esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Moisture Sorption  
Moisture sorption (Figure 5) was studied as one of the essential sorption properties which are of great importance for the utilization of chitosan-treated jute fabrics as water-binding geo-prebiotic polysaccharide supports. Theoretically, the moisture sorption values provide information about the accessibility of the fibers to water vapor [27]. A high  
\begin{table}
\begin{tabular}{c c c} \hline
**Wavenumber, cm\({}^{-1}\)** & **Interpretation** & **Origin** \\ \hline
2917, 2852 & C–H stretching vibration of CH\({}_{2}\) and CH groups & cellulose and hemicellulose \\ \hline
1332, 1316, 1102 & fingerprint region & polysaccharides, especially cellulose \\ \hline
1025 & C–O stretching bond structure & alcohol’s functional group \\ \hline
1593, 1422 & aromatic skeletal vibrations and ring breathing with C–O stretching & lignin \\ \hline
1502, 1454 & aromatic ring C = C of the phenylpropane group & \\ \hline
1234 & ring breathing of guiaacyl “G” & \\ \hline
895 & \(\beta\)-glycosidic linkages between the glycosidic units & cellulose \\ \hline \end{tabular}
\end{table}
Table 1: Wavenumber of IR absorption bands and their interpretation according to the literature [19; 20; 21; 22; 23; 24; 25].  
Figure 4: Zeta potential and isoelectric points (IEP) of J and J2.0% CS.  
moisture sorption value of raw jute fabric (J) is due to the presence of hydroxyl groups (originating from the main jute constituent, cellulose) that are capable of binding water molecules in the jute fiber amorphous regions as well as on the crystallite surfaces [25]. As the histogram reported, chitosan-treated fabrics J0.5% CS, J1.0% CS, and J2.0% CS possessed 39.2, 57.7, and 77.6% higher moisture sorption ability than J fabric, respectively (Figure 5). Their enhanced moisture sorption can be attributed to the hygroscopic nature of chitosan, i.e., a high ability of its hydroxyl and amino groups to form hydrogen bonds with water [28]. This is additionally supported by the good linear correlation (\(r=0.985\)) between the fabric moisture sorption values and chitosan solution concentration used for fabric functionalization.
# tRNA and tsRNA: From Heterogeneity to Multifaceted Regulators
## 1 Introduction  
The traditional view holds that transfer RNAs (tRNAs) are universally expressed housekeeping molecules, stably expressed and involved in cellular protein synthesis processes. However, the expanding evidence supports that cellular, mature tRNA pools, similar to the transcriptome, exhibit strong tissue specificity and cell type specificity, which further brings to light the concept of \"tRNA heterogeneity\" to characterize their diversity [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 4  
during various biological processes [12; 13], such as environment stress response [14], stem cell fate determination [15; 16], tissue development [17], and cancer progression [18; 19; 20; 21].  
Additionally, it has been suggested that tRNAs can function beyond translation control. Some tRNAs could serve as primers for reverse transcription transposons [22] or bind to DNA to hinder the transcription of tRNA genes [23]. Investigating this could expand our understanding of tRNA regulatory mechanisms. Moreover, it is fascinating that tRNAs not only function as intact molecules but could also be reborn from fragmentation [24]. The generation of tRNA-derived small RNAs (tsRNAs) from mature tRNAs revealed a brand new identity and utility for fragmented tRNAs. As a class of non-canonical regulatory small RNAs, tsRNAs could not only undergo the direct regulation of gene expression on transcriptional, post-transcriptional, and translational levels but also engage in crosstalk with other epigenetic factors, such as DNA methylation and histone modifications, to enrich the information compacity and regulatory dimensions of tRNA/tsRNA code [25; 26].  
With the advancement of high throughput sequencing technology, growing evidence has unveiled distinctive tRNA pools and tsRNA signatures between cancer samples and normal tissue [27; 28], revealing the diagnostic and prognostic potential of tRNA and tsRNA profiles in various diseases. Moreover, due to the genetic decoding capacity of tRNA molecules, it is promising to engineer tRNAs to develop new therapeutic strategies for diseases induced by genetic nonsense mutation [29; 30]. Based on the increasing body of literature about tRNA and tsRNA, here, we aim to provide an up-to-date summary of the recent advances in our understanding of tRNA and tsRNA, highlight their regulatory mechanisms in various biological processes and diseases, and discuss their potential therapeutic applications based on the new knowledge of tRNA and tsRNA.
###### Abstract  
This paper addresses the problem of controlling a heterogeneous system composed of multiple Unmanned Aerial Vehicles (UAVs) and Autonomous Underwater Vehicles (AUVs) for formation and containment maintenance. The proposed approach considers actuator time delay and, in addition to formation and containment, considers obstacle avoidance, and offers a robust navigation algorithm and uses a reliable middleware for data transmission and exchange. The methodology followed uses both flocking technique and modified L1 adaptive control to ensure the proper navigation and coordination while avoiding obstacles. The data exchange between all the agents is provided through the data distribution services (DDS) middleware, which solves the interoperability issue when dealing with heterogeneous multiagent systems. The modified L1 controller is a local controller for stabilizing the dynamic model of each UAV and AUV, and the flocking approach is used to coordinate the followers around the leader or within the space delimited by their leaders. Potential Field (PF) allows obstacle avoidance during the agents' movement. The performance of the proposed approach under the considerations mentioned above are verified and demonstrated using simulations.  
underwater vehicle system; unmanned aerial vehicle system; L1 adaptive control; flocking technique; formation control; containment control +
Footnote †: journal: Applied Sciences  
1
###### Abstract  
Skin cancer is a significant global health issue, placing a growing burden on individuals and society. Conventional diagnostic methods like visual examination and biopsy have limitations in invasiveness and accuracy. As a result, alternative tools such as infrared thermography have gained attention in skin cancer diagnosis. Tissue-mimicking phantoms have been instrumental in facilitating research in this field, offering controlled environments. While they do not fully replicate human skin complexity, physical skin models provide stability, ease of fabrication, and control over properties. Agarose phantoms are employed in this study. This research focused on testing and comparing cooling techniques for human skin in the context of skin cancer diagnosis using dynamic infrared thermography. Six cooling methods were investigated: a cool pack, an aluminum medal, ice, alcohol, a vortex cooler and a Zimmer Cryo 6 cooler. The experimental setup involved an infrared camera (Optris Xi400) with microscope optics positioned above an agar phantom mimicking flat skin and an ulcerating skin lesion. Based on experiments conducted on the skin phantom, it was observed that convective cooling methods offered more consistent and uniform cooling. Conversely, conductive methods proved effective for flat objects but posed challenges in achieving uniform cooling for bulging skin or ulcerated lesions. Ice or alcohol were deemed unsuitable due to artifacts influencing the infrared radiation and thermal camera view. A decision matrix assessed cooling techniques based on criteria such as uniformity, repeatability, view obstruction, efficiency, workload, patient comfort, clinical suitability, noise exposure, consumables, additional equipment, and price. The Zimmer Cryo 6 cooler emerged as the most suitable cooling method after evaluating various factors.  
infrared thermography; skin cancer; cooling; skin-mimicking phantom; agarose gel; finite element model +
Footnote †: journal: Applied Sciences
## 3 Proposed Methodology
### Feature Extraction
#### 3.2.2 Features for Modulation Type Recognition:  
To present the modulation type recognition, the higher-order cumulant-based features have been computed for the AMC process. In mathematics, moments are employed to describe the probability distribution of a function. The \(p\)th order and \(q\)th conjugation moment for a received signal \(x[n]\) in time-step n are defined as [39]:  
\[M_{pq}=E[x[n]^{p-q}(x[n]^{*})^{q}] \tag{7}\]  
where \(E(.)\) and \([]^{*}\) are the expected operator and complex conjugate, respectively. Equation (7) can be approximated as [29]:  
\[M_{pq}=\frac{1}{n}\sum_{n=0}^{N-1}x[n]^{p-q}(x^{*}[n])^{q} \tag{8}\]  
In this work, the magnitude of the 2 nd, 4 th and 6 th [21] cumulants are computed to distinguish between QPSK, 8 APSK, 16 APSK, and 32 APSK:  
\[C_{21}=M_{21}=E[|x(n)|^{2}] \tag{9}\]  
where C and \(M\), and index, respectively present Cumulant, Moment and their order.  
\[C_{42}(x)=M_{42}-2M_{21}-|M_{21}|^{2} \tag{10}\]  
\[C_{63}=M_{63}+18M_{20}M_{21}M_{22}+12(M_{21})^{3}-9M_{21}M_{42}-3M_{20}M_{43}- 3M_{22}M_{41} \tag{11}\]  
Notably, different orders of cumulant-based features were tested, and the best results were achieved using the above orders (\(|C21|\), \(|C42|\) and \(|C63|\)).
## 2 Instrumentation and Methods
### Applications of Empirical Models of GPP, Re, and NEP
Re is the hourly respiration in the sampling period. Coefficients of A\({}_{\mathrm{i}}\) and B\({}_{\mathrm{i}}\) are determined using hourly observational datasets (solar radiation, water vapor pressure, GPP, and Re). More detailed results about the empirical model (EMGPP, EMRe, and EMNEP) developments and evaluations are reported in references [40; 41]. Based on previously reasonable simulations of GPP, Re, and NEP from 2013-2016 [40; 41], these empirical models (EMGPP, EMRe, and EMNEP) were further applied to investigate GPP, Re, and NEP and their long-term variations from 1 January 2003-31 December 2017 in this forest using the hourly observational data (CO\({}_{2}\) fluxes, solar radiation, and meteorological variables) measured by the Qianyanzhou station. Figure 3 shows a graphical diagram of the study. Firstly, the hourly GPP and Re values from 2003-2017 under all sky conditions were computed using the 2-factor EMGPP and EMRe, respectively, considering the direct solar radiation was not measured by the Qianyanzhou station. Secondly, the hourly NEPs under all sky conditions were calculated as the difference between GPP and Re (NEP \(=\) GPP \(-\) Re). Under all sky conditions, two types of atmospheric conditions (S/Q \(<\) 0.5 and S/Q \(\geq\) 0.5) were also considered. The EMGPP or EMRe used different coefficients to describe different roles of PAR and photochemical terms to GPP or Re in two S/Q situations, respectively [40; 41]. Due to the lack of S/Q from 2003-2017, AF factor (Equation (3) was used as an indicator of S/Q and to identify atmospheric conditions (i.e., high or low GLPs). \[\mathrm{AF_{i}}=1-\mathrm{PAR_{i}}\times\mathrm{m/PAR_{Mmax}} \tag{3}\]  
where \(\mathrm{AF_{i}}\) and \(\mathrm{PAR_{i}}\) were hourly averages of AF and the observed PAR during one h, respectively. \(\mathrm{PAR_{Mmax}}\) is the maximum of the observed PAR in a month. When using the empirical models of GPP and Re, \(\mathrm{AF_{i}}<\) 0.5 and \(\geq\)0.5 were applied to represent the conditions of S/Q \(<\) 0.5 and S/Q \(\geq\) 0.5, respectively, and combined considering PAR level (high or low). The range of AFi was from 0 to 1, corresponding to that of S/Q from 0 to 1.
## 6 Simulation Results
### Network Performance Parameters: Definitions
#### 6.2. Network Performance Parameters: Discussions  
This subsection discusses the network parameters that are used to evaluate the performance of our proposed scheme in comparison to the selected existing schemes.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Network size & From 100 m \(\times\) 100 m to 10 km \(\times\) 10 km \\ Number of nodes & 10 and 100 \\ Initial energy of normal nodes & 100 J \\ Data aggregation factor & 0.6 \\ Packet size & 70 bytes \\ Transmission range of the sensor node & 20 meters \\ Number of AUVs & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters used in the simulation.
# A Study on Design Requirement Development and Satisfaction for Future Virtual World Systems
## 4 Virtual World Architecture Inspection
### Hybrid-II
#### 4.4.2 Limitations  
Similar to the P2P architecture, the Hybrid-II architecture is limited in resource provisioning for resource-limited devices. In the SAM service, objects stored on a VN have the identifiers close to the VN's address [90]. That is to say, objects are not grouped by their users but by mapping identifiers to the address space of the DHT overlay [144]. Thus, a SAM node may not have all the content of a user for running complete simulation logic. Moreover, even though cloud resources have been introduced, which may have higher computation capabilities, they are partitioned to many VNs for service and data storage backup. Thus, the cloud gaming functions (in C/S and Hybrid-I) cannot be run on the Hybrid-II architecture for code offloading [109] or interactive remote rendering [69].  
For reliability, though the two-layer data replication mechanism can double the reliability of the SAM service, as in the P2P architecture, the state synchronization issue between replicas is not addressed by the authors. When an uVN fails, it may not timely and successfully synchronize the latest state to its bVN or overlay neighbor, leading to state loss or inconsistency. The extent of inconsistency between replicas depends on the length of the synchronization interval. However, state consistency could be critical to the integrity of a game, such as in-game trade and user inventory [77].
## 2 Material and Methods
### Experimental Trials  
Endive lettuce (_Cichorium endivia_) was purchased from a local producer on the day of usage. The unpackaged lettuce was obtained as harvested without any pre-processing steps. Washing of endive lettuce was performed with or without the addition of PTW at different steps of the washing chain. Thereby, six different washing procedures were tested. Briefly, the washing chain included, if not otherwise stated, 180 s of whole-lettuce dipping (pre-bath), cutting into pieces, 10 s of pre-rinsing, 120 s for the first wash, 180 s for the second wash, and 10 s of final rinsing. Trial A was performed without the addition of PTW (washing with tap water only). The next experiment (trial B) was conducted without washing of whole lettuce before cutting with plasma-treated water added for 10 s during pre-rinsing. The following experiment (trial C) was an analog of trial B, but the time of pre-rinsing was extended to 30 s. The next experiment (trial D) included the washing of whole lettuce in PTW for 180 s, and trial E was conducted again without a pre-bath of whole lettuce with PTW added during the second wash for 180 s. The last experiment (trial F) included washing of whole lettuce in PTW for 180 s, and pre-rinsing with PTW for 30 s, along with the addition of PTW in the second wash for 180 s. Further details regarding the experimental procedure are described by [PERSON] et al. [19]. Briefly, 5 kg of endive lettuce was used for each washing process. The lettuce was cut into pieces of approximately 5 \(\times\) 5 cm. Samples were taken at six different sampling points: (1) raw material; (2) endive lettuce after pre-bath; (3) endive lettuce after pre-rinsing; (4) endive lettuce after first wash; (5) endive lettuce after second wash; (6) endive lettuce after final rinsing. At each sampling point, 150 g of lettuce was taken in triplicate, stored at temperatures below 5 \({}^{\circ}\)C in sterile bags (Whirl-Pak(r), Carl Roth GmbH Karlsruhe, Germany), and analyzed either within 24 h or after storage for seven days at 2 \({}^{\circ}\)C.
## 6 Discussion
### Comparative Analysis with Existing Studies  
This study aimed to capture and quantify how attackers exploit vulnerabilities with what purpose in OT/ICS environments from an offensive security perspective and how easy it is for attacks to succeed. Table 13 compares the evaluation attributes and application environments considered in this study and previous studies based on this.  
Evaluation attributes are split into static, which do not change over time, and dynamic, which do. Static attributes include inherent vulnerability characteristics, like attack vector and access rights. Impact factors cover confidentiality, integrity, and availability. Dynamic  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{**Related Work**} & \multicolumn{2}{c}{**Static**} & \multicolumn{2}{c}{**Dynamic**} & \\ \hline \multirow{2}{*}{Type} & \multirow{2}{*}{Paper} & Vulnerability & \multirow{2}{*}{Impact Factor} & \multicolumn{2}{c}{Ease of Exploitation} & Attacker’s & **Applicable** \\  & & Characteristic & & & & **Domain** \\ \hline \multirow{3}{*}{\begin{tabular}{c} Severity \\ Assessment \\ \end{tabular} } & [6] & 5 & 3 & 1 & 0 & IT \\  & [7] & 5 & 3 & 1 & 0 & IT \\  & [8] & 5 & 3 & 1 & 0 & IT \\ \hline \multirow{3}{*}{\
\begin{tabular}{c} Exploitability \\ Assessment \\ \end{tabular} } & [9] & 0 & 0 & 2 & 0 & IT \\  & [10] & 0 & 0 & 2 & 0 & IT \\ \hline \multirow{3}{*}{
\begin{tabular}{c} Threat Risk \\ Scoring \\ \end{tabular} } & [5] & 0 & 0 & 0 & 3 & IT,OT/ICS \\  & [12] & 0 & 0 & 0 & 3 & IT,OT/ICS \\ \hline \multicolumn{2}{c}{Our work} & 0 & 0 & 2 & 3 & IT,OT/ICS \\ \hline \hline \end{tabular}
\end{table}
Table 13: Comparison results with existing studies.  
attributes focus on the ease of exploitation and attacker's motives, reflecting the likelihood of a successful attack and the motivation of an attacker to carry out an attack.  
Unlike previous research, this study considered both the attacker's goals and ease of exploitation for a complete evaluation of OT/ICS system vulnerability. We found vulnerabilities that pose a significant risk of being exploited based on the attacker's goals. This shows a thorough evaluation rather than just identifying vulnerabilities based on a single objective of past research.
## 5 Numerical Results
### Network Size  
Here we study the effect of increasing the network size, on the optimal values of rate, power and delay. Network size here means the number of CRSN nodes. We thus vary the number of SUs on the channel from 5 to 30 and report the results in Figure 4.  
As seen from Figure 4a-c, the total transmission power, total transmission rate and total transmission delay are increasing functions of number of CRSN nodes. This trend can be observed in all the four cases. This is attributed to the fact that increasing the number of SUs on the network leads to more data packets being transferable which in turn leads to increased total rate, total delay and total power.  
The use of case 3 leads to less power and rate, with a higher transmission delay. The use of case 4 however leads to the highest power and rate, with a relatively smaller delay.
## 3 Modeling
### Notation Description  
To construct the mathematical model, this paper employs a series of symbols, the definitions and descriptions of which are shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Notation** & **Description** & **Notation** & **Description** \\ \hline \(i\) & Index of order, \(i=1,2,\ldots,\) & \(C_{c}^{setup}\) & Production setup cost of \\ \(l\) & & & part C \\ \(j\) & Index of sub-order, \(j=1,\) & & Sterilization quantity of \\ \(2,\ldots,J\) & & \(t_{ij,p}\) & product \(p\) in sub-order \(j\) of \\  & & & order \(i\) \\ \(p\) & Index of product, \(p=1,\) & & Demand quantity of \\ \(2,\ldots,P\) & & \(q_{ijp}\) & product \(p\) in sub-order \(j\) of \\ \(c\) & Index of part, \(c=1,2,\ldots,C\) & \(B_{min}\) & Minimum production \\ \(k\) & Index of order, \(k=1,2,\ldots,\) & \(B_{setup}\) & batch size \\ \(m\) & Index of raw material, \(m=1,2,\ldots,M\) & \(C_{mat}\) & Total budget for material \\ \(s\) & Index of specification, \(s=1,2,\ldots,S\) & & scheduling \\ \(r\) & Index of process, \(r=1,\) & & compatible, \(0=\) \\ \(\theta\) & Penalty coefficient for & & maximum production \\ \(\theta\) & order splitting quantity & \(C_{plan}\) & capacity of the equipment \\ \(\alpha\) & Safety stock coefficient & \(C_{run}\) & & management cost \\ \(\epsilon\) & Smoothing factor & \(C_{k}\) & & Equipment unit-time \\ \(l\) & Set of orders & \(C_{switch}\) & & operating cost \\ \(J\) & Set of sub-orders & \(C_{dep}\) & & Capacity of equipment \\ \(M\) & Set of raw materials & \(T_{delay}\) & & cost \\ \(\eta_{\rm lab}\) & Hourly wage & \(\tau_{ijp}^{(r)}\) & & product \(p\) in sub-order \(j\) at \\ \(P\) & Set of products & \(B_{m,c}\) & & \\ \(R\) & Set of processes & \(v_{m}\) & & \\ \(\mathcal{K}\) & Set of equipment & \(L_{p}\) & & \\ \(S\) & Set of specifications & \(\eta_{k}\) & & \\ \(D_{jp}\) & Quantity of product \(p\) in & & & \\ \(Q_{ij,p}\) & sub-order \(j\) & & & \\ \(Q_{ij,c}\) & Part quantity of product \(p\) & & & \\ \(Q_{ij,c}\) & in sub-order \(j\) of order \(i\) & & & \\ \(y_{i}\) & Quantity of order \(i\) & & & \\ \(\gamma^{plan}\) & Planned management unit & & & \\ cost & & & & \\ \(\gamma^{qu}\) & Unit cost of equipment & & & \\ and mold replacement & & & & \\ \(\gamma_{1}^{mat}\) & Logistics unit cost & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Indices, parameters, and variables.