esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The growth of the urban population and the increase in e-commerce activities have resulted in challenges for last-mile delivery. On the other hand, electric vehicles (EVs) have been introduced to last-mile delivery as an alternative to fossil fuel vehicles. Electric vehicles (EVs) not only play a pivotal role in reducing greenhouse gas emissions and air pollution but also contribute significantly to the development of more energy-efficient and environmentally sustainable urban transportation systems. Within these dynamics, the Electric Vehicle Routing Problem (EVRP) has begun to replace the Vehicle Routing Problem (VRP) in last-mile delivery. While classic vehicle routing ignores fueling, both the location of charging stations and charging time should be included in the Electric Vehicle Routing Problem due to the long recharging time. This study addresses the Capacitated EVRP (CEVRP) with a novel Q-learning algorithm. Q-learning is a model-free reinforcement learning algorithm designed to maximize an agent's cumulative reward over time by selecting optimal actions. Additionally, a new dataset is also published for the EVRP considering field constraints. For the design of the dataset, real geographical positions have been used, located in the province of Eskisiehr, Turkiye. It also includes environmental information, such as streets, intersections, and traffic density, unlike classical EVRP datasets. Optimal solutions are obtained for each instance of the EVRP by using the mathematical model. The results of the proposed Q-learning algorithm are compared with the optimal solutions of the presented dataset. Test results show that the proposed algorithm provides remarkable advantages in obtaining routes in a shorter time for EVs.  
capacitated vehicle routing problem; electric vehicle; charging stations; time windows; last-mile delivery; reinforcement learning; q-learning Article
# Change in Water Quality of the Vistula River During the Emergency Discharge of Untreated Wastewater
## 1 Introduction
### Water Sampling  
The research was based on monitoring data performed by certified laboratories covering the chemical parameters of the water in the Vistula. Samples for laboratory tests were collected daily into 1-L bottles. The following water quality parameters were analyzed: total nitrogen (TN), ammonium (N-NH\({}_{4}\)\({}^{+}\)), nitrite (N-NO\({}_{2}\)\({}^{-}\)), nitrate (N-NO\({}_{3}\)\({}^{-}\)), total phosphorus (TP), orthophosphates (P-PO\({}_{4}\)\({}^{3+}\)), suspended solids (SS), total dissolved solids (TDS), pH, electric conductivity (EC), phosphate (SO\({}_{4}\)), chloride (Cl), total organic carbon (TOC), water temperature (WT), chemical oxygen demand (COD), dissolved oxygen (DO). Value pH, EC, and WT were measured directly in situ and determined using a Multi 340i meter (WTW, Weilheim, Germany). Other chemical parameters of water were measured using the spectrophotometric method with the AQUALYTIC PC Spectro (AQUALYTIC, Langen, Germany) [36]. Samples for water quality analyses in Vistula River were taken during the failure, i.e., from 28 August to 20 October 2020 [35]. Monitoring included daily  
Figure 1: Location of study area.  
Figure 2: Scheme of the sewage collector transmission system.  
sampling for water quality testing at 8 stations. Stations S1-2 were located before the point of failure, and stations S3-8 were located after the place of failure (Table 1). The criterion of good ecological status (the standard threshold value) was used to assess the impact of pollutants on the quality of surface waters caused by the discharge of untreated sewage [37]. As surface water is used to supply people, chemical parameters have also been referenced in drinking water quality standards [38; 39; 40; 41]. Threshold values of water quality parameters are presented in Table 2.
Electronics_**2014**, \(3\), 675-688; doi:10.3390/electronics3040675  
_electronics_**ISSN 2079-9292**  
www.mdpi.com/journal/electronics  
_Article_  
**Kronecker-Based Fusion Rule for Cooperative Spectrum Sensing with Multi-Antenna Receivers**  
**[PERSON] 1,2,*, [PERSON] 3, [PERSON] 1 and [PERSON] 1**  
\({}^{1}\) Telecommunications and Systems Engineering, Universitat Autonoma de Barcelona,  
Bellaterra 08193, Spain; E-Mails: [EMAIL_ADDRESS] (G.S.-G.); [EMAIL_ADDRESS] (J.A.L.-S.)  
\({}^{2}\) Electrical Engineering, University of Engineering and Technology of Peshawar,  
25000 Peshawar, Pakistan  
\({}^{3}\) School of Electrical Engineering, KTH-Royal Institute of Technology, SE-100 44 Stockholm,  
Sweden; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +34-664837630.  
External Editors: [PERSON], [PERSON], [PERSON]  
_Received: 20 August 2014; in revised form: 10 November 2014 / Accepted: 13 November 2014 / Published: 10 December 2014_  
**Abstract:** This paper considers a novel fusion rule for spectrum sensing scheme for a cognitive radio network with multi-antenna receivers. The proposed scheme exploits the fact that when any primary signal is present, measurements are spatially correlated due to presence of inter-antenna and inter-receiver spatial correlation. In order to exploit this spatial structure, the generalized likelihood ratio test (GLRT) operates with the determinant of the sample covariance matrix. Therefore, it depends on the sample size \(N\) and the dimensionality of the received data (_i.e._, the number of receivers \(K\) and antennas \(L\)). However, when the dimensionality \(\{K,L\}\) is on the order, or larger than the sample size \(N\), the GLRT degenerates due to the ill-conditioning of the sample covariance matrix. In order to circumvent this issue, we propose two techniques that exploit the inner spatial structure of the received observations by using single pair and multi-pairs Kronecker products. The performance of the proposed detectors is evaluated by means of numerical simulations, showing important advantages with respect to the traditional (_i.e._, unstructured) GLRT approach.  
**Keywords:** spectrum sensing, GLRT, kronecker, cognitive radios
# Advancing Brain Tumor Segmentation with Spectral-Spatial Graph Neural Networks
## 2 Methods and Material
### Data Preprocessing  
In the preprocessing part of this study, we normalized and standardized brain MRI data to ensure comparability and optimize its suitability for downstream analysis. We further investigated the creation of supervoxels, evaluating different algorithms to identify the one that generates the most robust and reliable supervoxel segmentation for our graph-based methodology. These preprocessing steps are critical for achieving accurate and reliable segmentation results.
# Algorithms in Low-Code-No-Code for Research Applications: A Practical Review
## 5 LCNC Used in Existing Research
### Applying AI/ML Algorithms
#### 5.1.1 Regression Algorithm  
Using LCNC platforms, existing studies in disaster management (e.g., landslides, tornadoes, bushfires, floods, earthquakes, etc.) and major event analysis have used both linear and logistic regression algorithms [25; 26; 27; 18; 21]. These researchers used drag-and-drop features of LCNC platforms (e.g., key influencer visualization of Microsoft Power BI [49]), where they did not have to code the regression algorithms. Implementation of a regression analysis automatically ranks the factors that matter, contrasts the relative importance of these factors, and displays them as key influencers for both categorical and numeric metrics. For numerical features, linear regression was performed using SDCA regression implementation from Microsoft's ML.NET [50]. On the other hand, for categorical features, logistic regression was performed using L-BFGS logistic regression from ML.NET [51; 52].
## 2 Radar Jamming Signal Analysis and Modeling  
### Noise Modulation Jamming  
Noise modulation jamming typically utilizes noise signals to modulate certain parameters of the jamming signal. These techniques involve high transmission power, enabling the generated signals to effectively jam the target echo signal at the radar receiver, thus preventing the radar from performing its normal detection functions [43; 44; 45]. This paper primarily introduces two types of noise modulation jamming: Amplitude Modulation (AM) and Frequency Modulation (FM).
## 3 Experiments and Discussions
### Displacement Measurement with Wavelength Correction
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Times** & \(\lambda_{cal}\) **(nm)** & \(\lambda_{meas}\) **(nm)** & \(\frac{\lambda_{cal}-\lambda_{meas}}{\lambda_{cal}}\) \\ \hline
1 & 635.2275 & 635.2294 & \(-2.90\times 10^{-6}\) \\
2 & 635.4802 & 635.4750 & \(8.18\times 10^{-6}\) \\
3 & 635.4761 & 635.4824 & \(-9.91\times 10^{-6}\) \\
4 & 635.4764 & 635.4803 & \(-6.13\times 10^{-6}\) \\
5 & 635.8709 & 635.8676 & \(5.18\times 10^{-6}\) \\ Average & 635.5069 & 635.5062 & \(-1.16\times 10^{-6}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Calibrated and measured laser wavelength. Figure 16: Relationship between \(\lambda_{cal}\) and the variation of slope \(k_{cal}\), and between \(\lambda_{cal}\) and the variation of the drift of positive first-order diffraction angle. Figure 17: Comparison results: (**a**) residual of measured displacement by the NMM-1 and 3 DOFLM systems with wavelength correction; (**b**) standard deviation of five measurements.
## 6 Outcome of the Study and Future Works  
For assessing the performance of computer networks, a novel parameter has been proposed in this paper. The parameter has been named the traffic congestion and packing density trade-off (TCPDTF) factor. The derivation, usage, and significance of this factor has been elaborated in this work. In addition, the performance of the TFBN proposed in an earlier work has been thoroughly analyzed in this study in comparison with the existing networks for HIN. The evaluated results of the several parameters has justifies that TFBN possesses several attractive features. To summarize, the TFBN can be characterized with the following features:  
* Low hop distance--The node diameter and the average distance are short in a TFBN.
* High wiring complexity--The BM of a TFBN consists of a flattened butterfly network, which needs many short length links, resulting in a high wiring complexity compared to other networks.
* High node degree-Using the extra links for the BM interconnections increases the node degree of the TFBN.
* High static costs--The TFBN's static cost is slightly higher than the TTN and TESH networks due to its high node degree, although the cost is substantially less expensive than the torus and mesh networks. So, TFBN can be an ideal candidate as a HIN topology for an MPC system with zeta-scale or exa-scale computation speeds.
* Low traffic congestion--TFBN yields lower traffic congestion with respect to the 3D-Torus network, which is practically implemented in industries for a lot of contemporary MPC systems.
* High packing density--The packing density of TFBN is higher than that of torus and mesh networks and almost similar to that of TTN and TESH networks.
* Low message traffic density--The MTD of the TFBN is far lower than that of both 2D and 3D-Torus and mesh, TESH, and TTN networks.
* Low TCPDTF--The TCPDTF of TFBN is quite lower than that of both 2D- and 3D-Mesh, TESH, 2D-Torus, and TTN networks. It is also considerably lower than that of 3D-Torus networks.  
Figure 5: A graphical illustration of the performance comparison of the packing density, message traffic density, and the traffic congestion and packing density trade-off factor (TCPDTF) of various networks.  
* Low latency--The TFBN has a low latency, i.e., round trip delay, due to the low hop distance parameters-average distance and diameter.
* High throughput--Because of the low hop distance parameters, the TFBN has a high throughput.  
The low hop distance, low message traffic density, and low TCPDTF indicate that the TFBN can yield good dynamic communication performance; however, apart from this study, there is still a chance of proceeding the related research work in terms of industry adoption of TFBN for the development of a new MPC system.
# Laser Sintering of TiO\({}_{2}\) Films for Flexible Dye-Sensitized Solar Cells
## 2 Experimental Equipment, Materials and Methods  
Typically, 3.0 g TiO\({}_{2}\) nanoparticles (Degussa, P25, Evonik, Essen, North Rhine-Westphalia, Germany) mixed with 7.0 g ethanol (Zhongrong Technology, Inc., Shanghai, China) were ball-milled at a speed of 350 rpm for 4 h to obtain a binder-free paste. Then, the paste was uniformly coated on an ITO-PEN substrate (14 \(\Omega\)/cm\({}^{2}\), Peccell Technologies, Inc., Yokohama, Japan) by a doctor-blade technique. After being dried at 120 \({}^{\circ}\)C for 15 min in air, the films were compressed in a cold isostatic pressing (CIP) machine (Xinghualong, Inc., Suzhou, Jiangsu, China). The applied pressure was 200 MPa and the holding time was 300 s.  
A schematic diagram of the equipment used for the laser sintering is shown in Figure 1a. A 355 nm Nd:YVO\({}_{4}\) laser (Inno Laser, Inc., Shenzhen, Guangdong, China) with the pulse width of 42 ns at 100 kHz was employed as the one laser source and the other laser source was a continuous wave (CW) fiber laser (IPG Photonics, Oxford, Mississippi, USA) with a wavelength of 1064 nm. Beam expanders were used to improve the laser beam quality. A 2D galvanometer scanner system was used to control the laser beam according to the designed pattern (Figure 1c). As an appropriate defocusing amount is beneficial for increasing the spot diameter and the uniformity of laser energy distribution on the film surface, and the sintering efficiency can also be improved by increasing the spot diameter, the focus of the laser beam was set above the TiO\({}_{2}\) film (Figure 1b).  
After laser sintering, the TiO\({}_{2}\) films were soaked in a 0.5 mM N719 dye solution for 18 h. Then the dyed films were attached with a Pt \(\dagger\) I\(\Gamma\)O \(\dagger\) PEN counter electrode. Finally, the electrolyte, comprising 1.0 M 1,3-dimethylimidazolium iodide, 50 mM LiI, 30 mM I\({}_{2}\), 0.5 M tert-butylpyridine, and 0.1 M guanidinium thiocyanate in a mixture of acetonitrile and valeronitrile (85:15, by volume) was injected into the cell.  
The microstructure of TiO\({}_{2}\) films was analyzed by a field emission scanning electron microscope (FE-SEM, FEI-Sirion 200, FEI, Hillsboro, Oregon, USA). The thickness of TiO\({}_{2}\) films was measured by a surface profilometer (Veeco Dektak 150). Raman measurement was carried out using a WITec confocal Raman spectroscopy (alpha 300R, WITec, Ulm, Baden-Wurttemberg, Germany) with the spectral resolution of 1 cm\({}^{-1}\). A 532 nm laser (2.33 eV) with the power below 0.1 mW was used as the laser excitation, and the laser beam was focused using a 100\(\times\)objective lens (NA = 0.95). The photovoltaic performance of the cell was evaluated using a Keithley 2400 Source Meter (Keithley, Beaverton, Oregon, USA) under a light intensity of 100 mW/cm\({}^{2}\) at air mass (AM) 1.5G, the IPCE used for optimizing the laser sintering parameters was the maximum value achieved from the same ten specimens. A 4 \(\times\) 4 mm\({}^{2}\) square mask was put on top of the device during the photovoltaic testing.
# Research on Human Exposure to Transport-Induced Vibration in Buildings
## 2 The Measurement Methodology and Evaluation of Human Exposure to Vibration in Buildings
### Measurement Point Selection  
Locating the measurement points is a critical stage that affects the accuracy of the results. Standards such as [18; 22; 23] define different approaches to selecting measurement points depending on the vibration perception. Also, [18] recommends placing measurement points at locations where vibrations enter the human body. For external vibrations like ground-transmitted vibrations, the measurement point should be in the central part of the floor, within one-third of the length/width acc. [22]. For internal vibrations, the measurement points should be located according to the modal analysis of the structure. According to [23], the measurement point should be placed in the center of the floor in the room closest to the vibration source.  
Therefore, if whole-body vibrations are analyzed, the more appropriate recommendations are those included in [18]. In the case of building occupants who perceive vibrations passively (no influence on the source of vibrations), then the appropriate ones are the recommendations included in [22; 23]. These approaches differ in the method of locating the measurement point, which is shown in Figure 1.  
The other problem in addition to the location of the measurement point is the equipment. Accelerometers are mostly used, but the problem is how to connect the sensor to the floor. Ref. [19] provides a description of a measurement disk that should be used during measurements of human perception of vibrations in buildings. The key specifications of this disk are outlined below:  
* The measurement disk should have a significant mass to ensure stability and accurate transmission of vibrations. It is recommended that the disk should weigh at least 30 kg. This mass is substantial enough to mimic the weight of an average human being, thereby providing a realistic measure of how vibrations would affect a person in the same position.  
Figure 1: The measurement point location acc. the two standards green [22] and red [23].  
* The diameter of the disk should be around 30 cm. This size ensures that the disk covers a sufficient area to interact with the vibration field effectively, capturing an accurate representation of the vibrations experienced in the building structure.
* The disk should be made of a material that does not significantly alter the characteristics of the transmitted vibrations. Typically, metals or composite materials that are dense and have minimal internal damping are used. The choice of material ensures that the disk does not absorb or dampen the vibrations excessively, which would otherwise lead to inaccurate readings.
* The disk is typically flat and circular to ensure uniform contact with the surface on which it is placed. This design minimizes the influence of the disk's shape on the vibration measurements and ensures that the disk's weight is evenly distributed.  
An example measurement disk is shown in Figure 2.
## 1 Introduction
[20] introduces a three-factor symmetric key authentication key agreement scheme for multiserver environments. The registration center calculates the shared key with the server based on the server's identity, using it to encrypt the authentication message for server authentication. However, the absence of updates throughout the sessions implies that an attacker gaining the shared key between the registration center and the server can recompute all previous session keys. The authors of [21] designed an authentication scheme for unmanned vehicles, where secret keys are shared between users and ground station servers, as well as servers and drones. This scheme shares the same weakness as [20], where the exposure of shared keys jeopardizes the security of the communication channel. Ref. [22] employs hash functions and XOR operations to achieve mutual authentication, key agreement, and message integrity. To establish a secure session key between the user and the Industrial IoT device, three messages are required, built through a series of hash and XOR operations. Ref. [23] proposes a distributed control plane with elliptic curve cryptography. The distributed authentication mechanism, immune to central failure and able to detect attacks, is analyzed for security using the AVISPA tool. It relies heavily on a secure channel on the distributed plane. Ref. [24] introduces an authentication framework for smart homes and industrial environments. The authors utilized Ascon-authenticated encryption with a hash function to construct their architecture. In contrast to existing schemes, the proposed LI2A takes a new approach to mutual authentication in IIoT environments. LI2A addresses the limitations identified in previous works by emphasizing limited interaction for seamless authentication while ensuring instant and secure communication. The proposed scheme is specifically designed to mitigate vulnerabilities caused by shared key exposure, a lack of session key updates, and a reduction in communication overhead, all of which are common flaws in many symmetric key authentication schemes. LI2A combines the advantages of limited user-device interaction with instant authentication, resulting in increased security and efficiency in the IIoT ecosystems.
## 3 Materials and Methods
### Testing Methods
This was accomplished through Multiple Stress Creep Recovery (MSCR) tests (EN 16659) on 25 mm diameter samples (parallel plate DSR geometry--testing gap 1 mm). Three test replicates for each material and four test temperatures (50, 60, 70, and 80 \({}^{\circ}\)C) were analyzed. A pre-conditioning time of 15 min at the testing temperature was again selected. For \(BIT\), the test protocol consisted of the application of consecutive creep-recovery cycles under different stress levels \(\tau\) (0.1 and 3.2 kPa); in particular, 10 cycles constituted by 1.0 s of creep loading and 9.0 s of rest period were repeated for each of the two stress levels on the same sample. Binder performance was assessed by averaging the results of the three test replicates. At a generic nth cycle, \(\varepsilon^{n}\)\({}_{0}\) is the deformation at the beginning of creep load application, \(\varepsilon^{n}\)\({}_{1}\) is the strain after the creep phase of 1.0 s, and \(\varepsilon^{n}\)\({}_{10}\) is the final strain at the end of the given cycle. Then, the \(R_{\%}\)\({}^{\tau}\) parameter was used to express the average recovery aptitude of the material for the selected stress level \(\tau\) (average of 10 cycles) according to Equation (1):  
\[\sum_{n\ =\ 1}^{10}R_{\%}\text{${}^{\tau}$}=100\ (\varepsilon^{n}\text{${}_{1}$}- \varepsilon^{n}\text{${}_{0}$})/\varepsilon^{n}\text{${}_{1}$} \tag{1}\]  
The non-recoverable creep compliance \(J_{nr}\)\({}^{\tau}\) can be calculated as reported in Equation (2):  
\[\sum_{n\ =\ 1}^{10}J_{nr}\text{${}^{\tau}$}=\ \varepsilon^{n}\text{${}_{10}$}/\tau \tag{2}\]  
Analogously, the \(J_{nr}\)/\(J_{tot}\)\({}^{\tau}\) ratio illustrated in Equation (3) represent the ratio between the compliance referring to the non-recoverable strain at the end of the cycle and the compliance referring to the maximum strain measured at the end of the creep phase:  
\[\sum_{n\ =\ 1}^{10}J_{nr}/J_{tot}\text{${}^{\tau}$}=[\left(\varepsilon^{n} \text{${}_{r}$}-\varepsilon^{n}\text{${}_{0}$}\right)/\tau]/[\left(\varepsilon ^{n}\text{${}_{c}$}-\varepsilon^{n}\text{${}_{0}$}\right)/\tau]=(\varepsilon^{n }\text{${}_{r}$}-\varepsilon^{n}\text{${}_{0}$})/(\varepsilon^{n}\text{${}_{c}$ }-\varepsilon^{n}\text{${}_{0}$}) \tag{3}\]  
where, \(\varepsilon^{n}\)\({}_{r}\) is the absolute strain value at the end of the cycle and \(\varepsilon^{n}\)\({}_{c}\) is the absolute strain value at the end of the creep phase. As far as the mastic scale (binder + filler) and the mortar scale (binder + filler + fine sand) are concerned, _MAS-L_, _MOR-L_, and _MOR-S_ were tested with the same AS, FS, and MSCR test protocols to identify their LVE properties and rutting potential, which will sensibly vary as a consequence of the stiffening effect due to the aggregates [33]. LVE strain limit resulting from AS was again used to carry out FS tests. In this case, MSCR tests were carried out at three consecutive stress levels \(\tau\) (i.e., 0.1, 3.2, and 10 kPa) in order to achieve a more representative picture of the blends' performance testing stiffer materials with respect to bitumen.