esa-sceva/satcom-chunk-collection huggingface dataset:
# Post-Processing for Shadow Detection in Drone-Acquired Images Using U-NET
## 2 Related Studies
### Shadow Detection Methods in Drone Images
#### 2.2.2 Model-Based  
Model-based is a method that includes physical and environmental information of shadows, such as light source direction, object geometry, and sensor localization. For example, a study by [18] detected shadows on UAV images of forested areas using in situspectral reflectance measurement. A digital surface model (DSM) was used to represent the elevation of the terrain. In [19], geometrical shadow detection was proposed, using solar position to map out shadow and non-shadow areas in VHR images. It was mentioned that sun position and DSM were used to reconstruct the geometric relationship between the ground and sun when generating binary shadow masks. One of the reasons why the model-based methods are not favorable is due to the presence of errors from the geometrical measurements that affect the accuracy of shadow detection [19].
# Performance Analysis of Undifferenced NRTK Considering Time-Varying Characteristics of Atmosphere
## 2 Positioning Model of BDS Undifferenced NRTK
### Generation and Interpolation of UD Error Correction Value for Users
In undifferenced NRTK, the error correction value of the same satellite is interpolated by reference stations \(A\), \(B\), and \(C\), and the error correction value is completely stations-independent; to finally obtain the comprehensive error correction value by comprehensive error interpolation, the interpolation coefficient can be expressed as:  
\[\left\{ \begin{aligned} &\alpha_{A}=\frac{1/S_{AU}}{(1/S_{AU}+1/S_{BU}+1/S_{CU})} \\ &\alpha_{B}=\frac{1/S_{BU}}{(1/S_{AU}+1/S_{BU}+1/S_{CU})}\\ &\alpha_{C}=\frac{1/S_{CU}}{(1/S_{AU}+1/S_{BU}+1/S_{CU})}\end{aligned} \right. \tag{16}\]  
In Equation (16), \(S_{AU}\), \(S_{BU}\), and \(S_{CU}\) are the geometric distances from stations \(A\), \(B\), and \(C\) to user \(U\), respectively. In the reference station triangulation network, the comprehensive error interpolation method is the most commonly used linear interpolation method. In addition to the linear interpolation method, there are also low-order surface models (LSM), Kriging interpolation  
Figure 2: Schematic representation of the regional error interpolation. methods [22], least squares collocation methods (LSC) [23], polynomial models, low-order spherical harmonic function models, and generalized trigonometric function models to model and interpolate ionospheric delays. Kriging is a commonly used spatial interpolation method in geostatistics, which can fully consider the spatial correlation and variability of observation data. At present, the Kriging algorithm has been used to establish an ionospheric VTEC model and has achieved ideal results. This paper focuses on verifying the PSD, so only the error correction value of user station is obtained through the comprehensive error interpolation method.
# A CNN Regression Approach to Mobile Robot Localization Using Omnidirectional Images
## 3 Solving Hierarchical Localization By Means of a Classification CNN and Regression CNNs  
As mentioned before, the main contribution of this paper is a solution to the localization of a mobile robot using CNNs. Specifically, a classification CNN is used to carry out a coarse localization and then a regression CNN performs a fine localization. In the first stage, the solution of the classification CNN is the room where the robot is located. In the second stage, the position of the robot is estimated more precisely inside this room, since the regression CNN estimates its X and Y coordinates. This section describes these two stages in detail. Section 3.1 describes the complete procedure of the localization method. Section 3.2 focuses on the first stage, where a classification CNN performs a coarse localization. Then, the second stage is described in Section 3.3, where a regression CNN per room is trained to carry out a fine localization in the room retrieved in the first stage. The experiments and results regarding each one of the stages of the localization procedure will be presented in Section 5.
## 2 Materials and Methods
### Electronic Hardware  
The central item of this unit is the processor or reader, whose role is to capture various signals coming from the antenna cables and process them accordingly, and then send them to the computer. In this unit, we used the Speedway Revolution Reader manufactured by IMPINJ (Impini) Speedway R420 UHF RFID Reader USA/FCC \"4 Port\") (Table 1). This reader by itself can only accommodate four antennas, as it has only four ports available, which means without any modification or extension, this reader can only monitor four honey bee colonies at once (Figure 3b). As a cost-effective solution, if envisioned experiments only require four or fewer antennas, the antenna hubs and the adaptor kit may not be required. In this setup, the four antennas coming from the beehives can be plugged directly into the four available ports of the reader (Figures 2 and 3). The reader will operate as expected with four antennas but well below its maximum capacity of 32 antennas. Therefore, to maximize the benefits and amount of data generated by this reader, we based the design of this unit on the reader's maximum capacity of 32 antennas.  
For this purpose, two additional items are required: an adaptor kit and four antenna hubs that can bring up the reader's capacity to a total of 32 antennas, as shown in Figure 2. That said, this unit does not need to always operate on 32 antennas. It can cover all experimental requirements, going from one single antenna to a full 32 antennas (maximum capacity of this unit). There are a few aspects and specifications related to the performance of this reader, which is controlled by the software that will be further discussed in this study.  
Figure 3: Modification and wiring required to operate the RFID 32-antenna unit. (**a**) shows the optimal tunnel parameters required to accommodate a 5 dBi antenna to identify and read the bee’s transponder. (**b**) explains the wiring of the various RFID items (reader, hubs, and adapter) located within the designated box. For clarity and simplicity, we only displayed the wiring of two antennas.
## 1 Introduction  
The development of a complete analytical strategic method requires the inclusion of a number of steps, starting from sample collection and ending with the final result report. Intermediate steps include sample storage, preparation, isolation, identification and quantification of analytes. Thereby, sample preparation represents a tedious and time-consuming step, which is also very often a source of imprecision and inaccuracy of the overall analysis. The basic principal objective of sample preparation for chromatographic as well as infrared spectroscopic analysis are dissolution of analytes in a suitable solvent and removal from the solution of as many interfering compounds as possible. For this purpose, solid-phase extraction (SPE) is applied in many protocols for the pre-concentration, clean-up and purification of different types of analytical samples as well as for the removal of toxic substances. Characteristic applications comprise enrichment of pesticides [1], determination of organic contaminants in water [2; 3], and environmental samples in general [4]. Biological samples, e.g., human serum, can be purified to selectively enrich active compounds of pharmaceutical interest [5]. The SPE technology itself was introduced in the early 1970s in order to avoid or minimize the disadvantages of liquid-liquid extraction (LLE) [6]. Compared to LLE, SPE reduces the time required, especially in case of automation, while also allowing handling of small samples (50-100 \(\mu\)L). The term SPE was not mentioned until 1985 and since then the number of applications has continuosly increased.
# Upgrading the Hydrogen Storage of MOF-5 by Post-Synthetic Exchange with Divalent Metal Ions
###### Abstract  
In metal-organic frameworks (MOFs), mixed-metal clusters have the opportunity to adsorb hydrogen molecules due to a greater charge density of the metal. Such interactions may subsequently enhance the gravimetric uptake of hydrogen. However, only a few papers have explored the ability of mixed-metal MOFs to increase hydrogen uptake. The present work reveals the preparation of mixed metal-organic frameworks M-MOF-5 (where M = N2+, Co2+, and Fe2+) (where MOF-5 designates MOFs such as Zn2+ and 1,4-benzenedicarboxylic acid ligand) using the post-synthetic exchange (PSE) technique. Powder X-ray diffraction patterns and scanning electron microscopy images indicate the presence of crystalline phases after metal exchange, and the inductively coupled plasma-mass spectroscopy analysis confirmed the exchange of metals by means of the PSE technique. The nitrogen adsorption isotherms established the production of microporous M-MOF-5. Although the additional metal ions decreased the surface area, the exchanged materials displayed unique features in the gravimetric uptake of hydrogen. The parent MOF-5 and the metal exchanged materials (Ni-MOF-5, Co-MOF-5, and Fe-MOF-5) demonstrated hydrogen capacities of 1.46, 1.53, 1.53, and 0.99 wt.%, respectively. The metal-exchanged Ni-MOF-5 and Co-MOF-5 revealed slightly higher H2 uptake in comparison with MOF-5; however, the Fe-MOF-5 showed a decrease in uptake due to partial discrete complex formation (discrete complexes with one or more metal ions) with less crystalline nature. The Sips model was found to be excellent in describing the H2 adsorption isotherms with a correlation coefficient \(\cong\) 1. The unique hydrogen uptakes of Ni- and Co-MOF-5 shown in this study pave the way for further improvement in hydrogen uptake.  
metal-organic frameworks (MOFs); hydrogen storage; metal exchange; clean energy +
Footnote †: journal: Applied Sciences
## 1 Introduction
If the borehole spacing can be increased without compromising the rock splitting process, it will not only produce less noise but will also involve minimum time and power consumption which can result in an economical and efficient excavation process to be conducted. However, there are no guidelines available to select the borehole spacing values based on rock strength parameters and other excavation parameters involved in this method. For this reason, it is needed to analyze the rock splitting behavior, fracture propagation, and borehole spacing values under different rock strength conditions using the non-vibrational rock splitting method. The main objective of this study is to investigate the optimum borehole spacing to be used for excavation process in different types of intact rock based on unconfined compressive strength. The three types of intact rock were further classified according to the modulus ratio concept [6; 7], as illustrated in Figure 1, which classifies the intact rocks depending on the ratio of Young's modulus to unconfined compressive strength. This classification was based on the laboratory tests of 613 rock specimens from different locations. In this study, numerical analysis was performed for the rock-splitting process using PFC\({}^{\text{2D}}\), a commercial code based on particle mechanics approach, due to the inherent function of PFC\({}^{\text{2D}}\) of crack formation and coalescence so that the fracture propagation due to rock splitting process could be investigated. Microscopic parameters used in PFC\({}^{\text{2D}}\) are needed to be calibrated for simulating the strength parameters of rock-like material. The behavior of intact rock simulated in PFC\({}^{\text{2D}}\) is dependent on the selection of such microscopic parameters that reflect the true mechanical behavior of rock. For this purpose, the concept of Representative Elementary Volume (REV) was also applied in the numerical simulations in order to reflect the true mechanical behavior of rock in rock splitting model.
# Measurements of Room Acoustic and Thermo-Hygrometric Parameters--A Case Study +
## 2 Materials and Methods
### Development of the Measurements  
Theoretically, the measurement of acoustic parameters requires the linearity and time invariance of the system. However, these conditions are virtually impossible in a normal enclosed space. Thus, the experiments in this paper aimed to test this hypothesis and attempt to quantify the changes in acoustic parameters for changes in room temperature, relative humidity, and air velocity. The measurements of the room acoustic parameters were carried out using a pre-equalised ESS (exponential sine sweep) signal [19]. All the recorded tracks were then post-processed using the inverse filter of the ESS and finally the acoustic parameters were calculated.  
Furthermore, there was only one operator for this investigation, and the same instrumentation was employed. The sound source and microphone were always in the same location and orientation throughout the experiment. In utilising this method, uncertainty in temperature and humidity variables was reduced [26]. The strength of the ventilation system was likewise periodically increased, reaching a maximum value as frequently as possible and then reducing in the same way until it was shut off completely.  
The impulse response was extrapolated using the Aurora plugin after a total of 1806 recordings were recorded in this experiment, with 6 tracks for each measurement, 2 binaural tracks, and 4 sound field tracks pertaining to 301 measurements. The following acoustic parameters were determined from the impulse responses:  
* Reverberation time, T\({}_{20}\) (s) and T\({}_{30}\) (s);
* Early-decay time, EDT (s);
* Strength, G (dB);
* Clarity, C\({}_{50}\) (dB) and C\({}_{80}\)(dB);
* Definitor, D\({}_{50}\) (%);
* Interaural cross-correlation, IACC;
* Lateral efficiency, LE;
* Lateral fraction, LF.
## 4 Proposed Video Latency Measurement Technique
### Encrypt/Decrypt Applications  
These application nodes run on FPGA targets and are responsible for encryption and decryption of data. The BITW (bump-in-the-wire) security solution, which is explained in [45], has been applied to video data for the purpose of this work. The efficient AES encryption algorithm implementation is used for encryption/decryption of the data. The AES was implemented in two phases [14]. In the first phase, AES was designed for a standard FPGA platform using Verilog and in the second phase the traditional FPGA implementation was imported to LabVIEW FPGA using the IPIN node.  
Data transfer between applications running on the real-time processor (camera client and camera server) and applications running on the FPGA (encrypt/decrypt) is based on writing data to controls and reading data from indicators created in the FPGA application. For this purpose, a block called read/write control is used by the software on the real-time processor. For each control and indicator, a fixed size register is created on the FPGA to be accessed by the real-time processor. The main advantage of this type of data transfer, when compared to other methods [49; 50], is low overhead. Additionally, with each call of the read/write control function, data transfer is initiated with minimum delay. The main disadvantage is in the fact that data transfer is not based on DMA (direct memory access) and, hence, requires the real-time processor to be available to handle it.  
In order to decrypt data, it is necessary that both encrypt and decrypt VIs have the same 128-bit key set. Figure 6 shows the block diagram of the FPGA application node. Once the data is encrypted/decrypted, the result (which has the same size as the input data) is written to the output register of the FPGA target and is read by the application (camera server or camera client) running on the real-time processor.  
Figure 5: Camera server application.
# Optical Flow Odometry with Panoramic Image Based on Spherical Congruence Projection
## 2 Related Work
### Panoramic Image-Based Odometry  
Recent years have witnessed a growing interest in using panoramic images for odometry and SLAM tasks, owing to their wide field of view (FoV) and the advantage of long-term feature visibility across frames. A variety of methods have been developed to exploit this modality, which can be roughly categorized into visual-only, LiDAR-assisted (VLO), and visual-inertial (VIO) approaches.  
One of the earlier works in this domain is ROVO [10], which employs multiple panoramic cameras with overlapping FoVs to improve pose estimation accuracy by enhancing feature redundancy. More recently, 360 ORB-SLAM [26] proposed a monocular SLAM system tailored for panoramic cameras, combining traditional ORB-based sparse feature tracking with a depth completion network. This design significantly improves both scale estimation and robustness under challenging visual conditions.  
Several methods have focused on integrating panoramic vision with LiDAR data to enhance robustness and geometric consistency. LF-VISLAM [27] incorporates panoramic imagery and LiDAR input into the LVI-SAM framework, extending the backend optimization and loop closure capabilities. Panoramic direct LiDAR-assisted VO [28] further establishes geometric alignment between panoramic images and 360\({}^{\circ}\) LiDAR point clouds, demonstrating improved pose estimation under sparse features or rapid motion.  
To improve performance in dynamic scenes or under aggressive motion, visual-inertial fusion has also been explored. ROVINS [29], an extension of ROVO, introduces IMU constraints into the panoramic visual pipeline to enhance motion estimation stability. [PERSON] al. [30] proposed a real-time VIO framework that robustly handles feature tracking even in the negative hemisphere of panoramic images. More recently, PAL-SLAM2 [31] has been used to map panoramic image features onto a unit sphere and perform tightly coupled visual-inertial bundle adjustment in the spherical domain, providing improved accuracy and loop closure performance.  
Although the aforementioned systems leverage panoramic imagery for odometry and SLAM, they often rely on conventional projection models such as equirectangular projection or image stitching, which introduce severe distortions near the poles or discontinuities across seams. Moreover, most methods are built upon sparse feature-based matching pipelines, which suffer from inconsistency in motion estimation under large pixel shifts or dynamic scenes. In contrast, our method introduces a dense optical flow-based odometry framework built directly on a distortion-free spherical pixel structure. This enables continuous, pixel-wise tracking across the full FoV and offers a new perspective for robust full-view visual odometry in panoramic scenarios.
# TrafficWave: Generative Deep Learning Architecture for Vehicular Traffic Flow Prediction
## 2 Methods
### Deep Learning Techniques  
#### 2.3.1 SAEs (Stacked Auto Encoders)  
An SAE model is a stack of autoencoders used as building blocks to create a deep network [9]. An autoencoder is a Neural Network that attempts to reproduce its input. It has an input layer, a hidden layer, and an output layer. Given a set of training samples \(\left\{X_{(1)},X_{(2)},X_{(3)}\ldots,X_{(n)}\right\}\) where \(X_{(i)}\in\mathrm{R}\) which can be considered to be the traffic flow at i-time, an autoencoder first encodes an input \(X_{(i)}\) in a hidden representation \(y\!\left(X_{(1)}\right)\) on the basis of (5):  
\[y(x)=f(W_{1}x+b), \tag{5}\]  
then it decodes the representation \(y\!\left(X_{(1)}\right)\) in a reconstruction named \(z\!\left(X_{(1)}\right)\) calculated as in (6):  
\[z(x)=g(W_{2}y(x)+c. \tag{6}\]  
being:  
* \(W_{1}\) a weight matrix,
* \(b\) a coding polarization vector,
* \(W_{2}\) a decoding matrix,
* \(c\) a decoding polarization vector,
* \(f\left(x\right)\) and \(g\left(x\right)\) sigmoid functions.  
An SAE model is created by stacking autoencoders to form a deep neural network taking the autoencoder output found on the underlying layer as current level input as shown in Figure 1. After obtaining the first hidden level, the output of the k-hidden layer is used as an entrance to the (\(k+1\))-th hidden level. In this way, more autoencoders can be stacked hierarchically.  
In order to use the SAE network for traffic flow prediction, it is necessary to add a standard predictor on the top level. A logistic regression layer is generally considered [9].  
Figure 1: Stacked denoising autoencoder.  
Stacked denoising autoencoders for traffic flow prediction are adapted to learn network-wide relationships, these are necessary to estimate missing traffic flow data, and thus predict the future traffic value as a missing point with respect to the input data.  
SAE networks have been used in [19; 22; 23]. Figure 2 reports the SAE architecture used in this work to perform tests and comparisons.
# Metaheuristics for a Flow Shop Scheduling Problem with Urgent Jobs and Limited Waiting Times
## 4 Heuristic Algorithms
### Initial Seed Sequence  
In general, the metaheuristic algorithm starts with an initial seed sequence, and the performance of the metaheuristic depends on the seed sequence. Thus, the method to obtain the seed is introduced. In this study, a two-step method is used. A feasible permutation is generated by a list-scheduling in the first step. After that, the permutation is improved by a constructive heuristic, called NEH algorithm [42], in the second step.  
List scheduling is a common method in practice because it is intuitive and very easy to implement. This method is triggered when a machine becomes available, and a job with the highest priority is assigned to the machine. Defining the priority for the jobs is the only requirement of this method. Here, a modified earliest due date rule (MEDD) is used to focus on the urgent jobs with the objective of minimizing their tardiness. For an unscheduled job \(i\) and a given partial schedule \(S\), the modified due date (\(d^{\prime}_{i}\)) is defined as \(d^{\prime}_{i}=\max\{d_{i},\,C(Si)\}\) if \(i\in U_{A}\), and \(d^{\prime}_{i}=C(Si)\) otherwise. Because the due dates of normal jobs are set to an infinite number, normal jobs may be placed on rear positions in the MEDD schedule. To avoid this schedule, the modified due dates of normal jobs are set to the completion times assuming that they are scheduled immediately after \(S\).  
In step 2, the NEH begins with the MEDD schedule and makes schedules in a constructive way. In each iteration of the NEH procedure, a partial schedule is created by inserting the job at the front position of the MEDD schedule into the best position of the current partial schedule, and the inserted job is removed from the MEDD schedule. After generating a complete schedule, a pairwise interchange method is applied to the complete schedule for improving it. A detailed procedure is given in Figure 1.