronic equipment. Popular and trusted standards include: American Society for Testing and Materials - ASTM Testing Standards, Electronic Industries Alliance standard EIA-364 Electrical Connector Performance Test Standards, International Electrotechnical Commission standard IEC 60068-2 Electronic Equipment & Product Standards, International Safe Transit Association - ISTA Package Testing, Joint Electrostatic Discharge - JESD22 Solid State Device Packaging Standards, Military Standard MIL-STD-202 Electronic & Electrical Component Testing Standards, MIL-STD-331 Department of Defense Test Standards, MIL-STD-810 Environmental Engineering Considerations and Laboratory Tests, MIL-STD-883 Micro electric Device Testing Procedures, Radio Technical Commission for Aeronautics - RTCA/DO-160 Airborne Product & Equipment Standards, and Society of Automotive Engineers' SAE J1455 Electronic Equipment Environmental Standards [10; 11; 12; 13].  
The outcome of the test may define the number of acceptable failures that can occur for a particular device/system; this is particularly the case for aerospace and automotive applications. For example, for an aviation system, depending on the criticality of the device on the operation of the aircraft, levels of acceptable failure are assigned:  
* Level A--Catastrophic: 10\({}^{9}\) failures/flight-hour
* Level B--Hazardous: 10\({}^{7}\) failures/flight-hour
* Level C--Major: 10\({}^{5}\) failures/flight-hour
* Level D--Minor: 10\({}^{3}\) failures/flight-hour
* Level E--No Effect: Not Applicable  
It can be seen that level A corresponds to 1 in 1 billion failures per flight hour, which is an incredibly hard outcome to meet and may only be met in reality with a degree of redundancy in the system.  
Hence, standards such as RTCA DO-160G Section 5 [14], for example, which deals with Temperature Variation Testing for Level A, are extremely stringent. This category is for equipment external to the aircraft or internal to the aircraft. The temperature variation testing is a minimum of 10 degrees Celsius per minute. Equipment meeting this description must undergo RTCA-DO-160 temperature testing from \(-55\) to \(125\)\({}^{\circ}\)C in 15 min.  
Table 2 outlines the hypothetical test plan for the PCB that was used as the test platform. For the majority of tests, MIL-STD 810 [15] and IEC 60068 [16] were used; theserelate to equipment/components that are subject to day-to-day operational requirements. Other tests employed were based on Aerospace Recommended Practice standard SAE ARP 6338 [17], EIA IS-749 [18], SAE J3168 [19], and Institute of Printed Circuits' and Joint Electron Device Engineering Council's Standard IPC-JEDEC J-STD-020D-01 [20]. These standards are devised for semiconductor wear out, component failure, plated through hole and solder fatigue tests [21].
## Underlying Theories  
### 5G-mmWave Technology  
A clear phenomenon of increased data traffic shows that mobile cellular services have become essential for providing very rapid access to information in support of human activities and the increasing quality of life. Global mobile data traffic is projected to increase by a factor of close to 5 to 164 EB per month by 2025 [12]. The deployment of previous mobile technologies (2G/3G/4G) is no longer able to meet human needs, so 5G, as a new technology, requires greater bandwidth availability.  
Technically, the 5G frequency band is divided into FR 1, which works in the frequency range below 6 GHz, and FR 2, which works in the 24-52 GHz frequency range. The FR 2 frequency band is called 5G-mmWave and is in accordance with ITU-WRC-19. 5G development on mmWave will be operated in FR 2 frequency bands, including 26 GHz and 28 GHz.  
As seen in Figure 1, according to the International Telecommunication Union's (ITU) ITU-R M.2083-0 recommendation, the potential 5G services can be grouped into the three following usage scenarios: enhanced mobile broadband, ultra-reliable, and low-latency and massive machine-type communications [13].  
Focusing on the use case scenario of enhanced mobile broadband, 5G technology should be operated on a wider bandwidth to establish use cases [14]. With regard to the 5G standard release 15, the peak data rate (Mbps) can be calculated on the basis of (1) [15], with the peak data rate showing a directly proportional relationship with the bandwidth.  
\[\text{Data rate }(\text{Mbps})=10^{-6}\times\sum_{j=1}^{J}\left(v_{Layers}^{(j)} \times Q_{m}^{(j)}\times f^{(j)}\times\ R_{max}\times\frac{N_{PRB}^{BW(j),\ H}x12}{T_{s}^{H}}\times\left(1-OH^{(j)}\right)\right) \tag{1}\]  
Equation (1) shows that the 5G peak data rate formula is calculated using the factors of bandwidth (\(BW\)), the number of aggregated component carriers in a band or band combination (\(j\)), the maximum number of layers (\(v_{Layers}^{(j)}\)), the type of maximum modulation order (\(Q_{m}\)), and the numerology of carrier spacing (\(\upmu\)) used in the Orthogonal Frequency Division Multiplexing (OFDM) technique.  
In the case of 5G-mmWave, the typical deployment uses a frequency carrier at 26-28 GHz, with a bandwidth of 100 MHz and 400 MHz. Table 1 shows the calculation for the peak data rate using (1) with a technical specification of 8 \(\times\) 8 MIMO at four modulation scheme.
# The Performance and Fouling Control of Submerged Hollow Fiber (HF) Systems: A Review
## 5 Parameters Affecting the Performance of a Submerged Hollow Fiber System
### Shear Stress on Membrane Surface by Non-Bubbling Techniques
#### 5.3.2 Particle Scouring
Effects of treating different types of wastewater [258; 259; 261; 262; 267] (e.g., using municipal versus synthetic wastewater [258; 258]), trace organics [278; 279], membrane type [273] (including effects on membrane integrity [271; 272; 275]), screen size [259], fluidized media [236; 244; 279; 280] (including size and packing amount [244; 267; 268; 271; 275]), operating conditions [260; 261] such as temperature [262; 263; 264; 262], scale [261], design [130; 261; 264; 265; 266; 267] (e.g., single (AFMBR) versus two-stage (AFBR-AFMBR) systems [130]), which collectively proved the efficacy of GAC in scouring the membranes. Different embodiments of the AFMBR include single (AFMBR) versus two-stage (AFBR-AFMBR) systems [130], as well as simplifications of the two-stage AFBR-AFMBR system termed an IAFMBR (i.e., integrated anaerobic fluidized-bed membrane bioreactor) [277], and hybrids such as the MFC (i.e., microbial fuel cell)-AFMBR [264] and the fluidized bed membrane bioelectrochemical reactor (MBER) [263]. A study on the extent of fouling mitigation by fluidized GAC in an HF module found that larger-sized GAC particles, higher packing densities, and a ratio of hollow fiber spacing to fluidized particle size ofapproximately 3-5 are beneficial for fouling control [271]. Collectively, these efforts prove the efficacy of particle fluidization in mitigating fouling in submerged HF systems. The benefits conferred by particle fluidization include low energy cost [24; 24; 258; 261; 264; 281], and amenability for scale-up and continuous operation [24; 235], all of which make it an attractive means to improve membrane operations. In particular, the AFMBR energy requirement was only 0.028 kWh/m\({}^{3}\), which is much less than that reported for AMBRs using gas sparging [24]. The momentum, velocity, and concentration of the fluidized GAC particles have been found to play significant roles in membrane fouling mitigation via both experiments and simulation [266; 267; 268; 269]. Note that membrane-particle interactions have to be managed to avoid membrane damage. Also, the module geometry must allow movement of the particles to avoid blockages, as highlighted in Section 4.
# Quality of Experience (QoE)-Aware Fast Coding Unit Size Selection for HEVC Intra-Prediction
## 4 Proposed Fast CU Size Selection Algorithm
### Level I(L-1) SVM Models: Features and Optimal Weight Calculation  
As illustrated in Section 2, the typical machine learning approaches rely on large data sets of coding information and CU split decisions for a selected number of training video sequences to build and train generic offline models. However, the dynamic nature of the video contents and diversity of required quality levels make these generic models less flexible and less effective when applying them to estimate encoding decisions. Hence, the proposed method relies on coding, texture, and statistical information accumulated online during the encoding process and dynamically generate SVM models which are unique for a given content. The following subsections illustrate the feature selection and SVM weight calculation utilised with the L-1 SVM models in the proposed method.
## 4 Discussion
### Alignment and Space Closure  
Regarding alignment and space closure, studies older than ten years have shown no advantage to using the PSLB system at the expense of CBs. According to a prospective clinical trial by [PERSON] et al. in 2007 [PERSON] et al. (2007), when treating mandibular crowding, there was no appreciable difference in the treatment duration time between PSLBs and CBs approach. However, the PSLB group showed 2.7 times faster correction for moderate crowding. In another study by [PERSON] in the same year [PERSON] (2007), there was no significant difference in the en masse space closure rate between passive Smart-Clip self-ligating brackets and conventional twin brackets tied with stainless steel ligatures. Additionally, the time for initial alignment was substantially quicker for conventional brackets than for active or passive self-ligating brackets in a randomized controlled trial by [PERSON] et al. in 2014 [PERSON] et al. (2014). However, there was no statistically significant difference in the passive, active, or overall space-closure times across the three groups examined.  
In a 2010 study by [PERSON] (2010), it was found that the retraction rate was faster with the conventional bracket than with PSLBs. Last but not least, distal retraction of the upper cuspids and anchoring loss of the first molars were comparable with CBs and PSLBs in a 2011 study by [PERSON] et al. [PERSON] et al. (2011). With self-ligating brackets (SmartClip), the rotation of the upper canines during sliding mechanics was nevertheless reduced.  
In our review, studies suggest that PSLBs may offer some advantages over CBs in terms of alignment, space closure, and improved patient comfort ([PERSON] et al., 2014; [PERSON] et al., 2016; [PERSON] et al., 2016). However, there are also studies that suggest no significant differences between the types of bracket systems (PSLBs, ASLBs, and CBs) ([PERSON] et al., 2016; [PERSON] et al., 2016; [PERSON] et al., 2016; [PERSON] et al., 2016). The choice of bracket type may depend on individual patient needs and preferences and the clinician's experience and training.
## 3 Soil Moisture Mapping Methodology  
Our approach for the operational soil moisture mapping uses the neural networks technique to invert the radar signal. The neural networks (NNs) are trained using a synthetic database of the C-band backscattering coefficients in the VV and VH polarizations (incidence angle \"\(\theta\)\" between 20\({}^{\circ}\) and 45\({}^{\circ}\)), soil moisture (\(mv\)), surface roughness (\(Hrms\)) and NDVI. The synthetic database is built using a parameterized Water Cloud Model (WCM) [26] combined with the modified Integral Equation Model (IEM) [15,17]. The WCM used was parameterized using a wide range of soil moisture values (between 4.0 and 39.7 vol %), surface roughness (between 0.7 cm and 4.6 cm), and NDVI (between 0.08 and 0.86), all of which were obtained from agricultural fields (mainly cereals). The development of the inversion method consists of five steps:  
1. [label=(0)]
2. Simulate the radar backscattering coefficients in the VV and VH using the parameterized WCM;
3. Generate a noisy synthetic SAR C-band database by noising the data simulated by the WCM (step 1) to use the SAR database close to the real SAR data. The NDVI values used as input to the WCM were also noised to better represent the real NDVI values computed from the optical images;
4.  
Figure 2: Sensitivity of the C-band (Sentinel-1 and ASAR) to the soil moisture in the VV (**a–c**) and VH (**d–f**) polarizations for four classes of NDVI (NDVI values between 0.1 and 0.9).  
3 Divide the synthetic database into two equal sub-databases, one for neural network training and one for neural network validation;
4 Train and validate the neural networks using the synthetic training and validation sub-databases, respectively;
5 Finally, apply the trained neural networks to the real database (French database) of the SAR and NDVI measurements computed from Sentinel-1 and Sentinel-2 images, respectively, to estimate the soil moisture.  
For the proposed approach, neural networks need to be trained on wide database (with soil and vegetation parameters the most encountered in agricultural environments) simulated from calibrated physical model (WCM coupled with the modified IEM). The NNs trained only one time could be next used to derive the soil moisture from S1 and S2 images. As a case study, the developed approach was applied in using the Sentinel-1 and Sentinel-2 images for operationally mapping the soil moisture at high spatial resolution (up to plot scale) over the Occitanie region in France.
# Performance Impact of Optimization Methods on MySQL Document-Based and Relational Databases
## 5 Performance Tests--Results and Analysis
### Update Operation
#### 5.2.2 Updating Multiple Items
When there is a small number of elements, the results for updating operations are similar because the search is relatively fast, but with the increase in the number of elements and the lack of indexes, relational MySQL takes a very long time compared with document-based MySQL. After applying the optimization methods on both databases, we can see from Figures 6 and 7 that document-based MySQL is faster than relational MySQL, but timing improvements are significant for relational MySQL. We noticed, as a result of the tests performed, that the indexes have a huge impact; thus, the proposed optimization methods, described above, help with gaining an improvement in the execution time over 60% in the case of both databases.