esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Primary Metabolite Profiling of Roasted/Unroasted Mahlab Seeds via GC-MS Analysis (Post-Silylation)
#### 3.1.4 Fatty Acids/Acyl Esters/Sterols  
Another considerable metabolite class present in all examined seeds was fatty acids, reaching 15.3% in white mahlab, \"WE\", from Egypt, also accounting for its fatty taste. The monounsaturated omega-9 fatty acid, oleic acid (peak 23), was most enriched in WE (4.8%), making it the major fatty acid, indicating a heart-healthy functional food with diminished LDL (low-density lipoprotein) cholesterol [22]. An oleic-acid-rich diet has been reported to reduce the risk of atherosclerosis and type 2 diabetes [23]. The essential omega-3 polyunsaturated \(\alpha\)-linolenic fatty acid (peak 25), detected at the highest levels in WE (4.1%), exhibits neuroprotective, anti-inflammatory, antioxidant and antidepressant effects [24]. White mahlab, \"WE\", showed the highest omega 3/6 ratio among all seed accessions, providing the best fatty acid profile with the highest oleic acid content. The omega 3/6 ratio was observed to decline upon roasting, particularly in WS. \(\alpha\)-Tocopherol (peak 65),a major constituent of vitamin E with a protective effect against atherosclerosis [25], and stigmast-5-ene were further detected in all mahlab seeds though at trace levels.  
With regard to fatty acyl esters, the highest levels were detected in mahlab from Egypt, \"WE\", \"RE\" and \"RRE\", and Greece, \"WG\" (9-15.6%), with glycerol monostearate (peak 18) and n-butylcaprylate (peak 15) as major esters compared to 0.2-0.6% only in samples from Sudan. Glycerol monostearate is an anti-staling additive for bakery products that is widely used to improve the appearance and taste of flour foods and its abundance can account for the common use of mahlab seeds in bakery, aside from its role as flavor enhancer [26]. Generally, monoglycerides act as emulsifiers, yielding a more stable air-dispersed baked cake with a fairly soft crumb [27]. Another acyl ester of potential flavor described as fruity is n-butylcaprylate, detected at maximal levels in roasted red mahlab, \"RRE\" (6.5%), which is used in various foodstuffs, further rationalizing how roasting improves mahlab flavor [28].
## 6 Experiments and Results
### Experiment Design  
Figure 8 presents the overall indoor experimental test environment. This system consists of a quadrotor Qball (Quanser) with a QuaRC-powered Gumstix embedded computer onboard, for the real-time control of the vehicle [32; 33]. Because the lack of GPS signals indoors, a network of 24 Opti-Track cameras is adopted for providing position and orientation information to the UAV's embedded computer and a ground station for command allocation and real-time data display.  
The area available for experimentation is a \(2\) m \(\times\) \(2\) m space, at a constant altitude \(0.7\) m. The global positioning is based on the following coordinates: \(X=[0,2]\) m and \(Z=[0,2]\) m.  
The positioning of the quadrotor is controlled through the Gumstix computer on-board of the quadrotor, using data of the IMU and the Opti-track Visual system and a Kalman filter to correct the position and orientation of the UAV. The quadrotor is constrained to the maximum linear velocities of \(v_{x}=0.15\) m/s and \(v_{z}=0.15\) m/s. For heading the velocity is limited to \((15*pi/180)\) rad/s. A simulated polluted environment with high advection was used, according to the dispersion model of pollutants previously explained, including dispersion, wind effects and stochastic noises.  
The cyber-sensors onboard the UAV captures the pollutant and wind vector in the physical position of the UAV. In the real application, the wind vector must be estimated, that is, the magnitude and the direction. The dynamic of a real environment have been included in the model used in the experiments, as explained in Section 2.  
The fixed initial position of the quadrotor for any mission is at \((Z,X)=(100,100)\) cm. Given the limited testing area, the numbers of waypoints were restricted to a maximum of 30 for each experiment. The vehicle starts performing a deterministic navigation to get the initial heuristic position and then the metaheuristic algorithm guides the vehicle while the LMLS is updated and used by the UAV to follow the most promising position of the source location.  
The parameters selected for the LMLS of the experiments were: \(L_{x}=10\) cm, \(L_{z}=10\) cm, \(\sigma_{x}^{2}=10\frac{cm^{2}}{s}\), \(\sigma_{z}^{2}=10\frac{cm^{2}}{s}\) and the probability \(\mu=0.95\).
## 4 Proposed Scheduling Algorithm
### The Finding Minimum Scheduling Time for Convergecast (FMSTC) Algorithm
The algorithm also adds the node to the tree that has the smaller \(id\) if the weights of two links are the same in the case of a node that is in range of both of the trees. The algorithm repeats, until it gets the complete independent set of tree \(t_{i}\). After it gets a complete set of tree \(t_{i}\), it starts to find sub-tree set \(t_{ij}\) from \(t_{i}\). This process is for the next step of the FMSTC algorithm. The FMSTC algorithm builds the line topology after constructing a minimum spanning tree. This sub-tree set, \(t_{ij}\), allows us to complete an independent set of tree \(t_{i}\) by repeating the process described above. Figure 3 explains the sub-tree set environment. In Figure 3, the spanning tree, \(t_{1}\), has Nodes \(v_{1}\), \(v_{2}\), \(v_{7}\), \(v_{8}\), \(v_{9}\) and \(v_{10}\); \(t_{1}=\{v_{1}\), \(v_{2}\), \(v_{7}\), \(v_{8}\), \(v_{9}\), \(v_{10}\}\). A node, \(v_{1}\), is connected to sink node, and Nodes \(v_{2}\) and \(v_{7}\) are connected to \(v_{1}\). With a tree, \(t_{1}\), we can produce two sub-trees \(t_{11}=\{v_{7}\), \(v_{8}\}\) and \(t_{12}=\{v_{2}\), \(v_{9}\), \(v_{10}\}\), i.e., \(t_{11}\subset t_{1}\) and \(t_{12}\subset t_{1}\).We eventually construct the line topology in the given minimum spanning tree set with this process. Figure 4 explains the algorithm to produce minimum spanning tree set \(t_{i}\). There are three minimum spanning trees: \(t_{1}\), \(t_{2}\) and \(t_{3}\). Figure 3: Building a sub-tree set. Three nodes (\(v_{1}\), \(v_{3}\), \(v_{5}\)) are selected from a sink node. The FMSTC algorithm starts to construct a minimum spanning tree rooted from each node (\(v_{1}\), \(v_{3}\), \(v_{5}\)). It produces three spanning trees, which are \(t_{1}\), \(t_{2}\) and \(t_{3}\). The tree, \(t_{1}\), has two leaf nodes, which need to be divided into two sub-spanning trees. The algorithm produces the sub-trees, \(t_{11}\) and \(t_{12}\), from \(t_{1}\). Therefore, there are five spanning trees produced in the graph:  
\[t_{1}=\{v_{1}\text{, }v_{2}\text{, }v_{7}\text{, }v_{8}\text{, }v_{9}\text{, }v_{10}\}\text{; }t_{11}=\{v_{7}\text{, }v_{8}\}\text{, }t_{12}=\{v_{2}\text{, }v_{9}\text{, }v_{10}\} \tag{2}\]  
\[t_{2}=\{v_{3}\text{, }v_{4}\text{, }v_{11}\text{, }v_{12}\text{, }v_{13}\text{, }v_{14}\text{, }v_{15}\text{, }v_{16}\} \tag{3}\]  
\[t_{3}=\{v_{5}\text{, }v_{6}\text{, }v_{17}\text{, }v_{18}\text{, }v_{19}\text{, }v_{20}\} \tag{4}\]  
the node, \(v_{10}\), is in the spanning tree, \(t_{12}\), because it is close to Node \(v_{9}\), even though it is also in the transmission range of \(v_{11}\), which is in \(t_{2}\).
## 3 Description of the Environment  
Most published works on RL-based path planning have utilized simulated maps that may lack realism, being generated solely for the environment at hand. In this study, a real, cartometric electronic navigation chart (ENC) of a marine port located in Gdynia, Poland, is utilized. Since the map is cartometric, the distances measured on the chart accurately correspond to real-world distances, a property that is essential to precise route planning for an AUV. The corresponding area is highlighted in red in the satellite image in Figure 0(a).  
The ENC S-57 map was converted into the PL-2000 (Zone IV) projected coordinate system (EPSG:2177) to preserve these cartometric properties--particularly the distance fidelity. The resulting port map in S-57 format is shown in Figure 0(b). From the layers defined in [65], only the \"LAND AREA\" layer (with the acronym LNDARE) was retained, as illustrated in Figure 0(c). This remaining layer was then saved as a 2-bit bitmap, as seen in Figure 0(d). In this binary representation, navigable and non-navigable areas in the underwater vehicle's operational domain are marked on a grid map.  
The map covers a total area of 2,156,492 m\({}^{2}\) within a 1468.5 m \(\times\) 1468.5 m square, corresponding to a resolution of approximately 1.1472 m/pixel. Black pixels denote regions inaccessible to the AUV, whereas white pixels indicate navigable areas. The AUV agent moves in continuous space, meaning its motion is not discretized. Nonetheless, determining whether the agent is located in a valid or an invalid region is performed by mapping the AUV's continuous coordinates to the nearest pixels on the bitmap. The overall simulation environment is built upon the OpenAI Gym framework and preserves its general structure.  
To prevent unbounded rollouts and properly handle collisions or successful arrivals, the following termination conditions are introduced when training the RL algorithms:* **Maximum Step Limit:** A fixed maximum number of time steps (e.g., 200) is specified. Once this threshold is reached, the episode ends automatically.
* **Collision or Out-of-Bounds:** If the AUV crosses the boundaries of the map or collides with an obstacle (a black pixel), the environment terminates the episode immediately and assigns a negative reward.
* **Goal Achievement:** When the AUV comes within a specified distance (e.g., 10 m) of the target, the episode terminates, and a small success bonus is awarded. The exact threshold value is determined by a curriculum parameter.  
This design prevents indefinite episode durations, promptly penalizes collisions, and rewards reaching the goal as soon as it is achieved.  
The workstation used for training and validating the algorithms was a Windows 11 machine featuring an Intel Xeon W-2255 3.70 Ghz CPU, 128 GB of RAM, and three NVIDIA RTX 3090 GPUs.
# Electochemically Engineered Mesoporous Sn-Oxide Rods for Anode Materials in Lithium-Ion Batteries
## 2 Experimental  
### Materials Fabrication  
High-purity Sn foil (0.18 mm thick, 99.9%, Alfa Aesar, MA, USA) was used as a substrate to fabricate 1-D Sn micro-rods through electrodeposition. Prior to deposition, the foil was pretreated in diluted sulfuric acid and rinsed with distilled water to remove native oxide layers and surface contaminants. The plating bath consisted of 1.5 M H\({}_{2}\)SO\({}_{4}\) (95.0%, Samchun Chemicals, Seoul, Republic of Korea) and 0.15 M SnO\({}_{4}\) (95.5%, Thermo Fisher Scientific, Waltham, MA, USA) in aqueous solution.  
Electrodeposition was carried out in a beaker cell, with Sn foil as the working electrode and a platinum mesh as the counter electrode, maintaining a 2 cm distance between the electrodes [34; 35]. Various current densities and deposition durations (1-5 A/cm\({}^{2}\) for 5 s, 1-3 A/cm\({}^{2}\) for 10 s), and voltages (4-8 V for 10 s) were applied to investigate the structural changes in the Sn rods. To form porous Sn-oxides, the as-prepared Sn rods on the Sn foil were subjected to anodization, using the same electrode setup. The process was conducted under different voltage conditions (4-10 V for 60 s) in a 1 M NaOH (97.0%, Junsei Chemical, Tokyo, Japan) aqueous solution [36; 37] to achieve controlled porous structures on the Sn rod surface. Both electrodeposition and anodization were performed at room temperature using an IviumStat (Ivium Technologies, Eindhoven, The Netherlands), without electrolyte stirring or inert gas purging. These experimental conditions largely follow those reported in previous works [34; 35; 36; 37], with notable difference in the target anodized structure.  
The morphologies of the fabricated Sn rods and porous Sn-oxides were analyzed using field-emission scanning electron microscopy (FE-SEM; Mira3, TESCAN, Brno, Czech Republic), while their chemical compositions were verified using energy-dispersive X-ray spectrometry (EDS; 51-XMZ1004, Oxford Instruments, Abingdon, UK). The crystal structures of the samples were determined using an X-ray diffractometer (XRD Ultima-IV, Rigaku, Tokyo, Japan).
## 5 Conclusions  
By combining MODIS and Landsat imagery, this study examined the impacts of climate and management interactions on vegetation phenology and GPP in native and introduced prairie pastures. Climate factors, namely Ta and RF, played an important role in controlling the SOS/LOS and VIs sum, respectively. GPP was controlled by RF more than Ta. However, the explanatory power was usually low (mostly R\({}^{2}<0.5\)). In general, drought shortened the LOS and decreased GPP, grazing reduced VIs, and burning caused early greening-up and enhanced vegetation growth. The interactions of climate and management practices were evident in specific years only (2011 and 2014). The interactions of drought and management (baling or grazing) can greatly affect vegetation phenology and suppress production. Burning plus baling might be a good management strategy in a good RF year to increase forage productivity. Similar analyses can be adapted in other regions to investigate localized management and climate impacts on vegetation dynamics. The impacts of climate and management interactions suggested that ranchers need to adjust management strategies (dynamic instead of static management plans) based on climatic conditions to maintain productive and sustainable grasslands.  
Conceptualization, Y.Z., S.M., P.W. and P.H.G.; methodology, Y.Z. and S.M.; software, Y.Z. and S.M.; validation, Y.Z., S.M. and P.W.; formal analysis, Y.Z. and P.W.; investigation, Y.Z. and S.M.; resources, P.H.G.; data curation, Y.Z.; writing--original draft preparation, Y.Z.; writing--review and editing, Y.Z., S.M., P.W. and P.H.G.; visualization, Y.Z. and S.M.; supervision, P.H.G.; project administration, P.H.G.; funding acquisition, Y.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This study was supported in part by research grants from the National Science Foundation (NSF) EPSCoR (OIA-1946093), the U.S. Geological Survey under Grant/Cooperative Agreement No. G18 AP00077, and the USDA-ARS Office of National Program (Project number: 3070-21610-003-00D).  
**Data Availability Statement:** The code and data used in this work are available from the corresponding author upon reasonable request.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Simulation of Underwater Electric Field Communication
### Analysis of Underwater Electric Field Communication Simulation Results
#### 3.2.1 Underwater Electric Field Communication Distance Simulation Testing  
The receiving process of target nodes in underwater electric fields is as follows: when a node receives the first bit of a data packet, it checks the received energy. If the energy is below the energy detection threshold for channel transmission, the packet will be discarded. If the energy reaches the corresponding threshold, NS-3.31 ultimately determines the bit error rate and packet loss rate based on the SNIR (Signal to Interference plus Noise Ratio), which determines whether the data packet is successfully received. The LogDistance-PropagationLossModel is an extension of the Friis free space model and can be accessed through the following paths with Config::Set and Config::Connect [29; 30]. It is used to predict propagation loss in a wide range of environments, while the Friis free space model is limited to unobstructed paths between the transmitter and receiver.  
The NS-3.31 simulation environment was used to evaluate the underwater electric field communication path propagation attenuation process in seawater, using the LogDistancePropagationLossModel. By using this model to calculate the received power, the transmission power was returned when requesting the path loss at a distance less than the reference distance. The correct reference attenuation value was obtained based on the frequency and propagation speed using the Friis model. Then, the attenuation value at any distance was calculated, allowing for communication experiments with two nodes at a fixed distance of 15 m to be conducted. The simulation results are shown in Figure 5.  
In the NS-3.31 simulation, the communication process between two nodes was observed by gradually increasing the distance between them by 1 m. The packet delivery rate at the receiving node was monitored, and when the packet delivery rate changed from 1 to 0, it indicated that effective data transmission was not possible at that distance. The communication distance was then gradually decreased to find the maximum communication distance. The simulation results showed that the minimum communication distance, Pmin, was 14.86 m, indicating that the maximum communication distance was within this range. Within the range of 14.86 m, the packet delivery rate for underwater electric field communication was 100%, while beyond this range, the packet delivery rate was 0. Therefore, for future AUV point-to-point dynamic experiments,  
Figure 5: Analysis of underwater electric field communication distance test results.  
within this range should be selected to validate the performance of underwater electric field communication.
## 2 Materials and Methods
### Automated Satellite-Based Surface Water Mapping
#### 2.3.2 Mapping Algorithm
The steps of implementing Fuzzy Otsu include (1) segmenting the pixels of water and lands using the initial threshold value; (2) randomly selecting features to calculate the minimum threshold value using [PERSON]'s method; (3) filtering the image based on the minimum threshold value and calculating the maximum threshold values; (4) calculating the midpoint (arithmetic mean) value of the minimum and maximum threshold values; and (5) inputting the midpoint into the Fuzzy Gaussian function to map water. Unlike the binary image results from Bmax Otsu's and Edge Otsu, the Fuzzy Otsu exported the image decimal values estimated from Gaussian distribution. A parameter (\"spread\") was set to \(0.2\) to define the shape of the Gaussian curve. An additional process was included in determining the surface water areas using a cutoff value based on the empirical judgment that pixel values equal to or greater than \(0.999\) are classified as water, and values less than \(0.999\) are nonwater.
## 3 Design of a Computer Vision-Based Roadside Occupation Surveillance System (CVROSS)
### Roadside Surveillance Technology Using the IoT  
To collect data effectively and efficiently, an IoT-based roadside surveillance system (CVROSS) has been designed and developed to tackle the problem of loading and unloading bays. As shown in Figure 2, the CVROSS was equipped with a set of solar power-enabled wireless HD vision devices, which enable the system to capture images from the roadside. To reduce electrical costs and avoid the risks of relying heavily on external power or solar energy, the devices connect wirelessly to a cloud platform, allowing continuous data transfer to the CVROSS and real-time monitoring of occupancy and vacancy data, retaining up-to-date roadside information for 24 h a day, seven days a week. By using application program interfaces (APIs), the collected data can be examined at the preprocessing stage by the proposed decision support model; thus, the roadside traffic information can be observed in real time. Via a vision module and machine learning, users are provided with hundreds of functions for acquiring images from a multitude of vision devices, for further processing  
Figure 2: Overview of the CVROSS.  
by locating features, identifying objects and measuring parts. In addition, machines can learn from empirical data, making predictions about future data. HD vision devices are expected to be used, to provide the best compromise between maximum observation accuracy and minimum overlapping field of view, to generate the best viewpoint. Image data are then processed by denoising and image tuning, leading to target object detection, recognition, identification, classification and calculation of available parking spaces. As a result, useful and easily accessible traffic information on real-time roadside occupancy and vacancy can be provided to road users. Furthermore, with the aid of machine learning techniques applied in a time-domain dynamic system, both the accuracy and the efficiency of the system are enhanced. Various reports can be generated for road users, logistics companies and the public, for decision-making via big data analysis.  
After installation of the wireless HD vision devices connected to the CVROSS, cloud-based servers can be used with IoT communication protocols. Data analytics and computation modules function as the back-end cloud server, and the results are retrieved and displayed in front-end applications for end users. The process flow of the proposed system is illustrated in Figure 3. Transparency of roadside activities and information can be enhanced and reports can be produced at the end of every timed loop, after image processing.
## 4 Cost Analysis  
Production is designed to ensure that maximum profits are realized, while at the same time, costs are minimized. Besides, different approaches appear to be desirable, forcing one to conduct a cost analysis and to evaluate the weaknesses and strengths associated with each alternative. Given the costs and benefits of different production components, it is possible to create different production paths and then assess their profitability. The option with the lowest cost becomes desirable as it may be used to achieve maximum profit. Table 1 shows the components of production investment and their respective costs.  
Figure 12: Variation in water temperatures.  
Figure 13: Quantity of distilled water with and without regulation.  
The total cost of the system is arrived at by totaling the fabrication costs, repair and maintenance costs, and the cost of operation. The investment's main aim is to recover the total costs. Initially, the amount laid as the investment cost is 10,775 \(\upepsilon\). Besides, the system is estimated to have a lifetime of 20 years, which must be taken into consideration while arriving at the most effective alternative.  
Assumptions:  
1. The system will produce 18 L per day.
2. The production will take place 10 months each year.  
Analysis:  
The total litres of water that will be produced = production rate per day \(\times\) total period of production \(\times\) lifetime = \(\frac{18\,\mathrm{L}}{1\,\mathrm{day}}\times\frac{300\,\mathrm{day}}{1\, \mathrm{year}}\times 20\) years = \(108,000\) L.  
The total cost of production (water per litre) = \(\frac{\mathrm{The\ initial\ total\ cost\ of\ investment}}{\mathrm{ The\ total\ capacity\ of\ water\ that\ is\ produced}}=\frac{10,775\upepsilon}{\mathrm{T}0,000\upepsilon}=0.09\upepsilon/\mathrm{L}\).  
The daily cost of production (water in litres per day) = cost of production of water per litre \(\times\) productivity = \(\frac{0.09\upepsilon}{1\,\mathrm{L}}\times 18\,\mathrm{L}=1.62\upepsilon\).  
The cost of maintenance = 0.01 \(\upepsilon\)/day.  
Net income = cost of production of water - the cost of maintenance = \(1.62-0.01=1.61\upepsilon\).  
Period for effective payback = \(\frac{\mathrm{Investment}}{\mathrm{Net\ earning}}=\frac{10,775\upepsilon}{1.61 \upepsilon}=6692\) days.  
As the system will take approximately 18 years, which is less than its lifetime, to repay itself, the investment is worth the cost.