esa-sceva/satcom-chunk-collection huggingface dataset:
# Validation of Cable-Driven Experimental Setup to Assess Movements Made with Elbow Joint Assistance
## 3 Results
### Optimization of Anchor Positions
#### 3.1.2 Case 2: Comfort to the Wearer  
In a cable-driven exosuit, the user experiences the pull generated by the cable and the reaction forces generated at the elbow joint because of the cable pull. To enhance the wearer's comfort, both the cable tension, \(\overrightarrow{T_{s}}\), and the reaction force, \(\overrightarrow{F}\), at the elbow should be minimized. The pressure exerted on the wearer's skin during actuation is another parameter used to assess comfort. However, the pressure experienced is proportional to the tension, and minimizing tension implicitly minimizes the pressure. Moreover, the pressure generated by the tension values presented in this work is within the pain pressure tolerance limit [32]. Therefore, we defined a minimization problem to reduce \(\overrightarrow{T_{s}}\) and \(\overrightarrow{F}\) with respect to the anchor positions \(d_{1}\) and \(d_{2}\), as presented in Equation (18). The bounds and range of motion for the optimization are the same as discussed in Case 1.  
Figure 8: (**a**) Variation in optimal anchor positions, \(d_{1}\) and \(d_{2}\), along the elbow joint angle in order to minimize the moment generated at anchor positions. (**b**) **I–III** depict the scenario of cable attached at different values of \(d_{1}\) and \(d_{2}\) for joint positions 55\({}^{\circ}\), 90\({}^{\circ}\), and 120\({}^{\circ}\). Green and red cables represent optimal and non-optimal positions.  
\[\text{min}\bigg{(}\Big{\|}\overrightarrow{I_{s}}\Big{\|}\ +\ \Big{\|} \overrightarrow{F}\Big{\|}\bigg{)}\bigg{|}_{(d_{1},d_{2})} \tag{18}\]  
Figure 9a shows that the optimal positions of \(d_{2}\) converge towards the upper bound. Similarly, \(d_{1}\) also converges mostly towards the upper bound, excluding the extreme joint angles. To comprehend the results, we presented three schematics--refer to Figure 9b--which represent three different joint positions: 55\({}^{\circ}\), 90\({}^{\circ}\), and 120\({}^{\circ}\). The green-colored cable in each of these schematics represents the optimal position and the red-colored represents the non-optimal position of \(d_{1}\) on the forearm. Figure 9b(I), the angle made by the cable, is in line with the anchor attachment. Thus, the tension in the cable is lower, and consequently, the joint reaction force is also lower. Figure 9b(I,III), the attachment of the anchor on the forearm at the farthest position from the elbow leads to minimal tension in the cable and the reaction force. The above optimization is performed at a load of 0.5 kg, and the trend remains consistent for other loads.
## 2 Materials
### Polymer Synthesis  
Two main methods for the synthesis of APs exist. They can be prepared by polycondensation of bifunctional raw materials, like self-condensation of hydroxy acids, condensation of diacids with diols, diacid chlorides with diols, or by transesterification reactions of diesters and diols [41]. Alternatively, APs can be obtained by ROP of lactones and lactides [42]. Pioneering research on polycondensation unveiled the formation, together with expected high molecular weight linear polyesters, of low molecular weight cyclic byproducts. Some of them, e.g., \(\epsilon\)-caprolactone, were purified and used as raw materials in the ROP, eventually leading to the synthesis of APs. However, it was necessary to wait a few decades for the methods of controlled polymerization of cyclic esters to be fully exploited.  
Nowadays, commercially available APs are synthesized employing both strategies. Polycondensation can be used for many different diols and diacids, but it usually requires high temperature, long reaction time, and removal of reaction byproducts for the achievement of high molar mass materials [43]. Furthermore, the final polymers do not display controlled chain lengths, and molecular weight distribution is about two. On the other hand, only a limited number of raw materials can be employed for ROP, but it can be performed under milder conditions, and high molar mass polymers can be obtained in shorter reaction times. A wide range of initiators andcatalysts have been studied for ROP [42]. In the more favorable case, ROP of lactones and lactides is a living/controlled reaction, thus by controlling the monomer-to-initiator molar ratio it is possible to predetermine the molecular weight of the final polymer. The most used initiator for ROP of APs is tin(II) bis-(2-ethylhexanoate), also called tin octoate (Sn(Oct)\({}_{2}\)). Since it has been authorized as food additive by the US Food and Drug Administration (FDA), its removal from the final product is not necessary [42]. More recently, also the enzymatic catalysis has been explored for APs synthesis [43]. Generally speaking, enzymatic reactions can be performed under very mild conditions. More importantly, enzymes can easily achieve high regiospecificity and stereospecificity [44]. Lipase catalyzed polymerization has been studied as ecofriendly alternative for the preparation of APs. Indeed, lipases are capable of catalyzing both the ROP of lactones (small to large rings) and cyclic diesters (lactides) and the polycondensation of hydroxy acids and diacids with diols.
# The Future for End-Stage Kidney Disease Treatment: Implantable Bioartificial Kidney Challenge
## 1 Introduction
### The End-Stage Kidney Disease and the History of Kidney Replacement Therapy  
The total number of patients affected by End-Stage Kidney Disease (ESKD) saved by the artificial kidney can hardly be estimated, but it is surely very high. The history of dialysis technology evolution has one of its milestones in 1943, as the first practical dialyzer composed of a 20 m long rotating cellophane tube wrapped around a 2 m horizontal drum serving as a semipermeable membrane was realized by a Dutch physician, Doctor [PERSON] [1]. The major limitation of this device, without considering the large extracorporeal volume required, was inappropriate body fluid removal, which is one of the most important issues during fluid overload in renal dysfunction. In fact, in this system, gravitation generates a transmembrane pressure that was not adequate to effectively achieve ultrafiltration as required by the patient's clinical status. Therefore, conditions characterized by fluid overloads, such as pulmonary edema or hypertension, could not be effectively treated by this device.  
The opening success of this prototype was followed by the implementation of a modified dialyzer that was enabled to bear higher transmembrane pressure to ensure fluid removal [2]. Subsequent development of precise ultrafiltration control systems using scales and flow control have made fluid removal precise and safe making the fluid balance control an achievable and customizable clinical goal for each individual clinical setting. As the very first chronic dialysis facility was founded in Seattle in 1960, renal failure was no longer a fatal disease.
The limited spatial coverage of each beam necessitates the use of either multiple beams with a predefined beamforming codebook to cover the entire region of interest or field of view (FoV), or beam sweeping operations where beams cover a spatial area during a time instant in a predetermined way and sweep through another area in another time instant. Such beam management procedures typically consume radio resources and are associated with large training overheads, which calls for AI/ML approaches to improve system efficiency. However, the intelligent systems, such as the automated driving cars, employing the ML-based beam predictions must ensure very high prediction accuracies to deliver the required performance targets and to mitigate risks and vulnerabilities, both for V2I and V2V systems [1,2]. Several studies have thus explored the application of ML techniques in mmWave communication systems. These studies leverage the capabilities of ML to address various challenges such as resource allocation, beam management, and overall network optimization [3]. The integration of ML techniques in wireless communication systems is vital as it ensures the improvement of network performance in diverse deployment scenarios [9,10]. By leveraging ML algorithms, wireless networks can adaptively optimize resource allocation, enhance real-time analysis, and improve user experience, which are poised to have a significant impact on wireless communication systems across various domains [10,11]. This work, therefore, investigates the application of ML algorithms on position-aided beam prediction using real-world mmWave V2I datasets. The contributions combine the use of low-complexity supervised learning-based multi-class classification algorithms on the one hand and the use of experimental, measurement-based datasets on the other hand, thereby extending the state-of-the-art on the topic. The rest of the paper is organized as follows. Section 2 presents the related works. The materials and methods employed are presented in Section 3. Section 4 presents and discusses the performance results for the employed ML algorithms and scenarios. The conclusions and future research directions are outlined in Section 5.
# High-Efficiency Clustering Routing Protocol in AUV-Assisted Underwater Sensor Networks
## 4 Simulation and Performance Evaluation
### Simulation Results
#### 4.2.2 Residual Energy of Nodes  
To evaluate the residual energy of the HECRA nodes in different network environments, experiments are conducted in this paper with different numbers of nodes and different transmission ranges.  
Figure 6a shows the residual energy of the nodes for 1000 rounds of network operation when the transmission range is 200 m and the number of sensor nodes is 450. The graphical results show that the residual node energy of LEACH decreases the fastest when the network runs, and the residual node energy of HECRA improves by 5.27%, 22.65%, and 35.25% compared to that of EECMR, EERBLC, and LEACH, respectively.  
Figure 5: Number of dead nodes. (**a**) Transmission range 200 m, 450 nodes in 1000 rounds. (**b**) Transmission range 150 m, nodes 50 to 450.  
Figure 6: Average residual energy of a node. (**a**) Transmission range of 200 m, 450 nodes in 1000 rounds. (**b**) Transmission range of 150 m, nodes 50 to 450.  
Figure 6b shows the residual node energy for the case where the transmission range is 150 m and the number of sensor nodes varies between 50 and 450 nodes, where the number of sensor nodes is varied every 1000 rounds of operation. The graphical results show that the residual energy of LEACH is greater than that of the other protocols when the number of nodes is less than 100 because, in sparse networks, LEACH has a lower success rate of network communication when the transmission range is small, which results in less node energy consumption. Nevertheless, as the number of nodes increases, the energy consumption of LEACH gradually increases. Compared to the other three protocols, HECRA maintains better performance in both sparse and dense regions of nodes. This is because HECRA optimizes the cluster head selection, data transmission, and dynamic relay nodes. HECRA maintains relatively good node residual energy in both node-sparse and -dense regions.
## 4 Results and Discussion
### Mucoadhesive Study  
Finally, to evaluate mucoadhesive properties of ChAlg/\(\alpha\)-M HF in the mucous layer, we observed the mucoadhesive time of ChAlg/\(\alpha\)-M HF using the fresh mouse mucosa [26]. The mucoadhesive time of Ch HF, ChAlg HF, and ChAlg/\(\alpha\)-M HF were 11.5, 23.8, and 46.7 min, respectively (Figure 7B). However, Alg HF was not detected because Alg HF totally dissolved in aqueous liquid. Based on the results, ChAlg/\(\alpha\)-M HF significantly improved the mucoadhesive time compared to ChAlg HF. Furthermore, the combination of Ch and Alg in ChAlg HF repaired the mucoadhesive properties of Alg HF. Swelling ratio and adhesive ability of polymers are closely related to the time of attachment abilities of HFs. The combination of polymers increase mechanical bonds between polymers and mucosa [45]. In addition, mucoadhesive ability of Ch based on the formation of hydrogen bond or ionic interaction between the positively charged amino moiety of Ch with negatively charged components of mucus including sialic acid, as well as epithelial surfaces [34; 46]. The mucoadhesive polymer system plays an important role in RAS that can cover oral lesions to prevent long-term deterioration of lesions and bacterial proliferation [47]. These results suggest that ChAlg/\(\alpha\)-M HF was able to increase the attachment time of ChAlg HF to the mucous layer.  
Figure 7: In vitro studies of ChAlg/\(\alpha\)-M HF. (**A**) In vitro release of \(\alpha\)-M from ChAlg/\(\alpha\)-M HF; (**B**) mucoadhesive study of ChAlg/\(\alpha\)-M HF. Each value represents the mean \(\pm\) SEM of three experiments. \({}^{*}p<0.05\), compared to the Ch HF. # \(p<0.05\), compared to the ChAlg HF.
# Diamonds for Life: Developments in Sensors for Biomolecules
## 5 Diamond-Based Electric Biosensors
### Biosensors for DNA  
[PERSON] et al. observed the recognition of DNA oligonucleotides at the surface of diamond thin films by frequency-dependent impedance measurements [53]. Measurements as a function of potential show that the impedance at \(10^{6}\) Hz was dominated by the space-charge region of the diamond film and that the hybridisation of a complementary DNA oligonucleotide was able to induce a field effect in the diamond space-charge layer. This effect altered the impedance of the diamond film, thus allowing for a real-time hybridisation-dependent response.  
[PERSON] et al. tested the electrical detection of DNA using two types of diamond thin films, one composed of non-doped diamond and another of BDD with dopant concentrations above \(10^{19}\) cm\({}^{-1}\). Both films were functionalised with undecenoic acid with subsequent attachment of a DNA probe chain [24]. Interestingly, monitoring DNA recognition by impedance measurements demonstrated that there was an insignificant change in impedance following hybridisation of DNA at the BDD-based biosensor, while the DNA sensor built from non-doped diamond exhibited a clear reduction in the impedance response. This was explained as the result of'reduced charge-transfer resistance of the molecular layers following the \(\pi\)-\(\pi\) stacking of the duplex DNA, as well as a reduction in the subsurface depletion due to an accumulation of holes'.  
A DNA impedimetric biosensor built from undecenoic acid/DNA-functionalised, non-doped nanocrystalline diamond films was reported by [PERSON] et al. [54]. This device operated in a similar way to that constructed by [PERSON] et al., that is, it showed a decrease in impedance when the complementary target DNA was added. Further tests included challenging the biosensor with a solution containing a DNA sequence that was not complementary to that of the receptor layer (with a single nucleotide mismatch, or 1-mismatch) to investigate if it could be differentiated from the target analyte. Results showed that differentiation between 1-mismatch and complementary target DNA was possible at frequencies between 100 and 1500 Hz. The biosensor still showed, however, some limitations for a wide range of applications, because its performance suffered negativelyfrom the low ionic content of the sample media. Hybridisation in 1\(\times\) PBS buffer was unsuccessful, while the separation between complementary and 1-mismatch hybridisation curves was very clear and reproducible when using the concentrated buffer, 10\(\times\) PCR buffer (at 30 \({}^{\circ}\)C).
# Cationic and Anionic Dye Adsorption on a Natural Clayey Composite
## 4 Conclusions  
The naturally occurring clayey composite RC can efficiently remove the cationic and anionic dye, like the CNB and ADY, from water. The outcomes suggest a clayey composite constituted by IPs, KPs, and LPs. The heterogeneous composition of naturally occurring clayey composite favors different adsorption mechanisms. It opens an avenue for the separation process's engineering. The IPs showed a strong affinity for CNB dye and the KPs and LPs for ADY dye.  
The proposed framework for studying the dye concentration effect on the adsorption capacity leads to the adsorbent dose for complete adsorption. At a pH of less than 4, the CNB and ADY removal percentage was 97% and 96%, respectively. At a pH greater than 8, the CNB and ADY removal was 75% and 25%, respectively. The study of the pH effect on dye removal allowed us to postulate the RC particles' CNB and ADY adsorption mechanisms. The CNB adsorption happened by chemisorption of a monolayer on IPs. In contrast, the ADY adsorption occurs by the physisorption of a monolayer on KPs.  
The Langmuir isotherm model fits very well with CNB experimental data. The Temkin model shows the best fit between the isotherm function and the ADY dye-adsorption data. The PSOM fits the CNB and ADY dye adsorption kinetic data on RC particles. The interparticle diffusion is not the limiting step in the CNB and ADY adsorption on RC particles at a concentration lower than 75 mg/g.  
Conceptualization and methodology, C.O.M. and V.J.G., software, C.O.M. and V.J.G., validation, J.R.G., formal analysis, V.J.G., F.F.-M. investigation, C.O.M. and V.J.G., resources, C.O.M. and V.J.G., writing--original draft preparation, J.R.G., C.O.M., V.J.G. and F.F.-M. writing--  
Figure 10: Adsorption kinetic profiles of ADY (**a–c**) and CNB (**d–f**) dyes onto RC particles at different initial adsorbent dosages and dye concentrations; T = 20 \({}^{\circ}\)C. The dashed lines are the theoretical data predicted by the PSOM, Equation (13).  
review V.J.G., visualization, A.C.R., supervision, A.C.R., project administration, A.C.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare express gratitude to the Vice-Rectorate of Post Graduate Studies and Research of the Universidad Nacional de Chimborazo (UNACH) through the Interdisciplinary Studies research group.  
The authors
# Fibre Wireless Distributed Antenna Systems for 5G and 6G Services
## 4 Experimental Results
#### Effect of Chromatic Dispersion and Power Fading
In addition, the performance of the proposed system is summarized and compared in Table 2 with the state-of-the-art relevant method of the 5G RoF system, which was evidenced in the previous work of the authors in [16] and others in [43], with the same link length and other settings. The wireless reception can be further enhanced by using wireless signal detection using the techniques discussed in [44]. Figure 10: Profile of increasing baud rate for varying optical power in terms of BER. Figure 9: Power fading w.r.t frequency range covering the two intermediate frequencies.
## 2 Preliminaries
### Glossary of Acronyms  
To ensure clarity and consistency, this paper adopts several commonly used technical acronyms. These acronyms are listed and defined in Table 2 below. Throughout the remainder of this paper, the abbreviations provided in the table will replace their full forms for brevity. This ensures efficient communication while maintaining readability for a technical audience.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Protocol** & **Frequency** & **Data** & **Communication** & **Power** & **Advantages** \\  & **(GHz)** & **Speed (Max)** & **Distance** & **Consumption** & **Advantages** \\ \hline Wi-Fi & 2.4/5 & 1 Gbps & 50–100 m & High & High-frequency bandwidth, high speed, supported by extensive infrastructure \\ Bluetooth & 2.4 & 3 Mbps & 10–100 m & Low & Low power consumption, suitable for small devices with long-term operation \\ Zigbee & 2.4 & 250 kbps & 10–100 m & Low & Low power consumption, supports multi-node mesh networks \\ LoRa & Sub-GHz & 50 kbps & 2–15 km & Ultra-low & Long-range coverage, ultra-low power consumption \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of specifications of common wireless communication protocols.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Acronym** & **Full Form** \\ \hline
5G & Fifth-Generation Mobile Network \\ AES & Advanced Encryption Standard \\ CPA & Correlation Power Analysis \\ D-H & Diffie–Hellman \\ IoT & Internet of Things \\ MAC & Message Authentication Code \\ MITM & Man-in-the-Middle \\ MTD & Moving Target Defense \\ RSA & Rivest–Shamir–Adleman Encryption \\ SCA & Side-Channel Attack \\ Wi-Fi & Wireless Fidelity \\ \hline \hline \end{tabular}
\end{table}
Table 2: Glossary of acronyms.
# Deep Anomaly Detection for In-Vehicle Monitoring--An Application-Oriented Review
## 4 Projects and Resources
### Resources  
Despite their compilation efforts, most surveys on deep anomaly detection for various scenarios only provide some very high-level outlines of the application conditions of the suggested models and a superficial review of the available datasets [1, 65]. Although these are useful tools to deepen the theoretical knowledge of anomaly detection strategies, the limitations imposed by the accessible data must be explored. Moreover, this exploration effort provides an overview of the conditions required for improving and finding new application scenarios for the proposed methods. The work of [PERSON] et al. [6] is as of yet the most detailed overview of the approaches taken by the current methods and their underlying intuitions. This is achieved by providing a comprehensive literature review while inspecting the current challenges and studying future opportunities for application. Furthermore, a collection of publicly accessible source codes and a large number of real-world datasets were presented by the authors. However, the work of [PERSON] et al. [6] is lacking application-oriented reviews of opportunities and challenges posed by the exploration of novel scenarios. To fully explore and understand the constraints and potential issues of a specific real-world scenario, rather than a generic formulation of a problem, the attention that only a dedicated review can offer is required.  
The availability of the code used to develop the models is essential not only to correctly replicate them but also to better understand some of the concepts that were presented in a paper. Table 6 lists some details on the code shared by the authors of some of the surveyed methods. The data used to create this table were originally compiled by [PERSON] et al. [39]. Every repository was verified to confirm its accessibility and if it contained sufficient information to be properly implemented without major adjustments. Some additional information was also gathered, such as the Machine Learning (ML) framework that was used. Regarding this last topic, there is an interesting tendency, evident by analysing Table 6, on the growing popularity of PyTorch as the preferred framework employed by researchers investigating video anomaly detection. This compilation is a starting point for selecting models that could be adapted to anomaly detection in new scenarios, such as monitoring passengers and objects inside vehicles.
## 3 Distance Vector-Hop (DV-Hop) and Differential Evolution (DE) Algorithms
### Differential Evolution Algorithm
#### 3.2.3 Crossover  
The target individual \(x_{i}^{t}\) and variant individuals \(v_{i}^{t+1}\) are crossed to generate an experimental individual \(u_{i}^{t+1}=\left[u_{i,1}^{t+1},u_{i,2}^{t+1},\cdots,u_{i,D}^{t+1}\right]\). The crossover process is shown in Equation (11).  
\[u_{i,j}^{t+1}=\left\{\begin{array}{ll}v_{i,j}^{t+1},&if\ \ \ r_{j}\leq CR\ \ \ or\ \ \ j=rnbr\_i\\ x_{i,j}^{t},&otherwise\end{array}\right. \tag{11}\]  
where \(r_{j}\) is the \(j-th\) random number between \([0,1]\); \(rnbr\_i\) is the random natural number between \([1,D]\); \(CR\) is the crossover rate, \(CR\in[0,1]\).