esa-sceva/satcom-chunk-collection huggingface dataset:
# Experiencing LoRa Network Establishment on a Smart Energy Campus Testbed
## 8 Conclusions  
LoRa is expected to provide long-range communication (up to several kilometers) with low power and low cost. This will enable us to communicate easily with the countless pervasive IoT devices that have been disconnected or connected only via an expensive cellular network. A LoRa network, once established correctly, will lead to the emergence of sophisticated IoT services. Existing research has revealed several fundamental properties of LoRa. However, some research questions that are confronted when establishing a LoRa network on a real-world application testbed have yet to be unanswered. \"What is the communication range of LoRa in a combined indoor and outdoor environment?\", \"How do national regulations and application parameters influence LoRa performance?\", \"Do we need to install more than two LoRa gateways on our campus?\" To answer these questions, we built a real-world LoRa testbed and ran experiments that enabled us to find a simplified path loss model for LoRa PHY, revealing that the radio signal could reach up to 630-1344 m with various SF values in the combined environment. This paper also enumerates the impacts of the transmission period, payload size, and LBT on LoRa MAC performance after conducting intensive experiments and analyses. Based on the experimental results, it is recommended that the data period and the number of nodes are adjusted together to achieve the communication reliability requirement. Another result shows that a sensing threshold of \(-65\) dBm in the Korean regulations does not work well in LoRa. The results suggest optimal values for channel sensing and the LBT threshold and demonstrate that an exponential back-off algorithm outperforms others in LoRa. Taking into consideration both experimental results and the corresponding analyses, it is concluded that the campus needs to install two LoRa gateways to reach 3000 nodes while satisfying performance requirements of the smart energy application.  
This paper finds room for performance advancement. For instance, an adaptive, dynamic back-off algorithm can be applied to LoRa, and such an action is expected to improve LoRa MAC performance. Once multiple gateways are equipped, nodes communicate with more than two gateways simultaneously, introducing a communication redundancy issue. Then, advanced scheduling and data processing algorithms must be developed to deal with these issues. These are interesting topics that remain to be studied in future research.  
**Author Contributions:** [PERSON]-K.L. contributed to conceptualization, project administration, and validation; [PERSON]-H.K. contributed to software, data curation, and visualization; and J.K. contributed to investigation. All authors together contributed to all parts of writing.  
**Funding:** This research was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry and Energy (MOTIE) of the Republic of Korea (No. 20168530050030).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Embedded Battery Tile Design and Construction
### Embedded Battery Tile Prototype Design
With two battery tiles, the CubeSat capacity grows to 14.2 Wh, which is more than typical 1U satellites and comparable to the 1.5U typical capacity (14 Wh);
* Carbon fiber (CFRP) skins. Due to the aforementioned limitations on the total tile thickness, the two CFRP skins layup consists of a single layer of 2 \(\times\) 2 fulfill-weave-woven carbon fabric. The layup sequence is \([\pm 45^{\circ}]\). The CFRP material is a commercial prepreg material system and belongs to the family of the Toray/Huntsman T300/LY 564 [24; 25]. Standard mold forming and autoclave curing procedures were adopted and are described below;
* Printed circuit boards (PCBs). The electrical power system circuitry is hosted on two COTS PCBs. The thickness of the inner PCB is 0.75 mm and the thickness of the outer PCB is 0.44 mm. From a structural point of view, the boards can be modeled as plates composed of continuous layers of copper and epoxy resin. Figure 7: Electrical energy capacity of the state-of-the-art CubeSats. The ARAMIS prototypes with the embedded battery tiles are indicated in blue.
## 3 Experimental Results
### Control Experiment  
The mass of particle cavities is slightly different. It is well known that the damper mass is an important factor affecting the damping effect, and the connection bolts between the cavity and the adapter may also affect the structural response. This section evaluates the effect of two factors on the test and compares the primary structure measurements with different cavities to the control scheme in Table 4. \"Without cover\" in the table means that the cover on both sides of the cavity is not installed.  
As shown in Table 5, the damper cavity has little effect on the response of the primary structure. The resonant frequency of the primary structure with the adapter and the damper cavity is slightly reduced. The amplification factor of the main structure with the particle cavity installed has a slightly reduced under sinusoidal excitation and random excitation conditions, but the variation amplitude is within 10%.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Component Name** & **Mass (g)** \\ \hline Primary structure & 11070 \\ A1 cavity & 110 \\ A2 cavity & 151 \\ B1 cavity & 123 \\ B2 cavity & 161 \\ C1 cavity & 160 \\ C2 cavity & 198 \\ Cover & 24 \\ Adapter & 70 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Mass of each component.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Scheme Name**} & \multirow{2}{*}{**Additional Mass (g)**} & \multirow{2}{*}{
\begin{tabular}{c} **Additional Total Mass** \\ **(Including Adapter) (g)** \\ \end{tabular} } & \multirow{2}{*}{\(f_{\text{sin}}\)} & \multirow{2}{*}{\(A_{\text{sin}}\)} \\ \hline Sup\({}_{-1}\) & 0 & 70 & 240.81 & 71.53 \\ \hline A1 (without cover) & 110 & 180 & 237.80 & 68.30 \\  & & (\(-1.25\%\)) & (\(-4.52\%\)) \\ \hline A1 & 158 & 228 & 236.89 & 68.20 \\  & & (\(-1.63\%\)) & (\(-4.66\%\)) \\ \hline C1 & 208 & 278 & 233.42 & 64.24 \\  & & (\(-3.07\%\)) & (\(-10.19\%\)) \\ \hline C2 (without cover) & 198 & 268 & 235.20 & 66.39 \\  & & (\(-23.3\%\)) & (\(-7.19\%\)) \\ \hline C2 & 246 & 316 & 231.49 & 65.89 \\  & & & (\(-3.87\%\)) & (\(-7.88\%\)) \\ \hline \hline \end{tabular}
\end{table}
Table 5: The effect of the cavity and the adapter: (**a**) Sinusoidal excitation; (**b**) Random excitation.  
It can be observed from experiments that only the damping change caused by the additional concentrated mass is small, and the main cause of the structural response change is several bolt connections between the end cap and the cavity. Therefore, in the subsequent tests, the screws at the joints were tightened with a constant torque electric screwdriver.
# Design and Simulation of Electromagnetic Linear Actuators for Jet Dispensers
## 5 Results and Discussion
### The relationship Among Applied Current, Stroke Length, and Thrust Force  
The modified SA, MCA, and MMA models were used to further investigate the effects of the stroke length and excitation current on the thrust force value. In performing the simulations, the \(z\) gap of the SA model was increased from 0.2-0.7 mm, while the coil windings of the MCA model and magnet core of the MMA model were displaced through a \(z\) gap of 0.0-0.5 mm. For each actuator, the thrust force was calculated at intervals of 0.5 mm along the stroke length. In addition, the simulations were performed at intervals of every 0.2 A in the considered current range (1-2 A for the SA and 1.6-2.6 A for the MCA and MMA). Figure 13 shows the corresponding simulation results. For each actuator, the thrust force increases approximately linearly with increasing current. Furthermore, for each model, the magnitude of the thrust force varies with the position of the movable part (i.e., plunger, coil, or magnet). For the SA model, the thrust force decreases by around 61% on average as the \(z\) gap increases from 0.2-0.7 mm. For the MCA and MMA models, the thrust force attains its highest value at the center position (\(z=0\) mm). However, as the coil or magnet moves away from this position, the thrust force drops by around 0.7% for the MCA model and 1.8% for the MMA model as \(z\) increases to 0.5 mm. Overall, the SA thrust force decreases significantly when the position of the plunger is far from the coil holder. For the MCA and MMA models, the thrust force appears to remain constant as the stroke length increases. However, a detailed inspection of the simulation results shows that \(F_{\textit{MCA}}\) and \(F_{\textit{MMA}}\) actually drop within a narrow range of around 0.2 N and 0.3 N, respectively.  
Figure 12: Thrust force of three models before and after modification.  
Figure 13: _Cont_.
###### Abstract  
We present an autonomous sensor to measure soil water content that uses a single heat pulse probe based on a transistor encapsulated in a porous block. The sensor uses a bipolar junction transistor, which performs as both a heating and temperature-sensing element. Since the sensor depends on a porous block to measure the matric potential of the soil, it does not suffer from accuracy problems if the contact between the probe and the soil is not perfect. A prototype of the sensor showed a temperature variation of \(\Delta T=2.9\,^{\circ}\mathrm{C}\) when the porous ceramic was saturated with water. The sensor presented an almost linear behavior for small changes in the matric potential of a red latosol when tested in the 1-kPa and 35-kPa pressure range, showing a sensitivity of \(\mathrm{S}=0.015\,^{\circ}\mathrm{C}\)/kPa. The ultra-low power signal conditioning circuit can read the sensor's temperature with a resolution of approximately \(0.02\,^{\circ}\mathrm{C}\), so the matric potential can be read in increments of at least 1.33 kPa. When powered only by a 2-F supercapacitor from the energy-harvesting system, the interrogation circuit is able to take one soil water content measurement per day, for eleven days.  
soil water content; porous ceramic; bipolar transistor; embedded circuits; heat dissipation soil moisture sensors; low-power circuits +
Footnote †: journal: Applied Physics Letters
# Grape By-Products in Sustainable Cosmetics: Nanoencapsulation and Market Trends
## 6 Nanoencapsulation Trends
### Nanoemulsions  
Nanoemulsions, Figure 3, are nano-scaled simple emulsions that can be utilised to create new functional compounds that improve water dispersibility and thermal stability and are able to preserve bioactive compounds in the active form during storage, among other qualities [111]. [PERSON] et al. [112] developed resveratrol-loaded nanoemulsions based on Caproyl 90 or isopropyl myristate as oil phase, and propylene glycol and ethanol as cosurfactants. The particles had a range from 23.4 to 422.2 nm of diameter and allowed an increase in transdermal penetration and deposition of resveratrol in the skin, by 896.2- and 10.2-fold after 24 applications, respectively, comparing to the drug solution group. Additionally, in an in vivo study, plasma concentration of resveratrol was shown to be maintained at high levels for a long time after topical delivery of the emulsions. [PERSON] et al. [113] synthesised a resveratrol nanoemulsion using vitamin E and sefsol as the oil phase, and Tween 80 and Transcutol P as surfactants. The obtained drug release was ~88% in 24 h. Resveratrol nanoemulsions, created using Sefsol 218\({}^{\text{\text{\textregistered}}}\) as oil, PEG 400 as co-surfactant, Tween 80 as surfactant and water as aqueous phase, were also capable of improving the permeability and antioxidant activity of resveratrol against UV-related skin damage [114]. In another work, grape seed oil was used as one of the oil phase components of nanoemulsions, designed for resveratrol encapsulation. Orange oil was used as the other component of the oil phase and Tween 80 was selected as surfactant. In this case, resveratrol stability was improved after UV-light exposure, and the nanoemulsion allowed an 88% retention of resveratrol, compared to 50% when using dimethylsulphoxide (DMSO) [115].
## 5 Computation Experiments
### Description of Test Data and Parameter Setting  
Since the MEIFM problem is proposed for the first time and there is not a standard test set for it, in order to guarantee the validity and practicability of the proposed model and algorithm, the paper constructs a test data set by combining the data set available in the related and high-cited literature, instead of randomly generating a data set. Specifically, the jobs processing data [45] and corresponding machine energy consumption data [46] are shown in Tables 2 and 3, separately, in which a solid line in a cell implies that machines is not available to perform that operation. The size of each machine [47] is shown in Table 4. Besides, these machines are located in a 17 m \(\times\) 14 m workshop. The minimum distance between machines is 0.5 m and the minimum distance between machines and the workshop boundary is 1.5 m. The speed and energy consumption per unit time of transporter are 2 m/s and 5 kw, respectively.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Job**} & \multirow{2}{*}{**Operation**} & \multicolumn{8}{c}{**Processing Time (min)**} \\ \cline{3-10}  & & **M1** & **M2** & **M3** & **M4** & **M5** & **M6** & **M7** & **M8** \\ \hline \multirow{3}{*}{Job 1} & \(  
In addition, the parameters of the proposed algorithm are determined after multiple trials. For the upper-level NSGA-II, the population size is 50, the maximal number of generations is 500, the crossover rate is 0.8, and the mutation rate is 0.1. For the lower-level GA, the population size is 40, the maximal number of generations is 100, the crossover rate is 0.8, and the mutation rate is 0.2.
###### Abstract
There are around 84 to 262 stroke patients per 1 million people in populations in rural areas and 334 to 424 stroke patients per 1 million people in populations in urban areas in India and there are similar rates in low- and middle-income countries [14]. In developing countries, many stroke patients cannot afford to visit rehabilitation centers for treatment. The majority of them are from remote areas [15]. Although there are many exoskeleton models, they are too heavy [16] for a stroke patient to help them in picking up objects for their daily usage. Exoskeleton models-based therapy is costly. Many systems do not have feedback to measure the effort required by the patient and to accordingly adjust torque of the system [17]. In addition, many of the conventional exoskeleton models are not smart enough to store any information about the patient's therapy at a rehabilitation center [16; 17], thereby denying the benefits of data analytics to the users and the medical practitioners. In this research work, a lightweight, easy-to-wear, low-cost, multi-control wearable device with IoT-based monitoring and evaluation of key parameters is proposed for the hand therapy of people affected by stroke. The device is shown in Figure 1. In addition to health data storage and analysis, IoT technology integrated into the device allows for real-time follow-up and feedback from therapists which increases the efficiency of the therapy. The device has the wearable part, the controller has the fixed gateway, sensors, an actuator, and a smartphone has the mobile gateway. It can be controlled by three different methods, including smartphone app-based speech, smartphone app-based touch, and IoT-based dashboard control. The sensor data is transmitted via Wi-Fi-enabled controller. The Bluetooth interface of the smartphone allows for the direct transmission of control commands for operation of the device to the controller which is also Bluetooth-enabled. A timestamp is inserted into the wearable data before the gateway forwards them to the cloud server. The device is portable so that it can be used even while travelling. The remainder of this paper is organized as follows. Section 2 presents the state-of-the-art wearable healthcare devices and platforms with IoT technology. This is followed by Section 3 which describes the hardware design, different architectures, and multiple control methods. Section 4 presents the technical background, experimental setup, and the case study involving volunteers. Section 5 elaborates on the results and discussion of the results which is followed by conclusion in Section 6.
# FDCNet: Frontend-Backend Fusion Dilated Network Through Channel-Attention Mechanism
## 3 Our Proposed Method--FDCNet
### The Loss Function
#### 3.4.2 The SSIM-Based Loss Function  
SSIM is an indicator widely used in the field of image quality assessment. It computes the similarity between two images in terms of local patterns including mean, variance, and covariance. The range of SSIM value is from \(-1\) to \(1\). The more similar two images are, the larger the value is. When the two images are identical, it is equal to \(1\).  
We add SSIM to the loss function following SANet [7]. First, an \(11\times 11\) normalized Gaussian kernel with standard deviation of \(1.5\) is used to estimate local statistics. Then, the weight is defined by:  
\[W=\{W(r)\big{|}r\in R,R=\{(-5,5),\cdots,(-5,5)\}\} \tag{5}\]  
where \(r\) is offset from the center and \(R\) contains all positions of the kernel. So for each location \(t\), we calculate the local statistics on the estimated density map \(F_{d}\) and  
\[\sigma_{D}^{2}(t_{D})=\sum\
olimits_{\tau_{D}\in R_{D}}W(r_{D})\cdot[F(t_{D}+r_{D} )-\mu_{D}(t_{D})]^{2} \tag{9}\]  
Thereout the local covariance estimation \(\sigma_{F_{d}D}\) between \(F_{d}\)  and \(D\) is calculated by:  
\[\sigma_{F_{d}D}(t)=\sum\
olimits_{\tau\in R}W(r)\left[F(t+r)-\mu_{F_{d}}\left( t_{F_{d}}\right)\right]\left[Y(t+r)-\mu_{D}(t_{D})\right] \tag{10}\]  
After calculating these indexes, the SSIM is calculated point by point as:  
\[\text{SSIM}(t)=\frac{\Big{(}2\mu_{F_{d}}\mu_{D}+Q_{1}\Big{)}\Big{(}2\sigma_{F _{d}D}+Q_{2}\Big{)}}{\Big{(}\mu_{F_{d}}^{2}+\mu_{D}^{2}+Q_{1}\Big{)}\Big{(} \sigma_{F_{d}}^{2}+\sigma_{D}^{2}+Q_{2}\Big{)}} \tag{11}\]  
\(Q_{1}\) and \(Q_{2}\) are randomly small constants to avoid division by zero and we set them following [7]. The SSIM-based loss function is introduced as:  
\[L_{s}=1-\frac{1}{M}\sum\
olimits_{t=1}^{M}\text{SSIM}(t) \tag{12}\]  
where \(M\) is the number of pixels in density maps.
## 4 Selection of Computational Intelligence and Reinforcement Learning Solutions  
There are many CI- and RL-based solutions in the literature, with different characteristics and suitability to specific applications. In this section, we motivate and discuss the selection of five different methods. More specifically, we will focus our investigation on five CI-based solutions: GA, PSO, QPSO, a variant of PSO, MPSO, and GWO, and two DRL-based approaches: DQN and PPO.  
The GA represents a robust and flexible optimization solution that can be applied to a wide range of problems, including service management ones [25; 48]. While their relatively slow convergence rate in some cases has made them less popular than other CI solutions in recent years, we chose to consider GAs as they represent an important baseline--a gold standard, so to speak. PSO is a simple CI solution that has proven incredibly effective in a wide range of problems, despite requiring careful attention to the parameter setting to facilitate convergence [49]. QPSO is a heavily revised version of PSO inspired by Quantum mechanics, which in our experience has consistently demonstrated solid performance [36; 50]. We decided to include PSO and QPSO as relatively simple, robust, and fast-converging CI solutions.  
Also, due to the popularity of these approaches within the related literature, we decided to consider the DQN and PPO as representative algorithms for the off-policy  
Figure 5: Optimizing a real system with offline Reinforcement Learning.  
and on-policy approaches of RL, respectively. Considering both approaches allows a comprehensive evaluation of RL and could provide valuable insights with respect to different aspects, such as sample efficiency. In Section 6, we will give further details about the specific implementations of the algorithms that we used.
# A Lightweight File System Design for Unikernel
## 3 Design Issues of Unikernel File Systems
### Approaches for Lightweighting File System  
Optimized file system design in Unikernel environments will be possible through characterization of file access patterns, I/O size, access frequency, etc. [17], but to the best of our knowledge, there are no specific characteristics known for file access characteristics in Unikernels.  
As Unikernel is designed to execute a single application per kernel, the memory footprint of a single Unikernel is not expected to be large. Thus, the goal of our design is to maximize performance while minimizing resource usage. Specifically, performance can be improved by simplifying or eliminating unnecessary file system functions, such as permission management for multiple users or quota restrictions for each user.  
This leads to a reduction in memory footprint, improving booting and execution performance. Moreover, if multiple hardware threads are not necessary for a Unikernel application, file systems can also assume a single hardware thread, and non-reentrant file systems with a single hardware thread enable a simplified and non-preemptive implementation [18]. Performance can be further accelerated by eliminating locking, which is a major cause of performance degradation in most file system implementations.
###### Abstract  
Due to the variations of aircraft types, sizes, orientations, and complexity of remote sensing images, it is still difficult to effectively obtain accurate position and type by aircraft detection, which plays an important role in intelligent air transportation and digital battlefield. Current aircraft detection methods often use horizontal detectors, which produce significant redundancy, nesting, and overlap of detection areas and negatively affect the detection performance. To address these difficulties, a framework based on RetinaNet that combines a multi-feature fusion module and a rotating anchors generation mechanism is proposed. Firstly, the multi-feature fusion module mainly realizes feature fusion in two ways. One is to extract multi-scale features by the feature pyramid, and the other is to obtain corner features for each layer of feature map, thereby enriching the feature expression of aircraft. Then, we add a rotating anchor generation mechanism in the middle of the framework to realize the arbitrary orientation detection of aircraft. In the last, the framework connects two sub-networks, one for classifying anchor boxes and the other for regressing anchor boxes to ground-truth aircraft boxes. Compared with state-of-the-art methods by conducting comprehensive experiments on a publicly available dataset to validate the proposed method performance of aircraft detection. The detection precision (P) of proposed method achieves 97.06% on the public dataset, which demonstrates the effectiveness of the proposed method.  
multi-feature fusion; rotating anchor; harris corner detection; retinanet; oriented aircraft detection +
Footnote †: journal: Applied sciences