esa-sceva/satcom-chunk-collection huggingface dataset:
# Encapsulated Probiotics: Potential Techniques and Coating Materials for Non-Dairy Food Applications
## 4 Biomaterials Utilized for Probiotics Encapsulation
### Carbohydrate Polymers  
#### 4.1.1. Alginate  
Among the carbohydrate polymers used, the most common biomaterial is alginate. Alginate can be produced by various brown seaweeds (_Laminaria digitata_, _Laminaria hyperborea_, _Laminaria japonica_, _Macrocystis pyrifera_, and _Ascophhylum nodosum_) and two genera of bacteria (_Pseudomonas_ and _Azotobacter_), making it abundant and comparatively low in cost [45]. Alginate is the preferred biomaterial in probiotic encapsulation owing to its non-harmful nature, ease in producing strong beads, and being promptly accessible. Alginate has good gelling, balancing out, and thickening properties, and is easy to manipulate, biocompatible, and biodegradable [1; 40; 45; 79]. Alginate is a pH-responsive polymer that is stable at lower pH and unstable in higher pH conditions which is beneficial in customizing release profiles. During the delivery, alginate beads tend to shrink in low acidic gastric environments. Hence, it prevents the release of the encapsulated probiotics from the beads. Once the beads reach the small intestine with alkaline conditions, the alginate transforms into a soluble alginic acid layer. Subsequently, they swell and release the encapsulated probiotics [75]. Unfortunately, alginate is sensitive to heat treatment, porous, unstable, and has poor barrier properties because of its high molecular mobility and weak interaction between the molecular chains [1; 45]. The weakness of alginate can be overcome through a crosslinking reaction with divalent cations or co-encapsulation with starch or by coating the alginate capsules with an extra layer (multilayer technique) made of a different type of biomaterial [92]. The ionic crosslinking of alginate chains with calcium cations could result in a strong gel structure. The presence of calcium cations could also disrupt the water coordination of the alginate network [79]. The synergistic effects of alginate and starch of the alginate capsules could protect the entrapped probiotics [45]. With the interaction of the negatively charged carboxylic groups of alginate with positively charged amine groups chitosan, stronger, ordered three-dimensional gel networks can be produced. The resulting capsules also have smoother surfaces with decreased water permeability [67].
# Feasibility Study of Real-Time Monitoring of Pin Connection Wear Using Acoustic Emission
## 2 Experimental Setup
### Inspecting the Pin Surface by Digital Microscopy  
A VHX-600E digital (from Keyence, Osaka, Japan) microscope (as shown in Figure 5a) was used to observe the worn surface of the pins, and to create the 3D models of the worn surfaces. The scope had a varying-focus lens from 20\(\times\) to 200\(\times\), and the observation range was from 19.05 to 1.14 (mm). When the specimen stage moved 15 mm, the repeat position precision was \(\pm\)0.5 \(\upmu\)m. A V-shaped support was used to support the pin (as shown in Figure 5b).  
Figure 4: A diagram to illustrate the instrument connection for acoustic emission.
# The Design and FPGA-Based Implementation of a Stream Cipher Based on a Secure Chaotic Generator
## 3 Hardware Implementation of the Proposed SCbSC and Evaluation of Its Performance
### Hardware Cost of the Proposed Secure PCNG  
In this section, we analyze the performance of the proposed SPCNG implementation in terms of resources used (area, DSP), speed (maximum frequency--Max. Freq., throughput), and efficiency. Four SPNG versions were realized to choose the best among them in terms of hardware resources, throughput, and statistical resilience (NIST test) for use in the SCbSC system (see Table 2). Furthermore, we give the efficiency (in terms of throughput/slices) of all versions. The efficiency parameter gives us an overall idea of the hardware metrics performance of the implementation.  
\[Max.Freq.=\frac{1}{T-WNS}[\text{Mhz}]. \tag{19}\]  
where \(T=8\) ns is the target clock period (\(F=1/T=125\) Mhz) and WNS is the worst negative slack of the clock signal in the intra-clock paths section.  
\[Throughput=N\times Max.Freq.[\text{Mbps}]. \tag{20}\]  
\[Efficiency=\frac{Throughput}{Slices}[\text{Mbps}/Slices]. \tag{21}\]  
The proposed SPCNG versions were implemented on a Xilinx XC7Z020 PYNQ-Z2 FPGA hardware platform.  
Figure 6: Behavioral simulation and Post-implementation timing simulation.  
The four SPCNG versions have the same general structure but are completely different in their output function and slightly different in their internal state. The differences between the versions of columns 1 and 2 on the one hand, and the versions of columns 3 and 4 on the other hand, are in the output function used, as shown in Table 2. Indeed, versions 1 and 2 use a chaotic multiplexing technique as output function, where the sequence X(n) is controlled by a chaotic sample \(X_{th}(n)\) and a threshold \(T_{th}\) is defined as follows:  
\[X(n)=\left\{\begin{array}{ll}XSC(n)&if\ 0<X_{th}<T_{th}\\ XTIC(n)&otherwise\end{array}\right. \tag{22}\]  
with \(X_{th}(n)=XLC(n)\oplus XSC(n)\) and \(T_{th}=0.8\times 2^{N}\).  
Version 2, compared to version 1, contains a LFSR in parallel with the 3D Chebyshev map. Version 4 is the one shown in Figure 2, and version 3 is the same as version 4, but without the LFSR. Moreover, all SPCNG versions successfully passed the 15 NIST tests. However, versions without LFSR did not pass certain sub-tests. For the chaotic multiplexing technique, we found only one failed sub-test out of 148 non-overlapping template sub-tests, and for the XOR operation, we found three failed sub-tests out of 148 non-overlapping template sub-tests. Therefore, based on all results in Table 2, we chose version 4, which is the best (in terms of resources used, throughput, and efficiency) compared to other versions, to be used in the SCbSC system.
## 2 Method and Theory  
### Overview of Experimental Setup  
The primary method used in this study is difference-frequency generation (DFG), a nonlinear, optical process in which two laser beams (a pump and a signal) interact within a nonlinear medium to generate an idler wave at a frequency equal to the difference between the pump and signal frequencies. DFG is particularly effective for generating mid-infrared (mid-IR) light, as it can convert the 1064 nm pump and a C-band signal (typically 1530-1565 nm) into an idler at approximately 3.5 um. This work leverages the availability of high-power, high-efficiency 1064 nm solid-state lasers and C-band fiber lasers combined with nonlinear materials like periodically poled lithium niobate (PPLN) to efficiently generate mid-IR radiation. The efficiency of DFG is influenced by several factors, including phase-matching conditions, beam intensities, and the properties of the nonlinear medium.  
For a clear overview of the experimental setup, Figure 1 illustrates the layout of key components involved in the experiment, including the pump and signal sources, nonlinear medium, and output coupler. The figure provides a high-level summary of the components before delving into the specific details of each element.  
Figure 1: Experimental schematic diagram of the DFG setup. The 808 nm diode laser is coupled to the Nd gain medium, which is followed by a dichroic mirror and a PPLN crystal for frequency conversion. The optical layout includes a collimator, half-wave plate, and a plano-convex lens for focusing.
## 1 Introduction and Related Work  
In the modern era, security and surveillance are important issues. Recent acts of theft/terrorism have highlighted the urgent need for efficient video surveillance and on-the-spot notification of ongoing thefts to house owners and other household members. A number of surveillance solutions are currently available on the market, such as CCTV cameras and digital video recorders (DVRs) that can record the unauthorized activities of a trespasser, but cannot distinguish between human and non-human objects [1, 2]. In recent times, the ratio of theft has increased tremendously due to a lack of awareness and low availability of smart-gadgets. The task of face detection and the recognition of an intruder become very difficult when the intruder hides their face partially or fully using some type of material, such as plastic, leather, or fabric.  
Legacy systems cannot provide real-time theft notification to the house owner nor detect partially of fully obscured faces. It is also challenging for old systems to detect the intruder in the dark using a CCTV camera without night vision capability. The major flaw with this kind of arrangement is that it demands the 24/7 availability of a house owner or member, or manual video surveillance, which is almost impossible [3; 4; 5]. In addition, it is a tedious task to go through all the recorded video clips after a possible theft has become known. It might be that the storage server contains a large amount of family member footage, which is of no use in identifying trespassers.  
The proposed approach can be applied to an IoT-based smart home monitoring system in near real-time. As shown in Figures 1 and 2, a smart home was designed and developed based on an integrated framework of sensors, cameras, and customized hardware to analyze unauthorized access. The system operates at two different levels: through a hardware interface and through a software interface. At the hardware interface level, an intelligent sensing node is deployed, connected to a central sensing node which acknowledges the data and sends it to the storage server. The software modules are further subdivided into several further levels, including data logging, data retrieval, and storage. The main objective of the software is to detect and report unsupervised human activity using large data handling techniques as close to real-time as possible.  
The work is presented as follows. The initial section provides an introduction on legacy systems, potential issues, and their impact on society. Section 2 describes the necessity of the present research. It also describes the intelligent features of the system that can detect obscured faces using graphical and statistical methods. Subsequently, Section 3 presents the design and experimental setup of the system. It also describes two critical features: (i) the detection of obscured faces and (ii) detection of
# Structure, Biology, and Therapeutic Application of Toxin-Antitoxin Systems in Pathogenic Bacteria
## 5 Exploitation of TA Systems for the Development of Novel Antibiotic Drugs
Recombinant toxin RNA or DNA enter pathogens via bacteriophage infection. The nucleic acids integrate into the bacterial genome in a lysogenic cycle. The recombinant toxins are produced and cause cell death. There are critical considerations for the design of antibiotic drugs using TA systems from pathogenic bacteria. Drug candidates, such as peptides or small molecules, should have strong binding affinity to the interface of the TA complex, enough to fully disrupt a stable pre-formed complex, because most known TA complexes are formed with a strong binding affinity. The binding commonly involves extensive electrostatic and hydrophobic interactions; the antitoxin wraps around the toxin. The large interface of the TA complex is difficult to bind strongly to small molecules. This obstacle has been frequently encountered in the development of inhibitors of protein-protein interaction (PPI). However, there are precedents that the binding interface of the protein-protein complex has a long and shallow pocket that is more easily accessible to small molecules [195]. Other issues are related to the limited information on a variety of TA interactions. The sequence and structure of antitoxins are highly divergent, although their cognate toxins show considerable structural similarity to other toxins of the same family. To inhibit the TA interaction, one could design a molecule that binds to the \(C\)-terminal toxin-binding site of the antitoxin. However, this binding site is a challenging target because it commonly adopts an intrinsically disordered structure. Furthermore, the artificial activation of the toxin has to be effective in cell death when TA operons are highly activated under stressful conditions such as nutritional starvation, antibiotic treatments, and oxidative stresses. There is concern that artificial activation of toxins to a moderate level could induce the formation of persister or dormant cells that contribute to chronic infection. In this regard, another strategy can be envisaged that resuscitates the dormant cell, rendering it susceptible to antibiotic drugs. It remains to systemically validate the effectiveness of diverse antibiotic strategies utilizing TA systems of pathogens. The supplementary materials are available online at www.mdpi.com/2072-6651/8/10/305/s1, Table S1: Toxin-Antitoxin pairs of human pathogenic bacteria addressed in the text. The information on TA pairs was collected from the web-based TA database (TADB). The minus sign indicates \"Not Applicable\". **Acknowledgments:** This work was supported by grants provided by the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (NRF-2007-0057059, 2014K1A3A1A19067618, and 2015R1A2A1A05001894), and the 2015 Brain Korea (BK) 21 plus Project for Medicine, Denistry and Pharmacy. **Conflicts of Interest:** The authors declare no conflict of interest.
# TDLearning: Trusted Distributed Collaborative Learning Based on Blockchain Smart Contracts
## 5 Experiment and Case Study
### Case Study
#### 5.3.1 Task-Information-Initialisation Phase  
Steps (a) to (c) in Figure 18 represent collaborative node-identity registration and authorisation, data registration and on-chain data retrieval, respectively. The prerequisite for collaborative nodes to participate in collaborative tasks is the registration of identity information and the assignment of role authority, i.e., their Ethereum account address is tied to the authority information. Step (a) shows the task owner node-identity registration and role-authority-assignment process, where the smart contract assigns the corresponding role authority to the node after its identity information has been successfully registered. As shown in this diagram, the assignment of authority triggers the UpdateNodeAuthority event, indicating that the node has completed the authorisation process, and the authorisation process is recorded and interacted with in an event manner. The identity-registration and authorisation process for the rest of the collaborating nodes is the same as that of the node, after which each node has the authority to participate in the on-chain collaboration.  
Step (b) shows the data-registration process for a research institution, where the smart contract completes the data registration and returns the transaction record information. As shown in this step, once the basic data information is filled in, the data are successfully registered and added to the data list via the DMC. The smart contract then creates an inverted index for the data and initialises its data-quality evaluation. Subsequently, the remaining three research institutions all completed data registration through the above steps with data hashes of eh86m1, 9 uthew and po8a7r, respectively. Step (c) shows the data-retrieval process; after successful retrieval, the smart contract returns the data hash value corresponding to the retrieval keyword and triggers the data-retrieval event.  
Figure 17: Experimental results in three scenarios: in different collaboration scenarios, with the increase in the number of fused pretrained models, the classification accuracy of the global model developed based on F-Attention has been steadily improved.  
In the information-initialisation phase of the collaborative task, each collaborative node completed the registration of identity information and was assigned the corresponding role authority by the smart contract. The four research institutes then uploaded local data information to the blockchain as participants of the task, and the smart contract completed data indexing and a data-quality evaluation, establishing a trusted on-chain database for the collaborative task. In summary, the allocation of role authority and the establishment of the trusted database marked the completion of the initialisation of the collaborative task information.
# WIRE: A Weighted Item Removal Method for Unsupervised Rank Aggregation
## 3 Weighted Rank Aggregation  
A typical rank aggregation scenario involves a group of \(n\) rankers \(U=\{u_{1},u_{2},\ldots,u_{n}\}\) that submits \(n\) preference lists \(l^{(u_{1})},\ldots,l^{(u_{n})}\) and an aggregation algorithm \(\mathcal{A}\) that combines these preference lists with the aim of finding a consensus ranking \(\mathcal{L}\). In this paper, we consider the most generic version of the problem, where the preference lists (i) can be of variable length \(|l^{(u)}|\) and (ii) may include only a subset of the entire universe \(S\) of the elements; such lists are called partial. In contrast, full lists contain permutations of all elements of \(S\), so they are of equal lengths.  
A typical non-weighted aggregator \(\mathcal{A}\) receives the preference lists as input and assigns a score value \(s_{i}\) to each element \(i\), according to its ranking in the input lists, the number of pairwise wins and losses, or other criteria. Then, it outputs the unique elements of the lists sorted in score order, thus producing a consensus ranked list \(\mathcal{L}\) as follows:  
\[\mathcal{L}=\mathcal{A}\Big{(}l^{(u_{1})},\ldots,l^{(u_{n})}\Big{)}. \tag{1}\]  
In contrast, a weighted aggregator \(\mathcal{A}_{w}\) employs an unsupervised mechanism \(\mathcal{E}\) that evaluates the importance \(w^{(u)}\) of each ranker \(u\), by taking into account its preferences and several other properties. In the sequel, it integrates these weights in the computation of the element scores as follows:  
\[w^{(u_{1})},\ldots,w^{(u_{n})}=\mathcal{E}\Big{(}l^{(u_{1})},\ldots,l^{(u_{n}) }\Big{)}, \tag{2}\]  
\[\mathcal{L}=\mathcal{A}_{w}\Big{(}w^{(u_{1})},\ldots,w^{(u_{n})},l^{(u_{1})}, \ldots,l^{(u_{n})}\Big{)}. \tag{3}\]  
In [13], the authors also introduced a post-processing technique \(\mathcal{P}\) that removes the low-ranked elements from each input list based on the weights of their respective rankers as follows:  
\[\mathcal{L}^{\prime}=\mathcal{P}\Big{(}\mathcal{L},w^{(u_{1})},\ldots,w^{(u_ {n})}\Big{)}. \tag{4}\]  
In this study, we propose a new list pruning mechanism with the aim of improving the quality of the aggregate list \(\mathcal{L}^{\prime}\). The block diagram of Figure 1 illustrates the sequential flow of the aforementioned procedure.
## 2 Problem Formulation
### Clustering Constraints  
This paper adopts multi-hop communication for intra-cluster information exchange, supported by intra-cluster Radio Resource Allocation (RRA). The performance of intra-cluster RRA is directly related to the cluster topology, such as network topology connectivity, number of cluster members, and cluster diameter. The cluster diameter is the diameter of the cluster network topology. Cluster connectivity can ensure that any pair of nodes in the cluster communicates through intra-cluster links. However, more cluster members will make the resource load of the cluster larger, while adding the cluster diameter will increase communication delay and cost.  
Therefore, we impose some constraints on the topology of each cluster as follows:  
1. The network topology of cluster \(C_{k}\) is connected.
2. The number of nodes (\(n_{k}\)) in cluster \(C_{k}\) can be at most \(n_{max}\), namely \(n_{k}\leqslant n_{max}\).
3. The diameter \(\omega_{k}\) of cluster \(C_{k}\) cannot exceed the maximum diameter \(\omega_{max}\), that is, \(\omega_{k}\leqslant\omega_{\max}\).  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Notation** & **Meaning** \\ \hline \(N\) & Number of UAVs \\ \(M\) & Number of task groups \\ \(K\) & Number of clusters \\ \(\mathcal{N}\) & UAV index set \\ \(\mathcal{G}\) & Index set of the task groups \\ \(\mathcal{K}\) & Cluster index set \\ \(\Omega_{i}\) & The neighbor set of UAV \(i\) \\ \(f_{ij}^{e}\) & The communication link correction between UAVs \(i\) and \(j\) \\ \(f_{ij}^{g}\) & The group correction between UAVs \(i\) and \(j\) \\ \(e_{ij}\) & The compound correction between UAVs \(i\) and \(j\) \\ \(IT\) & Coalition structure or partition \\ \(\widehat{\Omega}_{l}(C_{k})\) & The neighbors of \(i\) in the same cluster \(C_{k}\) and the same group \\ \(\sigma_{k,l}(\mathcal{P})\) & The switch operation that transfers UAV set \(\mathcal{P}\) from \(C_{k}\) to \(C_{l}\) \\ \(\mathcal{R}(\varepsilon_{k,l}(\mathcal{P}))\) & The gain of switch operation \(\sigma_{k,l}(\mathcal{P})\) \\ \(\mathcal{O}\) & The switch operation set \\ \hline \hline \end{tabular}
\end{table}
Table 2: Major notations.  
Figure 1: The network of UAV swarms consisted of several clusters with different task groups.
## 2 FRA Applications on Power Transformers
### FRA Measurement Results  
The measured FRA signatures for the SC and OC cases along with the signature for normal winding conditions are compared and shown in Figure 4. It can be seen from Figure 4a,b that the effect of SC turns on the transformer FRA signature is obvious in the frequency range 20 Hz to 10 kHz in which the magnitude of the winding transfer function is reduced, and the resonance point is shifted toward a higher frequency range compared to the normal signature. The variation in magnitude and resonance frequency increases with the increase of the SC level. On the other hand, Figure 4c shows a significant variation in the FRA signature obtained for OC turns in the low-frequency range. At the frequency range higher than 10 kHz, the FRA signature of the OC winding becomes similar to the winding normal signature. This may be attributed to the fact that, at such a high frequency and short OC gap, current can pass through the short gap based on the electromagnetic induction principle [28].  
Figure 4: FRA measurement for the tested transformer winding at (**a**) Normal vs. SC at tap 2 and tap 3 (**b**) Normal vs SC at tap 4 and tap 5 (**c**) Normal vs. OC.