esa-sceva/satcom-chunk-collection huggingface dataset:
# Resource-Efficient Multicast URLLC Service in 5G Systems
## 3 Analyses of Possible Solutions and Their Adaptation to mURLLC
### RB Allocation  
For each slot, the gNB determines which RBs are used for transmission of enqueued multicast packets. The RB allocation procedure consists of two steps. First, the gNB divides RBs between various multicast streams (packets). Second, it allocates particular RBs to each multicast packet.  
[PERSON] et al. [28] evaluate various scheduling policies for unicast URLLC traffic. They show that the well-known Earliest Deadline First (EDF) policy provides high network capacity for URLLC and has very low complexity. This policy can be adapted to mURLLC traffic as follows. Let \(\mathcal{P}=\{1,...,P\}\) be the set of multicast packets pending transmission and \(\mathcal{B}=\{1,...,B\}\) be the set of available RBs. First, the gNB sorts enqueued multicast packets in the ascending order of their remaining lifetimes: \(RT_{p}=D^{QoS}-D_{p}\), where \(D_{p}\) is the packet \(p\) queuing delay. Note that packets with \(D_{p}>D^{QoS}\) are dropped. Having sorted a set of packets \(\hat{\mathcal{P}}\), the gNB considers the first packet and allocates RBs to this packet as described in the following paragraph until all its bytes are transmitted or no free RBs are available. Then the gNB considers the second packet in \(\hat{\mathcal{P}}\) and so on.  
Two approaches can be used to allocate particular RBs to packets: Frequency-Selective (FS) and non-FS scheduling. With FS scheduling, the gNB takes into account the channel quality in the considered RBs and allocates the best RBs to packets in order to minimize resource consumption. For multicast traffic, the FS scheduling is implemented as follows. For each RB \(j\in\mathcal{B}\), the gNB determines the recipient with the worst SINR, i.e., \(SINR_{j,p}~{}=~{}\min_{i\in U_{p}}SINR_{i,j}\), where \(U_{p}\) is the set of recipients for packet \(p\) and \(SINR_{i,j}\) is SINR of UE \(i\) in RB \(j\) given by (1). Then, the gNB sorts free RBs in set \(\mathcal{B}\) in the descending order of \(SINR_{j,p}\) and allocates free RBs until all bytes of the considered packet are transmitted or no free RBs are available. With non-FS scheduling, the gNB assumes that RBs have the same quality. Thus, it can select RBs sequentially or randomly.  
FS and non-FS scheduling approaches both have their benefits and drawbacks. In terms of performance, FS scheduling provides lower resource consumption and, thus, increases the network capacity. However, in terms of complexity, FS scheduling requires the calculation of the precoder in each RB (i.e., to estimate \(SINR_{j,p}\)). In contrast, with non-FS scheduling, the gNB only needs to calculate the precoder in the allocated RBs. In Section 4, we study in detail this tradeoff between performance and complexity.
# Hybrid Engineered Water-Polymer Flooding in Carbonates: A Review of Mechanisms and Case Studies
## 4 Hybrid Engineered Water-Polymer Flooding (EWFF)
### Potential Risks Associated with EWPF  
The potential risks associated with EWPF, as investigated by [PERSON] et al. [PERSON] et al. (2019), are as follows:  
* The viscosity of a LSP solution is more sensitive to brine salinity compared to a solution containing high-salinity water (HSW). A slight increase in the brine salinity can reduce the polymer viscosity significantly, making it necessary to consider this factor while designing a LSPF project;
* The contamination of the LSP slug with already present high-salinity formation water also poses risks for polymer viscosity loss and increased adsorption;
* Another risk in this process is a delay of the incremental oil recovery due to the higher polymer slug needed to reach the required adsorption level as a result of a lower polymer concentration;
* The reduced injectivity levels of polymer and chase fluid in the presence of EW can also be a potential limitation involved in this hybrid method [PERSON] et al. (2019).  
However, these risks can be partially or completely offset by lowering the maximum adsorption and increasing the hydrodynamic acceleration of the polymer in the LSW. [PERSON] et al. [PERSON] et al. (2019) carried out a de-risking study in order to minimize the risks of using LSW for PF and showed that due to polymer adsorption, a LS slug is formed in between the HSW and LSP, which prevents direct contact of the polymer with the HSW, meaning the polymer slug efficiently displaces the HSW slug without deterioration. Despite the risk of delayed oil production, a two- to four-fold reduction in the polymer concentration and the resulting cost savings could be enough to justify the economic feasibility of LSPF, particularly in high-salinity formations, which otherwise may not satisfy the criteria for conventional PF.  
Several variable-rate coreflood experiments were conducted by [PERSON] et al. [PERSON] et al. (2019) to assess the effects of smart water on polymer injectivity. Resistance factors and residual resistance factors for smart water cases were slightly higher than those for injection water cases, indicating a negative impact of smart water on polymer injectivity. Although polymer and chase fluid injectivity rates were slightly reduced due to increased polymer chain expansion in the smart water, the reduced polymer consumption and retention still showed that the hybrid method has potential for successful field-scale implementation in high-salinity, high-temperature carbonate formations, as it can result in significant cost savings.  
Figure 15: (**a**) A higher reduction in the zeta potential and (**b**) an increased wettability shift to water-wetness in the presence of HPAM polymer in smart water [PERSON] et al. (2019).
# Accounting for Training Data Error in Machine Learning Applied to Earth Observations
## Guidelines and Recommendations
### Step 2: Minimize Design-Related Errors
#### 4.2.2 Training Data Sources  
The requirements for temporal representativeness make the source of training imagery a critical consideration for projects that rely on image interpretation. The use of basemap imagery is not recommended for training maps of dynamic features, given their broad range and uneven distribution of image acquisition dates [61], unless the age of the imagery being classified can be matched to that of the training imagery. Otherwise, there is substantial potential for introducing error into the mapping algorithm (e.g., Figure 1), and its impact may be hard to assess, particularly if the map reference sample is collected from the basemap. The goal of temporal representativeness must be balanced with the need to have a sufficiently high spatial resolution to accurately interpret the smallest target features/classes (i.e., the MMU; see Step 3). Beyond matters of cost, this tradeoff is one reason that HR/VHR basemaps are widely used [61]. Newly available commercial imagery such as PlanetScope [128] are collected at high temporal frequency (near-daily) and have a spatial resolution sufficient for many visual interpretation tasks (3-4 m) and, therefore, may be a preferable source of training imagery for developing maps representing the post-2016 period. Finally, in designing an image-based sample, it is also important to consider additional characteristics that can influence interpreters' judgement, such as atmospheric quality (e.g., clouds, haze), sensor view angle, sun angle, spectral band selection, and image contrast stretches [74].
## 4 Results
### Rider Influence  
As explained in Section 3.3, the literature offers different examples of rider modelling. These references often analyse the influences of the rider on weave and wobble stability. For example, reference [42] investigates the effect of the rider yaw combined with the connection to the handlebar. Reference [7] proposes the eigenvalue analysis with the 3-DoF rider also used in the present work. In order to obtain a better overview of the single influences, this section aims to briefly summarise the essential differences between the three different DoF models used for rider modelling. This is shown in Figure 15. The effect of the single DoF can be summarised as follows:  
* The rider lean \(\alpha_{\mathrm{ri}}\) stabilises the weave and destabilises the wobble at high speed. The increased stability of weave is a reasonable result because during the weave oscillation the rider lean is almost in counterphase to the motorcycle's roll [41], thereby damping out this motion.
* The rider yaw \(\gamma_{\mathrm{ri}}\) combined with the connection to the handlebar destabilises the weave, massively stabilises the wobble and also increases its eigenfrequency. This is in accordance with [42] and is a reasonable result, as the connection acts in a similar way to a steering damper, which also stabilises the wobble while destabilising the weave.
* The rider's hip lateral motion \(y_{\mathrm{ri}}\) hardly affects the eigenmodes, as also reported by [31]. The only remarkable influence is the increase in the wobble frequency at high speed.  
Figure 14: Compass plot of the weave eigenvector at different speeds.  
Figure 16 shows the effect of the whole rider model, containing the three DoF \(\alpha_{\text{ri}},y_{\text{ri}},\gamma_{\text{ri}}\). In the weave the effect of \(\alpha_{\text{ri}}\) overcomes the others, thereby causing the already mentioned stabilisation at high speed. When concerning wobble, the effect of \(\gamma_{\text{ri}}\) and the connection with the handlebar are dominant, leading to an increase in both frequency and damping.  
Figure 15: The rffects of the single rider’s DoF on the weave and wobble modes.
# Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series
## 4 Experimental Results  
This section aims at evaluating the TempCNN architecture presented in Section 2.3. We designed a set of six experiments in order to study:  
1. how the proposed TempCNN architecture makes the most of both spectral and temporal dimensions,
2. how the filter size of temporal convolutions influence the accuracy,
3. how pooling layers influence the accuracy,
4. how wide and deep the model should be,
5. how the regularization mechanisms help training the model,
6. what values should be used for batch size.  
A last section is dedicated to the visual analysis of the produced land cover maps.  
As explained in Section 3.2, all the presented Overall Accuracy (OA) values correspond to average values over five folds. When displayed, the interval always correspond to one standard deviation. All details of the trained networks are available at [[https://github.com/charlotte-pel/temporalCNN](https://github.com/charlotte-pel/temporalCNN)]([https://github.com/charlotte-pel/temporalCNN](https://github.com/charlotte-pel/temporalCNN)).
## 3 Materials and Methods
### Sample Analysis
\begin{table}
\begin{tabular}{l l l l l} \hline \hline  & & & \multicolumn{2}{c}{**Treatments**} \\ \hline
**Fiber Type** & **Sample Size** & **Temperature** & **Time** & **Sample Agitation** \\ \hline \multirow{4}{*}{85 \(\upmu\)m CPDMS} & 0.25 mL & 25 \({}^{\circ}\)C & 1 h & None \\  & 0.50 mL & 24 h & 0.20 cm \(\times\) 0.50 cm Stir bar @ 1000 rpm \\  & 0.25 mL & 37 \({}^{\circ}\)C & 1 h & None \\  & 0.50 mL & 24 h & 0.20 cm \(\times\) 0.50 cm Stir bar @ 1000 rpm \\ \hline \multirow{4}{*}{75 \(\upmu\)m CPDMS} & 0.25 mL & 25 \({}^{\circ}\)C & 1 h & None \\  & 0.50 mL & 24 h & 0.20 cm \(\times\) 0.50 cm Stir bar @ 1000 rpm \\  & 0.25 mL & 37 \({}^{\circ}\)C & 1 h & None \\  & 0.50 mL & 32 \({}^{\circ}\)C & 24 h & 0.20 cm \(\times\) 0.50 cm Stir bar @ 1000 rpm \\ \hline \multirow{4}{*}{50/30 \(\upmu\)m DVB/CPDMS} & 0.25 mL & 25 \({}^{\circ}\)C & 1 h & None \\  & 0.50 mL & 25 \({}^{\circ}\)C & 24 h & 0.20 cm \(\times\) 0.50 cm Stir bar @ 1000 rpm \\ \hline \multirow{4}{*}{100 \(\upmu\)m PDMS} & 0.25 mL & 25 \({}^{\circ}\)C & 1 h & None \\  & 0.50 mL & 24 h & 0.20 cm \(\times\) 0.50 cm Stir bar @ 1000 rpm \\  & 0.25 mL & 1 h & None \\  & 0.50 mL & 37 \({}^{\circ}\)C & 24 h & 0.20 cm \(\times\) 0.50 cm Stir bar @ 1000 rpm \\ \hline \multirow{4}{*}{65 \(\upmu\)m PDMS} & 0.25 mL & 25 \({}^{\circ}\)C & 1 h & None \\  & 0.50 mL & 24 h & 0.20 cm \(\times\) 0.50 cm Stir bar @ 1000 rpm \\ \cline{1-1}  & 0.25 mL & 1 h & None \\ \cline{1-1}  & 0.50 mL & 37 \({}^{\circ}\)C & 24 h & 0.20 cm \(\times\) 0.50 cm Stir bar @ 1000 rpm \\ \hline \multirow{4}{*}{65 \(\upmu\) PDMS/DVB} & 0.25 mL & 25 \({}^{\circ}\)C & 1 h & None \\ \cline{1-1}  & 0.50 mL & 24 h & 0.20 cm \(\times\) 0.50 cm Stir bar @ 1000 rpm \\ \cline{1-1}  & 0.25 mL & 1 h & None \\ \cline{1-1}  & 0.50 mL & 37 \({}^{\circ}\)C & 24 h & 0.20 cm \(\times\) 0.50 cm Stir bar @ 1000 rpm \\ \hline \hline \end{tabular} Abbreviations: CAR/PDMS = Carboxen polydimethylsiloxane; DVB/CAR/PDMS = divinylbenzene/ Carboxen polydimethylsiloxane; PDMS = polydimethylsiloxane; PDMS/DVB = polydimethyl-siloxane/divinylbenzene.  
\end{table} 
Table 3: Experimental treatments and the different fiber types used in the experimental design. and ChemStationTM (Agilent, Santa Clara, CA, USA) software programs were used for data acquisition (Figure S10). The aromagram was formed when an odor event occurred and was defined in an area of chromatographic separation. During the odor event panelists were responsible for recording the period in which the odor originates and ends, editable odor character descriptors, and perceived odor intensity. The aroma intensity was evaluated on a 0%-100% scale with 0% indicating no odor, 15% indicating a questionable odor, 30% indicating a faint odor, 60% indicating a medium odor, 80% indicating a strong odor, and 100% indicating an intense odor.
## 3 Results and Discussion
### Experimental Results
We previously established [55] that the combustion temperatures of composite fuels based on filter cake are 1000-1400 \({}^{\circ}\)C. It is generally agreed that combustion temperatures of 850 \({}^{\circ}\)C and a gas residence time of 2 s or 1000 \({}^{\circ}\)C and a gas residence time of 1 s are necessary for total destruction of polychlorinated dibenzodioxins and dibenzofurans (PCDD/Fs) in flue gases. Since decomposition increases exponentially with temperature and incineration temperature of 1200 \({}^{\circ}\)C, which is typical of chlorinated hydrocarbon wastes, requires a residence time measured in milliseconds for total destruction [56]. The critical temperature zone for the maximum rate of PCDD/Fs from precursors such as chlorophenol is 350-400 \({}^{\circ}\)C depending on the precursor concentration. Furthermore, the rate of PCDD/Fs formation from these precursors is 1 or 2 orders of magnitude greater than dioxin formation rates from conventional de novo synthesis. The maximum rate of dioxin formation from de novo synthesis is in excess of 300 \({}^{\circ}\)C [57].Therefore, in order to minimize PCDD/Fs formation in this temperature zone it is necessary to cool the gases through the critical temperature zone as rapidly as possible. In general, according to [PERSON] [56] effective PCDD/Fs minimization can be achieved by applying the following [56]: \"In the first case, combustion temperature should be above 1000 \({}^{\circ}\)C, combustion residence time should be greater than 1 s, combustion chamber turbulence should be represented by a Reynolds number greater than 50,000. In the second category, very rapid gas cooling from 400 to 250 \({}^{\circ}\)C should be achieved, semi-dry lime scrubbing and bag filtration coupled with activated carbon injection adsorption as end-of-pipe treatments can all play a role in prevention or minimization of dioxins in the final flue gas emission to the atmosphere\". The design of typical industrial coal-fired boilers can provide all the necessary conditions to minimize PCDD/Fs concentrations in flue gases from the combustion of composite fuel.
## 4 Case Study Description
### Mysteryland 2017--The Netherlands  
Every year a large dance festival is held at the Floriade terrain near Amsterdam in The Netherlands. Within this area of approximately 600,000 m\({}^{2}\) in total almost 100,000 visitors enjoy different sorts of dance music for one or two days. In 2017, this event was held on August 26 th and 27 th, two days with almost no rain, a temperature of 24 \({}^{\circ}\)C and limited cloud cover. The population of the event is fairly young (18-35 years old). The visitors generally moved around in groups. Furthermore, both genders were equally represented at the festival terrain. Reference [6] illustrates that the activity choice behavior of the crowd showed two distinctive patterns. Either visitors walked around visiting a number of stages with a distinct music taste or visitors remained at their preferred stage for large parts of the day. In order to manage these crowds effectively, it is essential to predict during the day at what moment in time the crowd is moving and the route that they take from one stage to the next.
## 4 Next Forwarding Node Selection in UWSNs
### Addressing Based
Moreover, the process of assigning the address should be updated periodically, which affects the performance of the protocol. Furthermore, H\({}^{2}\)-DAB did not utilize any energy and link quality metrics for assigning an address to each node, which leads to an increase in energy consumption and packet loss. A novel energy-efficient multi-hop scheme, the adaptive power controlled routing (APCR) protocol for underwater sensor network, has been proposed [46]. APCR uses different power levels to assign nodes to various layers around the sink and allows the nodes to switch between groups of transmission power levels to handle the mobility of the network. APCR is divided into two phases, namely layer assignment and communication phase. In the first phase, each node has been assigned a layer ID. To this end, the sink sends a beacon message at the maximum power level. The receiver nodes extract the layer ID included in this message and set its layer ID based on the extracted layer ID. Then, the sink decreases the power level and layer ID, and broadcasts the message. The receiver node compares the layer ID in the received message to its layer ID and chooses the smallest one. At the end of this phase, each node has been assigned a layer ID. Figure 9 shows the layer assignment phase. In the communication phase, the sender broadcasts a hello packet using its minimum power level to  
Figure 8: Data packet forwarding in 2H-ACK. find the next forwarding nodes among its neighbors. Only the nodes in a layer above the sender would reply to the message if they are located in the communication range of the sender. If the sender does not receive any reply message, it increases the power level and broadcasts the message again until it finds the forwarding sets. Receivers then reply to the message with their ID, layer ID, and residual energy level. The sender then adds this information to the table and forwards the data packets to the node with maximum residual energy. An acknowledgment message is sent to the sender from the receiver node, embedded with its updated residual energy. This process is continuously repeated until the data packet reaches one of the sinks. The advantages of APCR are as follows. It solves the communication void problem by changing the power level during the process of finding the next forwarding nodes, which leads to an improvement in the packet delivery ratio. Moreover, it does not utilize full location information provided by GPS. Furthermore, it employs residual energy for selecting the best candidate as it balances the energy consumption. However, APCR suffers from some problems. The proposed architecture has high energy consumption due to the maximum power level used. Moreover, the use of a different power level while selecting the next forwarding nodes increases the energy consumption. Furthermore, APCR does not consider the link quality parameter in selecting the next forwarding nodes, which has a direct effect on the packet delivery ratio.
# Stationary Phases for Green Liquid Chromatography
## 3 Stationary Phases Used in Pure Water Conditions
### Instability of Stationary Phases--A Motivation to Search for Solutions  
In most cases, the thermal stability of analytes is not a significant problem, and a wide range of substances may be analyzed. Another serious problem is the thermal stability of the stationary phase used. Most of the materials used as stationary phases that are stable under ambient temperature HPLC may not be stable under superheated water conditions. Despite the temperature range, the van 't Hoff relationship remains linear for both ambient temperature HPLC and SHWC [59; 60].  
One of the leading examples of a thermally unstable stationary phase is the most commonly used chromatographic material--octadecyl-modified silica (ODS)--which undergoes degradation at temperatures above 80 \({}^{\circ}\)C [7]. For this reason, it is essential to develop new materials or modify existing ones in order to ensure their thermal stability when using pure water as the only eluent. When analyses are carried out with ODS silica materials at water contents higher than 95%, a sudden decrease in retention times occurs while maintaining efficiency. This is due to a phenomenon called the \"phase collapse\" effect, which is associated with the stacking of long-chain alkyl ligands [61]. In this case, the adsorptive surface area of the stationary phase is strongly reduced. However, the \"phase collapse\" process is reversible, and this can be achieved by using a mobile phase with a high organic modifier content. This effect is explained by the de-wetting of the phase surface, which lowers the effective volume of the column by removing water molecules from the pores [62].  
Many attempts have been made to prevent this effect. For example, polar groups were incorporated between the hydrophobic octadecyl chains and the silica surface; this led to the obtaining of mixed hydrophilic-hydrophobic phases. Such materials have improved wettability in high-water-content or even in purely aqueous mobile phases. Novel materials were named polar-endcapped, polar-embedded, or aqueous stationary phases [39; 63].  
With SHWC, and using phases with embedded polar groups, changes in retention were also observed. This was achieved by cooling the column and leaving it without flow for a period of time; however, this effect can occur suddenly--even between performed analyses. The overall behavior of stationary phases under aqueous conditions was described in detail by [PERSON] et al. [64].  
Consequently, the inability to successfully use commercial octadecyl phases has forced the search for other stationary phases that are thermally stable under pure water conditions. Currently, several chromatographic phases are used in SHWC and other techniques using pure water as the only eluent. The most common are: modified silica phases, polymer phases, zirconia-based phases, other metal oxide phases, carbon phases, and hybrid phases [6; 7].