esa-sceva/satcom-chunk-collection huggingface dataset:
# Recent Applications of Deep Eutectic Solvents in Environmental Analysis
## 1 Introduction
The unique characteristics of DESs make them ideal green solvents with great versatility due to the huge number of possible components and combinations available, as well as the ease of carrying out the synthesis process. In addition, they show non-flammability, low volatility, low cost, and toxicity; good biocompatibility, biodegradation, and reusability capacity, among others [6; 10]. In this sense, diverse compounds have been used as HBAs, notably choline chloride (ChCl), one of the most widely used in general but, particularly, in the field of environmental analysis. Nevertheless, other quaternary ammonium salts have also been applied in this field. In addition, as mentioned above, the application of HDESs and quasi-HDESs has recently increased in this area, making use of terpenoids or large chains of quaternary ammonium salts with long-chain fatty acids. Another remarkable group of DESs is the one constituted by natural components (NaDESs). These solvents are exclusively constituted by primary or secondary metabolites, as well as other compounds presented in cells that make them less- or non-toxic and assure their biocompatibility. These characteristics have enhanced the interest in the application of NaDESs for the evaluation or treatment of environmental matrices due to the low impact of these solvents in such media performing more sustainable procedures [11]. Indeed, a brief evaluation of the recent literature indicates that NaDESs make up the majority of DESs used in the field of environmental analysis for the study of organic and inorganic compounds in liquid and solid matrices. In addition, although not extensively, on some occasions those NaDESs were combined with magnetic nanomaterials in order to harness the advantages of using both kinds of novel materials and developing simpler procedures with better specificity and higher efficiency. The sharp increase in applications using DESs in recent years necessitates continuous revisions of the literature in which the compilation, description and discussion of the main trends, advantages, and difficulties of the use of these materials are carried out. In fact, in recent years, diverse review articles have been published in this sense [7; 8; 9]. However, to the best of our knowledge, a specific study of the use of DESs in the field of environmental analysis has not been carried out up to date. That is why, in this work, we compiled the most remarkable applications of DESs in this area, describing and discussing the main goals and achievements to date, as well as the major difficulties found in the application of these novel materials for the evaluation and treatment of environmental matrices.
# Robust Damage Estimation of Typhoon Goni on Coconut Crops with Sentinel-2 Imagery
## 7 Conclusions  
We have assessed the damage caused by Typhoon Goni to coconut trees in the Philippines. Our image analysis is based on canopy density estimation in multi-spectral Sentinel-2 images, which is able to recognise a wide range of damages to the tree canopies. Accurate mapping of reduced and damaged foliage may help to anticipate yield loss in the mid- andlong-term that may impact livelihood and well-being of coconut producers (many of which are smallholder farmers).  
Overall, we estimated that 14.1 M coconut trees were affected by typhoon Goni inside our study area. We observed a larger impact on provinces that were directly under the typhoon's path, including Camarines Sur, Quezon and northern Samar, all of which are home to large coconut plantations.  
While we rely on optical satellite imagery to map even small damages to the trees' foliage, our method is challenged by cloud cover. We believe that a promising direction for future research is the combination of optical and SAR satellite imagery. Some research already exists that advocates the joint usage of SAR and optical sensors [76; 77], which could possibly allow us to combine the best of both worlds: robustness against clouds and sensitivity to subtle changes of foliage.  
With climate change increasing the likelihood and severity of natural hazards like typhoons [78], methods that allow for large-scale, consistent damage assessment will play an important role to support mitigation measures against negative impacts on the local population. Our change estimates could, for example, be used to make predictions of future economical impacts and ultimately of the livelihood of coconut farmers. Studies analysing the mid- and long-term impact of similar events have been published after typhoon Haiyan in 2013 [5; 31; 33], but took several years to complete. We hope that the large-scale, high-resolution maps that our method is able to generate in a short time can support stakeholders with quantitative evidence to take better informed decisions with less time lag.  
[PERSON] was responsible for the design and development of the methods, for running the experiments, and for writing the paper. [PERSON], [PERSON], [PERSON] and [PERSON] were responsible for creating the research design, for writing the paper, and for editing. All authors have read and agreed to the published version of the manuscript.  
This research received funding from Barry-Callebaut AG as part of a research agreement.  
The change maps are available online at [[https://doi.org/10.528](https://doi.org/10.528)]([https://doi.org/10.528](https://doi.org/10.528)) 1/zenodo.5595921 (accessed on October 2021). The manually labelled reference data is available on request.  
The authors declare no conflict of interest.
## 3 Results
### The Effect of BenPros on Prostate Weight and Prostate Index in BPH Rats  
In order to test the potential effect of BenPros on BPH, changes in the prostate weight (PW) and prostate index (PI) were observed using the TP-induced BPH model rats. As shown in Table 1 and Figure 5A, the PW and PI of rats in the TP-induced BPH group (PW: 2.74 \(\pm\) 0.15; PI 0.81 \(\pm\) 0.05) were significantly increased compared with the NC group (PW: 1.40 \(\pm\) 0.14; PI: 0.37 \(\pm\) 0.03, respectively), whereas the parameters in the finasteride (PW:1.83 \(\pm\) 0.05; PI: 0.53 \(\pm\) 0.01) and BenPros groups (200 mg/kg, PW: 2.22 \(\pm\) 0.09; PI: 0.68 \(\pm\) 0.02 and 400 mg/kg, PW: 1.99 \(\pm\) 0.10; PI: 0.60 \(\pm\) 0.02) were significantly decreased compared with the BPH group. As shown in Figure 5B, the size of the prostate in the BPH group was increased, whereas the finasteride- or BenPros-treated groups showed a significant reduction in prostate size. Furthermore, there were no significant differences in rat body weight between the groups (Table 1). No other organs were affected by BenPros treatment (Supplementary Figure S2).  
Figure 4: Effects of BenPros on expression of cyclooxygenase-2 (COX-2) and apoptosis proteins in BPH rats. Representative western blots of COX-2, B-cell lymphoma 2 (Bcl-2) and BCL2 Associated X (Bax). Blots are normalized to \(\beta\)-actin and values are presented as the mean \(\pm\) standard error of the mean of 6 rats per group. *** \(p\) < 0.001 vs. NC; # \(p\) < 0.05, # \(p\) < 0.01 and *** \(p\) < 0.001 vs. BPH. Fina; finasteride, Saw; saw palmetto.
# Interference-Aware Radio Resource Management for Cognitive High-Throughput Satellite Systems
## 3 Challenges of RRM in Cognitive HTS Systems
### Hybrid CCI  
In the cognitive HTS systems with FFR, CCI is the major obstacle for increasing the overall system throughput. Specifically, the CCI is comprised of the intra-system CCI from frequency reuse among multiple beams and inter-system CCI from spectrum sharing between HTS and terrestrial systems--referred to as hybrid CCI. To facilitate effective elimination of interference, the first issue is to establish a theoretical model to analyze the hybrid CCI, as in our prior work [33]. It is then important to study how to realize the friendly coexistence between satellite and terrestrial networks. Motivated by the promising vision of future satellite systems in 5G and beyond, the intuitive objective of RRM is to maximize the throughput of satellite communications in a multiuser scenario with diverse service requirements, while guaranteeing the normal operation of the primary licensed systems.
## 2 Description of the LMS-Based Algorithms
### Filtered-X LMS Algorithm Applied to a Feedback Configuration  
The feedback system is typically implemented in all those situations in which the noise is predictable, for example, for the attenuation of transformer noise outdoors [25] or for the cancellation of fan noise [26]. Since the reference microphone is absent, the reference signal is internally modeled starting from the measurement of the error signal. The choice of this system makes it possible to contain the costs associated with the sensors and to protect the system against the acoustic feedback mentioned in the previous paragraphs. However, the feedback system generally provides a limited attenuation, in particular when the environment makes the modeling of the reference signal cumbersome, for example, in enclosed spaces. Moreover, this system is affected by amplification at high frequency due to an intrinsic phenomenon called waterbed effect [30], which could also lead to instability. Considering the scheme shown in Figure 5, the feedback system works as explained in the following. The filtered output signals are evaluated as:  
\[y^{\prime}_{ji}(n)=\sum_{m=0}^{M-1}s^{\prime}_{j,m}(n)y_{j}(n-m). \tag{14}\]  
These contributions are subtracted from the error signals in order to model the reference signals, for which the subscript is changed from \(i\) to \(l\) for the sake of simplicity in the notation:  
\[x_{l}(n)=x_{i}(n)=e_{i}(n)-\sum_{j=1}^{2}y^{\prime}_{ji}(n). \tag{15}\]  
The procedure becomes identical to the one of the feed-forward case described previously in Section 2.1. The filtered reference signals are thus computed as:  
Figure 5: Schematic representation of FXLMS algorithm for a feedback configuration.  
\[x^{\prime}_{l,ji}(n)=\sum_{m=0}^{M-1}s^{\prime}_{j,m}(n)x_{l}(n-m). \tag{16}\]  
The updating law presented in Equation (3) becomes:  
\[w_{lj,k}(n+1)=w_{lj,k}(n)-\mu\sum_{i=1}^{2}e_{i}(n)x^{\prime}_{l,ji}(n-k)\ \ \ k=0,1,\ldots,K-1 \tag{17}\]  
and, finally, the output signals are evaluated as:  
\[y_{j}(n)=\sum_{l=1}^{2}\sum_{k=0}^{K-1}w_{lj,k}(n)x_{l}(n-k). \tag{18}\]  
This procedure is repeated at any generic instant \(n\).
# Comparative Analysis of Major Machine-Learning-Based Path Loss Models for Enclosed Indoor Channels
## 3 Data Preparation and Models Setup
### Hyperparameters Tuning Setup  
Hyperparameter tuning techniques play an essential role in searching for the best hyperparameter for machine learning applications. Machine learning algorithms are based on complex hyperparameters that create complicated black boxes and lead to optimization challenges. Moreover, determining the hyperparameters' values can be time-consuming if we try all possible combinations of hyperparameters. However, several techniques have been proposed to select the best hyperparameters for a particular model, including Bayesian Optimization Automate Hypermarameter Tuning (Hyperpot) [68], Spearmint Bayesian optimization [69], Sequential Model-Based Optimization (know as SMAC) [70], Autotune: A derivative-free optimization [71], Google vizeier [72], Genetic Algorithm [73], and Optuna Approach [74]. These approaches aim to select the best hyperparameter that minimizes the mean square error and maximizes the accuracy, such as R-square. This is performed by training the machine learning techniques in all the hyperparameter possibilities, then selecting the ones that lead to the objective (i.e., the best performance). The tuning techniques mainly assist in looping through predefined hyperparameters and fitting the estimator (model) on the training set.  
Since machine learning techniques require hyperparameter tuning to achieve both the efficiency of the model training process and the resulting model accuracy, the best hyperparameter of machine learning algorithms requires determinination. This work has applied the Optuna approach to select the best hyperparameter that leads to suitable data fitting. The Optuna approach is used in our work for multiple reasons. Optuna is a software framework for automating the optimization process of these hyperparameters. It automatically finds optimal hyperparameter values using various samplers such as grid search, random [75], Bayesian, and evolutionary algorithms [74]. It has eager search spaces that use automated searches for the optimal hyperparameter using Python conditionals, loops, and syntax. It is also a state-of-the-art algorithm that efficiently searches large spaces and prunes unpromising trials for faster results. In addition, the Optuna can parallelize hyperparameter searches over multiple threads or processes without modifying code. Finally, the best machine learning techniques depend on identifying the proper hyperparameters, avoiding both overfitting and underfitting, which is the trend of this research to achieve objective prediction accuracy.
## 1 Introduction
Then, for source optimization, a weighted cost function, which is the superposition of the CDE and normalized image log slopes (NILS) incorporating the impacts of random MAT errors and SWA variations, is innovated to improve image fidelity and process stability. The stochastic gradient (SGD) algorithm is applied to optimize the source. Next, to further increase the PW, the cost function for the NA and process parameters optimization is the DOF at a specific exposure latitude (EL). As these parameters are not of the same order of magnitude, the large-scale parameters are likely to be obstructed by small-scale parameters during the optimization procedure. We employ the normalized stochastic gradient (NSGD) algorithm to optimize the NA and process parameters. Finally, in consideration of the interaction of source, NA, and process parameters on lithography performance, the optimization procedures are implemented alternately to get better performance. The SNPCO method is tested on two types of layouts at 14 nm technology node. Simulations of 1D mask pattern demonstrate that for the system with extreme mask absorber errors, the CDEs of the proposed method are reduced by 62.1% and 58.9%, respectively. In such cases, the corresponding DOFs at EL = 5% are increased by 40.3% and 36.4%, respectively. The results indicate that the proposed method mitigates the CDEs caused by mask absorber errors and improves the process robustness effectively in the DUV lithography system. The remainder of the paper is organized as follows. In Section 2, the source pattern representation, weighted cost function definition, and inverse optimization problem formulation are described. In Section 3, the gradient-based inverse DUV lithography optimization algorithm is developed. Simulation results are presented in Section 4. Finally, conclusions are provided in Section 5.
## 3 System Modeling  
### Network Model  
In this paper, we considered a heterogeneous WSN in which all sensor nodes are deployed uniformly in a field bounded by \(L\times L\left(m^{2}\right)\) region. Each static sensor node with a unique ID can acquire its neighbors' ID by sharing a probe message as discussed in [22; 26]. Also, we assumed that all sensor nodes follow the asynchronous working-sleeping cycle strategy with their working time as \(W_{V}\) and sleeping time as \(S_{V}\). In this model, we assumed that the BS has unlimited energy, storage space and powerful computation capability for communication between CHs and the BS to collect the aggregated sensory data and perform comprehensive evaluations for detection of an event. Furthermore, we also assumed that each sensor node has its own CH and each sensor node has limited energy and limited storage capacity.  
Figure 1 illustrates our network model in which we have considered a single BS, some CHs and many sensor nodes connected to each CH. It is evident from Figure 1 that each sensor node acting as CMs or CHs and BS have fixed locations after deployment in the network. Each CH acquires the sensory data from its CMs. CHs can communicate with the BS or any other CH directly or through multiple hops. Moreover, every sensor node is aware of its position using the energy-efficient localization method and we can estimate the distance between the CM and CH and the CH and BS using the Relative Signal Strength Indicator (RSSI) parameter.  
[PERSON] et al. [26] utilized the OR in WSNs using the asynchronous working-sleeping cycle strategy for sensor nodes deployed in neighborhood area networks (NANs) of smart grids (SGs). For the efficient utilization of OR, we have to model it using the working-sleeping schedule (\(W_{v}/S_{v}\)), and the status transition frequency (\(F_{ST}\)) of each sensor node in the network. As the concept of OR is quite realizable in real-time scenarios of WSNs, we adopt the asynchronous working-sleeping cycle strategy for every sensor node deployed in our network. The list of notations is given in Table 1.
## 5 Restoration Plan Strategy after a Blackout in a Wide Area or a Country
### Parallel Restoration Model
#### 5.1.1 Modeling of the Restoration Time  
The model for the restoration time aims to reduce the amount of time (\(\tau_{\ell,\text{b,t}}\)) that the load (\(\ell\)) of the bus (b) is not supplied. F\({}_{1}\) is the objective function and is the total of all the hours that any load was not in service, as can be seen in (2). The number of hours will be calculated starting from the moment the blackout occurs. Additionally, the model makes a distinction between the higher importance loads (\(\ell\in\text{HI}\)), such as government buildings or military facilities, and the less significant loads (\(\ell\in\text{LI}\)).  
\[\text{minF}_{1}=\sum_{t=1}^{\text{T}}\sum_{b=1}^{\text{B}}\sum_{\ell\in\text{ HI}}^{\text{L}}\tau_{\ell,\text{b,t}}+\sum_{t=1}^{\text{T}}\sum_{b=1}^{\text{B}} \sum_{\ell\in\text{LI}}^{\text{L}}\tau_{\ell,\text{b,t}} \tag{2}\]  
where:  
F\({}_{1}\): The first objective function.  
T: The programming horizon.  
t: The time period set (set in 1 h).  
\(\tau_{\ell,\text{b,t}}\): Time that the load (\(\ell\)) of the bus (b) is not supplied.  
B: Total number of buses.  
b: Bus number.  
L: Total number of loads.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{3}{c}{**How the Incident on 8 January 2021 affected the Greek Grid**} \\ \hline
**Stage** & **Location** & **Events** \\ \hline
**Loss of angle stability** & & \\  & & \\  
\(\ell\): Number of load.  
\(\ell\in\) HI: Higher importance loads.  
\(\ell\in\) LI: Lower importance loads.  
Constraint (3) relates the value of variable \(\tau\) with the value of the binary variable \(\mathrm{u}_{\ell,\mathrm{b},t}\).  
The relative position of the set \(t\) is indicated by the operator ord(t). If we consider a one-day programming horizon, then the values of ord(t) are [1, 2, 3,..., and 24]. The last variable represents the status of restoration of the load (\(\ell\)), which is equal to 1 when the supply of this load is restored. M is a significant enough number.  
\[\tau_{\ell,\mathrm{b},t}\geq\mathrm{ord(t)}-\mathrm{M}\cdot\mathrm{u}_{\ell, \mathrm{b},t},\;\mathrm{b}=1,\ldots,\mathrm{B};\;\ell=1,\ldots,\mathrm{L},\; \mathrm{t}=1,\ldots,\mathrm{T} \tag{3}\]  
In relation to load relevance, constraint (4) specifies that a certain number of high importance loads (X) must be restored before beginning to restore the first low importance load. Additionally, when the load is successfully served again, the restoration status variable \(a_{\ell,\mathrm{b},t}\) is set to 1.  
\[\sum_{\mathrm{t}=1}^{\mathrm{T}}\sum_{\mathrm{b}=1}^{\mathrm{B}}\sum_{\ell\; \in\;\mathrm{HI}}^{\mathrm{L}}a_{\ell,\mathrm{b},\mathrm{t}}\geq\sum_{ \mathrm{t}=1}^{\mathrm{T}}\sum_{\mathrm{b}=1}^{\mathrm{B}}\sum_{\ell\;\in\; \mathrm{LI}}^{\mathrm{L}}a_{\ell,\mathrm{b},\mathrm{t}}+\mathrm{X} \tag{4}\]
## 4 Results and Discussion
### Effects of Other Types of Settlement  
Different types of subgrade settlement have different effects on the deformation of the slab track and the contact force of the gravels in the subgrade. Site investigation shows that the angular type and faulting type subgrade settlement often occur at the transition zones. Therefore, the situations under differential subgrade settlement with angular type and faulting type are also studied in this paper. It is assumed that the longitudinal length of the angular type settlement is 15 m, and the settlement amplitude is 10 mm. The amplitude of the faulting type settlement is taken as 10 mm. The deformation curves of the track structure are shown in Figure 14.  
It can be seen from Figure 14a that the deformation curve of the slab track is in good agreement with the shape of the subgrade settlement with angular type. The deformation curve of the slab track is smoother compared to the settlement curve of the subgrade. As shown in Figure 14b, the slab track undergoes severe settlement in the faulting type settlement section. There is a significant difference between the track deformation and the subgrade settlement. In this case, there is a clear gap between the concrete base and subgrade.  
The calculation results indicate that the average contact force of the gravel grains in the subgrade under angular type settlement is 2.93 \(\times\) 10\({}^{2}\) N. The maximum contact force is  
Figure 14: Deformation of the slab track under other types of settlement.  
Figure 13: Contact force characteristics of gravel grains in the subgrade at different settlement amplitudes.  
\(1.01\times\)\(10^{4}\) N. The average and maximum contact forces of the gravel grains in the subgrade under faulting type settlement are \(2.91\times 10^{2}\) and \(1.30\times 10^{4}\) N, respectively. The growth rates of the gravel contact forces under angular and faulting types settlement compared to the unsettled condition are shown in Figure 15. It can be seen that the maximum contact forces of the gravel grains are significantly increased by the angular and faulting types settlements, especially the faulting type settlement.
## 3 Security Aspects Regarding Wireless Body Area Framework
### Special Consideration
#### 3.4.2 Human-Body Communication as Security Enhancer  
HBC, by using the human body as a medium for data transmission, presents an innovative way to augment WBAN security. It is particularly effective for the secure transmission of encryption keys within the WBAN, utilizing the physical proximity required for communication as a natural security measure. However, HBC's utility is inherently limited by its short-range capability, confining its application to scenarios where devices maintain close physical contact.
# A Study on Design and Control of the Multi-Station Multi-Container Transportation System
## 3 Results  
### Overview of the Controlled Module Characteristics  
#### 3.1.1 The Characteristics of Forward Motion Module  
As mentioned above, the forward motion conveyor module consists of many small sizes of forward motion conveyors to arrange a proper line. With multiple component conveyors, the module can certainly transport many containers at the same time. Two RFID readers are placed on both two ends of the modules to help identify a container entering or leaving the module, as shown in Figure 10. Moreover, these RFID readers can determine the number of containers on the module at any given time.  
To avoid the fact that the entire forward motion module, which includes multiple conveyors, is turned on at the same time, whether there are many containers or only one container, electrical counters and proximity sensors are used. With the detection of proximity sensors and counters, the number of containers on each conveyor is determined. Then, each conveyor is only activated when there is at least one container on the conveyor (counter value greater than 1). Otherwise, the conveyor will not be activated. This process is described in Figure 11 below.  
Figure 9: An brief introduction of the hung conveyor system.