esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient Runtime Firmware Update Mechanism for LoRaWAN Class A Devices
## 3 Developed System
### Implementation of the Proposed System
#### 3.5.1 Hardware  
In order to validate the proposed update technique, an electronic device for RFID access control was designed and implemented, equipped with LoRaWAN communication. The PIC18 LF27K42 microcontroller [18] was chosen to control the electronic device, while the LoRa radio transceiver RA-07H [19] was selected to provide LORAWAN communications due to its stack accessible via UART and AT commands [20]. The electronic device was designed to be energy efficient, with a consumption of less than 10 \(\upmu\)A in sleep mode. Figure 8 depicts the printed circuit board of the constructed electronic device, along with an image of the assembled hardware.
# Applying Topological Information for Routing Commercial Vehicles Around Traffic Congestion
## 2 Materials and Methods
However, in order to advance the existing models, there is a need to focus on a practical tool that implements truck-specific characteristics such as weight, height, and road restrictions combined with real-time traffic data to optimize truck alternative route selection and meet operational, safety, and economic criteria. The authors in [22] used machine learning to enhance the efficiency of rail traffic diversion. The study proposed a predictive model for strategic planning for rail traffic disruption. This model supports long-term planning and is more suitable for pre-planned conditions; however, it does not provide an immediate decision-making tool. Therefore, a truck rerouting framework at a corridor level that can be used as an immediate decision-making tool is needed. Additionally, to build upon the existing literature and advance previous rerouting models, there is a need to implement a geographic visualization and spatial planning that integrates feasibility analysis to ultimately present a practical application to assist transportation planners in making truck diversion decisions based on observed conditions. Strategies that divert truck traffic to an alternative route can be used as congestion mitigation strategies. The alternative route consequently carries both the diverted traffic and its regular traffic load. Therefore, the alternative route should be carefully selected, and the safety and efficiency of the overall network should be considered when evaluating truck traffic diversion options. While traffic diversion strategies are deployed in many regions, there is minimal consideration of alternative route requirements when the optimal diversion route for trucks is chosen. Nonetheless, the criteria for choosing alternative truck routes should be carefully defined to consider truck characteristics and to select routes that can efficiently accommodate truck traffic. Most previous studies of this issue focused more on enhancing traffic conditions for passenger vehicles than on conditions for trucks. Hence, limited consideration has been given to a comprehensive analysis of the economic impacts of rerouting decisions. The proposed framework extends the scope beyond the selection of alternative routes by featuring a cost-benefit analysis to quantify the savings from delay time and fuel consumption for the rerouted trucks, which provides decision makers with measurable benefits that justify diversion strategies.
# Deployment Protection for Interference of 5G Base Stations with Aeronautical Radio Altimeters
## 2 Methodology
### Integrated Interference Model
#### 2.1.2 RA Antenna  
The RA antenna is the primary coupling path for 5G interference [3]. It is typically a microstrip patch antenna, and the antenna pattern can be described as Equation (2) [1].  
\[G_{RA,AB}(\varphi)=-\frac{12}{\varphi_{3 dB}}\varphi^{2}+G_{RA,ABi} \tag{2}\]  
In Equation (2) \(G_{RA,AB}(\varphi)\) is the antenna gain at the azimuth angle (\(\varphi\)), \(\varphi_{3 dB}\) is 3 dB lobe width of the radio altimeter antenna, and \(G_{RA,ABi}\) is the maximum gain of the radio altimeter antenna.  
As shown in Figure 5(a),b the radio altimeter antenna offers a gain of 2-8 dBi and a coverage angle of 35-60\({}^{\circ}\) to the 3 dB point of the antenna pattern. Such wide antenna beams are imperative due to the wide range of pitch and roll angles that an aircraft can reach in flight. Regrettably, radio altimeter antennas are inherently exposed to numerous interference sources, including 5G BSs. Since the antennas are pointed towards the Earth's surface, they are susceptible to all possible interference sources that may be encountered during operation.
# EMI Threat Assessment of UAV Data Link Based on Multi-Task CNN
## 3 Data Preprocessing  
Before modeling based on deep learning, the received original signal needs to be preprocessed and represented in an appropriate format [20]. The state parameters of the data link are normalized and converted into histograms to represent the state information under different EMI conditions. The STFT spectrogram is obtained through time-frequency analysis of I/Q data to extract the time-frequency information of the EMI signal. In addition, after standardizing the I/Q data, the constellation diagram is drawn to obtain the phase information of the EMI signal.
# A Survey on Adaptive Data Rate Optimization in LoRaWAN: Recent Solutions and Major Challenges
## 3 Review of Adaptive Data Rate Algorithms
### Throughput
The authors formulated a joint SF and TP distribution problem to maximize the minimum UL throughput of the end nodes, dependent on co-SF and inter-SF interferences and power constraints. They based their strategy of SF allocation on the matching theory. Once the end nodes have been allocated SFs, optimization of the power distribution parameters is performed so as to maximize the minimal throughput achieved on each SF. The authors addressed the intractability of the joint SF and power distribution problem by splitting it into two subproblems: SF allocation under fixed TP, and TP distribution under fixed SFs. They use both linear and quadratic approximation to make the non-linear inequalities in the feasibility problem tractable. Their simulation results showed that in spite of severe co-SF and inter-SF interferences, the propositional algorithms outperform standard algorithms, jointly with regards to minimum end node data rates, user fairness, and average end node throughput. The two propositional linear and quadratic approximation methods to tackle the non-linear feasibility problem for TP distribution produced efficient TP solutions, resulting in significant energy conservation, whilst further improving minimal throughput and user fairness.
# Trajectory Tracking Control of a Manipulator Based on an Adaptive Neuro-Fuzzy Inference System
## 4 Hydraulic Manipulator System Modeling and Controller Design
### Mathematical Model of the Hydraulic System  
The mathematical model of the hydraulic system mainly includes the fluid flow of the valve, the input and output flow of the cylinder (motor) and the piston dynamics [23]. The valve-controlled hydraulic cylinder model is shown in Figure 6; the derivation process of the kinematics equation is as follows.  
The linearized flow equation of the valve is:  
\[Q_{L}=k_{q}x_{o}-k_{c}P_{L} \tag{9}\]  
where \(x_{v}=k_{p}\cdot u\), \(x_{v}\) is the displacement of the servo valve spool, \(k_{v}\) is the valve gain, \(u\) is the input voltage, \(P_{L}\) is the load pressure, \(k_{q}\) is the flow gain of the servo valve and \(k_{c}\) is the valve flow-pressure gain.  
Ignoring the external leakage, the flow continuity is:  
\[Q_{L}=A\frac{dy}{dt}+C_{ip}P_{L}+\frac{V_{t}}{4\beta_{e}}\frac{dP_{L}}{dt} \tag{10}\]  
where \(A\) denotes the effective working area of the piston, \(C_{ip}\) denotes the leakage coefficient in the cylinder, \(V_{t}\) represents the working volume of the cylinder and \(\beta_{e}\) is the effective bulk modulus. \(y\) represents the displacement of the piston, and the nonlinear transformation between it and the joint angle can be obtained from Figure 5:  
\[y_{i}=\sqrt{L_{1i}{}^{2}+L_{2i}{}^{2}-2L_{1i}L_{2i}\cos(\pi-\theta_{i})} \tag{11}\]  
Figure 6: Valve-controlled hydraulic cylinder model.  
When the spool of the servo valve begins to move, a pressure difference between the two chambers of the cylinder will be generated, causing the piston position to change. The force generated on the cylinder is obtained using [PERSON]'s second law [24]:  
\[F=P_{L}A=m\frac{d^{2}y}{dt^{2}}+B_{c}\frac{dy}{dt}+F_{L}+F_{f} \tag{12}\]  
where \(m\) is the mass of the piston, \(B_{c}\) is the viscous damping coefficient, \(F_{L}\) is the load force and \(F_{f}\) is the friction force, which is composed of static friction, Coulomb friction, viscous friction and Strobeck effect [25].
## 6 Conclusions  
In summary, we have reviewed recent applications of 3D-ccDLS and CORENN methods for applications in optical biosensing and time-dependent monitoring of nanoparticle-biofluid interactions. The methods allow to differentiate rotational and translational motion, as well as agglomerate formation. Time dependent monitoring of protein-nanorod interactions and monitoring of serum together with nanoparticles allows to analyse either changes of hydrodynamic radii of the components of the particles or of monitoring nanoparticle motion in the presence of serum. The CORENN method shows robust results over a large range of concentrations of serum or protein concentrations, which is expected to contribute to the developments of new standards in dynamic light scattering.  
Conceptualization, E.F. and S.S.; methodology, E.F. and S.S.; software implementation, E.F., validation, E.F. and S.S.; formal analysis, E.F.; investigation, E.F.; resources, S.S.; data curation, E.F. and S.S.; writing--original draft preparation, S.S.; writing--review and editing, S.S. and E.F.; visualization, E.F. and S.S.; supervision, S.S.; project administration, S.S.; funding acquisition, S.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by HES-SO, grant numbers HENAPAMAT IA-EXT18-22-90667 and MIPAMO IA-EXT18-50-90666.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  
Figure 5: Gold nanospheres together with mouse serum (2.3%). Cumulants and CORENN results.
# Land Cover Classification Using a KOMPSAT-3A Multi-Spectral Satellite Image
###### Abstract  
New sets of satellite sensors are frequently being added to the constellation of remote sensing satellites. These new sets offer improved specification to collect imagery on-demand over specific locations and for specific purposes. The Korea Multi-Purpose Satellite (KOMPSAT) series of satellites is a multi-purposed satellite system developed by Korea Aerospace Research Institute (KARI). The recent satellite of the KOMPSAT series, KOMPSAT-3A, demonstrates high resolution multi-spectral imagery with infrared and high resolution electro-optical bands for geographical information systems applications in environmental, agricultural and oceanographic sciences as well as natural disasters. In this study, land cover classification of multispectral data was performed using four supervised classification methods: Mahalanobis Distance (MahD), Minimum Distance (MinD), Maximum Likelihood (ML) and Support Vector Machine (SVM), using a KOMPSAT-3A multi-spectral imagery with 2.2 m spatial resolution. The study area for this study was selected from southwestern region of South Korea, around Buan city. The training data for supervised classification was carefully selected by visual interpretation of KOMPSAT-3A imagery and field investigation. After classification, the results were then analyzed for the validation of classification accuracy by comparison with those of field investigation. For the validation, we calculated the User's Accuracy (UA), Producer's Accuracy (PA), Overall Accuracy (OA) and Kappa statistics from the error matrix to check the classification accuracy for each class obtained individually from different methods. Finally, the comparative analysis was done for the study area for various results of land cover classification using a KOMPSAT-3A multi-spectral imagery.  
land use; land cover; classification; KOMPSAT-3A; multi-spectral imagery; Mahalanobis Distance; Minimum Distance; Maximum Likelihood; Support Vector Machine
## 4 Discussion
### Impacts of Meteorology Data on GPP Estimations  
In this study, GPP_MOD strongly underestimated (68.0%) daily mean GPP compared to GPP_EC (Table 3 and Figure 5). The difference between GPP_MOD and the GPP_MODR1 was only the meteorology data inputs in GPP estimations (Table 1). Unexpectedly, ground meteorology data (Tmin, VPD, and PAR) used in GPP_MODR1 generated a slightly higher underestimation (RPE of 73.0%) with mean daily RMSE of 2.8 g C m\({}^{-2}\) than the default DAO data with mean daily RMSE of \(-2.47\) g C m\({}^{-2}\) that caused 6.6% of less integrated estimations in annual mean GPP (Table 2 and Figure 5). Bulks of previous studies demonstrated that the DAO meteorology data was coarse in the GPP algorithm [25; 26; 27; 66] that stems from the inaccurate observations on three meteorological factors of Tmin, VPD, and net surface solar radiation [33; 67; 68]. [PERSON] et al. (2003) concluded that the VPD and minimum temperature data in the DAO performs well when compared to flux tower measurements whereas PAR has a large positive bias, which might be a reason for the deteriorate of GPP_MODR1. However, similar to [PERSON] et al. (2008), our study also revealed that the coarse meteorology data in the MOD17 algorithm did not exert a significant affection in the MODIS GPP products.  
Figure 5: Inter-annual GPP variations from the field GPP measurement (GPP_EC) and five MODIS-based GPP estimations (GPP_MOD, GPP_MODR1, GPP_MODR2, GPP_MODR3, and GPP_MODR4) (**a**) and the difference among different estimation methods (**b**). The error bars in b are the standard deviation of the mean annual GPP estimation from 2009 to 2012. The bar with the same lowercase indicates no significant difference among diverse GPP estimation methods (\(p>0.05\)).
# Comparison of GPM IMERG and TRMM 3B43 Products over Cyprus
###### Abstract  
Global Precipitation Measurement (GPM) Integrated Multi-satellite Retrievals for GPM (IMERG) high-resolution product and Tropical Rainfall Measuring Mission (TRMM) 3B43 product are validated against rain gauges over the island of Cyprus for the period from April 2014 to June 2018. The comparison performed is twofold: firstly, the Satellite Precipitation (SP) estimates are compared with the gauge stations' records on a monthly basis and, secondly, on an annual basis. The validation is based on ground data from a dense and well-maintained network of rain gauges, available in high temporal (hourly) resolution. The results show high correlation coefficient values, on average reaching 0.92 and 0.91 for monthly 3B43 and IMERG estimates, respectively, although both IMERG and TRMM tend to underestimate precipitation (Bias values of \(-\)1.6 and \(-\)3.0, respectively), especially during the rainy season. On an annual basis, both SP estimates are underestimating precipitation, although IMERG estimates records (R = 0.82) are slightly closer to that of the corresponding gauge station records than those of 3B43 (R = 0.81). Finally, the influence of elevation of both SP estimates was considered by grouping rain gauge stations in three categories, with respect to their elevation. Results indicated that both SP estimates underestimate precipitation with increasing elevation and overestimate it at lower elevations.  
GPM; IMERG; TRMM; precipitation; Cyprus Article
# Vegetation Classification in Urban Areas by Combining UAV-Based NDVI and Thermal Infrared Image
## 2 Materials and Methods  
### Research Process  
Figure 1 shows the process of conducting this study.  
First, the study site was selected, and the coordinates of the ground control point (GCP) were acquired by performing a VRS (Virtual References Station) survey to secure the location accuracy of the UAV image.  
Additionally, Canon S110 NIR camera (Jeonju, South Korea) and thermoMAP camera (Jeonju, South Korea) were mounted on UAV to obtain multispectral and thermal infrared images, respectively. In addition, a flight plan was established using eMotion S/W to set the UAV flight altitude and speed, and the longitudinal and lateral overlap of images.  
Figure 1: The research process.  
By merging images using Pix4D Mapper S/W, orthomosaic images for each blue, green, red, near infrared, and thermal infrared band were created. Additionally, by combining the images for each band using ArcGIS S/W, NDVI value was calculated. Field surveys were conducted to verify the NDVI results analyzed through UAV images, and a method to solve the problem of misclassification as vegetation such as basketball courts and waterproof coating roofs was studied.  
Finally, a new technology was developed to analyze vegetation in urban areas by combining NDVI and thermal infrared images.
## Materials and Methods  
### Experimental Site, and Treatments of the OMET System  
This trial was established in a greenhouse setting in 2021 and was repeated in 2022 between November and December at the Green Biotechnologies Research Centre of Excellence (GBRCE), University of Limpopo, South Africa (23\({}^{\circ}\)53\({}^{\prime}\)10\({}^{\prime\prime}\) S, 29\({}^{\circ}\)44\({}^{\prime}\)15\({}^{\prime\prime}\) E). The site is located 2126 m above sea level. The temperature and relative humidity in the greenhouse ranged from 18 \({}^{\circ}\)C to 24 \({}^{\circ}\)C and 40% and 50%, respectively. A randomized complete block design was used to layout a 2 \(\times\) 3 factorial trial consisting of two growing conditions (OMET and non-OMET) and three Amaranth species (_A. caudatus_, _A. cruentus_, and _A. tricolor_) in triplicates. The OMET system was prepared on greenhouse benches; a layer of growth medium (25 cm thick) was sandwiched using high density polyethylene (HDPE) plastic (flexible, translucent/waxy, weatherproof, good low temperature toughness (\(-60\)\({}^{\circ}\)C), easy to process by most methods, low cost, and with good chemical resistance) to form an enclosed trough [10]. The growth medium comprised of steam pasteurized loam soil, sand, and Hygromix growth medium (Hygrotech, Pretoria North, South Africa) at a ratio of 2:1:1. Holes for planting the seedlings under OMET were made on the top enclosing sheet with row spacing of 20 cm \(\times\) 20 cm between seedlings. The non-OMET system (control) was prepared in the same way but without plastic.
## 2 Materials and Methods
### Singular Value Decomposition
The shape of the window doesn't change as the columns are shifted. This can be achieved in several ways, one of which is simply applying a wrap around condition so that values off the end of a matrix column, for example, are inserted at its beginning (and vice versa). An example of window shifting is described in Figure 3.  
The resulting shifted window (\(\mathbf{M_{sw}}\)) resembles the matrix from Figure 1, and can be described by the equation:  
\[\mathbf{M_{sw}}=\chi(\mathbf{M_{w}},\mathbf{s}) \tag{4}\]  
where \(\chi\) is the \"shift\" operator, \(\mathbf{M_{w}}\) is the window subset from \(\mathbf{M}\) and \(\mathbf{s}\) is the shift vector. The \"shift\" operator takes a matrix and a shift vector as its arguments and shifts the columns of the matrix based on the values prescribed by the shift vector. For example, if the value of \(\mathbf{s}\) for column \(j\) is \(n\), then we would replace an element \(M_{i,j}\) with the element \(M_{i+n,j}\). The matrix decomposition used in local SVD and SOSVD can also be described with the Equation (5):  
\[\mathbf{M_{w}}\approx\sum_{k}\chi(\mathbf{u^{k}v^{k}}^{T},-\mathbf{s^{k}}) \tag{5}\]  
where \(\mathbf{s^{k}}\) is the shift vector corresponding to the \(k^{th}\) pair of left and right singular vectors \(\mathbf{u^{k}}\) and \(\mathbf{v^{k}}\). By using Equation (5) and plugging in \(\mathbf{M}\) for \(\mathbf{M_{w}}\), the matrix \(\mathbf{M}\) from the previous example can be presented with a single outer product coupled with a shift vector as shown in Figure 4. A simple quantitative example that shows the effectiveness of column-shifting with a wave similar to the one from Figure 4 can be found in the Appendix A.  
Figure 3: (**a**) The original data matrix \(\mathbf{M}\). (**b**) A selected window \(\mathbf{M_{w}}\) from the original data matrix \(\mathbf{M}\). (**c**) The matrix \(\mathbf{M_{sw}}\), obtained by shifting the columns of \(\mathbf{M_{w}}\) to maximize their correlation. Figure 2: (**a**) A matrix showing a wave arriving to the receivers at different times (**b**) A pair of singular vectors obtained by applying SVD to the matrix. (**c**) The outer product of the pair of singular vectors which is very different from the original matrix. Unfortunately, both local SVD and SOSVD seem to fail to accurately denoise seismic data which contains interfering waves with different dips. In such scenarios, numerical artifacts appear around the area of intersection. We solve this problem by developing shifted-matrix decomposition (SMD) which uses a very different process for determining the shift vector. The following subsection will give a detailed description of the processes SMD uses to obtain the shift vectors and the pairs of singular vectors.
# Swin Transformer Assisted Prior Attention Network for Medical Image Segmentation
## 4 Experimental Results
### Experiment Setup
#### 4.1.2 Implementation Details  
The proposed Swin-PANet is implemented on a single NVIDIA RTX 3060 Ti. The experimental setup includes PyCharm 2021, Python 3.5.7, and PyTorch 1.9.0 framework on an Ubuntu 20.04 server. The code snippets of the network structure can refer to Appendix A. For the final step of segmentation in enhanced attention block, bilinear interpolation in attention guiding decoder is applied with scale factors being 2. All images on above datasets are resized to 224 \(\times\) 224 considering the memory-efficient and computation-efficient strategy. To better explore the performance of the Swin-PANet, the comparative experiment is conducted with the process of data augmentation including vertical and horizontal flip, random rotating, and random scale change (limited to 0.9-1.1). Although data augmentations can change the size and shape of images for enriching the samples, on the other hand, we can enhance the generalization ability of the Swin-PANet in modeling global context dependencies. To achieve faster convergence on training, the Adam optimizer is employed in network with a learning rate of 0.005 to optimize the performance of the network. Adam optimizer [40] can be straightforward to implement, performs memory-efficient and computation-efficient strategies, and is very suited for networks that are large in terms of parameters. The batch size is set to be 2 and the model is trained for 300 epochs. The above dataset divisions follow the same experimental protocols in UCTransNet [5]. In addition, pre-trained weights are not implemented for training the proposed network. The cross-entropy loss and dice loss are integrated as the loss function to be implemented in the Swin-PANet. Note that the above settings and loss function are utilized for training all the models in experiments.