esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Recent Advances in Encapsulated Biosensors and Bioremediation Agents  
Some PGPMs, in addition to their ability to enrich plant growth and health and their ability to maintain soil quality and fertility, have great potential for remediation and decontamination of polluted environments [87]. The advantage of encapsulating PGPM in bioremediation is to overcome the limiting effect of contaminants on survival and to protect cells from the adverse conditions of polluted environments [88]. The genetically modified rhizobacterium _Pseudomonas fluorescens_ F113 Rifpcb has been used as a biosensor and bioreactor of soils contaminated by polychlorinated biphenyls (PCBs), very dangerous chemical compounds carcinogenic and toxic for the environment. Alginate beads loaded  
\begin{table}
\begin{tabular}{l l l l l l l} \hline
**Microorganism** & **Carrier** & **Additives** & **Method** & **Purpose** & **Plant** & **References** \\ \hline _Bacillus subtilis + Pseudomonas_ & Sodium alginate & Skimmed milk & Cross-linking (extrusion) & Plant growth promotion & Maize & [83] \\ _curgata_ & & & & & & \\ _Pseudomonas fluorescens + Burkholdera_ & Sodium alginate & Skimmed milk & Cross-linking (extrusion) & Biofertilizer in salinized soil & Wheat & [73] \\ _curgata_ & & & & & & \\ _Klbsiella cartouca_ & Sodium alginate & - & Cross-linking (extrusion) & Biofertilizer in salinized soil & Cotton & [84] \\ \hline _Sinorhizobium melidi_ & Canola oil + xanthan & - & Emulsification & Modulation and plant growth promotion & Alfalfa & [85] \\ _melidii_ & gum & - & - & - & - & - \\ \hline \multirow{3}{*}{_Bacillus subtilis_} & \multirow{3}{*}{Alginate} & Bentonite + starch + titanium dioxide & Cross-linking (extrusion) & Biocontrol of _Rhizocatania salani_ and plant growth promotion & \multirow{3}{*}{Beans} & \multirow{3}{*}{[86]} \\  & & & & & & \\ \cline  
with _Pseudomonas fluorescens_ applied to contaminated soil allowed the detection of PCBs and the restoration of the contaminated site. Immobilization in alginate beads allowed a more controlled release and reduced the risk of unwanted diffusion of the genetically modified strain. This system could facilitate the application of depollution bacteria in contaminated soils [74]. On the other hand, the use of encapsulated microorganisms for the desalination of saline soils has been widely reported. The _Pantoea agglomerans_ strain KL isolated from saline soil used as inoculum encapsulated in alginate beads has allowed, when applied to rice plants, for reducing stress caused by salt concentration; increasing length, biomass, and the rate of photosynthetic pigment; and reducing the rate of proline and malondialdehydedehyde. This application helps to decrease sodium accumulation and improves rice growth in the presence of salt [78]. In a similar study, spray-dried microcapsules of _Bacillus megaterium_ NCT-2 showed very significant remediation potential when applied in highly saline soil, resulting in a significant reduction of more than 45% in NO3\({}^{-}\), and electrical conductivity was observed [29].
## 2 Modeling of Residual Life Prediction for Electrical Connector
### Overall Scheme of Residual Life Prediction for Electrical Connectors  
The overall scheme of the prediction method for the residual life of electrical connectors is shown in Figure 8. Firstly, the HSMM of the electrical connector's using state is constructed. Then, the feature of intermittent failure is extracted, which is the input for the HSMM. Further, the using state estimate model and the residual life prediction model can be constructed by training the HSMM. After collecting the intermittent failure data, the using state can be estimated according to the trained using state estimation model. Combined with the estimated state and the trained residual life prediction model, the residual life of electrical connectors can be predicted.
# Design and Evaluation of Large-Scale IoT-Enabled Healthcare Architecture
###### Abstract
In addition, the implementation of the reaction, which results from thinking, is achieved in a real-time manner [7]. Electroencephalography (EEG) is one of the important techniques that measures brain signals. The choice of signal-processing technology at each processing stage is very important for creating a robust BCI system. The description of different BCI processing techniques in addition to EEG classifications are stated in [8]. Nevertheless, the IoT-based BCI research field faces many challenges, such as measuring of certain brain activities affected by the state of emotions and some environmental factors. Furthermore, the technologies which are used to measure brain signals such as EGG have low signal/noise ratios in addition to lack of sufficient spatial resolution [9]. Based on the previous discussion, it is difficult to achieve all targets for IoT-enabled healthcare systems without a powerful communication system that relies on more than one method to cover devices and make them permanently connected without defect. Therefore, this paper proposes a large-scale IoT-enabled healthcare architecture in which more than one coverage tool is used to achieve communication between the medical and other devices regardless of their type or location. In addition, to obtain high efficiency in the proposed IoT-enabled healthcare architecture, it is supported with a simple and effective management scheme. The contributions of this paper are as follows:  
* the proposal of IoT-enabled healthcare architecture,
* applying a large-scale coverage scheme to guarantee communication between healthcare devices,
* usage of clustering and prioritization methodologies in the proposed IoT-enabled healthcare architecture,
* a simulated environment to measure the performance of the proposed IoT-enabled healthcare architecture, and
* discussion of the simulation results and recommendations. The remainder of this paper is structured as follows. Section 2 discusses the literature review. Section 3 demonstrates the proposed IoT-enabled healthcare architecture. Section 4 presents the simulated environment specifications. Section 5 shows and discusses the simulation results. Finally, the paper is concluded, and future work is proposed in Section 6.
# Zero-Shot Detection of Visual Food Safety Hazards via Knowledge-Enhanced Feature Synthesis
## 3 Methodology
### Food Safety Knowledge Graph  
At the core of our approach is a specialized Food Safety Knowledge Graph (FSKG) that encodes relationships between food categories and visual safety attributes. Unlike general food knowledge graphs that focus on nutritional or culinary aspects, our FSKG specifically models visual characteristics relevant to food safety hazards.
# Automatic Spatial Audio Scene Classification in Binaural Recordings of Music +
## 2 Materials and Methods
### Corpus of Binaural Audio Recordings  
This section provides a description of how the audio recordings were generated. Initially, it explains how the raw audio material was selected and preprocessed. Then, it describes the incorporated binaural-room-impulse-responses (BRIRs) as well as the spatial synthesis techniques employed in the study.
# Ion Exchange MIEX(r) GOLD Resin as a Promising Sorbent for the Removal of PFAS Compounds
## 3 Results and Discussion
### Interference of Organic and Inorganic Matter on PFAS Removal  
#### 3.3.1 Interference of Organic Matter in PFAS Removal  
Groundwater and wastewater commonly contain natural organic matter and organic pollutants that can compete with the removal of PFAS compounds leading to a decline in PFAS removal during the treatment of water [34; 35; 36]. One of the common organic  
Figure 4: Schematic diagram of PFAS removal by resin and regeneration of PFAS-adsorbed MIEX® GOLD resin in MeOH with saturated NaCl.  
pollutants in water and wastewater is humic acid, which shows an important effect on PFOA adsorption [34]. In our study, two pollutants, namely, humic acid (HA) and tannic acid (TA), were selected to test the interference of NOM on the removal of PFAS compounds. In the absence of NOM, the removal of all studied PFAS was >99% (see above). Whereas in the presence of 1 ppm NOM, little influence was observed with >99.94% removal for all PFASs still achieved. Further study on the impact of NOM on PFAS removal was performed at higher concentrations of NOM when the concentration of the PFAS compounds was maintained at 1 ppm (Table 1). Even in the presence of 10 ppm NOM (i.e., 5 ppm HA acid + 5 ppm TA), high removal efficiencies of >99% were still achieved. Conclusively, there is negligible interference of NOM in the removal of PFAS compounds using MIEX(r) GOLD resin (Figure 5), even when the concentration of NOM is 10 times higher than PFAS.
## 4 Relative Jammer Screening Algorithm Based on Mixed-Strategy Game  
### Primary Jammer Set and Strategy Set  
The distributed primary jammer \(\mathbf{J}_{ip}\) is the player involved in the relative jammer allocation game. \(\mathbf{J}_{-ip}\) denote the primary jammers, other than the \(\mathrm{i_{P}}\)th primary jammer. The strategy of the \(\mathrm{i_{P}}\)th primary jammer is \(X_{ip}\), which refers to the vector of feasible strategies that the primary jammer may adopt. The full set of strategies, \(S_{i}=(X_{1},X_{2},\ldots,X_{N})\) for all N primary jammers, is called an overall strategy combination. The set of strategies \(S_{i}\) formed by the primary jammer is the Cartesian product of the strategies \(X_{ip}\) of each primary jammer.  
For the \(i_{P}\)th primary jammer, one of the strategies made by it, \(X_{ip}\), may consist of K optional relative jammers, as shown in Equation (2).  
\[X_{ip}=\left[x_{1}^{ip},x_{2}^{ip},x_{3}^{ip},\ldots,x_{K}^{ip}\right]^{T} \quad\quad o=\{0,1\} \tag{2}\]  
Figure 3: Adaptive primary and relative jammer determination diagram.  
where \(x_{k}^{ip}\) is whether the kth relative jammer is selected as the interference signal forwarding carrier, 0 indicates no selection, and 1 indicates a selection. The constraint is \(\sum_{k=0}^{K}x_{k}^{ip}\geq 1\), i.e., each primary jammer selects at least one relative jammer as an interference signal forwarding carrier.
## 2 Literature Review
### Time Delay-Based Approach  
In [30], authors propose a routing protocol named Congestion-aware Fibonacci Multipath Load Balancing (Congestion-aware FMLB). This protocol discovers routes based on the minimum RTT. The data packet distributes through different routes based on the Fibonacci sequence number; therefore, the route with the smallest RTT is used more often. The analysis of results in this paper shows the superiority of RTT over the number of hops routing mechanism. This protocol's main issue is the high chance of finding lengthy routes due to the absence of a mechanism to restrict the route lengthiness. These lengthy routes participate in the practice of data transmission according to the load balancing mechanism.  
An RTT-based probabilistic routing protocol for content-centric networking (CCN) known as a receiver-driven network architecture is proposed in [31]. This method selects the paths based on the message content; therefore; one packet may pass to multiple servers and accordingly the RTT measurement is not accurate.  
Authors in [32] proposed a novel delay metric named sojourn time backlog (STB) employed with the back-pressure (BP) algorithm. The STBP protocol was introduced to overcome the problem of having long end-to-end delays in the BP scheme. STB considers queuing delay instead of queue length to select the routes. However, this method is yet not a loop-free routing method; therefore, the provided delay is unsuitable for real-time network applications.  
In [33; 34], two different routing protocols of Reliable Cluster-based Energy-aware Routing (RCER) protocol and load-balanced Multipath routing protocol with energy constraints (EE-LB-AOMDV) were proposed respectively to optimize the routes based on the three metrics of residual energy, hop count, and RTT. RCER mainly aims to maximize the network lifetime by clustering the network into the geographical segments based on the nodes' residual energy and the node neighboring. This protocol is suited for a network with low nodes mobility to avoid frequent cluster reconstruction. EE-LB-AOMDV load balances the data over different discovered routes based on the route score to increase the lifetime of the network. Due to the absence of a restriction mechanism for the lengthiness of routes in this protocol, it is likely to send data through routes with high end-to-end delay.  
An efficient routing algorithm in VANETs that considers multiple challenges such as congestion avoidance, vehicle energy, and end-to-end delay is desired. In this paper, we propose a routing algorithm called TMR used to reduce the time taken by the packets to transmit from source to destination.
# Energy Efficient Access Point Placement for Distributed Massive MIMO
## 2 System Model and Problem Formulation
### Mathematical Formulation  
We aim to reduce the transmit power by optimizing the AP locations, while providing the desired signal coverage in terms of minimum average received signal power at all potential user locations. _In other words, we target to use the minimal amount of transmit power while ensuring that the users receive sufficient signal power wherever they are within the covered region._ In order to formulate the AP placement optimization problem, we define the following decision variables: the transmit power per AP \(p_{\mathbb{T}}\); \(x_{l}=1\) if an AP is deployed at a location \(l\), otherwise \(x_{l}=0\); \(z_{i}=1\) if a user location \(i\) is covered with sufficient signal, \(z_{i}=0\) otherwise. In addition to the aforementioned parameters, we define \(P_{\text{MIN}}\) to denote the minimum required average received signal power at each user location and \(V\in[0,1]\) to denote the signal coverage level in percentage (\(V=0\) corresponds to zero signal coverage and \(V=1\) corresponds to full signal coverage). The AP placement optimization problem can then be formulated as Equations (6)-(9). The notation utilized for the formulation is given in Table 1.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameters** & \\ \hline \(\mathcal{L}\) & set of potential AP locations in the region \\ \(\mathcal{R}\) & set of potential user locations in the region \\ \(T\) & total number of APs in the D-mMIMO deployment \\ \(P_{\text{MIN}}\) & minimum average received signal power, required at each user location \\ \(V\) & signal coverage level as a percentage \\ \hline
**Variables** & \\ \hline \(p_{\mathbb{T}}\) & non-negative, transmitted power per AP \\ \(x_{l}\) & binary, equals one if an AP is deployed at location \(l\) \\ \(z_{i}\) & binary, equals one if user location \(i\) is covered with sufficient signal \\ \hline \hline \end{tabular}
\end{table}
Table 1: Notation.  
Transmit power minimization:  
_minimize_ \[p_{\Upsilon}\] (6) _subject to_  
Placement selection:  
\[\sum_{l\in\mathcal{L}}x_{l}=T \tag{7}\]  
Average received signal power:  
\[Tp_{\Upsilon}\sum_{l\in\mathcal{L}}\widetilde{\beta}_{il}x_{l}\geq P_{ \Upsilon\Upsilon}z_{i}\qquad\qquad\forall i\in\mathcal{R} \tag{8}\]  
Signal coverage calculation:  
\[\sum_{i\in\mathcal{R}}z_{i}\geq V|\mathcal{R}| \tag{9}\]  
Objective function (6) minimizes the transmit power per AP. Constraint (7) guarantees that \(T\) locations are selected for AP deployment. Constraints (8) ensure that the average received signal power at a covered user location is not lower than the minimum required amount \(P_{\Upsilon\Upsilon}\). Constraint (9) ensures the desired signal coverage level.
# Features of Structure and Absorption in the Jet-Launching Region of M87
## 4 Discussion
### Jet Interruption with Absorption  
As we have mentioned in Section 3, an interruption of the northern limb between 5 and 7 mas from the center of the image is seen at 22 GHz. On the restored 22 GHz image, the peak of the northern limb around \(d=6.0\) mas is below \(5\sigma\). According to the results of 22 GHz KaVA monitoring and 15 GHz MOJAVE survey on M87, at many epochs in a period longer than 20 years, an interruption is seen in the northern limb somewhere between \(d=5\) and 10 mas, thus the jet in M87 shows a single-ridgeline morphology in this region, e.g., at 22 GHz, 2 March 2014 and 3 May 2014 [7]; at 15 GHz, 1 November 1998, 9 August 2004, 11 February 2010, 27 August 2019, et al.  
Inverted spectrum with \(\alpha\) up to 2.2 is detected around the interruption on our 22-43 GHz spectral-index map, suggesting a strong absorption feature in this area. Interestingly, we find that the inverted spectrum is also seen at a similar location around \(d=7\) mas on the 22-43 GHz spectral-index map in [31]. Since \(\alpha\) is very close to the upper limit of SSA (if we set the cutoff to \(3\sigma\), the \(\alpha\) is almost 3), free-free absorption may probably be the main absorption mechanism here. Since this feature is between 2000-4000\(R_{s}\) from the central engine, \"winds\" are still believed to be the main absorption medium. With our intensity slices, we estimate the opacity of this absorption feature: on the restored 22 and 43 GHz images, the intensity peak of the northern limb at \(d=6.0\) mas is 1.5 mJy beam\({}^{-1}\) and 2.3 mJy beam\({}^{-1}\) respectively. If emissions were optically thin here, assuming the optically thin spectral index is \(-2\), then the intensity peak in this region should be 8.8 mJy beam\({}^{-1}\) at 22 GHz. If \(\mathrm{S}_{\mathrm{obs}}=\mathrm{S}_{\mathrm{pre}}\mathrm{e}^{-\Theta}\), then this implies an opacity of \(\alpha\sim 1.8\) at 22 GHz.  
The interruption of the northern limb between 2000 and 4000\(R_{s}\) from the central engine is an indication that the external medium is not uniform in the jet-launching region. As the jet propagates outward, it may go through regions with high f-f opacity. In addition, the pattern-like spectral-index distribution along the jet might be an indication of non-uniform external medium as well.
###### Abstract  
High mountain terrains, with steep slopes and deep valleys, are generally challenging areas to monitor using satellite earth observation techniques since the terrain creates perspective distortions and differences in illumination that can occlude or obfuscate a significant proportion of the land. This is particularly prominent in synthetic aperture radar (SAR) data, where the oblique geometry can result in large areas of layover and shadow, which must be excluded from any analysis. Interferometric SAR (InSAR) is an established technique for monitoring ground motion and this study assesses its potential for geohazard monitoring in mountainous areas using Lake Sarez in Tajikistan as a case study, applying SAR data from the Sentinel-1 mission. It is shown that, although the effect of layover and shadow is severe, a judicious combination of ascending and descending satellite passes is still capable of surveying 88% of the land surface. It is also demonstrated that, through the use of an advanced InSAR technique (the APSIS(tm) Intermittent Small Baseline Subset technique), near-complete coverage of ground motion measurements is possible, despite intermittent snow cover. Moreover, this is achieved without the need for ground control, which can be hazardous to establish in such areas. It is concluded that a combination of satellite passes and advanced InSAR techniques greatly facilitates the remote monitoring of ground motion hazards in high mountain areas.  
oehazards; InSAR; ground motion; landslides; Sentinel-1; Lake Sarez Article  
Remote Monitoring of Ground Motion Hazards in High Mountain Terrain  
1 Nottingham Geospatial Institute, University of Nottingham, Nottingham NG7 2 TU, UK; [PERSON]SS]  
2 Tera Motion Limited, Ingenuity Centre, Nottingham NG7 2 TU, UK; [PERSON]S] (A.S.); [PERSON]DRESS] (D.G.); [PERSON]ESS] (A.A.)  
3
## 7 Conclusions  
The GA-DTC approach was developed and used in this research to optimize the KP, KI, and KD PID controller parameters for a doubly fed induction motor driven by two voltage inverters, utilizing this algorithm's optimized controller parameters and a weighted combination of the performance indices ISE, IAE, and ITAE. The main goal of this study focused on a comparison of different controls with different objective functions, used separately and in combination. The proposed controls showed significant efficiency in monitoring speed and torque references.  
Performance metrics, such as speed overshoot and rejection time, fluxes and torque ripples, and current THD, all showed significant improvements under the GA-DTC using combined objective functions. The following bullet points sum up the improvements made to the DFIM performances:  
* Eliminating speed overshoot for all reference speed variations, both with and without torque.
* A14.53% improvement in rejection time reduction.
* Lowering the torque ripples and flux ripples on the stator and rotor by 27.88%, 15.13%, and 4.375%, respectively.
* Respective increases of 32.45% and 71% in the THDs of the stator and rotor currents, which are acceptable.  
The GA-DTC control improved the classic DTC control's resilience by boosting its effectiveness, quickness, rapidity, and stability under transient and dynamic conditions.  
Future research will focus on the following areas to boost technical and scientific development:  
* Enhancement of DFIM performances by using artificial neuron networks.
* Using an experimental test bench to validate the GA-DTC approach.
* Using other optimization algorithms to tune the PID controller (ACO, ABC).
* Establishment of H infinite technique to improve DFIM performances.
* Theoretical and experimental validation of the DTC for the DFIM by using ANFIS.  
Conceptualization, S.M. ([PERSON]), M.A.M. ([PERSON]), and N.E.O.; methodology, S.M. ([PERSON]), A.D., M.E.M. and S.M. ([PERSON]); software, S.M. ([PERSON]) and M.A.M. ([PERSON]); validation, S.M. ([PERSON]), M.A.M. ([PERSON]), A.S.A.-S. and N.E.O.; formal analysis, S.M. ([PERSON]), M.A.M.  
[PERSON]. [PERSON]), and S.M. ([PERSON]); investigation, S.M. ([PERSON]), A.D., N.E.O., M.E.M. and A.S.A.-S; resources, S.M. ([PERSON]), M.A.M. ([PERSON]), N.E.O. and A.D.; data curation, S.M. ([PERSON]), A.D.,M.E.M. and S.M. ([PERSON]); writing--original draft preparation, S.M. ([PERSON]) and M.A.M. ([PERSON]); writing--review and editing, S.M. ([PERSON]), M.A.M. ([PERSON]), A.S.A.-S. and S.M. ([PERSON]); visualization, S.M. ([PERSON]) and M.A.M.; supervision, M.A.M. ([PERSON]), A.S.A.-S. and A.D.; project administration, M.A.M. ([PERSON]), S.M. ([PERSON]), A.D. and A.S.A.-S.; funding acquisition, A.S.A.-S. and M.A.M. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research was supported by Khalifa University as part of research excellence award # 8474000427.  
The data are available upon request from the corresponding author (M.A.M.) ([PERSON]).  
The authors declare no conflict of interest.
# DRL-Based Dynamic Destroy Approaches for Agile-Satellite Mission Planning
## 3 Modeling Process
### Markov Decision Process Modeling  
The interaction between agent and environment is depicted in Figure 4. Firstly, the state vector is embedded based on the clustering graph discussed in Section 3.1. Subsequently, the embedded initial state is fed into the actor-network to obtain the destroy nodes and the coefficients of destroy range of the current solution. Once the environment receives an action from the agent, a new state and the corresponding reward of this action are  
Figure 3: Actor-network. The attention mechanism is incorporated into the network architecture, where the initial input tasks are embedded as graph nodes. These graph nodes undergo multiple layers of GAT networks to extract node features, ultimately obtaining the set of nodes to be destroyed in the next iteration.  
calculated. The agent repeatedly outputs actions until the terminal state is reached. The state, action, and reward of the MDP are introduced in this section.