esa-sceva/satcom-chunk-collection huggingface dataset:
# Valorization of Carrot Pomace: UVC Induced Accumulation of Antioxidant Phenolic Compounds
## 3 Results and Discussion
### Phenolic Profile and Individual Phenolic Compounds
A similar trend was observed for CHA, where the 60 min UVC treatment showed the highest accumulation at 24 h of storage, which was 46.8% higher than the control and 80.9% higher than the CBS. Regarding gallic acid derivative, the compound accumulated in the tissue at concentrations between 47.7 and 68.2 mg/kg, where the lowest content was observed in the tissue treated with UVC for 60 min, and no significant difference was observed between the other UVC treatments (30 and 120 min) and the control (Figure 3B). Interestingly, the treatment that showed the highest accumulation of hydroxycinnamic acids (3,5-dicaffeoylquinic acid and CHA) showed the lowest accumulation of gallic acid derivative, suggesting that the treatments redirected the use of shikimic acid as a precursor for the biosynthesis of aromatic amino acids rather than their conversion into 3-dehydroshikimic acid and for their further transformation into gallic acid [37]. At 48 h of storage, the concentration of protocetachuic acid and 3,5-dicaffeoylquinic acid decreased to low levels (Figure 3A,C). For the gallic acid derivative, the highest accumulation (26.5% higher than the control) was detected in the 60 min UVC treatment. Regarding CHA, the content remained constant in the 30 min UVC treated samples but decreased by 60.9% from 24 h to 48 h storage time. Interestingly, the control and 120 min UVC treated samples showed the largest CHA content increase, which was 143.6% and 129.9% higher than the CBS, respectively. In addition to the phenolic compounds mentioned, ferulic acid was also detected at 48 h of storage of the carrot pomace (Figure 3E). The highest accumulation of ferulic acid was observed in the tissue treated with UVC for 120 min, whose content was 46.6% higher than the control. The ferulic acid accumulation also supports that the wound-induced activation of lignin was activated in carrot pomace since it is an essential precursor for its biosynthesis [32]. As described for total phenolics, the accumulation of individual phenolics results from the balance between their biosynthesis rate and utilization rate for the production of other molecules or even their oxidation/degradation rate [38]. For instance, the high accumulation of CHA observed at 48 h of storage in the control and in the 120 min UVC treated samples indicates that its accumulation rate was higher than its utilization rate for lignin biosynthesis.
## 2 Results and Discussion
### Catalytic Conversion Kinetics of MoS\({}_{2}\)-NG for Polysulfides
As shown in Figure 4c, the cyclic voltammetry (CV) curves of the symmetric cells assembled with MoS\({}_{2}\)-NG/PP separator exhibit four distinct peaks at 0.15 V/ \(-\)0.47 V and \(-\)0.16 V/0.46 V. The peaks at lower potentials with higher current responses indicate that MoS\({}_{2}\)-NG has a stronger catalytic effect on polysulfides compared to MoS\({}_{2}\) and MoS\({}_{2}\)-G, which show peaks at higher potentials with lower current responses. Additionally, to further confirm that MoS\({}_{2}\)-NG can more effectively promote the nucleation and growth of Li\({}_{2}\)S, we conducted potentiostatic discharge tests at 2.05 V using MoS\({}_{2}\)/PP, MoS\({}_{2}\)-G/PP, and MoS\({}_{2}\)-NG/PP as separators, carbon fiber felt as the current collector, and Li\({}_{2}\)S as the active material. The discharge curves are shown in Figure 4d-f. A comparative analysis reveals that the peak current appears earliest with the MoS\({}_{2}\)-NG/PP separator, followed by MoS\({}_{2}\)-G/PP, and lastly with the MoS\({}_{2}\)/PP separator. Moreover, the area under the peak, calculated according to [PERSON]'s law and corresponding to the capacity released by Li\({}_{2}\)S deposition, indicates that the specific capacity is significantly higher for the MoS\({}_{2}\)-NG/PP separator (526.67 mAh g\({}^{-1}\)) compared to the MoS\({}_{2}\)-G/PP (492.84 mAh g\({}^{-1}\)) and MoS\({}_{2}\)/PP (473.96 mAh g\({}^{-1}\)) separators. These results collectively demonstrate that MoS\({}_{2}\)-NG exhibits higher activity compared to MoS\({}_{2}\) and MoS\({}_{2}\)-G. It not only optimizes the redox kinetics of the battery system but also significantly enhances the catalytic conversion of polysulfides, leading to more effective Li\({}_{2}\)S nucleation and growth.
# Design of Universal Code Generator for Multi-Constellation Multi-Frequency GNSS Receiver
## 2 Introduction to GNSS Spread Spectrum Codes
### Spread Spectrum Code Generation Methods Based on LFSR
#### 2.2.1 m-Sequence  
The m-sequence is a pseudo-random sequence of length \(2^{N}-1\) using a register of length N shifted by linear feedback. The m-sequence has good autocorrelation and cross-correlation characteristics.  
Signals that utilize m-sequence codes include GPS L2C [2], GLONASS L1 OF, and L2 OF signals. The structure of the spread spectrum code generated by the m-sequence is typically simpler. For example, the GLONASS L1 OF signal [12] employs the simplest PRN code generator structure, as depicted in Figure 1. It consists of a 9-bit shift register with feedback taps at Positions 5 and 9. The register is initialized with a value of one. The generated sequence corresponds to an m-sequence. The period of this sequence is \(L=2^{9}-1=511\) chips.  
For the GPS L2C signal, its ranging code is a truncated m-sequence. The code generator consists of a 27-bit shift register that generates different pseudocodes for different satellites by configuring different initial phases. It should be noted that the output of the last bit of the code generator is fed back to both the first bit and the intermediate bits. The code generator specified in the ICD [13] uses the Galois configuration (as shown in Figure 2), while most generators use the Fibonacci configuration. Each Fibonacci LFSR has an equivalent Galois LFSR that produces exactly the same output sequence [17; 18]. The Fibonacci configuration is achieved by configuring the shift register with the correct initial phase to fix the position of the feedback taps, maintaining consistency with previous m-sequence structures.  
To simplify the hardware structure of the code generator, the Fibonacci configuration is used in this paper. To convert to a Fibonacci configuration, the correct initial phase of the m-sequence shift register must be found. Therefore, we used a generator in the form of a 27-bit LFSR in Ref. [2] (Figure 3). The feedback taps for this register is given by the polynomial \(f(x)=1+x^{3}+x^{4}+x^{5}+x^{6}+x^{9}+x^{11}+x^{13}+x^{16}+x^{19}+x^{21}+x^{24 }+x^{27}\). The output sequence is taken from the last bit of the register.
# A Radiation-Hardened Low-Power SRAM with Enhanced Write Capability for Space Applications
## 3 Implementation Results and Analysis
### Read Capability Comparison  
Read stability refers to the ability of an SRAM cell to preserve the stored value at a storage node during a read operation, without being disturbed by noise or internal coupling. It is typically evaluated using the RSNM, which is measured by precharging both BL and BLB to VDD and extracting the voltage transfer characteristics through the butterfly curve method. A higher RSNM indicates stronger noise immunity and better read stability [17]. During read operations, the proposed EWS-16T cell utilizes only one pair of access transistors to connect the storage nodes to the bitlines, while the remaining pair remains electrically present but inactive. These unused transistors introduce parasitic coupling between the storage and internal nodes, acting as additional load elements that weaken voltage stability. Furthermore, the feedback structure between the storage and internal nodes, although beneficial for soft-error resilience, increases the susceptibility of the cell to noise during read access, thereby reducing RSNM.  
As shown in Figure 9, the EWS-16T cell exhibits a lower RSNM than other comparison cells such as QCCS, SCCS, SAW16T, HP16T, RHSCC16T, and S8P8N. This indicates that while the proposed design delivers strong write performance through its dual-access structure and optimized pull-up ratio, its read stability is comparatively limited due to the added complexity of its internal node structure and feedback paths. To enhance RSNM, read-assist techniques such as negative bitline (NBL) or boosted word line can be employed to increase the voltage differential between bitlines during read operations [18; 19]. In addition, layout optimization and careful transistor sizing can help achieve a balanced trade-off between read stability and write efficiency.
# Nanomaterials for Healthcare Biosensing Applications
## 4 Organic Nanomaterial Applications for Healthcare Biosensing
### Nanostructured Films
The sensor was functional in the 4-400 uM concentration range with a 1.5 uM limit. Liu and co-workers reported an ultrasensitive electrochemical glucose biosensor using Prussian blue nanocube (PBNC) films [240]. Graphene was used to promote the conjugation of GOx with PBNC. The PBNC film acted as an \"artificial peroxidase\". An LOD of 10 uM with a detection range of 0.001-0.8 nM was achieved.
# Void Hole Avoidance for Reliable Data Delivery in IoT Enabled Underwater Wireless Sensor Networks
## 4 Proposed Schemes
### Ca-Dbr  
In this section, we describe the CA-DBR routing protocol, which also finds the set of next hop forwarders using the greedy opportunistic forwarding mechanism. CA-DBR selects those nodes that have the minimum number of neighbor nodes to avoid the collision, as shown on the left side of Figure 4. In this scheme, when a node receives a packet, it manages the data packet as follows:  
Figure 4: Proposed system model illustrating B-DBR and Collision Avoidance-based (CA)-DBR.  
* If the node is not within the effective forwarding range of the preceding hop, it does not forward the data packet, and it solely updates its neighbor table. Moreover, it enqueues information if the packet has not been transmitted already.
* Whereas, if the node is not within the transmission range of the forwarding node, then it will wait for the subsequent data packet. This means that the packet is inside the suppression area. This will reduce the collision and interference on the acoustic channel.
* The fall back and nomination of forwarder node mechanisms are used together for minimal energy consumption and a high packet delivery ratio.
* After updating the neighbor table, if no different transmission of the packet is heard towards the destination, it forwards the packet towards the sink node.
## 2 Experimental Details
### Electrochemical Measurements  
The electrochemical properties i.e., cyclic voltammetry (CV), charge-discharge curves (GCD), and electrochemical impedance spectroscopy (EIS) of the CuCo\({}_{2}\)S\({}_{4}\) electrodes prepared in different solvents were measured in a 2 mol per liter solution of KOH (potassium hydroxide). All the measurements were performed in a three-electrode cell assembly constituted od a working electrode of CuCo\({}_{2}\)S\({}_{4}\)@Ni (1 \(\times\) 1 cm), a graphite plate as a counter electrode, and a reference electrode-saturated calomel electrode (SCI). ZIVE SP5 (Wona Tech; Seecho-gu, Seoul, Republic of Korea) potentiostat/galvanostat/impedance analyzer was used to measure the electrochemical properties. The mass of active material was 3.2 mg/cm\({}^{2}\) for CCS-EG and 3.9 mg/cm\({}^{2}\) for CCS-Glycerol electrode.
# Research on Clustering-Based Fault Diagnosis during ROV Hovering Control
## 3 Configuration of ROV
### Control System  
Figure 11 illustrates the control system of the ROV. Microcontroller Unit 1 (MCU1) is a control thruster, MCU2 is used for gathering sensor data, and MCU3 measures the back-EMF period to calculate the RPM. Additionally, a mini PC processes algorithms, collects data, and communicates with a PC on land. For measuring the attitude of ROV, E2 BOX \"9 DOFV5\", an attitude and heading reference system (AHRS) sensor, was used. Its accuracy is \(\pm\)0.2 degrees, and it has a maximum output speed of 1 kHz. For depth measurement, BlueRobotics \"MS5837\", was used. This sensor operates on the principle of converting physical pressure into electrical signals and can measure up to 300 m with a resolution of 0.2 cm. For measuring the consumption current of the thruster, the current sensor used is the same product as the one described in Section 2.2.  
As shown in Figure 12, a graphical user interface (GUI) was developed using C# to monitor and control the ROV. It allows real-time monitoring of the attitude data of the ROV, data feedback for thrust compensation, the transmission of motion-related commands to the ROV, and adjustment of the proportional-integral-derivative (PID) gain values for hovering control.  
Figure 11: Diagram of the ROV control system.
## 3 Results and Discussion  
The optimized method was validated in terms of linearity, precision, detection and quantification limits (Table 1). The calibration curves generated by plotting detector response versus the amount spiked of each standard showed a coefficient of detection (r\({}^{2}\)) \(>\) 0.99 for all the quantified volatiles. Detection (LOD) and quantification (LOQ) limits were calculated following the IUPAC approach [19]. The method showed an average LOD = 0.05 \(\mu\)g L\({}^{-1}\) and LOQ = 0.15 \(\mu\)g L\({}^{-1}\). The precision of the method was carried out by intraday repeatability and intralaboratory reproducibility studies. The repeatability of the developed method was determined by analysing three different aliquots of the same sample under identical experimental conditions in the same day and in different days for the intralaboratory reproducibility. Considering the peak area obtained for each component during the different analyses, the coefficient of variation (CV) resulted \(<\)12% for all the quantified components. The recovery was determined by the standard addition method: each analyte was spiked in a wine sample used as blank materials at five levels; the spiking levels were \(\geq\)3 times the level present in the blank. The extraction recoveries were included between 98.9% and 102.6%. Table 2 reports all the identified sesquiterpenes together with their retention time (RT), linear retention index (LRI) and odor; quantitative data are reported in Table 3. Sixteen sesquiterpenes, acyclic, monocyclic, bicyclic and tricyclic, were identified mainly hydrocarbons but also derived oxygenated compounds. The structures of each sesquiterpene is reported in Figure 1 while Figure 2 reported the HS-SPME-GC-MS chromatogram of a wine sample in SIM mode monitoring the following ions: _m/z_ 161, _m/z_ 189, _m/z_ 204 _m/z_. \begin{table}
\begin{tabular}{l c c c c c c} \hline  & **LOD** & **LOQ** & **Linear range** & **Intraday** & **Interday** & **Recovery** \\  & **(\(\mu\)g L\({}^{-1}\))** & **(\(\mu\)g L\({}^{-1}\))** & **(\(\mu\)g L\({}^{-1}\))** & **(RSD) (\%)** & **(RSD) (\%)** & **(\%)** \\ \hline \(\alpha\)-Gurjunene & 0.04 & 0.13 & 0.12–1.02 & 2 & 5 & 94 \\ \(\alpha\)-Cedrene & 0.04 & 0.13 & 0.12–1.08 & 1 & 4 & 99 \\ (E)-\(\beta\)-Farnesene & 0.05 & 0.16 & 0.14–0.24 & 2 & 3 & 98 \\ Valencene & 0.03 & 0.10 & 0.10–1.31 & 2 & 3 & 97 \\ (E)-Nerolidol & 0.07 & 0.23 & 0.20–5.98 & 1 & 4 & 104 \\ \hline \end{tabular}
\end{table} 
Table 1: Analytical features of the proposed method.
## 1 Introduction
It has a 5 days revisiting period for the same area. Until now, the CSES has been steadily operating in orbit for over four years. Its identical successor satellite will be launched to the same orbit space around February 2023. This paper briefly reports how the CSES scientific application center analyzes seismic activities using satellite data.
## 3 Construction of Time Series Based on the UTC Scale
### Assessment of the Construction Method of the Proposed Time Series Based on the UTC Scale
The comparison of the obtained values of residuals and the values of all the prediction quality measures indicates, that the best quality of predicting the deviations has been obtained using the TS1_1 and TS2_1 time series. This is due to the application of _xb_(_t_) values determined with a one-day interval, thanks to the application of the PCHIP function. The research results for these time series for a longer period of time presented in [10,24] confirm that the TS1_1 and TS2_1 enable the best quality for predicting deviations. However, these studies show that the TS1_1 is more suitable for practical application. The TS2_1 is characterized by a slightly worse quality of prediction and is more time-consuming to prepare. 6. The values of the TS3_1 are determined only on the basis of the _xb_(_t_) deviation values published by BIPM. The achieved unfavorable large value of _xb_(_t_) for the MJD 54499 day, amounting to 26.6 ns (Figure 7), directly influenced the large residual value for MJD 54509, as well as the MSE error and its components MSE\({}_{1}\) and MSE\({}_{3}\). Such a large residual value results from a large change in UTC-UTC(PL) deviations in the period from MJD 54479 to MJD 54509. The proposed TS3_1, despite the non-optimal residual values and the determined quality measures of prediction achieved, is a good alternative for predicting _xb_(_t_prad) in the case of absence of the _xa_(_t_) values due to a failure of the atomic clock or its replacement. Figure 7: Values of _xb_(_t_) deviations for a period of one month (from MJD 54479 to MJD 54509) after interpolation with the PCHIP function.
## 1 Introduction
As a result, the propeller forces fluctuated, and that changed the behavior of the bending moment as well as the stability of the propulsion shafting system. In the case ship of this study, several related works have already been published [25], but there is still an issue that needs to be clarified. It is well-known that the measurement of the bending moment and shaft alignment can be performed via the bending stress obtained by strain gauges with telemetry systems. This technique does not require disassembling the shaft line. It was introduced early by [PERSON] [26], [PERSON] and [PERSON] [27]. This method can be conducted while the ship is dioat, as opposed to more traditional methods which often require dry docking of the ship. Cdr [PERSON] et al. [28] carried out propulsion shaft alignment measurements on an alfloat warship. [PERSON] et al. [29] identified the specific bearing load based on a three-section strain gauge. A study by [PERSON] et al. [30] used strain gauges to verify the satisfactory alignment of the entire shafting system as well as the accurate gearbox positioning of a high-speed naval craft. There are many other studies that also used this method [31; 32; 33; 34]. By the same manner, in the previous work on this case ship [25], the authors determined the nominal bending moment though a single bridge of strain gauge. This affects the position of the shaft in the bearing as well as the pressure distribution of the lubrication film. It is noted that the nominal bending moment has only one value which does not change during a shaft rotation. It is related to the quasi-static condition. In fact, the propeller force varies at every angle of rotation. This fluctuation leads to a continuously changing oil film pressure in the bearing, which repeats itself for every rotation of the shaft, resulting in the instability of the bearings. It is related to the dynamic condition which is not accessible by the nominal bending moment only. Establishing the true dynamic bending moment is needed to evaluate the effect of propeller forces on the bearings as well as whole propulsion system. Ordinarily, two bridges of strain gauge at 90 degrees are needed. This research explores the novelty in establishing the true dynamic bending moment using only one bridge of strain gauge. This new technique provides reliable results compared to the conventional method requiring double bridges. It helps to simplify the equipment and, therefore, to reduce the measurement cost. The experiments were carried out under the rapid turning transient states of the target ship as port turn and starboard turn, which significantly affected the working conditions of the propeller. A half-bridge of strain gauge was installed at the close vicinity of forward stern tube seal box as a means to establish the true dynamic bending moment. The final purpose was to investigate the effect of the propeller force on the bearings and propulsion system because of the sudden change in the stern's wake-field during rapid turning. Displacement measurement was also performed simultaneously to confirm the accuracy of this technical method.
## 6 Performance Evaluation
### Evaluation for the Designed Protocol
#### 6.1.2 Comparison for LBRP with Three Others  
In Figure 5, four key performance metrics are compared between our proposed LBRP and three other protocols: DBF, RTF and LSGR. In these figures, we mainly validate the influence of vehicular traffic density on performance metrics, including the path construction successful probability, the path lifetime, the number of transmission hops for one constructed path and the overall transmission delay for the target content.
# Machine Learning Prediction Approach to Enhance Congestion Control in 5G IoT Environment
## 3 Proposed Congestion Control Prediction Approach
### Development Phase  
This research discusses the improved SCTP (IMP-SCTP) congestion control mechanism of sensors in the 5G IoT environment. To achieve the third objective, improved slow-start and congestion avoidance techniques must be implemented. The improved approaches were used to solve the limitation of conventional congestion control in the 5G IoT environment. Various development processes were applied to create the final shape of IMP-SCTP based on an improved congestion control mechanism that uses DT capabilities. Two-stage testing with different scopes was implemented to develop IMP-SCTP. In the first stage, IMP-SCTP congestion control provided optimal window management according to the best I-selection. In the second stage, I-selection was led by DT. The evaluation criteria of DT techniques were queue size, throughput, packet loss, and cwnd. The improvedapproaches implement optimal window management through congestion avoidance and slow-start mechanisms, as shown in Figure 1.  
At this point, all possible experiments, including related scenarios, have been covered and presented. Figure 1 depicts the stages of the IMP-SCTP congestion control mechanism. Based on these stages, two experiments were designed and developed. The details of these experiments are discussed in the next section. The improved mechanisms of congestion control utilize adapted approaches associated with slow-start and congestion avoidance mechanisms. Figure 2 demonstrates the processes of the proposed approaches and clarifies that each step relates to the congestion control model of the improved SCTP. To achieve the optimal I value, a machine learning approach is applied using C4.5, which is an algorithm utilized to accomplish DT that was proposed by [PERSON] in 1970 [PERSON] (1970). J48 is an open-source implementation of the C4.5 algorithm. Thus, J48 obtains the optimal alternative and predicts the new optimal alternative based on the training table, which consists of a composite of specific performance factors. J48 generates a DT through the following steps.  
Figure 1: Understanding of the development phase. DT: decision tree.  
Figure 2: Flowchart of the proposed congestion control mechanism. ACKed: acknowledgement; ORG-SCTP: original stream control transmission protocol.