esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Simulations  
We verify our routing by simulations using the ns2 network simulator [20]. To create a realistic simulation environment, we simulated EAR based on the characteristics and parameters of the MICAz motes, as shown in Table 1. All nodes could be used to work with three power levels and they will work in the minimal power level as the default transmission power. Many-to-one traffic pattern is used, which is common in WSN applications. This traffic is typical between multiple source nodes and one of the sinks. There are 100 nodes distributed in a 100 m \(\times\) 100 m region as shown in Figure 4. We randomly select four nodes as source nodes, and place 1-4 sinks in the simulation areas as node 99, 98, 97 and 96 respectively. Each source generates constant bit rate (CBR) traffic periodically. The real-time packet miss ratio and packet dismiss ratio by delay estimate as well as energy consumption are assigned as the main metrics for evaluating the performance of EAR. The real-timepacket miss ratio (we use \"miss ratio\" in the following paragraph) is the ratio of all packets missed because of the delay bound to the total packets sent out.  
The packet dismiss ratio by delay estimate (we use \"dismiss ratio\" in the following paragraph) is defined as the ratio of packets discarded by delay estimate and the total sending packets. The energy  
\begin{table}
\begin{tabular}{l c} \hline
**Parameter** & **Value** \\ \hline Propagation model & Shadowing \\ Shadowing deviation & 4.0 \\ Reference distance & 1.0 \\ PhyType & Phy/WirelessPhy/802\_15\_4 \\ MacType & Mac/802\_15\_4 \\ CSThresh\_ (carrier sense threshold) & 5.29754e-11 \\ RXThresh\_ (reception threshold) & 5.29754e-11 \\ Pt\_ (transmit power) & 5.35395e-05/0.000214158/0.000481855 \\ Freq\_ & 2.4e+9 \\ Traffic & CBR \\ Traffic packetSize\_ & 70 \\ Traffic Interval\_ & 0.0969 \\ Node Initial energy & 3.6J \\ \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.  
Figure 4: Simulation grid.  
consumption is the average energy consumed for each sensor during the simulation. Within the simulated area, a fire breaks out 30 seconds after the simulation is started which means the first 30 seconds of the simulation. The node in the network is static. At 30 seconds after the simulation begins, a fire occurs randomly in the network area and then spreads to its neighbors continuously every 10 seconds. When the fire reaches a sensor node, it will lead to a terminal node failure after 10 seconds.  
We compare our protocol with minimal hop count routing and RPAR protocol to make performance evaluation. The two comparing routing mechanisms are operated with the initial power as the default transmission power in EAR. RPAR is a real-time power-aware routing mechanism that achieves this by dynamically adapting transmission power and routing decisions based on packet velocity calculated by geographical distance and time left.
# Concept Justification of Future 3 DPVS and Novel Approach towards its Conceptual Development
## 3 Conceptual Design Approach for 3 DPVS
### Introducing the Engineering Design Process (EDP) into 3 DPVS Development
#### 3.1.1 Traditional EDP  
Traditional engineering design processes generally contain four stages, but generally follow the same 4-step principles, that is, analysis of problem as initiation, concept design, embodiment design and product validation. To be specific, Initiation is to define the problem, analyze the task restraints and needs, and develop the requirement lists under design specification. Conceptual design is the most vital design phase in EDP. This phase generates the design concepts and validates it. Embodiment design is to develop specifications of function, strength, geometrics, physical compatibility and financial availability. Detailed design is to further identify and establish the properties of all components inside the product. Simple logic is shown in Figure 2a follows:In addition to the typical four-stage process, some researchers have suggested the design initiation to be integrated inside conceptual design, thus making the whole process contain three phases. They believe that initiating the conceptual design would literally cover tasks in design initiation regarding defining problem and gathering information. Conceptual design, therefore can integrate the design initial stage [26]. Figure 2b shows a typical three-stage EDP process with eight sub-steps.  
Besides the above three-stage EDP process, there exists another definition of categorizing the design phase. It proposes that embodiment and detail design could be integrated together [27]. The unified part would be named detailed design, to make a distinction with conceptual design which includes the full stages before it. In this way, the design for engineering product will include only two major stages: conceptual design, which integrates the traditional design initiation and conceptual design, and detailed design, which includes both the traditional embodiment design and detailed design, seen in Figure 2c. Two-stage EDP defines design process in crystal clear way, and is easier to understand. For the development of 3 DPVS, this EDP will be utilized.
## 3 Results and Discussion
### Effect of Wall Height on the Bead Geometry  
By comparing the _RWTH_ between Groups I and II in Figure 4, we can conclude that the upper layers (in Group II) obtain larger _RWTH_ than the lower layers (in Group I) under the same process parameters. This is consistent with the above analysis that the heat dissipation condition becomes worse as the wall height increases, thereby slowing down the solidification of the molten pool. Besides this, their difference becomes more noticeable for larger _WFR_. This is because the higher the heat input is, the more difficult the heat dissipation through the upper layers becomes. This explains why it is a great challenge for WAAM to maintain consistent bead geometry from the bottom up. For example, if a 10.5 mm width wall is to be fabricated, the required _WFR_ is about 5 m/min for lower layers according to Figure 4a. If this _WFR_ remains the same regardless of the increase in the wall height, the obtained bead width is about 12.5 mm at upper layers. Only when the _WFR_ is reduced to 3.2 m/min at upper layers can consistent bead geometry be achieved. Nevertheless, it should be noted that the deposition rate (\(DR\)) is also associated with the _WFR_ as follows:  
\[DR=\frac{\rho\pi d^{2}}{4}\times WFR \tag{3}\]  
where \(\rho\) is the material density and \(d\) is the wire diameter. According to this equation, when the _WFR_ is reduced from 5 m/min to 3.2 m/min, the corresponding deposition rate is also reduced from 30.5 g/min to 19.5 g/min, which greatly sacrifices productivity. Besides this, this adjustment relies on experience to a large extent and greatly complicates the build process.
# A Gesture-Based Teleoperation System for Compliant Robot Motion
## 1 Introduction
Because of the complexity, low reliability, and high cost of haptic devices, the applications of haptic feedback systems are limited [24]. For vibrotactile feedback, [PERSON] added vibrotactile feedback to a myoelectric-controlled hand. When visual feedback was disturbed, it improved performance during a functional test [25]. [PERSON] conveyed shape information with vibrotactile feedback to aid in the recognition of items when tactile perception was hindered [26]. [PERSON] carried out a pick-and-place experiment with ten subjects. Vibrotactile feedback significantly improved the performance in task execution in terms of completion time, exerted force and perceived effectiveness [27]. Therefore, the vibrotactile device is an attractive option for implementing force feedback in teleoperation systems. Therefore, this paper proposes a novel gesture-based teleoperation system, which can generate precise robot motions as well as compliant robot motions. First, the effects of the low-frequency movement of human hands are mitigated by a scalable human-robot motion mapping mechanism. Second, a new interaction logic, scalable human-robot motion mapping mechanism and single-axis mode are used to improve the teleoperation's accuracy. Third, to expand the sensing range of the leap motion, the clutch mode is introduced. Then, to meet requirements of complaint assembly skill, a vibration-based force feedback system was developed to let the operator feel the contact force. Lastly, an active force control mechanism was also designed to restrict the contact force within a safe range. Compared to the other gesture-based teleoperation method, the proposed one combines both hand gestures and hand movements, which results in more flexible robotic motions. The force feedback capabilities and multiple operation modes make it possible to perform compliant and precise robot motion. The remainder of the paper is organized as follows: Section 2 introduces the system's human-machine interface, including the leap motion-based gesture recognition, hand-robot-tool mapping and force feedback loop. Section 3 describes refining the above interfaces to meet the requirements of actual tasks and integrating them into the system. The pick-and-place and peg-in-hole tasks were used to test the effectiveness of the developed teleoperation system, as described in Section 4. Section 5 gives some conclusions and outlines future work.
# Development of Compact and Robust Physical System for Strontium Optical Lattice Clock
## 4 Environmental Testing  
To extend the application of optical clocks to as many scenarios as possible, it is imperative to address the challenges posed by the complex and dynamic environmental conditions outside the laboratory. Therefore, we conducted exhaustive evaluations to determine the adaptability of the physical system in magnetic, mechanical, and temperature environments.
# Solving the Fuzzy Transportation Problem by a Novel Particle Swarm Optimization Approach
## 6 Case Studies and Experimental Results
\begin{table}
\begin{tabular}{c c c c c} \hline  & & **20 Particles** & **35 Particles** & **50 Particles** \\ \hline  & Mean & 132.418 & 132.317 & 131.483 \\  & St. Dev & 4.483 & 5.066 & 3.589 \\ Pr. 14 & Min & 130.680 & 130.680 & 130.680 \\  & Max & 147.390 & 148.710 & 146.730 \\  & CV\% & 3.386 & 3.829 & 2.730 \\ \hline  & Mean & 2703.450 & 2738.700 & 2445.350 \\  & St. Dev & 251.641 & 400.107 & 138.771 \\ Pr. 15 & Min & 2327.000 & 2330.000 & 2261.000 \\  & Max & 3275.000 & 3585.000 & 2795.000 \\  & CV\% & 9.308 & 14.610 & 5.675 \\ \hline  & Mean & 951.250 & 951.250 & 951.250 \\  & St. Dev & 0.000 & 0.000 & 0.000 \\ Pr. 16 & Min & 951.250 & 951.250 & 951.250 \\  & Max & 951.250 & 951.250 & 951.250 \\  & CV\% & 0.000 & 0.000 & 0.000 \\ \hline  & Mean & 829.900 & 826.775 & 823.813 \\  & St. Dev & 15.601 & 11.120 & 3.498 \\ Pr. 17 & Min & 821.250 & 821.250 & 821.250 \\  & Max & 878.000 & 868.000 & 834.750 \\  & CV\% & 1.880 & 1.345 & 0.425 \\ \hline  & Mean & 149.450 & 149.000 & 149.150 \\ Pr. 18 & St. Dev & 1.468 & 0.000 & 0.671 \\  & Min & 149.000 & 149.000 & 149.000 \\  & Max & 155.000 & 149.000 & 152.000 \\  & CV\% & 0.982 & 0.000 & 0.450 \\ \hline  & Mean & 68.100 & 67.400 & 67.250 \\  & St. Dev & 2.685 & 0.671 & 0.000 \\ Pr. 19 & Min & 67.250 & 67.250 & 67.250 \\  & Max & 79.000 & 70.250 & 67.250 \\  & CV\% & 3.942 & 0.995 & 0.000 \\ \hline  & Mean & 3641.096 & 3641.297 & 3641.029 \\ Pr. 20 & St. Dev & 0.802 & 1.266 & 0.787 \\ Pr. 20 & Min & 3640.56 & 3640.560 & 3640.560 \\  & Max & 3643.24 & 3644.580 & 3643.240 \\  & CV\% & 0.022 & 0.035 & 0.022 \\ \hline  & Mean & 3896.413 & 3896.413 & 3896.413 \\  & St. Dev & 53.852 & 53.852 & 53.852 \\ Pr. 21 & Min & 3844.000 & 3844.000 & 3844.000 \\  & Max & 4020.000 & 4020.000 & 4020.000 \\  & CV\% & 1.382 & 1.382 & 1.382 \\ \hline  & Mean & 295.900 & 298.667 & 295.900 \\  & St. Dev & 0.000 & 12.372 & 0.000 \\ Pr. 22 & Min & 295.900 & 295.900 & 295.900 \\  & Max & 295.900 & 351.230 & 295.900 \\  & CV\% & 0.000 & 4.142 & 0.000 \\ \hline  & Mean & 551.030 & 551.030 & 551.030 \\ Pr. 23 & St. Dev & 0.000 & 0.000 & 0.000 \\ Pr. 23 & Min & 551.030 & 551.030 & 551.030 \\  & Max & 551.030 & 551.030 & 551.030 \\  & CV\% & 0.000 & 0.000 & 0.000 \\ \hline  & Mean & 4518.150 & 4465.515 & 4446.878 \\ Pr. 24 & Min & 4389.600 & 4386.450 & 4399.350 \\  & Max & 4857.300 & 4629.000 & 4580.550 \\  & CV\% & 2.352 & 1.332 & 0.979 \\ \hline  & Mean & 4518.150 & 4465.515 & 4446.878 \\ Pr. 24 & St. Dev & 106.249 & 59.477 & 43.525 \\ Pr. 24 & Min & 4389.600 & 4386.450 & 4399.350 \\  & Max & 4857.300 & 4629.000 & 4580.550 \\  & CV\% & 2.352 & 1.332 & 0.979 \\ \hline \end{tabular}
\end{table} 
Table 12: _Cont._
# Arithmetic Optimization AOMDV Routing Protocol for FANETs
## 3 AO-AOMDV Routing Protocol
### Route Process
#### 3.4.2 Route Maintenance Process
Route Discovery Initiation: The source node S initiates the route discovery process by broadcasting an RREQ message to its neighboring nodes. 2. RREQ Propagation: The RREQ message is propagated from one node to another based on the routing protocol's algorithm. The RREQ message contains information about the source node S, destination node D, and other necessary parameters. 3. Reverse Path Setup: Intermediate nodes and/or the destination node D, upon receiving the RREQ message, establish reverse paths to the source node S. These reverse paths will be used later for returning the RREP message. 4. RREP Generation and Propagation: Once the RREQ message reaches the destination node D, it generates an RREP message. The RREP message is then propagated back to the source node S through the pre-established reverse paths. 5. Multiple Forward Paths Establishment: The source node S and the destination node D establish multiple forward paths between them. These forward paths are designed to be disjoint, ensuring redundancy and reliability. 6. Fitness Function Evaluation: For each of the established forward paths, a fitness function is applied to calculate their fitness values. The fitness function considers factors such as path holding time, energy consumption, congestion degree, etc. 7. AO Path Selection: We will sort the fitness values of all routes calculated through arithmetic optimization in descending order. The route from the source node to the destination node will automatically select the top-ranked route to transmit data packets. In the event of a link disruption, the second-ranked route in the sorting will be chosen to transmit data packets, and so on, in a sequential manner.
# Current Promising Strategies against Antibiotic-Resistant Bacterial Infections
## 2 Discovery of New Antibiotics
### Searching for New Antibiotics from Peculiar Sources  
The traditional approach of screening secondary metabolites from soil actinomycetes has led to the discovery of most antibiotics in use today. However, repetitive utilization of this methodology results in the disappointing outcome that most isolated active compounds are already existing antibiotics or their analogs [50]. Thus, many researchers are starting to search for new antibiotics from peculiar sources that include marine samples (inter vertebrates or algae), invertebrate organisms (such as insects), and microbiomes (Figure 2). Four natural compounds purified from the marine fungal _Stachybotrys_ species MF347, have shown anti-MRSA activity [51]. Furthermore, a bactericidal compound MC21-A was isolated from the methanol extract of the marine bacterium _Pseudoalteromonas phenolics_ sp. O-BC30T [51] and was effective against MRSA and VRE, likely through the permeabilization of the cell membrane. Thanatin is an insect-derived antimicrobial peptide (AMP) that shows potent activity against various drug-resistant GNBPs and GPBPs [52]. Natural AMPs have intrinsic disadvantages in terms of toxicity, low stability, and high cost. Thaatin variants acquired via engineering had greater tolerance to host proteinases, were non-toxic to human cells, and displayed systemic _in vivo_ efficacy against a wide range of drug-resistant pathogens [53; 54; 55; 56; 57]. These studies strongly suggest a high potential of thanatin-based products in overcoming the above-mentioned obstacles on the way to clinical success [58]. Lugdunin, a novel thiazolidine-containing cyclic peptide antibiotic produced by nasal _Staphylococcus lugdunensis_ strains, has exhibited bactericidal properties against MRSA and VRE, effectiveness in animal models, and low risk in terms of resistance development in _S. aureus_[59], showcasing that human microbiota can be used as a source for mining new antibiotics. Since the majority of microorganisms in nature with a promising potential to be a source of new antibiotics cannot be cultured in artificial growth media, the [PERSON] laboratory at Northeastern University has developed several methods to address this issue. The group successfully recovered approximately 50% of cells from soil samples by cultivating them in their natural environment or using specific growth factors [60]. An extract termed teixobactin from a new species of \(\beta\)-proteobacteria provisionally named _Eletheria terrae_ showed good activity against GPBPs and _Mycobacterium tuberculosis_. Further mechanistic studies revealed that teixobactin contributes to membrane disruption by concentrating the long hydrophobic tails of lipid II within the supramolecular structure using its C-terminal headgroup to bind the pyrophosphate-sugar moiety of lipid II and N-terminus to coordinate the pyrophosphate of another lipid II molecule [61]. This novel antibacterial mechanism can be utilized to combat current antibiotic resistance.
# Assessing the Security and Privacy of Baby Monitor Apps
## 3 Materials and Methods
### Analysis Environment  
Baby monitoring apps constitute a video surveillance solution by combining the capabilities of two different smartphones. This surveillance solution uses one monitoring device, which transmits live video streams to the second device, the parental control device. Therefore, a fundamental requirement for investigating baby monitoring apps is running instances of the same monitoring app on two smartphones. Our experiments were carried out using two physical mobile devices running the Android 12 operating system. We chose physical devices to create a realistic analysis environment, which means, among other things, establishing connections to Wi-Fi and cellular networks. The parent device was connected to a separate Wi-Fi network only accessible through the Internet to emulate a remote parent device. We pre-installed each investigated app on both devices.
# Research on the Mechanism and Dynamic Characteristics of Intermitt Failure of Electrical Connectors
## 2 Analysis of the Electrical Connector Intermittent Failure Mechanism  
### Contact Resistance Model  
It is well known that the contact surface of electrical connector is rough, and there exists many asperities on the surfaces. The two surfaces are contacted and extruded with each other, forming a real contact. Among the real contact area, a small part belongs to the metallic contact area. This metallic contact area where the current can pass through is called contact spot, or \"a-spot\". The current will shrink when it passes through \"a-spot\", as shown in Figure 1.  
The contact resistance is composed of contraction resistance \(R_{s}\) and velamen resistance \(R_{b}\). The contact resistance of the pairs can be expressed as  
\[R_{c}=R_{s}+R_{b} \tag{1}\]  
Because each \"a-spot\" is usually formed after the surface film is destroyed mechanically, the impact of contraction resistance can be ignored. Assuming that the shape of the \"a-spot\" is a circle, the contact resistance of \"a-spot\" is expressed as follows:  
\[R_{s}=(\rho_{1}+\rho_{2})/4a \tag{2}\]  
where \(\rho_{1}\) and \(\rho_{2}\) represent the electrical resistivity of the contact metal and \(a\) denotes the radius of the \"a-spot \". Once the contact material is determined, the contact resistance is mainly determined by the radius of the \"a-spot \".
## 4 Experimental Results
### Transportation Network  
The real data were collected from Amsterdam city, and the original mapping of the inner-city network included 7512 roads [17]. The Amsterdam network of roads has been reduced to 208 roads as illustrated in Figure 7. The present study concentrates only on one day of data, which is a weekday, and the road speeds are estimated from the individual travel times. Moreover, the average speed information is available every time period of 10 min from 7 a.m. to 3 p.m. for the 208 roads. The day has a total of 48 periods, and the maximum speed of the road is 40 m/s.
## 3 Results
### Patient Characteristics and Mortality Rates
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Variables** & **Alive** & **Death** & **\(\boldsymbol{p}\)** & **Alive** & **Death** & **\(\boldsymbol{p}\)** & **Alive** & **Alive** & **Alive** & **\(\boldsymbol{p}\)** \\  & **48 h** & **48 h** & \(\boldsymbol{p}\)** & **7 Days** & \(\boldsymbol{7}\) Days & \(\boldsymbol{p}\)** & **30 Days** & **30 Days** & **\(\boldsymbol{p}\)** \\ \hline Respiratory & 16 & 21 & \(<\)0.001 & 16 & 20 & \(<\)0.001 & 16 & 18 & \(<\)0.001 \\ rate, median (IQR) & (15–18) & (16–29) & & (15–18) & (16–26) & & (15–18) & (16–24) & \\ NEWS category & & & & & & & & \\ respiratory rate, & & & & & & & & \\ n (\%) & & & & & & & & \\ \(<\)8 & 36 (0.1) & 0 (0) & & 36 (0.1) & 0 (0) & 36 (0.1) & 0 (0) & \\ g-11 & 219 (0.8) & 0 (0) & & 219 (0.8) & 0 (0) & 218 (0.8) & 1 (0.2) & \\
24,593 & & & & 24,500 & & 24,237 & 24,237 & 415 (64.5) & \\
12–20 & (90.7) & 59 (48.4) & & (90.8) & & (91.1) & 116 (3.4) & 68 (10.6) & \\
21–24 & 1186 (4.4) & 18 (14.8) & & 1173 (4.3) & 31 (11.7) & & 1136 (4.3) & 68 (10.6) & \\ \(>\)25 & 1082 (4) & 45 (36.9) & & 1045 (3.9) & 82 (30.9) & 968 (3.6) & 159 (24.7) & \\ NEWS & & & & & & & & \\ Respiratory & & & \(<\)0.001 & & & & & \\ rate, n (\%) & & & & 24,500 & & & 24,237 & \\
0 & (90.7) & 59 (48.4) & & (90.8) & 152 (57.4) & & (91.1) & 415 (64.5) & \\
1 & 219 (0.8) & 0 (0) & & 210 (0.8) & 0 (0) & 218 (0.8) & 1 (0.2) & \\
2 & 1186 (4.4) & 18 (14.8) & & 1173 (4.3) & 31 (11.7) & & 1136 (4.3) & 68 (10.6) & \\
3 & 1118 (4.1) & 45 (36.9) & & 10,813 (4.0) & 82 (30.9) & & 1004 (3.8) & 159 (24.7) & \\ SpO\({}_{2}\), (\%) & 98 & 94 & & 98 & 94 & 98 & 95 & 95 \\ median (IQR) & (97–99) & (89–96) & \(<\)0.001 & & (97–99) & (90–96) & (97–99) & (92–97) & \(<\)0.001 \\ NEWS category & & & & & & & \(<\)0.001 & \\ SpO\({}_{2}\), n (\%) & & & & & & & & \\ \(<\)91 (3) & 750 (2.8) & 43 (35.2) & & 708 (2.6) & 85 (32.1) & & 647 (2.4) & 146 (22.7) & \\
92–93 (2) & 745 (2.7) & 14 (11.5) & & 727 (2.7) & 32 (12.1) & & 686 (2.6) & 73 (11.4) & \\
94–95 (1) & 2084 (7.7) & 31 (25.4) & & 2061 (7.6) & 54 (20.4) & & 1997 (7.5) & 118 (18.4) & \\ \(>\)96 (0) & 23,537 & & & 23,477 (87) & 94 (35.5) & & 23,265 & 306 (47.6) & \\  & (86.8) & 34 (27.9) & & & & & (87.5) & \\ FIO\({}_{2}\), & 303 & 29 & \(<\)0.001 & 282 & 50 & \(<\)0.001 & 247 & 85 \\ n (\%) & (1.1) & (23.8) & & (1) & (18.9) & (0.9) & (13.2) & \\ \hline \end{tabular}
\end{table} 
Table 3: Distribution of vital parameters, NEWS and category in relation to death at 48 h, 7 and 30 days.