esa-sceva/satcom-chunk-collection huggingface dataset:
# Sustainable Additives for the Production of Hydrogen via Sodium Borohydride Hydrolysis
## 3 Results
### Lag Time  
This section discusses the influence of the studied additives on the lag time values. Table 2 shows the lag times obtained in the presence and the absence of the different additives, following an increasing order. SDS reported the lowest value of lag time (50 s), while the test in the absence of additives offered the highest value (211 s). SDS can negatively impact H\({}_{2}\) production by accelerating the decomposition of NaBH\({}_{4}\), causing an uncontrolled release of hydrogen gas, which can lead to safety concerns and reduce the overall yield of H\({}_{2}\). On the other hand, the precursor CMC, which showed the shortest stabilization release time, offered a threefold decrease in the lag time (70 s) in comparison to the value achieved without additives. In the case of PAM, the lag time was higher than in the case of the rest of the additives (115 s) but still half the lag time of the control test. This is because PAM acts as a dispersant, which helps to break up the agglomerates of the catalyst, allowing for better dispersion of the catalyst in the reaction mixture. This leads to an increase in the surface area of the catalyst available for the reaction, resulting  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Additive** &
\begin{tabular}{c} **H\({}_{2}\) Rate** \\ **(mL\(\cdot\)min\({}^{-1}\cdot\)gcat\({}^{-1}\))** \\ \end{tabular} & **Conditions** & **Ref** \\ \hline BCD & 788 \(\pm\) 120 & 10 wt\% NaBH\({}_{4}\), 7 wt\% NaOH, 1 wt\% additive & This work \\  & & Catalyst: Ni-Ru & \\ \hline None & 980 & 10 wt\% NaBH\({}_{4}\), 7 wt\% NaOH & \\  & & Catalyst: Ni-Ru & \\ \hline None & 220 & 10 wt\% NaBH\({}_{4}\), 5 wt\% NaOH & \\  & & Catalyst: Co-Al\({}_{2}\)O\({}_{3}\) & \\ \hline None & 400 & 10 wt\% NaBH\({}_{4}\), 5 wt\% NaOH & \\  & & Catalyst: Ni-Ru nanocomposite & \\ \hline None & 1466 & 10 wt\% NaBH\({}_{4}\), 5 wt\% NaOH, & \\  & 2446 & Ni-Catalyst: Ni-Co\({}_{3}\)O\({}_{4}\) & \\  & & Catalyst: Co-Co\({}_{3}\)O\({}_{4}\) & \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._in a shorter lag time and faster onset of hydrogen generation. Moreover, PAM can also enhance the mass transfer in the reaction mixture, which can improve further the efficiency of the hydrogen production process. Finally, BCD achieved a lag time of 80 s, which can be considered acceptable. The values obtained in this section demonstrate that the use of additives can improve the lag time required for H\({}_{2}\) release. Thus, all the additives can be used to accelerate the initial hydrogen release, which is desirable when small but rapid hydrogen production is necessary for process control. Furthermore, for higher demands, the use of CMC, BCD, and PAM additives can facilitate the production of larger quantities of hydrogen safely.
## 3 Results and Discussion
### Principal Component Analysis (PCA) and Multivariate Correlation Analysis (MCA)  
Principal component analysis (PCA) is a valuable statistical tool, as it enables the simplification of complex data sets. Three technical PCA iterations were performed to ensure the validity of the results. The three different tested variables, TPC, FRAP, and DPPH, in water-cress leaf extract displayed a positive correlation, as shown in Figure 2. The variable \(X_{4}\), temperature, also exhibits a positive correlation. These data are perfectly reasonable since, as described in Table 4, higher temperatures (up to 65 \({}^{\circ}\)C) seem to benefit the enhancement of the TPC, FRAP, and DPPH variables. In addition, the extraction technique was found to play the greatest role among the different extraction parameters. As found in Tables 1 and 3, different techniques and combinations of techniques appeared to strongly favor extraction for the amplification of each of the TPC, FRAP, and DPPH variables. This is the reason that, in Table 5, the different variables present low correlations with each other (with a peak correlation value of 1).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Responses**} & \multicolumn{5}{c}{**Optimal Conditions**} \\ \cline{2-6}  & **Maximum Predicted** & **Technique** & **C (\%, \(v/v\))** & **t (min)** & **T (\({}^{\circ}\)C)** \\  & **Response** & **(X\({}_{\textbf{1}}\))** & **(X\({}_{\textbf{2}}\))** & **(X\({}_{\textbf{3}}\))** & **(X\({}_{\textbf{4}}\))** \\ \hline TPC (mg GAE/g dw) & 25.72 \(\pm\) 3.59 & PEF + US + ST (4) & 50 (3) & 60 (2) & 65 (4) \\ FRAP (\(\mu\)mol AAE/g dw) & 79.82 \(\pm\) 6.48 & ST (1) & 25 (2) & 90 (3) & 50 (3) \\ DPPH (\(\mu\)mol AAE/g dw) & 43.98 \(\pm\) 9.51 & US + ST (3) & 50 (3) & 30 (1) & 65 (4) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Optimal extraction conditions and maximum predicted responses for the dependent variables.  
Figure 2: Principal component analysis (PCA) was applied to the measured variables, with each \(X\) variable represented in blue.
## 3 Results and Discussion  
### Source Water Characterization  
The three source waters were chosen to represent a range of types of water in the Midwestern US. The pH, conductivity, and turbidity of each source water were measured and are shown in Table 2.  
The pH of the pond water was approximately 8% higher than the tile water and approximately 1.5% higher than the stream water. The pond water had a 78% and 63% higher conductivity than tile or stream water, respectively. The pond water had the highest turbidity, 3.25 times and 4.3 times greater than tile and stream water, respectively.
## 3 Results and Discussion
### Deprotection Kinetics Test  
Generally, in solid-phase peptide synthesis (SPPS), a solution of piperidine in DMF is used for removing the Fmoc group from the \(\alpha\)-amino function. In this study, a deprotection kinetics test was conducted to evaluate the time required by the solvent, DMM, in combination with piperidine, to remove the Fmoc group and to assess any side reactions that may have occurred between the solvent and the base used [26]. The test was performed with a 20% solution of piperidine in DMM, and the formation of dibenzofulvene (DBF) was monitored by HPLC-UV at times 0, 4, 6, 8, 15, and 30 min. The deprotection kinetics were compared to the method developed by [PERSON] et al. [27]. For both protocols, Fmoc-Val-OH was examined. With the standard solution of 20% piperidine in DMF, the peak relative to Fmoc-Val-OH disappeared after 6 min, while with the green solvent (piperidine at 20% in DMM), after 30 min (Figure 1(a),1(b)). Although the times are longer, they are acceptable for a deprotection reaction. Certainly, DMM does not bring side reactions, and this step of SPPS has been improved in terms of eco-sustainability. DBF and DBF-base adduct were characterized by 1H-NMR spectroscopy in Figure S17.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & & & & & & & & \\ \multicolumn{1}{c}{} & & & & & & & & & & \\ \multicolumn{1}{c}{} & & & & & & & & & & \\ \hline \multicolumn{1}{c}{} & & & & & & & & & & \\ \multicolumn{1}{c}{} & & & & & & & & & & \\ \hline \multicolumn{1}{c}{} & & & & & & & & & & \\ \hline \multicolumn{1}{c}{} & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Solubilization efficacy of coupling reagents and mixture Fmoc-AA(PG)-OH with coupling reagents (A–B) in DMF and DMM 1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{} & & & & & \\ \multicolumn{1}{c}{} & & & & & \\ \multicolumn{1}{c}{} & & & & & \\ \hline \hline \multicolumn{1}{c}{} & & & & & & \\ \hline \hline \multicolumn{1}{c}{} & & & & & & \\ \hline \hline \multicolumn{1}{c}{} & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Solubility of Fmoc-His(Trt)-OH amino acid in mixture NMP/DMM 1.
## 4 Simulation Experiment Analysis
The communication channel had three Rayleigh paths, and the specific communication channel equalization energy control results are shown in Figure 11.  
Figure 11: Results of communication channel equalization energy control. Figure 6:0 Comparison results of three methods of average energy consumption for successful transmission of a single packet. It can be seen from Figure 11 that the bit error rate of the communication channel control conducted by study [2] and study [3] was higher, whereas that of the communication channel equalization energy control conducted by this method was lower, showing a downward trend. Using this method to balance the energy of the communication channel can optimize the system well, and its security performance is good. Because of the small energy loss, the life of communication network can be greatly extended. From what has been discussed above, the information fusion performance of big data numerical attribute information clustering processing was better, the automatic search ability of data clustering center was stronger, and the fuzzy weighted clustering retrieval ability of communication big data numerical attribute was improved. This method had good application value in cloud computing analysis and clustering of communication data.
# Alternative Pedicle Screw Design via Biomechanical Evaluation
## 1 Introduction
When the strength and stiffness of the screw material are not enough, bending strength and pull-out strength are also low. Several improved designs that take these factors into account have been reported, such as dual screws with single and double threads [7]. The opinion of field clinicians is that the use of the dual screw significantly reduces problems after implantation. Nevertheless, because many manufacturers still want to develop alternative pedicle screws, their pedicle fill, shape, and properties are to be determined by a rigorous design procedure. To release a new product, it is first necessary to evaluate its reliability under the new design conditions. Although several studies have obtained useful results, they were limited because the improvement of screw design or the analysis of vertebrae has been considered separately. [PERSON] et al. [8]  
Figure 1: Radiographs after a lumbar interbody fusion surgery for degenerative disc disease. compared the pull-out strengths of different screw designs, while [PERSON] et al. [9] explored the strength with several screw leads and [PERSON] et al. [10] investigated the cause of the breakage of the thoracic and lumbar vertebrae due to the screw insertion method. Despite these efforts, the biomechanical models generated in most studies has been concerned separately for vertebrae or pedicle screws instead of creating an integrated model that takes into account the interactions between them. In particular, standard tests and detailed analysis models must be combined to solve problems that arise at an early stage after surgery. This should comprise a comprehensive platform to understand the interaction between the vertebrae and pedicle screws and to investigate their behavior after the surgery. This will allow researchers to evaluate or verify the design of alternative pedicle screws. Therefore, herein, a comprehensive platform based on experimental effort and coordinated modeling is presented. Moreover, the platform was applied to evaluate an alternative dual pedicle screw design with varying shapes and dimensions. The structural stability of the screws was evaluated via testing based on the ASTM F1717 standard. An integrated finite element analysis (FEA) model of vertebrae and screws was also established to investigate their mechanical stresses and interactions. Finally, we present the efficacy of the design platform by the alternative dual screw design results.
## 4 Results and Analysis
### Impact of Climate Change on Wetland Changes
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Period**} & \multirow{2}{*}{**Region**} & \multirow{2}{*}{**Major change**} & **Local area ratio (\%)** & **Total area ratio (\%)** & **Precipitation (mm/year)** & **Temperature (\(\mathbf{c}\)C/10 years)** & **H humidity (\(\mathbf{10^{-3}/year}\))** \\ \hline \multirow{4}{*}{1990–2004} & \multirow{2}{*}{I} & Lakes, reservoir & \multirow{2}{*}{90.0} & \multirow{2}{*}{28.6} & \multirow{2}{*}{2.6} & \multirow{2}{*}{0.06} & \multirow{2}{*}{4.3} \\  & & \& ponds increase & & & & \\ \cline{2-3} \cline{5-8}  & II & Swamp decrease & 44.6 & 12.0 & 1.1 & 0.05 & 2.1 \\ \cline{2-3} \cline{5-8}  & III & Swamp decrease & 68.0 & 27.8 & 1.9 & 0.08 & 1.8 \\ \cline{2-3} \cline{5-8}  & IV & Swamp decrease & 50.0 & 0.2 & \(-\)1.0 & 0.07 & \(-\)3.6 \\ \hline \multirow{4}{*}{2004–2012} & \multirow{2}{*}{I} & Lakes, reservoir & \multirow{2}{*}{92.1} & \multirow{2}{*}{11.5} & \multirow{2}{*}{10.0} & \multirow{2}{*}{0.01} & \multirow{2}{*}{17.3} \\  & & \& ponds increase & & & & \\ \cline{1-1} \cline{2-3} \cline{5-8}  & II & Lakes, reservoir & \multirow{2}{*}{69.7} & \multirow{2}{*}{33.2} & \multirow{2}{*}{11.1} & \multirow{2}{*}{0.01} & \multirow{2}{*}{20.4} \\  & & \& ponds increase & & & & \\ \cline{1-1} \cline{2-3} \cline{5-8}  & III & Lakes, reservoir & \multirow{2}{*}{49.1} & \multirow{2}{*}{19.3} & \multirow{2}{*}{7.1} & \multirow{2}{*}{0.04} & \multirow{2}{*}{10.4} \\  & & \& ponds increase & & & & \\ \cline{1-1} \cline{2-3} \cline{5-8}  & IV & Swamp decrease & 83.3 & 0.5 & 5.0 & 0.02 & 6.2 \\ \hline \hline \end{tabular} Note: humidity Region: I: 0.3–0.4, II: 0.4–0.5, III: 0.5–1.0, and IV: \(>\)1.0; local area ratio = area of major wetland change/area of each humidity region; and total area ratio = area of major wetland change/area of wetland changes in whole region.  
\end{table} 
Table 4: The relativity statistics between wetland and climate changes in the TRH region from 1990 to 2012. During 2004-2012, precipitation increased significantly while the rate of temperature increase slowed; therefore, wetland changes were tightly associated with climate humidification. Among these regions, the maximum wetland area increase was in the Region II, accounting for one third of the total change of area. Meanwhile, the _HI_ growth rate was also the maximum in this region, reaching \(20.4\times 10^{-3}\)/year. In the Region IV, the _HI_ also showed an increasing trend, however, the annual growth rate was the minimum among regions. Wetland changes mainly presented as swamp decreases, accounting for merely 0.5% of total wetland area change. Therefore, the regional humidity differences and the interannual variation trend, caused by the change of precipitation and evaporation, were the main driving factors for the dynamic variation of wetland change in the TRH region. In general, the increase of _HI_ in the THR region since the 1990s, especially in the western TRH region, has continuously contributed to wetland increases.
## 3 Materials and Methods
### Methods
#### 3.1.3 Data Processing  
Agisoft Methashape Professional was used to process the images obtained from the flights, which included alignment, orientation, scaling and the generation of point-cloud models and orthophotographs. This software, together with the number of images and data to be processed, had immense hardware requirements; an ASUS WS X299 PRO ATX Workstation with 128 Gb of DDR4-2666 RAM and Intel(r) Core i9-10900X processor at 3.7 GHz, 2 NVIDIA GeForce RTX 2080 Super 8 GB GDDR6 graphics cards with 3072 NVIDIA(r) Cores and HD SSD of 1 Tb capacity were used.
# Flying Watchdog-Based Guard Patrol with Check Point Data Verification
###### Abstract  
The effectiveness of human security-based guard patrol systems often faces challenges related to the consistency of perimeter checks regarding timing and patterns. Some solutions use autonomous drones for monitoring assistance but primarily optimize their camera-based object detection capabilities for favorable lighting conditions. This research introduces an innovative approach to address these limitations--a flying watchdog designed to augment patrol operations with predetermined flight patterns, enabling checkpoint identification and position verification through vision-based methods. The system has a laser-based data transmitter to relay real-time location and timing information to a receiver. The proposed system consists of drone and ground checkpoints with distinctive shapes and colored lights, further enhanced by solar panels serving as laser data receivers. The result demonstrates the drone's ability to detect four white dot LEDs with square configurations at distances ranging from 18 to 20 m, even under deficient light conditions based on the OpenCV detection algorithm. Notably, the study underscores the significance of achieving an even distribution of light shapes to mitigate light scattering effects on readings while also confirming that ambient light levels up to a maximum of 390 Lux have no adverse impact on the performance of the sensing device.  
drone; patrol; OpenCV; laser; monitoring; security; data communication; computer vision; IoT; watchdog +
Footnote †: journal:  
00 2023 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
# AHC: Advanced Network-Hiding Access Control Framework
## 2 Related Work  
The most critical aspect of network services is the security of port access. However, issues arise when ports are left open or access is allowed without proper authentication, making it easier for unauthorized users to access the server. Currently, numerous domestic and international scholars have conducted extensive research on network-hiding technologies. Overall, these technologies have primarily evolved through three stages: the first generation is represented by Port Knocking, the second generation is represented by Single Packet Authorization (SPA), and generation 2.5 consists of enhanced SPA technologies. Both Port Knocking and SPA are access control technologies. Port Knocking only reveals network-protected resources to users who can prove knowledge of a pre-shared port sequence. SPA is a technique designed to protect hidden network services from unauthorized users.  
Port Knocking uses the port fields in TCP and UDP packet headers to transmit information. While Port Knocking can effectively restrict access to services, it still has many limitations [10; 11; 12]. First, encrypted knocking sequences inevitably involve information transmission, and the information volume depends on the encryption algorithm used. Second, out-of-order data delivery complicates decryption, and the service-side mechanism for reordering out-of-sequence packets can be problematic. Third, the limited data transmission capability of Port Knocking makes it challenging to effectively defend against replay attacks, as a malicious third party can easily disrupt the knocking sequence by injecting an additional data packet disguised as part of the client's port sequence. Finally, any Intrusion Detection System (IDS) capable of monitoring traffic between the client and server can easily detect the knocking sequence as a port scan.  
SPA and its enhanced technologies represent a relatively new protocol that retains all the benefits of Port Knocking while addressing its limitations [13; 14]. SPA and its enhancements provide similar security advantages for protecting services that use packet filters configured with a default-drop policy. SPA and its enhanced technologies offer clever solutions to the many restrictions of Port Knocking. These solutions enable SPA and its enhanced technologies to resolve replay issues, support data transmission rates with asymmetric encryption, thwart simple spoofing attacks, and evade IDS detection that monitors for network port scans.  
Therefore, we summarize the current research status of the three developmental stages of network-hiding technologies as follows:
# Leader-Follower Formation Control of Wheeled Mobile Robots without Attitude Measurements
## 2 Kinematic Model  
Consider a group of differential-drive mobile robots as it is shown in the Figure 1. According to the figure, the Cartesian position and orientation of each robot are denoted by \(\mathbf{x}_{i}=\text{col}(x_{i},y_{i})\in\Re^{2}\) and \(\theta_{i}\in\Re\), respectively. The posture of each robot can be described by the unicycle kinematic model as follows  
\[\dot{\mathbf{x}}_{\text{i}} =\mathbf{\Theta}_{\text{i}}v_{\text{i}}, \tag{1a}\] \[\dot{\theta}_{i} =\omega_{i},\quad i=\ell,\text{f}_{j},\quad j=1,...,n, \tag{1b}\]  
where \(\mathbf{\Theta}_{i}\triangleq\text{col}(\cos(\theta_{i}),\sin(\theta_{i}))\in\Re^ {2}\) represents the direction of the Cartesian velocity and \(n\) is the number of slave robots. Finally, \(v_{i}\) and \(\omega_{i}\) are the linear and angular velocities, respectively. The orientation angle and the Cartesian velocity satisfy the nonholonomic constraint  
\[\dot{y}_{\text{i}}\cos(\theta_{\text{i}})-\dot{x}_{\text{i}}\sin(\theta_{ \text{i}})=0 \tag{2}\]  
or equivalently,  
\[\tan(\theta_{\text{i}})=\frac{\dot{y}_{\text{i}}}{\dot{x}_{\text{i}}}. \tag{3}\]  
The nonholonomic constraint (3) implies that the velocity in the direction of wheel axis is zero, i.e., the robot cannot move in the lateral directions.