esa-sceva/satcom-chunk-collection huggingface dataset:
# An Overview on Atomization and Its Drug Delivery and Biomedical Applications
## 1 Introduction  
The process of the destruction of feed liquid into drops or fine spray by the influence of surface tension together with interior or exterior forces is referred to as atomization. When there is no influence of disturbances, the surface tension of the liquid will pull it into a sphere-shape in which the surface energy is less. When it is disturbed with other forces, the surface tension will result in instability that leads to the destruction of feed liquid (thin ligaments or sheets of liquid) into smaller drops [1]. Some examples of atomization that we come across in our day-to-day life are antiperspirant or hair spray, shower head, garden sprinkler, cologne mist, etc. The outcome of the atomization process is to generate spray, which is a gathering of fluid beads that are in an organized motion and each sphere-shaped drop is also known as a particle [2]. Thus, to generate particles of preferred thinness, the measure of the external force or any kind of force that causes disruption, should either level or top the force applied by the surface tension of the particular fluid [1]. There are factors that affect the atomization process and its result of the fine spray of liquid droplets. One of the major factors is the properties of the fluid that is being used in the process and those are surface tension, density and viscosity of the fluid. All the three have a similar effect, that is the higher these properties, the larger the droplets. Another factor that can affect this is the temperature at which atomization is taking place. The temperature has an inversely proportional effect on surface tension of the fluid, i.e., higher temperature reduces the surface tension of the fluid and thus affects the droplet size [2].  
Recently scientists are exploring the utilization of atomization in various biomedical applications. One crucial challenge associated with biomedical implantations are infections caused by biofilm formation on the implanted biomaterial [3]. Micro or nano level modification of the implant surface is one of the remedies. Atomization techniques like electrospraying are used to modify the biomaterials to generate drug distributing transporters, embellished microarrays, implant coatings, effective scaffolds for tissue engineering, oral vaccine delivery etc. for various biological applications [4; 5; 6]. The various biomedical applications of atomization are briefly discussed in the later section.
# A Traceable and Verifiable Tobacco Products Logistics System with GPS and RFID Technologies
## 3 The Proposed Scheme
### Arbitration Phase  
When any access party doubts the authenticity of the origin of the tobacco products, they can arbitrate its legality through a third-party arbitrator. The arbitration flow is shown in Figure 14. The detailed steps are described as follows:  
1. [label=Step 0.A]
2. AP provides the specific tobacco products' TID to the AU.
3. AU sends a request message with their signature and TID to the BCC.
4. BCC checks the signature; if the signature is valid, then the AU responds to a list of signatures to the AU.
5. The AU starts to check the validity of signatures. 1. [label=.]
2. If the RT signature is illegal, then the record is forged by the RT. 2. Then, the AU extracts the list of the \"Transport Record\" of tobacco information and obtains the RCP and SHP signature. 3. If the RCP signature is illegal, then the record is forged by the RCP. 4. If the SHP signature is illegal, then the record is forged by the SHP. 5. Confirm that the record is the last data of the list of \"Transport Record\". If it is not, go to step 4b, otherwise go to the next step. 6. If the MF signature is illegal, then the record is forged by the MF. 7. If there are no illegal signatures, then the logistics record is legal and verified by the AU.  
Figure 13: The audit mechanism in the verification phase.
## 2 Miniaturized X-ray/Gamma-ray Imaging Payload
### CdTe Sensor for Enhanced Efficiency of Photon Detection  
The pixel detector is equipped with a radiation-sensitive semiconductor sensor of a high-Z material (CdTe) and increased thickness (2000 \(\upmu\)m thick). This detector-sensor configuration is customized for enhanced detection of high energy X rays and gamma rays. The detection efficiency of the photon component depends on the sensor material and thickness as shown in Figure 5. For the sensor configuration used, the detection efficiency for 500 keV gamma rays is at the level of 2%.
## 1 Introduction
The system achieved a 13% increase in the recovery rate, along with a 2-8 times reduction in the scaling rate. Moreover, the effect of EMF was stronger when it was used simultaneously with the antiscaling agent, and the recovery rate of the system reached 89.3%. There are also a few researchers who have used the electric field directly on the RO, using the electric potential as a tool to prevent biofouling; when biofouling was present, it was shown that the RO was subjected to potentiodynamic polarization for 30 min and was able to recover 33-44% of the osmotic flux [33]. In summary, the application of electric fields is highly beneficial in preventing the fouling of filtration membranes with pollutants and increasing the permeate flux through the membranes. However, there remains a paucity of research on the direct impact of electric fields on RO desalination, particularly on the direct incorporation of electric fields into completed RO membranes. Consequently, this study aims to directly introduce electric fields to finished roll membranes to examine their influence on RO desalination performance, as well as their effect on both inorganic and organic fouling. The findings from this research will contribute to a deeper comprehension of the mechanisms by which electric fields affect the performance of RO systems, offering valuable insights into the implementation of electric fields in completed RO operations.
# Mobility-Multihoming Duality
## 1 Introduction
### Benefits of Harmonised Mobility and Multihoming  
There are proposals that enable mobility or multihoming individually, but it is not clear whether they can work together seamlessly in harmony. Often, such mechanisms are designed and implemented separately, and so trying to make them work together retrospectively could result in complex and fragile engineering solutions. For example, the standard Mobile IP solution today from the Internet Engineering Task Force (IETF) cannot work with multihomed systems, as, at the very least, they will use different control plane protocols. Additionally, Mobile IP and other IETF offerings require the use of agents/proxies and tunnelling, introducing additional complexity and overhead into the service provisioning.  
Multihoming is often implemented by requiring a network service provider to implement special configuration or functions, for example, IP routing, with the use of very specific addresses and additional routing table entries. This results in a loss of flexibility and portability for a given solution for the end user.  
While some transport protocols, such as Multipath TCP (MP-TCP) and QUIC, do offer _multipath_ capability, they are transport layer (layer 4) protocols, and so they rely on IP layer (layer 3) mechanisms to provide the _multihoming_ capability--they cannot provide it themselves. Additionally, if a new transport protocol is required, the use of multihoming and multipath capability will have to be implemented from scratch for that new protocol. Finally, even if a host is multihomed, existing protocols--TCP and UDP--cannot use the multipath capability: new protocols must be utilised.  
Currently, there are no known general solutions to provide _harmonised mobility and multihoming_ for an IP--that is, _the combined, seamless use of both mobility and multihoming together_. More specifically, there are no solutions at the IP layer (layer 3) to allow _any_ transport protocol to function over _any_ (different) layer 2 protocols. This would include using multiple network connectivity without any need for special configuration or functions from a network service provider.  
Overall, our solution has the following benefits:  
* A harmonised, combined, mobility and multihoming mechanism for hosts/end systems, with a single, simple, unified control plane protocol. No other solution currently exists for this.
* For mobility, there is no need for the use of agents/proxies/middleboxes, tunnelling, address translation, or special mechanisms in the network: only normal unicast routing is required.
* For multihoming, there is no need for reliance on special routing or provisioning by a network service provider: again, only normal unicast routing is required.
* Our approach implements an IP layer (layer 3) mechanism that can be used by _any_ transport layer (layer 4) protocol, including existing protocols such as TCP and UDP, which we demonstrate in our evaluation.
# Deep Learning-Based Spread-Spectrum FGSM for Underwater Communication
## 3 Proposed SS-FGSM Scheme
### SS-FGSM Scheme
Such a process is considered as a high computation detector and the ML decoder complexity is increasing exponentially with all of these estimated parameters. Therefore, a low-complexity detection algorithm is required to relax the complexity of the SS-FGSM.
# Evaluation of Manual Skill Degradation Due to Automation in Apparel Manufacturing
## 4 Discussion  
The study aimed to detect manual skill loss due to automation and non-use for an extended period. The study also aimed to determine how much time it takes for refresher training to achieve previously attained learning levels. The performance test after initial training was conducted to support the idea that all groups have acquired the same skill level before the start of the retention period. The findings demonstrate that exposure to high and excessive automation for an extended period decreases manual performance in an apparel manufacturing sewing task. The degraded manual sewing skills of the operators demand a counter-skill decay strategy in the form of refresher training.  
The results supported all three of our hypotheses. The RTG maintained its skill level despite a decrease in learning level as compared to the MMG, while the highest skill degradation occurred in the AG. The AG exhibited the highest manual sewing skill performance after retention interval while the sewing performance of the MMG increased. The results are consistent with earlier research that skill retention is inversely proportional to the period of non-use [21]. However, the learning level achieved at the initial training made a substantial impact on minimizing skill decay. In addition, quality and the analytical way of training at the start results in more skill retention.  
The RTG performance in manual skill looks almost the same at the retrieval assessment phase but with a reduced learning speed of only 2% in six weeks. The result suggests that non-use for a short period first reduces the learning level, and as the non-use period increases, this skill level goes into the depreciation phase. The results also suggest that frequent refresher practice sessions reduce the chances of skill loss to a considerable degree. The time interval for refresher training depends on the manual task difficulty and the level of learning at the skill development phase.  
In manual task analysis as shown in Figure 3, alignment and positioning of fabric parts and active sewing include 70-80% of the task. However, the sewing operators were only doing the pick and dispose of tasks in the automation group. This suggests the dexterity loss during the skill retrieval stage among the AG since the operators did not practice needing the machine and alignment of parts on automated machines. As a result, the average increase in time(s) during skill retrieval is only in needle board (3.2 s) and pinboard (3.5 s) dexterity.
## 4 Experimental Process and Discussion
### Calibration Results and Response Time  
To measure the relationship between the 2f SA and C\({}_{2}\)H\({}_{2}\) concentration, 10 C\({}_{2}\)H\({}_{2}\) samples with concentrations of 10, 20, 30, 40, 50, 60, 70, 80, 90, and 100 ppmv are prepared by dynamic gas preparation. The prepared C\({}_{2}\)H\({}_{2}\) sample was sequentially introduced into the gas chamber at intervals of 5 min. As shown in Figure 6a, the 2f SA obtained in the experiment was expressed as max (2f). The 2f SA is linear with the C\({}_{2}\)H\({}_{2}\) concentration, and Equation (15) is obtained by linear fitting (where C\({}_{C2H2}\) is the measured C\({}_{2}\)H\({}_{2}\) concentration).  
\[\mathrm{max}(2f)=0.0241C_{C2H2}-0.0015 \tag{15}\]  
Then, Equation (16) is derived as follows.  
\[C_{C2H2}=41.4938\mathrm{max}(2f)+0.0622 \tag{16}\]  
The 2f SA can be converted to C\({}_{2}\)H\({}_{2}\) concentration by using Equation (16) with a linear correlation of 0.9997, and the fitted curve is shown in Figure 6b.  
The response time of the system depends on the time of data processing and the speed of gas diffusion. The detection system can obtain the concentration value in one stair-stepping segmented signal cycle, and 10 concentration values are displayed after average processing. In the case of static gas preparation (i.e., using an air bag to inject C\({}_{2}\)H\({}_{2}\) into the gas chamber), the response time is approximately 2 s.
## 3 Wireless Power Transfer System in Seawater
### Power Loss of Coupler with Ferrite Core  
In order to increase the mutual inductance between the transmitter and receiver and shield the stray electromagnetic fields, we usually add ferrite cores with high permeability to the bottom of the coils, as is shown in Figure 5b. However, the presence of ferrite will cause the magnetic field to have a nonlinear distribution, so traditional electromagnetic theory cannot be used for analysis. Most previous studies are based on numerical simulations or empirical equations. In addition, the presence of ferrite cores brings additional power loss, which is defined as core loss and causes the AC impedance of the coil, the eddy current loss of the seawater and the weight of the coupler to increase.  
Based on the simulation model in the previous section, we added a circular ferrite core with a thickness of 5 mm and the same diameter as the outer diameter of the coils. The material of the ferrite core is set to high-performance PC95. Figure 9 shows the simulation results of the influence of distance change in power loss and mutual inductance. The simulation results in Figures 8 and 9 have the same changing trend. The slope of the curves in Figure 9 is greater than the curves marked with the same symbol in Figure 8 because of the existence of the ferrite core.  
Figure 8: Simulation results of power loss and mutual inductance of the coreless structure vary with the distance between the turns. (**a**) Power loss. (**b**) Mutual inductance.  
Figure 9: Simulation results of power loss and mutual inductance of the ferrite core structure vary with the distance between the turns. (**a**) Power loss. (**b**) Mutual inductance.  
In addition, the size of the ferrite core and the distance between the core and the coil will also affect the transmission performance of the coupler. These parameters will be considered in the subsequent design.
## 2 Materials and Methods
### Search Strategy  
Six databases were systematically searched from their inception to January 2025: CINAHL, PubMed, Scopus, Web of Science, ScienceDirect, and PEDro. The following search terms were used: (\"Multiple Sclerosis\" [MeSH] OR \"multiple sclerosis\" OR \"disseminated sclerosis\") AND (\"Core Stability\" [MeSH] OR \"core\" OR \"trunk\" OR \"lumbopelvic hip complex\" OR \"Pelvic Floor\" [MeSH] OR \"pelvic floor\" OR \"diaphragm\" OR \"Abdominal Muscles\" [MeSH] OR \"abdomin\"' OR \"low back\" OR \"multifiq\" OR \"lumbar\" OR \"quadratus lumborum\" OR \"eractor spinae\" OR \"external oblique\" OR \"internal oblique\") AND (\"Resistance Training\" [MeSH] OR \"Endurance Training\" [MeSH] OR \"train\"' OR \"Exercise\" [MeSH] OR \"Exercise Therapy\" [Mesh] OR \"Exercise Movement Techniques\" [MeSH] OR \"exercis\" OR \"Pilates\" OR \"strength\"' OR \"stabili\" OR \"intervention\" OR \"Rehabilitation\" [MeSH] OR \"rehabilitation\") AND (\"gait\" OR \"exercise capacity\" OR \"walking\" OR \"cardiorespiratory outcome\" OR \"locomotion\"). The complete search strategy is detailed in Appendix A.  
The reference lists of all included and cited studies were manually searched to minimize publication bias and include relevant gray literature. This strategy aimed to identify those studies that were not captured by the database search.
## 1 Introduction
(2006) used NIR hyperspectral reflectance imaging (650-1100 nm) to determine the quality of durum-wheat kernels based on PLS discriminant analysis (PLS-DA) and reported 94% classification rate [17]. [PERSON] et al. (2017) classified the geographical origin of white rice (Korea and China) by using visible and NIR (VNIR) HSI technology (400-1000 nm) and PLS-DA with several pre-processing methods as well as first and second derivatives [4]. [PERSON] et al. (2019) selected discolored and visually diseased rice seeds through HSI within the 400-1000 nm wavelength range, and they applied several multivariate analysis methods, such as support vector machine (SVM), linear discriminant analysis (LDA), and quadratic discriminant analysis (QDA) methods. They reported that each prediction model shows over 92% accuracy according to the five pre-processing methods with respect to standard normal variate (SNV), normalization, and smoothing [18]. The objective of this study is to develop a classification model for multiple grain flours using multiple HSI modalities (VNIR, fluorescence, and short-wavelength infrared (SWIR)) in the ranges of 400-1000 and 900-1700 nm. The specific objectives of this study are as follows: (1) collect the multimodal hyperspectral images of grain flours by using three line-scan HSI modalities, and (2) develop a classification model for multiple grain flours using spectral data and apply the model to the hyperspectral images to differentiate grain flours.
# Deterministic and Robust Optimization Approach for Single Artillery Unit Fire Scheduling Problem
## 3 Fire Scheduling Problem  
The objective of an artillery unit is to reduce the combat capabilities of the enemy and support friendly operations by firing at the targets that are assigned by higher units or that are requested by maneuvering units. In a war, artillery units are required to fire at numerous enemy targets of varying types and sizes. When the war commences, these enemy targets may cause severe damage to friendly forces. Because an artillery unit cannot fire at multiple enemy targets simultaneously, the firing sequence for the targets should be determined.  
When a single artillery unit fires at enemy targets sequentially, enemy targets that are yet to be attacked by the artillery unit can continue performing missions that cause damage to friendly forces. The basic military strategy is to minimize the damage and achieve the mission objective. Therefore, artillery units must determine the firing sequence that will minimize the total threat of the enemy to friendly units until the end of the firing operation. When determining the firing sequence, artillery units must consider the threat levels of the enemy targets, destruction time, and preparation time to fire at the next target.  
In this paper, we make the following assumptions:  
1. The threat of enemy targets is determined by the type and size of the target.
2. While firing at one target, an artillery unit cannot fire at another target.
3. After firing at one target, it takes time for the artillery unit to fire at the next target.
4. An enemy target that is not attacked by an artillery unit causes damage to friendly units.
5. An enemy target that is attacked by an artillery unit does not cause damage to friendly units.  
The following notations are used in the mathematical formulations.  
\begin{tabular}{l l} \hline \hline Notations & Definitions \\ \hline \multicolumn{2}{l}{Decision variables} \\ \hline \(x_{j}\) & Firing start time for target \(j\) \\ \(y_{j\
eq j}\) & If target \(j\) takes precedence over target \(j^{\prime}\), 1; otherwise, 0 \\ \hline \multicolumn{2}{l}{Parameters} \\ \hline \(j\) & Target \(j,\ j\in\mathrm{N}=\{1,\ \ldots,\ n\}\) \\ \(\frac{f_{j}}{f_{j}}\) & Nominal threat level of target \(j\) \\ \(\frac{f_{j}}{f_{j}}\) & Maximum threat level of target \(j\) \\ \(\frac{f_{j}}{f_{j}}\) & Uncertain threat level of target \(j\) \\ \(\frac{f_{j}}{f_{j}}\) & Difference between \(\widetilde{f_{j}}\) and \(f_{j}\), \(\widetilde{f_{j}}=\widetilde{f_{j}}-f_{j}\) \\ \(s_{j\
eq j}\) & Preparation time to fire at target \(j^{\prime}\) after firing at target \(j\) \\ \(p_{j}\) & Amount of time required to destroy target \(j\) \\ \(M\) & Large positive value \\ \hline \hline \end{tabular}
# A Survey of GIS and IoT Integration: Applications and Architecture
## 3 GIS and IoT Integration Use Cases
### Utilities
With the development of sensing in IoT, they emphasize the use of this technology to operate and maintain water supply and sewage systems. As an example of GIS and IoT integration, [PERSON] [140] examined the concentration and influence of toxic gases in the urban underground sewage pipe network by the data gathered by IoT. GIS is used as a tool for pipeline 3D visualization, real-time display of accident disposal, material distribution, and path visualization.
# Coupling of Modes in Step-Index Plastic Optical Fibers by Using D-Shape Technique
###### Abstract  
This article presents a technique for reducing the stabilization length of steady-state modes in step-index plastic optical fibers (POFs), which is significant for sensor networks, Internet of Things, signal processing, and data fusion in sensor systems. The results obtained with the com- putational tool developed suggest that the D-shape created in the POF effectively reduces the stabi- lization length of the modes and, by extension, minimizes the dispersion effects of the modes by filtering out high-order modes. Applying the analysis to commercial POFs, the authors experimen- tally verified a reduction in the stabilization length of modes from 27 to 10 m and from 20 m to 5 m. Reducing the mode stabilization length minimizes the bit error rate (BER) in short-length SI-POF- based optical links operating at 250 Mbp/s. A reduction from 7.6 \(\times\) 10\({}^{-7}\) to 3.7 \(\times\) 10\({}^{-10}\) was achieved.  
POF; steady-state distribution (SSD); D shape +
Footnote †: journal: Sensors