esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### Advantages of Sentinel and Landsat Missions in Coastal Monitoring  
Satellite remote sensing technologies, specifically those related to Sentinel and Landsat missions, have been highly preferred in advancing knowledge and monitoring regarding coastal erosion and shoreline dynamics [21; 95]. With their public open-access data source archive, these major satellite systems overcome traditional limitations by providing large-scale, high-resolution data that enable accurate monitoring of changes along coastlines [13; 15]. Sentinel-2, over time, has proven effective due to its high spatial resolution and frequent revisit times that capture detailed information on coastal shifts that are usually quite difficult to identify [39; 96]. The Landsat series, particularly Landsat 8, offers a long-term historical record, which is crucial for understanding coastal processes over decades [18; 69]. Studies have continuously reiterated that such technologies yield valuable insights into mapping erosion and accretion patterns along coastlines, providing crucial insights into how natural forces and human interventions shape coasts [12; 19]. Based on this review, the major technological application lies in the Digital Shoreline Analysis System derived from the GIS process, which caters to the majority of shoreline change calculations. Metrics like EPR and LRR work with long-term shore movement trends and give surprisingly good predictions about future changes. For example, the study by [PERSON] et al. [11] on the coast of Quang Nam Province in Vietnam identified a total of 800 erosional and accretional sections that provide crucial information on how both natural processes and human activities of land reclamation and urbanization drive changes along the coast. Such an analysis is fundamental to informing management practices of coasts and mitigating the impacts brought about by erosion on vulnerable communities.
# Recent Advances and Applications of Semiconductor Photocatalytic Technology
## 4 Common Preparation Methods
### Liquid Phase Method
#### 4.4.3 Liquid Deposition Method  
In the liquid deposition method, a substrate is immersed in an appropriate reaction solution, and a uniform and dense film is deposited onto the substrate. These films obtained by the liquid deposition process usually consist of oxides or hydroxides [207; 208; 209; 210]. The liquid deposition method is suitable for substrates of various shapes, and it does not require a high temperature during film formation or expensive equipment. The liquid deposition method can be used not only to prepare a single oxide film but also to prepare a composite oxide film, a multi-component oxide film, a metal fine-particle-dispersed oxide film, a laminated oxide film, and so on. The reaction principle of liquid deposition method is not complicated. The process is simple, and the film formation rate is high. The benefit/cost ratio is large, while the pollution to the environment is small. In addition, several disadvantages of liquid deposition methods have been solved, which means that liquid deposition methods have wide application prospects.  
For example, [PERSON] et al. [212; 213; 214] immersed a substrate glass sheet in a reaction solution to obtain a transparent anatase type TiO\({}_{2}\) film at 35 \({}^{\circ}\)C. The reaction solution containing a complex TiF\({}_{6}\)\({}^{2-}\), F\({}^{-}\) ion trapping agent H\({}_{3}\)BO\({}_{3}\) and a crystallization-inducing agent of TiO\({}_{2}\). The thickness of the film obtained by the liquid phase deposition method increases as the deposition time increases, and when the deposition time is 9 h, the resulting film thickness is about 260 nm. The photocatalytic activity of TiO\({}_{2}\) thin film after heat treatment at different temperatures was evaluated by the photocatalytic degradation of a methylene blue experiment. The results showed that the TiO\({}_{2}\) film heated at 300 \({}^{\circ}\)C has the highest photocatalytic activity, and its activity is equivalent to five times that of TiO\({}_{2}\) film deposited at 35 \({}^{\circ}\)C.
# Study on Noise Reduction and Data Generation for sEMG Spectrogram Based User Recognition
The experiment's results revealed a 78.7% user identification accuracy based on the transformation of the raw sEMG signals into sEMG spectrograms for 40 subjects. By contrast, when the resting state signals were removed and transformed into sEMG spectrograms, as proposed herein, the user identification performance was 85.4%, which was a 6.7% improvement compared with using raw signals. Furthermore, when data were augmented by generating sEMG signals after removing the resting state signals, the performance increased by 10% to 96.1%, compared with before increasing the data amount. This paper is organized as follows: The research trend involving sEMG signals is presented in Section 2. The proposed sEMG spectrogram-based user identification of this study is described in Section 3. The experimental methods employed for the proposed user identification and results analysis are presented in Section 4. Finally, the conclusions and future research directions are presented in Section 5.
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} BER & Bit Error Rate \\ CIR & Channel Impulse Response \\ CSI & Channel State Information \\ DD & Direct Detection \\ FoV & Field-of-View \\ FSO & Free Space Optical \\ IM & Intensity Modulation \\ IRS & Intelligent Reconfigurable Surfaces \\ LED & Light-Emitting Diode \\ LoS & Line-of-Sight \\ Non-LoS & Non-Line of Sight \\ OOK & ON-OFF Keying \\ PD & Photo-Diode \\ RF & Radio Frequency \\ SINR & Signal to Interference plus-Noise-Ratio \\ SLM & Spatial Light Modulator \\ SNR & Signal-to-Noise-Ratio \\ SISO & Single Input Single Output \\ VLC & Visible Light Communication \\ V2V & Vehicle to Vehicle \\ \end{tabular}
## 2 Sparse Reconstruction
### Vector Case  
Consider the problem of finding a vector \(x\in\mathcal{R}^{n}\) that satisfies  
\[Ax=b, \tag{1}\]  
where \(A\in\mathcal{R}^{m\times n}\) and \(b\in\mathcal{R}^{m}\) are given. If \(m\geq n\), there exist a unique solution except for the rare case. We can derive a reasonable solution using, e.g., the least-squares method. If\(m<n\), no unique solution exists. If we have prior knowledge that \(x\) is sparse (i.e., almost elements of \(x\) are zero), then \(x\) may be uniquely determined. The problem of finding a spare vector \(x\) from (1) is called the sparse reconstruction problem. The sparse reconstruction problem is equivalently rewritten as the following problem:  
\[\min_{x\in\mathcal{R}^{n}}\|x\|_{0}\ \ \text{s.t.}\ \ Ax=b, \tag{2}\]  
where \(\|x\|_{0}\) is the number of nonzero elements of \(x\). A sparse solution can be obtained by solving (2). Since Problem (2) is NP-hard, we consider the following relaxed problem:  
\[\min_{x\in\mathcal{R}^{n}}\|x\|_{1}\ \ \text{s.t.}\ \ Ax=b, \tag{3}\]  
which can be equivalently rewritten as a linear programming problem. A solution to (3) is equal to that to (2), except for in rare cases [43].  
Next, we consider the block-sparse reconstruction problem. Suppose that the vector \(x\) is decomposed to \(p\) subvectors, i.e., \(x=[x_{(1)}^{\top}\ x_{(2)}^{\top}\ \cdots\ x_{(p)}^{\top}]^{\top}\) (\(x_{(i)}\in\mathcal{R}^{n_{i}},\sum_{i}n_{i}=n\)). We may permutate \(x\) in advance. The vector \(x\) may be uniquely determined, based on prior knowledge that \(x\) is block-sparse (i.e., almost subvectors of \(x\) are zero, and all elements of a few subvectors may be non-zero). Then, a convex relaxation of the block-sparse reconstruction problem is given by the following problem:  
\[\min_{x\in\mathcal{R}^{n}}\sum_{i=1}^{p}\|x_{(i)}\|_{2}\ \ \text{s.t.}\ \ Ax=b. \tag{4}\]  
In the case where there are priorities for subvectors, we may add weights as follows:  
\[\min_{x\in\mathcal{R}^{n}}\sum_{i=1}^{p}w_{(i)}\|x_{(i)}\|_{2}\ \ \text{s.t.}\ \ Ax=b, \tag{5}\]  
where \(w_{(i)}\geq 0\) is a given weight. See, e.g., [31; 32] for further details.
# Event-Based Emergency Detection for Safe Drone
## 4 Case Study
### Simulation Environment and Settings  
To further explain the details of the implementation of our proposed method inside our simulator, the PID control loop operated at a frequency of 10 Hz, which means the sensor data processing and position prediction was conducted every 0.1 s. The physical simulation functionalities provided by AirSim [29] and Unreal Engine [30] were utilized to illustrate randomized noise in terms of drone control outputs with motors. On the other hand, we implemented a custom noise system to sensor measurements. Finally, we designed our event-based controller to perform regular evaluations at a frequency of 2 Hz and perform event-based emergency diagnosis at each waypoint arrival event.  
We attempted to simulate several common emergency situations that a drone might encounter in the real world. We considered several possible events, such as collisions (due to deviation and unexpected sudden collisions), system failures (motor, blade, electronics, etc..), sensor malfunctions, and strong winds. In particular, we conducted experiments on and studied three cases: normal case, extreme wind condition, and air collision. We tried to compare the above cases under the terms of the time-window concept. These cases are difficult to detect or respond to with existing control methods, so the effectiveness of our proposed control method should be shown clearly.  
Regarding the approach of our system, verifying whether a drone is in a normal state requires estimating the expected time-window before the actual flight mission. The very first step is to define the physical specs of the drone. In our case, we defined the drone as a quadcopter with a wheelbase of 1.13 m, a weight of 3.8 kg, a maximum wind-resistant speed of 6.7 m/s, and a desired flight speed of 5 m/s.  
Next, we defined our mission to be a straight-line path traversal starting from waypoint A and proceeding through the mission in alphabetical order as \(\mathrm{A}\rightarrow\mathrm{b}\rightarrow\mathrm{c}\rightarrow\mathrm{d}\rightarrow\mathrm{e}\)\(\rightarrow\mathrm{F}\). After the mission was set, we conducted experiments to configure the equations and variables required for time-window calculations. In order to calculate the time-window for each waypoint, we attempted to average out the amount of time the drone spends while moving various distances. After conducting over 1000 trials, we were able to get the data of expected acceleration speed and ascent speed. We not only tested cruise, acceleration, and de-acceleration conditions but also took various wind conditions in the count. After getting the data, we were able to plot equations for the drone to use during the mission.
## 2 Materials and Methods
### Evaluation of Antioxidant Activity  
#### 2.4.1 Ferric Reducing Antioxidant Power (FRAP) Assay  
A modified version of the FRAP assay was used to assess the ferric reducing capacity of _G. hederaca_ extracts [42]. The reduction of a pale-yellow-colored ferric complex (Fe\({}^{3+}\)-tripyridyl triazine) to a blue-colored ferrous complex (Fe\({}^{2+}\)-tripyridyl triazine) by electron-donating antioxidants at low pH is the basis for this approach. The working FRAP reagent was prepared daily by mixing 10 mL of 300 mM acetate buffer of 3.6 pH, with 1 mL of 10 mM TPTZ (2,4,6-tri(2-pyridyl)-s-triazine) in 40 mM hydrochloric acid and with 1 mL of 20 mM ferric chloride. The reaction mixture was incubated for 15 min at 37 \({}^{\circ}\)C in a water bath before use. An aliquot of 3 mL of the freshly prepared FRAP reagent was added to 100 \(\upmu\)L of plant extracts or standard (Trolox) and incubated for 5 min at 37 \({}^{\circ}\)C in a water bath before analysis. Then, the absorbance of the samples was measured at 593 nm using a Varian Cary 300 bio UV-VIS spectrophotometer (Agilent, Santa Clara, CA, USA). All measurements were carried out in triplicate. Trolox was used as the standard and the calibration curve was plotted in the range of 150-3000 \(\upmu\)mol/L [42]. The FRAP value was determined as previously reported [38]. Based on the obtained FRAP value, the final antioxidant activity (AA) in each sample was expressed as a Trolox equivalent (TE) in terms of \(\upmu\)mol Trolox equivalent TE/g of extract, according to the following equation [43]:  
\[\text{AA}\left(\upmu\text{mol}\text{TE}/\text{g}\text{ extract}\right)=\text{FRAP value }\left(\upmu\text{mol}/\text{L}\right)/\text{sample }\left(\text{g}/\text{L}\right) \tag{2}\]
# A Cost-Optimized Model for Dynamic Port Hinterland
###### Abstract  
This study presents a methodology for determining port economic hinterlands through comprehensive logistics optimization. The research advances traditional geographical approaches by developing an integrated cost model that considers maritime transport, inland transportation, warehousing expenses, and time-dependent factors. Testing this methodology on the Spanish port system (with three main ports: Valencia, Algeciras, and Barcelona), we demonstrate how product characteristics and origins significantly influence optimal port selection and hinterland boundaries. The analysis examines both perishable goods, using bananas and oranges as case studies, and non-perishable cargo from Asian and American origins. Results reveal distinct patterns of port influence depending on goods type and origin. The study's findings, visualized through innovative color-coded mapping, provide valuable insights for port authorities and logistics planners in optimizing supply chain decisions. Our approach contributes to port competition theory by establishing a quantitative framework for dynamic hinterland delineation based on actual logistics costs and operational factors.  
p ARTICLE, 2020 15 96
# Design of a Human Muscle-Powered Flying Machine
## 5 Selected Design Aspects of the Wing
### Tail and Stability  
In most modern aircraft designs, the vertical and horizontal stabilizers are located behind the wing, which is positioned closer to the center of mass. This arrangement facilitates maintaining equilibrium and stability of the aircraft. Equilibrium is achieved when the forces and moments acting on the aircraft balance each other out. Under ideal conditions, at a constant velocity, this allows for straight flight. Stability refers to the aircraft's behavior when disturbed from equilibrium. It means that in the event of disturbance, the aircraft will return to its normal flight parameters due to stability. Two types of stability are recognized: static stability, where the aircraft tends to return to its original position after a disturbance, and dynamic stability, where the aircraft automatically returns to its initial parameters. Longitudinal static stability is achieved when the derivative of the pitching moment is negative. Positioning the stabilizer at the rear of the aircraft and designing it according to specific dimensional parameters will ultimately result in a reduced angle of attack.  
To determine the appropriate size of the stabilizer, volume ratios are utilized for the horizontal tail (stabilizer) \(\kappa_{H}\) and vertical tail (stabilizer) \(\kappa_{V}\), respectively [47]:  
\[\kappa_{H}=\frac{S_{H}\cdot I_{H}}{S\cdot S_{C_{A}}}\ \, \tag{6}\]  
Figure 4: Typical wing structure [49].  
\[\kappa_{V}=\frac{S_{V}\cdot I_{V}}{S\cdot b}, \tag{7}\]  
where \(S_{H}\)--horizontal tail area m\({}^{2}\), \(S_{V}\)--vertical tail area m\({}^{2}\), \(S_{Ca}\)--mean aerodynamic chord of the wing m\({}^{2}\), \(S\)--wing area m\({}^{2}\), \(I_{H}\)--length between the aerodynamic centers of the wing and horizontal tailplane m, and \(I_{V}\)--length between the aerodynamic centers of the wing and vertical tailplane m.  
Depending on the aircraft's application, different volume values are adopted. For gliders, which are similar in application to muscle-powered aircraft, the volume characteristics for the horizontal stabilizer typically oscillate around 0.5, and for the vertical stabilizer, they are approximately 0.018.  
During aircraft stability analysis, a crucial aspect is determining the neutral point of stability, where the pitching moment is independent of the angle of attack. If the center of mass coincides with the neutral point, the aircraft is neutrally stable. When the center of mass is forward of the neutral point, the aircraft is statically stable, while when it is atf of the neutral point, the aircraft becomes statically unstable. This implies that the neutral point serves as the aft limit for the center of mass.
# Medical Image Segmentation Network Based on Dual-Encoder Interactive Fusion
## 4 Experiments and Results
### Evaluation Metrics
#### 4.3.2. Hausdorff Distance at the 95 th Percentile (HD95)  
The Hausdorff distance (HD) serves as a metric for evaluating the congruence of boundaries between segmentation results and their corresponding ground truths. It measures the distance between the peripheries of these entities, thereby focusing on the precision of boundary delineation. In contrast to the DSC, the HD places a greater emphasis on the accuracy of boundary localization.  
For two non-empty sets, \(A\) and \(B\), the HD is defined by the following equation:  
\[HD(A,B)=\max\Biggl{\{}\sup_{a\in A}\frac{\inf_{b\in B}d(a,b),\sup_{b\in B}\inf _{a\in A}d(a,b)\Biggr{\}}\]  
Here, \(d(a,b)\) represents the Euclidean distance between points \(a\) and \(b\). Essentially, HD computes the greatest of all the shortest distances from points in one set to the nearest points in the other set. In the context of segmentation tasks, it is common to use HD95, which quantifies the maximum distance for 95% of the points in set \(A\) to their nearest points in set \(B\). A superior alignment of the segmentation result with the ground truth can be indicated by a lower HD95 value.
# Map Matching Based on Seq2 Seq with Topology Information
## 5 Conclusions  
To solve the matching problem between GPS tracking and real road networks, this paper proposes a method based on seq2 seq combined with topological information, modeling road network matching into seq2 seq sequence model, creating multiple dependency dictionaries according to the topological relationship of a road network, fine grid division, and limiting the matching results to road network. Using the BPE algorithm to compress the grid dictionary, the length of the word vector is reduced and the training time is optimized. Standard trajectory data sets are established through simple path search through NetWork X, and a large number of noise trajectories are generated based on Gaussian noise within a certain Frechet distance to support large data volume in deep learning. Bi-GRU is used as a bidirectional recurrent neural network to extract the information of both historical trajectory sequence and future trajectory sequence. We added attention mechanisms to enhance the ability of extracting sequence hidden value information. The experimental results show that this road network matching method is effective and has higher matching accuracy and less model training time than other methods.  
Methodology, Y.B.; Software, Y.B. and G.L.; Formal analysis, W.Z.; Validation, Y.B. and T.L.; Resources, W.Z.; Data curation, Y.B.; Writing--original draft, Y.B.; Writing--review & editing, T.L.; Supervision, Y.W.; Project administration, Y.W.; Funding acquisition, Y.W. and Y.F. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Talent Introduction Project of Sichuan University of Science & Engineering (2020 RC20), Innovation Fund of Postgraduate, Sichuan University of Science & Engineering (Y2022106) and the Sichuan Key Provincial Research Base of Intelligent Tourism (No. ZHYJ23-03).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflict of interest.  
Figure 16: Data compression controlled experiment.
## 4 Deployment Scenario and Prototype
### Physical Layer  
The physical layer has 12 sensor nodes. Currently, each sensor node consists of an Arduino microcontroller and a Xbee shield, with a Series 1 802.15.4 Xbee module connected and a Grove shield with a sensor connected. Table 1 shows the sensors used.  
All sensors used (see Table 1) form part of the Grove system (Figure 4) from Seeedstudio (San Leandro, CA, USA, www.seeedstudio.com/wiki/GROVE_System) that allows an easy plug connection with the grove base shield.  
In addition, the physical layer consists of three control units. Each control unit consists of an Arduino microcontroller and an Xbee shield with an Xbee module connected and a wifi shield. The function of each control unit is to process the data received from the sensors within range to send  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sensor** & **Magnitude** & **Type** \\ \hline Ambient temperature & \({}^{\circ}\)C & Analogue \\ Water temperature & \({}^{\circ}\)C & Analogue \\ Rain & Yes/No & Digital \\ Humidity & \% & Analogue \\ Light & Lm & Analogue \\ Ultrasound & Cm & Analogue \\ Movement & Yes/No & Digital \\ Sound & Db & Analogue \\ Atmospheric pressure & Pa & Analogue \\ Electricity usage & W & Analogue \\ Air quality & level & Analogue \\ Smoke & level & Analogue \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sensors used.  
to the logical layer. These control units are distributed to cover the full extent of the urbanization. The communication between the sensor nodes and the control unit uses the Zigbee protocol. Sensor nodes have limited functionality: they spend most of their time in sleep mode and wake up only to send information when asked for it (or to update it, if the sensor node has an actuator installed). Thus, sensor nodes behave periodically as active Zigbee nodes. Some of the sensors produce data that should be normalized into the control unit before being sent to the logical layer. When the data is ready, the control unit sends it through the Wifi connection, which allows the use of the TCP/IP network infrastructure. This wireless connection can be used in control units because these devices are connected to the electric current, therefore they can meet the energy needs of these connections and produce benefits such as increased bandwidth, ease of configuration, and flexibility in the network. The status of the three microcontrollers can be seen in Figure 5.  
Figure 6 shows the code executed by the control unit to read data from the rain sensor and send it to the logical layer. After taking a rain sensor reading, in this case a digital sensor that provides discrete values 0 and 1, an HTTP POST request is made to update the field assigned to this sensor.  
Figure 4: Groove system sensors.  
Figure 5: Location of sensors and microcontrollers in SmartResidents.