esa-sceva/satcom-chunk-collection huggingface dataset:
# Plasma Surface Modification of PDMS-Glass Microfluidic Chips for Oil Recovery Studies
###### Abstract  
Wetting hysteresis is the most important characteristic of microfluidic chips for modeling multiphase flows in rocks, including for oil production problems. Plasma modification of surface wetting characteristics is well studied, but there is a problem of stabilizing the resulting surface for use in a liquid hydrocarbon media. In this work, a simple and accessible technology for modifying the surface of PDMS and glass using a dielectric barrier discharge in a chamber based on the d'Arsonval apparatus has been developed. The surface wetting hysteresis for PDMS and glass was studied as a function of the plasma treatment time. It is shown that with the help of plasma treatment it is possible to change the wetting angles of the walls of microfluidic chips in a very wide range, thereby simulating the conditions of both hydrophobic and hydrophilic rocks. At the same time, PDMS has the widest possible range of changes in the wetting angle; the advancing contact angle decreases from 120\({}^{\circ}\) to 10\({}^{\circ}\); receding contact angle--from 70\({}^{\circ}\) to 0\({}^{\circ}\) during plasma treatment. It has been shown that plasma treatment of a microfluidic chip, together with a 30 min primary extraction with oil and salt water, leads to a significant change in the wetting characteristics of its surface. This in total leads to an increase in the oil displacement efficiency from the chip by about 10%. In general, the results of the study showed that plasma surface treatment for the hydrophilization of microfluidic chips is a simple and affordable technology for controlling the wetting characteristics of microfluidic chips. PDMS in this case is a promising material.  
laboratory-on-a-chip; surface modification; contact angle; plasma; primary extraction; rock flooding; crude oil; glass; PDMS Article
# Dwarf Mongoose Optimization-Based Secure Clustering with Routing Technique in Internet of Drones
## 4 Conclusions  
In this study, a new DMOSC-MHRS algorithm was devised for secure cluster-based communication in the IoD environment. The DMOSC-MHRS technique proficiently chooses CHs and optimal routes to a destination. In the presented DMOSC-MHRS approach, a new DMOSC technique is utilized to choose CHs and create clusters. A FF involving trust as a major factor is included to accomplish security. Besides, the DMOSC-MHRS technique designs a WHOMHR scheme for the optimal route selection process. To demonstrate the enhanced performance of the DMOSC-MHRS model, a comprehensive experimental assessment is made. Extensive comparison studies illustrate the better performance of the DMOSC-MHRS model over other approaches, with maximum reliability of 96.99%. Therefore, the proposed model can be employed in future real-time applications such as environmental monitoring, forest fire detection, disaster management, search and rescue, and smart cities. In the future, the performance of the DMOSC-MHRS algorithm can be enhanced by the utilization of data aggregation approaches, thereby enhancing overall network efficiency. In addition, the overall network performance can be improved by the use of unequal clustering techniques to mitigate hot spot problems.  
Conceptualization, F.S.A. and J.S.A.; methodology, K.A.A.; software, A.M.H.; validation, H.A., M.A.E. and A.Y.; formal analysis, A.Y.; investigation, A.M.; resources, A.M.; data curation, A.M.H.; writing--original draft preparation, F.S.A., A.A., J.S.A. and H.A.; writing--review and editing, K.A.A. and A.A.; visualization, A.M.; supervision, A.M.H.; project administration, J.S.A.; funding acquisition, F.S.A. and A.A. All authors have read and agreed to the published version of the manuscript.  
Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2022R319), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia. The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code: 22 UQU4340237 DSR47. Research Supporting Project number (RSP2022R444), King Saud University, Riyadh, Saudi Arabia.  
Data Availability StatementData sharing is not applicable to this article as no datasets were generated during the current study.  
The authors declare no conflict of interest.  
Figure 9: Reliability analysis of DMOSC-MHRS approach under distinct drones.
# FL-SDUAN: A Fuzzy Logic-Based Routing Scheme for Software-Defined Underwater Acoustic Networks
## 4 Methodology
### Minimum Spanning Tree Algorithm Based on Fuzzy Path Optimization  
If \(L\langle p,q\rangle\) is a non-tree arc, there exists a unique path from node \(p\) to \(q\) on the tree \(T\) that connects the two nodes to form a loop. For a non-tree arc \(L\langle p,q\rangle\in E-E^{\prime}\), if the weights of all arcs in \(T(N,E^{\prime})\) are less than the directly connected arc weights of \(p\) and \(q\), then \(T\) is a spanning tree of \(G\). The MST algorithm based on fuzzy path optimization (FPO-MST) is also known as the greedy approach. The graph \(G\) is constructed by CHs. Fuzzy inference is used to calculate the path weights between CHs. In FPO-MST, the arcs with the smallest weights are joined to the subtree \(T\). If the arc does not form a ring when connected to \(T\)the arc is added. Then the next arc is considered until all nodes are traversed. Finally, it constructs the tree. The FPO-MST pseudo code is shown as follows (Algorithm 2).  
```
1:Input\(G(V,E),E=\infty\), \(E_{new}=\varnothing\)//initialization graph \(G\) and arc value
2:Output\(MST(G)\)
3:Procedure FPO-MST(0)
4:\(V_{new}\leftarrow\left\{V_{0}\left|V_{0}\right.\right\in N\right\}\);
5:\(T\leftarrow\left(V_{0},\varnothing\right)\);
6:For each node \(V_{i}\in V_{new}\) and \(V_{j}\in V-V_{new}\)
7:\(\widetilde{A}_{ij}=Residual\left(\widetilde{V}_{j}\right)\); //residual energy of node
8:\(\widetilde{B}_{ij}=Resource\left(\widetilde{V}_{j}\right)\); //resource occupancy of node
9:\(\widetilde{R}_{ij}=\widetilde{A}_{ij}\times\widetilde{B}_{ij}\);
10:\(\widetilde{W}_{V_{i}\to V_{j}}\leftarrow\max\left\{\underset{i\in 1}{\overset{n}{\underset{j}{\sqcup}}}\sqrt{\underset{j\overset{n}{ \underset{j}{\sqcup}}}{\overset{n}{\underset{j}{\sqcup}}}\mu\left(\widetilde {R}_{ij}\right)}\right\}\);
11:\(W_{V_{i}\to V_{j}}\leftarrow\min\left(\widetilde{W}_{V_{i}\to V_{j}} \right)\); //pick the arc weight
12:if\(\left(W_{V_{i}\to V_{j}}<W_{V_{i+1}\to V_{j+1}}\right)\)
13:\(T=T\cup\left(V_{i}\to V_{j}\right)\);
14:elseif\(T\cup\left(V_{i+1}\to V_{j+1}\right)\in C_{i+1,j+1}\)
15:\(MST(G)=\varnothing\); //unable to construct an MST tree
16:else
17:\(V_{new}\leftarrow\left\{V_{i}\left|V_{i}\in N\right.\right\}\); //add the node \(V_{i}\) to \(V_{new}\)
18:\(V_{new}=V_{new}\cup\left\{V-V_{new}\right\}\); //update \(V_{new}\)
19:\(E_{new}\gets E_{V_{i}\to V_{j}}\); //add the arc \(E_{V_{i}\to V_{j}}\) to \(E_{new}\)
20:\(E_{new}=E_{new}\cup\left\{E-E_{new}\right\}\); //update \(E_{new}\)
21:\(T=T\cup\left(V_{new},E_{new}\right)\); //update T
22:\(MST(G)\gets T\); //construct T
23:End if
24:End for
```  
**Algorithm 2** The FPO-MST algorithm
## 3 The FQS Algorithm
### Correctness and Complexity Analysis  
#### 3.3.1 Correctness Analysis  
The correctness of the FQS algorithm essentially follows from the correctness of the QS algorithm. In the search phase, the FQS algorithm uses two bad character shift arrays in two steps. When comparing pattern \(P\) to text \(T\), FQS first checks the position, \(pos\) in \(P\), the expected maximal shift position, comparing it to the position, \(j+pos\) in \(T\). If there is a mismatch, it uses the shift array, \(next\), to shift the pattern to the next right position. The shift value is at most \(pos+1\). It will not miss any potential matching position. After the first symbol comparison (\(P[pos]\)_vs._\(T[j+pos]\)), the remaining steps are the same as in the QS algorithm.
# A Research Study on Protocol Stack of Space-Based Optical Backbone Network
## 3 Design of the Protocol Stack
### Design of Transport Plane
#### 3.2.3 Service Adaptation  
From the design discussed above, we know that the protocol stack builds an LSP for each service to transmit it. So, there is a very important problem here: how to let the service move into its corresponding LSP? The solution is to use a service adaptation method provided by the adaptation module. In the process of building an LSP, the signal protocol has already created an FIB (forwarding information base), which is used to adapt services. Each FIB item includes parameters such as FEC (forwarding equivalence class), OutPort, OutLabel and so on. When a service package arrives, the adaptation module firstly creates the FEC by extracting specific parameters from the package, and then uses the FEC to query the FIB to obtain the corresponding OutPort and OutLabel. Then the protocol stack creates the AOS frame, including the Outlabel and service package, and then sends it through the port specified by OutPort.  
In this paper, we completed the adaptation for IPv6 services.  
As shown in Figure 8, it can be seen that the IPv6 service adaptation is designed as follows:  
1. When an IPv6 package arrives, the adaptation module firstly creates the FEC, in accordance with the rules for extracting the destination address from the package directly as the FEC. Therefore, for the two IPv6 packages shown in the figure, two corresponding FECs (FEC1 and FEC2) are created.
2. Using the FEC created above, the adaptation module then queries the FIB to find out the corresponding OutPort and OutLabel.
3. Having found the OutPort and OutLabel, the adaptation module creates the corresponding AOS frame with its label field filled with the corresponding OutLabel and its data field filled with the corresponding complete IPv6 package.
4. Finally, the adaptation module sends the AOS frame through the port specified by the corresponding OutPort.  
By the process designed above, the adaptation module can adapt the IPv6 services in real time. According to the rules of creating an FEC, it can be seen that the adaptation module uses the destination address to differentiate between services, which means all services that go to the same node (with the same address) are adapted into the same LSP.  
Figure 8: IPv6 service adaptation.  
However, the rules can be improved according to the demands of services. If there is a demand for different services to the same node to be differentiated, the adaptation module only needs to improve the rules by extracting more parameters, which can differentiate between the services, to create the FEC. Then, even if the services go to the same node, they are still adapted to the different LSPs according to the improved rules.  
As can be seen, the adaptation module could provide service adaptations easy to expand and support the transmission for multiple services.
# Performance Modeling Framework for IoT-over-Satellite Using Shared Radio Spectrum
###### Abstract
Then, we estimate the uplink signal and the terrestrial interference using our proposed framework. In order to evaluate this framework under realistic environments, we adopt several parameters in the commonly used LoRaWAN standard [7] over a Low Earth Orbit (LEO) satellite. Then we evaluate the performance of the link in terms of SINR. The main contributions of this paper are summarized as follows:  
* Providing an evaluation framework of predicting the uplink SINR in IoT-over-satellite communication. * Providing a model for obtaining the aggregated uplink interference of terrestrial shared ISM band users as seen from the satellite's perspective. * Providing a model for predicting satellite link-availability based on a given antenna beamwidth. * Finding the optimal beamwidth of satellite antenna beamwidth to maximize link-availability. The main focus of the paper is to provide a simulation framework to estimate the impact of terrestrial uplink interference to the LEO satellite in the shared spectrum, and it does not necessarily address only ISM or any specific radio frequency band. Although the 1.6 GHz and 2.5 GHz is a suggestion by the 3 GPP [8; 9], there is no restriction on what frequency bands to be used in IoT-over-satellite communication, and it is interesting to explore the use of a license-free spectrum, such as the ISM band for shared terrestrial-satellite access use since the ISM band devices are commonly used by the public.
# An Asymmetric Encryption-Based Key Distribution Method for Wireless Sensor Networks
## 4 Analysis and Comparison
### Security Analysis  
#### 4.3.1 Mutual Authentication  
In both subschemes, mutual authentication of BS and sensors (including CHs) is assured via the challenge-response mechanism. Terminals without legal identifiers (_ID_CHi_ or _ID_si_) cannot pass the identity authentication. Since the identifier is stored in the chip of each sensor with a tamper proof mechanism and encrypted for transmission, its confidentiality and integrity can be guaranteed. We added 10 nodes to the test network and distributed them evenly in 3 clusters. They simulated nodes that gained illegal access to the sensing network, randomly generating their identification codes _ID_si_. Since the identifiers _ID_si_ used by these 10 nodes in constructing the _Ciperh3_ were not included in the authorized and legitimate user list of the base station, the shared session key could not be obtained via the base station in the test. As a result, the reliability of the authentication scheme is fully demonstrated.
## 6 Latest Advances of Honey Applications in Wound Care
Pectin, a natural polyurethane, has been used to produce Manuka honey-loaded hydrogels (PHH) to treat excisional wounds in rats, with significant acceleration of wound contraction compared with direct liquid honey application or control (no treatment). It is interesting to note that at the end of the observation time, new hair follicles and organised fibrous tissue with reduced inflammation could be observed not only in the PHH group but also on the pectin blank hydrogel group [132]. This suggests that the polymer itself played an important role in achieving complete healing and indicates the paramount importance of selecting the most suitable material for the designated medical application. The inclusion of stingless bee honey and curcumin within composite nanofibrous membranes improved their therapeutical potential in terms of the radical scavenging ability, compared to direct application of these healthful natural agents on the wound. The polymeric structures were shown to be highly cytocompatible, although in this study by [PERSON] et al., the honey-loaded membrane did not significantly reduce the wound closure time compared to the conventional treatment (povidone iodine). However, over 95% inhibition of all the bacterial strains tested was obtained upon honey addition to the gelatin membrane, while a maximum of 25% was achieved when only curcumin was loaded into the membrane [138]. Iranian honeys from three different provinces were compared by [PERSON] et al. on a rat burn model, with the thymol-rich Damavand sample showing the greatest antibacterial effect with early onset of signs of wound recovery. Honey incorporation in an alginate scaffold further shortened the healing time (from 16 to 14 days) with remission from infection. Interestingly, in the same study, the analysis of the honey sample from the Ardabil province showed a higher level of sucrose (right below the maximum concentration allowed, above which it is indicative of adulteration) and reduced diastase activity denoting possible excessive heating as compared to the other samples. Ardabil honey's ability to promote wound healing was lower compared to the other samples, suggesting that honey authenticity and quality is a cardinal parameter to be considered for all the therapeutically effective honey varieties to ensure remarkable medical properties [139].
# GDPR and Large Language Models: Technical and Legal Obstacles
## 2 Detailed Analysis of GDPR Articles Challenging for LLMs
### The Explainability Gap in Transformer-Based Models  
Understanding and interpreting the decision-making process of Large Language Models (LLMs) remains a fundamental challenge, particularly in the context of GDPR's transparency requirements. Traditional Explainable AI (XAI) techniques, such as SHAP and LIME, are well-suited for structured data but struggle to provide meaningful insights into the highly non-linear and distributed representations used in Transformer-based architectures [40]. These methods often rely on perturbation-based explanations, which fail to capture the intricate contextual dependencies across long text sequences processed by LLMs [67].  
Recent interpretability approaches tailored for Transformers include attention weight visualization, attention rollout, and gradient-based saliency mapping, which attempt to trace decision-making pathways within multi-layer, multi-head attention models [68]. However, studies indicate that attention distributions do not always correlate with model reasoning, raising concerns about their reliability as interpretability tools [69]. Additionally, methods such as causal mediation analysis and concept-based interpretability frameworks are being explored to bridge this gap, yet they remain computationally intensive and lack standardization [70]. Addressing this explainability gap is critical for regulatory compliance and user trust, necessitating further research into robust, scalable, and legally viable interpretability solutions for LLMs.  
Figure 3 provides a visual summary of the challenges associated with implementing key data subject rights in LLM systems. The diagram consolidates the challenges for the Right to Erasure (Article 17) [8], Right of Access (Article 15) [6], Right to Rectification (Article 16) [7], and restrictions on Automated Decision-Making (Article 22) [12]. It highlights common issues such as data-parameter entanglement, non-transparent processing, and the inherent opacity of LLMs, and suggests potential solutions including machine unlearning, model auditing, targeted fine-tuning, and explainable AI techniques. This figure serves as an effective visual aid to contextualize the technical hurdles and possible strategies for achieving GDPR compliance in automated decision-making systems.
# Genus, Species, and Subspecies Classification of _Salmonella_ Isolates by Proteomics
## 2 Materials and Methods  
### Selection of Bacterial Isolates  
Bacterial isolates were obtained from the stock culture collection at the Food and Drug Administration/Center for Food Safety and Applied Nutrition. _Salmonella_ reference collection A (SARA), the so-called \"Typhimuriumium complex\" representing _Salmonella_ Typhimuriumium and its closest serovars, was selected to challenge the method [30]. This covers _Salmonella_ Typhimuriumium (str. SARA2, SARA18, and SARA19), _Salmonella_ Saintpaul (str. SARA23 and SARA24), _Salmonella_ Heidelberg (str. SARA31, SARA36, and SARA39), _Salmonella_ Paratyphi B (str. SARA50), and _Salmonella_ Muenchen (str. SARA63). _Salmonella_ Enteritidis (str. SESa and SE22) was included due to its high prevalence. Strains of a different subspecies (_Salmonella enterica_ subsp. _arizonae_ str. SARC5 and SARC6) and a different species (_Salmonella bongori_ str. SARC12) were also included to test the discriminatory power of the method. _Escherichia coli_ (O157:H7 and str. K-12) and _Shigella_ (_S. sonnei_ str. S864 and _S. flexneri_ str. 2457M) were selected due to their close relatedness to _Salmonella_ and their known difficulty of differentiation from each other. The isolates and taxonomic positions are summarized in Figure 1.
## 5 Experimental Results and Discussion
### Time Skew with Differential Driven Mode  
Finally, the impact of time skew was experimentally tested. Figure 10a shows the received signal spectra with different skew values at the BTB case. Similar to Figure 4c, spectral narrowing was introduced by time skew. Differently, as shown in Figure 10b, time skew was able to confront with power fading after 20 km SSMF transmission. A sharp depression inside the spectrum could be observed with 0 ps skew (black curve). The fading frequency components could be gradually lifted as the skew value increased.  
Figure 10: Received signal spectra versus time skew (**a**) at BTB and (**b**) after 20 km SSMF transmission.  
Figure 9: Measured BER versus amplitude ratio and bias voltage for (**a**) PAM-6 and (**b**) PAM-8 signals after 20 km SSMF transmission. Measured ROP sensitivities with \(-\)15% amplitude ratio for (**c**) PAM-6 and (**d**) PAM-8 signals after 20 km SSMF transmission and for (**e**) PAM-6 and (**f**) PAM-8 signals at BTB, respectively.  
Figure 11a,b shows the measured BERs as a function of time skew for 34 Gbaud PAM-6/8 formats, both at BTB and after 20 km SSMF transmission. For the BTB case, the larger skew values lead to the rising of BER, which was caused by more serious ISI in the time domain or low pass filtering in the frequency domain. For 20 km SSMF transmission, the BERs could be brought down quickly as the skew increased and then reached an error floor when the skew was larger than 20 ps. The lowest BERs were 2.43 \(\times\) 10\({}^{-4}\) and 5.39 \(\times\) 10\({}^{-3}\) with 26 ps skew for PAM-6 and PAM-8 signals, respectively. Figure 11c-f measures the ROP sensitivity for PAM-6/8 signals after 20 km SSMF transmission and at BTB, respectively. The skew was controlled as 20 ps considering both BTB and transmission performance, suitable for practical implementation with a fixed delay line. After 20 km SSMF transmission, the ROP sensitivities were promoted to \(-\)5.1 dBm and \(-\)3.9 dBm at the 7% and 20% HD-FEC thresholds, which was the best result among three imbalances. For the BTB case, the penalties were 1.3 dB and 1.2 dB after introducing time skew for PAM-6 and PAM-8 signals. It can be explained as the low pass filtering effect.
## 3 Design of Main Working Parts
### Seedbed Preparation Device
#### 3.1.2 Theoretical Aggregate Amount of Soil  
As shown in Figure 5, while moving forward, the shaper needs to push soil in front, transport the soil to the lateral side, and extrude the soil so as to form the soil-covering belt of seedlings. In order to ensure the correct amount of soil collected on the soil-covering belt of seedlings and the thickness of the covering soil on the seedlings in the subsequent rotating tillage process, the amount of soil collected by the shaper needs to be calculated theoretically. The volume of soil collection is calculated through the effective cross-sectional area of the shaper multiplied by a certain length. The amount of soil collected at a forward distance of \(Y\) for the entire machine [14] is:  
\[Q_{1} =\gamma Y\left[h_{z}L_{1}+\frac{L_{1}^{2}\tan 15^{\circ}}{2}+L_{2}(L_{1} \tan 15^{\circ}+h_{z})\right] \tag{1}\]  
where \(Y\) is the advancing distance of the whole machine which is 1 m;  
\(\gamma\) is the bulk density of soil (yellow soil) (1300 kg/m\({}^{3}\)) [15];  
\(h_{z}\) is the depth of the short side of the shaper into the soil (0.07 m);  
\(L_{1}\) is the cross-sectional length of the shaper (0.25 m);  
\(L_{2}\) is the distance between the two shapers (0.2 m).  
It can be calculated from Formula (1) that \(Q_{1}=69.2\) kg.  
When the advance distance of the whole machine is \(Y\), the amount of covering soil required by the seedlings is  
\[Q_{2}=\frac{2\gamma YHL_{1}}{\cos 15^{\circ}} \tag{2}\]  
where the thickness of covering soil required by seedlings \(H\) is 0.07 m.  
It can be calculated from Formula (2) that \(Q_{2}=37.6\) kg.  
Because \(Q_{1}>Q_{2}\), the soil collection operation of the shaper meets the theoretical requirement.  
Figure 4: Schematic Diagram of Operation Process of Seedbed Preparation Device. 1. Seedling soil-covering belt. 2. Seedbed. 3. Ridge body. 4. Non-working area. 5. Seedbed preparation device.