esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Simulation and Experiment
### Experimental Results  
This section presents the results obtained from experiments conducted using the actual equipment. An image of the equipment is provided in Figure 12. The experimental setup consists of a TENG, a DC-DC converter, an operational amplifier (op-amp), and a micro-controller (MCU). The op-amp is used for voltage measurement, while the microcontroller performs control calculations. Additionally, the dielectric layer of the TENG is composed of silicon, while the electrode is made of copper plates. This TENG is measured using an oscilloscope with an internal impedance of 10 M\(\Omega\), recording a maximum voltage of 265 V. As a result, it generates approximately 0.065 W in instantaneous values; however, this output is only temporary and lacks sustainability.  
As a comparison with the proposed control method, experiments were conducted using the commonly used PI control. The experimental setup is identical to the one shown in Figure 12. The parameters are the same as those in Table 2, except for the time constant. Note that the proportional gain is 0.6, and the integral gain is 0.7.  
A comparison of Figures 13 and 19 reveals that the proposed method results in smaller fluctuations in the output voltage.
# Emergency Communication System Based on Wireless LPWAN and SD-WAN Technologies: A Hybrid Approach
## 5 Scenario and Simulation Report
### Scenario 1: LPWAN Simulation
\[\text{PDR}=\frac{\text{PACKETS RECEIVED}}{\text{PACKETS SENT}}100\%=\frac{5570}{5760}100\%=96.7\% \tag{1}\]  
\[\text{LPR}=\frac{\text{INTEFERED FAILURE}}{\text{PACKETS SENT}}100\%=\frac{190}{5760}100\%=3.3\% \tag{2}\]  
Figure 7 visually compares the results to emphasize the simulation output. A sufficient firm data transfer with the fewest possible losses is offered with the gateway installed in the middle of the simulation field. Consequently, every single LoRa node in this instance is completely covered. As a result, it is possible to address all 3-axes sensors while the resulting data are successfully received from the network server. Other indoor and outdoor simulation scenarios can be held to simulate real-life environments. Therefore, we can determine the number of gateways needed, and their exact position, to reduce deployment costs.
# A Survey on Variable Neighborhood Search for Sustainable Logistics
## 2 VNS: Concept, Versions, and Extensions
Other adaptive strategies used with VNS in sustainable logistics are in a hybrid way, such as with a Genetic Algorithm (GA) [19; 26], Particle Swarm Optimization (PSO) [27], an Electro-Magnetism Algorithm (EMA) [28], or an Artificial Bee Colony (ABC) [29; 30]. Another way to use adaptivity in VNS using sustainable logistics, such as in [31], is with a mechanism that automatically chooses between the first improvement and the best improvement strategies in the improvement phase of VNS (see [32]). Other variants of VNS have few applications in sustainable logistics. The Skewed VNS (SVNS) is the version introduced by [33], where a move to a worse solution is admitted if it is only \"slightly\" worse and \"sufficiently\" distant from the current solution. Condition \(f(x^{\prime})<f(x)\) is replaced by \(f(x^{\prime})<f(x)-\alpha\delta(x^{\prime},x)\) for the appropriate value \(\alpha\) and the function \(\delta(.,.)\). Parallel BVNS with SVNS are applied in network design for reverse logistics in [34] and in [35] for a routing problem minimizing emissions. A Skewed GVNS (SGVNS) is applied to solve a battery swap station location/routing problem with capacitated electric vehicles in [36]. SVNS is also applied to sustainable logistics in [37; 38]. In the Variable Neighborhood Decomposition Search (VNDS) variant proposed in [6], after shaking the incumbent solution by changing some of its attributes, the local search works on the subproblem containing only the attributes that have been changed while keeping the remaining ones fixed. The size of the subproblem plays the role of the shaking neighborhood parameter. No publications on the application of VNDS in sustainable logistics have been found in the literature. The VNS Branching proposed by [39] is applied in [40] to an electric vehicle routing problem with time windows. The simplest version of VNS is the Fixed Neighborhood Search (FNS), which considers a single neighborhood structure in the shake procedure and for the local search [41] and coincides with the first variant of the Iterated Local Search [42]. This method is applied in [43] to a routing distribution problem with drones. The Nested VNS (NVNS) consists of applying a VNS procedure to the solutions of a predefined neighborhood structure, rather than only to an initial solution [44; 45]. If the main loop also uses VNS, the procedure is referred to as a two-level GVNS [46]. Some combinations of VNS variants could also be used, such as a General VNDS or a Skewed General VNDS [8], by replacing VNS with VNDS in GVNS or SGVNS, respectively. In the reviewed literature, no publications have been found on the application of these VNS variants in sustainable logistics.
# Automatic Generation of Guidance for Indoor Navigation at Metro Stations
## 6 Conclusions  
This study acknowledges the unique challenges posed by metro stations, such as their complex architecture, lack of natural light, and limited visibility. These factors make traditional navigation methods less effective, thereby highlighting the importance of innovative solutions like the landmark-based approach. This research also points out the need for inclusivity in navigation systems, ensuring that they cater to the diverse needs of all users, including those with disabilities.  
The analysis of the performance of the implemented algorithm for different cases leads to the conclusion that attention should be paid not only to the construction of the rules that create the navigational directions but also to the data model. Establishing correct relationships between the layers of the data model, setting appropriate attribute values, and proper preparation of the geometry enable the optimal operation of the algorithm. This relationship is evident in the examples discussed. Correctly modelled paths make it easier to determine whether there is a left turn, a right turn, or a straight run. Such a determination may depend on the subjective feeling of the user at a given point indoors, so it is up to the cartographer to prepare the data model to anticipate the user's expectations and delineate the geometry accordingly. Similarly, when generalising staircases, it is possible to force the algorithm to work in a specific way by setting appropriate attributes for topographic objects.  
The algorithm presented in this paper has been implemented and tested on mobile devices for different routes at different metro stations in Warsaw. After several testing iterations, the project team developed a quality algorithm, allowing the mobile application to be made available on Android Play and the AppStore. The system launch is planned for the end of 2024. Making the application available to customers will enable the collection of broader feedback necessary for maintaining and further developing the technology. The unique experience gained during this research will be used to design solutions for other cities with more extensive underground urban transport infrastructure.  
Conceptualisation, J.B.M.; methodology, J.B.M.; software, B.W.; validation, B.W.; investigation, J.B.M. and B.W.; resources, B.W.; data curation, B.W.; writing--original draft preparation, J.B.M. and B.W.; writing--review and editing, J.B.M. and B.W.; visualisation, B.W.; supervision, J.B.M.; project administration, B.W.; funding acquisition, B.W. All authors have read and agreed to the published version of the manuscript. This research was funded by The National Center for Research and Development (NCBR), grant number \"Rzczczy sa dla ludzi/0049/2020\", and the APC was funded by NCBR.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
## 3 Results and Discussion  
### General Behavior of Columns  
In the present study, all the columns were subjected to monotonic loading, increasing gradually till failure. The general behavior of the tested columns was assessed by the load carrying capacity, ductility, and load-displacement responses. The load-axial-displacement behavior of all columns was divided into three different stages, as presented in Figure 4 [33]. The primary stage of axial load-displacement responses denotes the load-carrying capacity of the gross cross-sectional area of the column prior to the cracking of the concrete cover. In this stage, the steel fibers and the traverse reinforcement do not affect the behavior of the tested specimens. During the second stage in load-displacement responses, a sudden decline in the axial load from the ultimate load (P\({}_{\text{max}}\)) can be seen, owing to the spalling of concrete cover. The last stage characterizes the behavior of the tested specimens after spalling of the concrete cover. In this stage, the load-displacement response of the tested columns is affected by the existence of fibers, steel reinforcement, and spacing of the transverse reinforcement. The incorporation of steel fibers significantly influences the post-peak load-displacement behavior of the tested columns. The addition of steel fibers softens the descendant portion of the load-displacement response and restricts the abrupt failure of the tested specimens. In order to study the effect of steel fibers on the ductility of the specimens, the ductility ratio is estimated for all specimens.  
Figure 3: The instrumentation setup to measure deformations in columns. The dial gauges 1 and 2 were installed for axial deformation, and 3 for lateral deformation measurements. The label 4 represents column specimen, while the base plate is marked with label 5.  
The behavior of the tested specimens in terms of ductility/brittleness was evaluated by calculating the ductility of the specimens, considering a displacement ductility ratio as an index [48; 49; 50]. The displacement ductility index \(\mu\) was computed as the ratio of the axial displacement of specimens at an axial load corresponding to 85% of the peak axial load on the descending portion of the load-displacement curve (\(\Delta_{\text{85}}\)) to the axial displacement corresponding to the yield limit of elastic behavior of the specimens (\(\Delta_{y}\)). Figure 4 presents the illustration of calculating the yield displacement corresponding to the limit of the elastic behavior of specimens. The ductility index of the tested specimens was computed using the following equation (Equation (1)):  
\[\mu=\frac{\Delta_{\text{85}}}{\Delta_{y}} \tag{1}\]
## 3 Observation Quality
\(\varphi_{i}\) and \(\varphi_{j}\) stand for the phase observation of frequency i and j. \(C_{i,j}\) denotes the phase ambiguity bias. Figure 3 illustrates the multipath analysis of four different BDS-3 frequencies from four stations. The selected stations are from the MGEX network, without obstruction, and are situated far from water areas. The figure depicts that the multipath errors for most epochs are within 0.5 m, and these errors decrease as the elevation angle rises. The mean values and STD of multipath for different BDS-3 frequencies from the four stations are presented in Table 4. The table shows that the mean multipath for each station \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**SNR [dB-Hz]** & **B1I** & **B1C** & **B2a** & **B3I** \\ \hline MET3 & 47.260 & 46.866 & 53.499 & 50.434 \\ MCHL & 46.409 & 48.043 & 50.325 & 48.022 \\ HOFN & 49.441 & 48.439 & 50.566 & 46.723 \\ VNDP & 48.531 & 46.888 & 49.255 & 50.010 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The mean SNR of different BDS-3 signals for four stations. Figure 2: The SNR of different BDS-3 frequencies for four stations with different receiver types. is close to 0 m. In terms of STD, the B3I signal exhibits the least multipath, approximately 0.17 m, and the value of B2a signal is close to B3I. The B1I signal has the most significant multipath, which is about 0.26 m.
## 4 Validation with Field Test
### Data Analysis and Discussion  
#### 4.3.1 Validation of the ResNet with High-Frequency Responses  
The architectural specifics of the ResNet are elaborated upon in Section 2.3.2. For the training of the ResNet, monitoring data from the second and third days were utilized. To be precise, two discrete datasets, each spanning a duration of thirty minutes, were merged to create a comprehensive time-series dataset. In the training phase, 70% of these compiled data was appropriated for the training of the ResNet model, 20% were earmarked for testing, and the remaining 10% were reserved for validation to ascertain the precision of the proposed methodology.  
The inputs to the ResNet encompassed the high-frequency responses measured at locations 04-S02 and 02-S02. The output target for the model was the high-frequency response at the midpoint cross-section, 03-S02. The validation outcomes, which illustrate the efficacy of the trained ResNet across both the time and frequency domains, are delineated in Figures 18-20, respectively.  
Figure 17: Verification of synchronous vibration in different locations. (**a–c**): the high\(-\)frequency bridge responses at three sensors measured on 7 March; (**d–f**): high\(-\)frequency bridge responses at three sensors measured on 28 August; (**g–i**): high\(-\)frequency bridge responses at three sensors measured on 6 October. (**a,d,g**): strain time history; (**b,e,h**): zoomed strain time history; (**c,f,i**): frequency spectrum. Circle: first bridge frequency; rectangle: potential vehicle frequency).  
Figure 18: Reconstruction results from ResNet and true monitoring data. (Solid line: reconstruction result with ResNet; dashed line: true monitoring data).  
In the time domain, as evidenced in Figure 18, the high-frequency strain time histories derived from the reconstructed data closely mirrored those extracted from the monitoring data, signifying a substantial concordance between the two datasets. This correlation affirms the fidelity of the reconstruction process.  
In the frequency domain, which is detailed in Figure 19, the major frequencies were observed to align with the first and second natural frequencies of the bridge. These frequencies were uniformly present in both the reconstructed and monitoring datasets. Moreover, the amplitudes associated with these frequencies exhibited a similar pattern, underscoring the consistency of the results.  
A more granular examination of the frequencies and their respective amplitudes, especially pertaining to the first and second natural frequencies, is depicted in Figure 20. The variations in amplitude at the first natural frequency of 3.915 Hz and the second natural frequency of 5.061 Hz were negligible. Such minimal discrepancies are indicative of the ResNet's proficiency in accurately simulating the mapping function for high-frequency strain responses.
# Ionospheric Error Models for Satellite-Based Navigation--Paving the Road towards LEO-PNT Solutions
## 3 Methodology
### Data Collection  
The Sun goes through a magnetic cycle every 11 years, influencing the quantity and size of sunspots, solar flares, and Coronal Mass Ejections (CMEs). These factors are indicators of solar activity, and they contribute to a rise in the electron content within the ionosphere [21; 57]. The solar cycle commences and ends with a phase of low activity known as the solar minimum, during which the influence on the ionosphere is minimal. The Sun reaches its peak activity level, known as the solar maximum, in the middle of the cycle, during which the impact on the ionosphere increases.  
In order to encompass an entire 11-year solar cycle, we collected IONEX files spanning a length of 11 years (2008 to 2018) from the Crustal Dynamics Data Information System (CDDIS) database, which is a database managed by National Aeronautics and Space Administration (NASA) that houses space geodesy data from multiple organizations, such as the IGS. For each month in a year, we collected three IONEX files, giving us a total of 396 files. These files contained the final GIMs, which we chose over other maps for their accuracy.  
Next, we used a Matlab code to read all the collected files and generate all the required TEC values, which were then stored in the \(Y\) matrix. The data collection and arrangement process is illustrated in Figure 4. The \(Y\) matrix has a total number of \(11\times 12\times 13\times 71\times 73\) VTEC values, representing 11 years (one complete solar cycle), 12 months, 13 time instances, 71 latitude values, and 73 longitude values. The year and month data are divided into a set of \(M_{a,b}\) matrices, where \(a\) and \(b\) correspond to the year and month, respectively. Every \(M_{a,b}\) matrix is divided into 13 \(T_{i}\) submatrices, where \(i\) represents the time index. These submatrices are arranged systematically, with a 2-h interval between adjacent submatrices. Lastly, every \(T_{i}\) matrix contains a total of \(71\times 73\) VTEC values, corresponding to 71 different latitude values and 73 different longitude values that span the entire Earth with a spatial resolution of 2.5 degrees for latitudes and 5 degrees for longitudes. This systematic arrangement of data provides a comprehensive representation of VTEC values by latitude, longitude, time of day, month, and year.  
It is to be remarked that such a number of parameters can be stored through some compression mechanisms and therefore could occupy below \(1\) MB of space, or even only a few tens of kB with adequate compression. In addition, as interpolation is further used to obtain exact values at any desired latitude-longitude pair, one can also investigate the situation when we have fewer parameters for the latitude and longitudes than the current 71 and 73 values. This remains a topic for further research.
# Advances in Random Fiber Lasers and Their Sensing Application
## 2 RFLs Based on Various Optical Fibers
### DCF-Based RFLs  
Because of its large negative dispersion, 1 km dispersion compensation fiber (DCF) can compensate for the dispersion of 4-8 km standard SMF. The RFL based on the DCF was first investigated in 2014 [88]. The experimental results demonstrated that the threshold of first-order output emission is only 0.45 W. The schematic setup of the RFL based on DCF is depicted in Figure 6. The pump light with the wavelength of 1366 nm is launched into 10 km DCF via 1365/1461 nm wavelength division multiplexer (WDM). The 1 km DCF provides additional Rayleigh scattering feedback for the RFL. The route is also revealed with the increase of pump power from first-order stokes light to second-order stokes light, which experiences three chaotic regimes, as shown in Figure 7.  
Figure 4: Schematic diagram of the experimental setup of the RFL based on erbium-doped fiber (EDF). OSA, optical spectrum analyzer; PM, power meter.  
Figure 5: Experimental setup of the super-continuous (SC) source. LD, laser diode; OFM, optical fiber mirror.  
Moreover, the position or length ratio of DCF and SMF has a great influence on the output of the RFLs (i.e., laser threshold, spectral stability, and shape). Lasing intensity can be considerably enhanced when the DCF locates at a higher pump power distribution position along the fiber [89]. These results are useful to understand the role of DCF in the RFLs and optical amplification. These studies provide theoretical supports for the flexible design and the optimization of the RFLs.
# Development of Potato Mass Estimation System Based on Deep Learning
## 2 Materials and Methods
### Potato Mass Estimation System Performance Analysis  
The objective of the potato mass estimation system developed in the present study is to estimate the mass of potatoes in real time using deep learning technology and a camera. To evaluate the estimation accuracy of the developed system, 200 potatoes are randomly selected from four groups (100 g or less, 100 to 150 g, 150 to 200 g, and 200 g or more) to measure their actual weights and sequentially put into the system to comparatively analyze them using the calculated estimates. Moreover, to evaluate the potato object detection rate of the system and the total mass error considering the actual harvest work, randomly selected potato objects are put in the conveyor with a rate of three potato objects per second. The test is repeated three times to improve the data reliability; in each test, 400 potato objects are put. At this time, the potato variety used for the test is superior.
# DARAL: A Dynamic and Adaptive Routing Algorithm for Wireless Sensor Networks
## 1 Introduction
Reliability, granted Quality of Service (QoS), energy efficiency, duty cycle control, synchronization, etc., are some necessities for modern wireless communications that can only be covered by advanced routing algorithms. _IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL-6 LoWPAN)_[13] or _Ad-Hoc On demand Distance Vector (AODV)_[14] are some of these well-known _IEEE 802.15.4_- based routing algorithms. Each one possesses some interesting features, but they also present disadvantages when they are exposed to a scenario like the Smart City. The present work focuses on the analysis of set-up time and protocol overhead due to the necessity of a fast configuration and a fast self-healing, due also to energy and throughput requirements (a lower overhead requires a lower consumption and allows a better communication channel use). This article addresses the problems of convergence time [7] through different scenarios, together with the control overhead at network formation phase, based on a comparison between other well-known routing algorithms. However, the current routing algorithms for WSN do not usually take into account these key features for Smart City scenarios, and they leave space for new proposals as the one presented here. _DARAL_ proposes a routing algorithm based on a dynamically-allocated hierarchical clustering, which uses the quality of the links as a reference parameter, minimizing the control message overhead, convergence time and energy consumption during the set-up phase. To achieve energy-efficiency, the nodes minimize their active time transferring the routing tasks to some dynamically-selected nodes. Additionally, _DARAL_ organizes the nodes in virtual sub-networks, containing the traffic by zones and also incrementing the efficiency in terms of convergence time. The rest of the paper is organized as follows: Section 2 provides an overview of the most relevant related work about routing algorithms for WSN. Section 3 describes _DARAL_, the routing algorithm proposal designed for a fast set-up and low-overhead in low-/medium-density scenarios. Section 4 presents the simulations and results obtained with our algorithm. Finally, the most relevant conclusions of this paper are explained in Section 5.