esa-sceva/satcom-chunk-collection huggingface dataset:
# Non-Thermal Methods for Ensuring the Microbiological Quality and Safety of Seafood
## 5 Pulsed Electric Fields--PEF
### Effects of PEF on the Quality of Fish and Saefood  
PEF, as a non-thermal method, can maintain the sensory properties and biochemical and nutritional value of treated foods [174; 179]. Table 3 lists different studies investigating the application of PEF for the treatment of fish and seafood products. [PERSON] et al. (2020) [12] investigated the physiochemical changes appearing in fresh, frozen-thawed, and supercooled yellowfin tuna fillets (_Thununus albacares_) stored at 4.0 \({}^{\circ}\)C, \(-\)3.2 \({}^{\circ}\)C, and \(-\)18 \({}^{\circ}\)C (freezing) for 8 days, respectively. Supercooled tuna fillets assisted using PEF (3.5 V\({}_{\text{rms}}\) at 20 kHz) and oscillating magnetic fields (OMF) (75 mT at 1 Hz). They concluded that PEF processing combined with supercooling could provide fresh tuna fillets with extended shelf life compared to conventional refrigeration. Similarly, [PERSON] and [PERSON] (2020) [178] documented higher sensory scores of PEF-treated shrimps at a higher electric field intensity (15 kV/cm, 600 pulses) than others after 10 days of storage at 4 \({}^{\circ}\)C.  
In fish processing, PEF treatment is also applied to improve the extraction of various organic compounds from fish and seafood [180; 181; 182; 183]. [PERSON] et al. (2020) [184] recommended the use of PEF treatment for improved antioxidant extraction from fish residues (gills, bones, and heads) of two commercial species (sea bream and sea bass). Fish bones, together with gills, and heads are the main fish residues reaching about 20-75% of a fish's total weight [185]. These products are commonly rejected without any further process, resulting in environmental pollution and waste of potentially valuable resources. In this sense, [PERSON] et al. (2014) [180] used PEF to extract chondro sulfate (CS) from fish bones and concluded that PEF extraction speed and CS content were much higher at 15-25 kV/cm.
## 4 Results
### Crop Parameters Retrieval Validation
#### 4.2.2 FVC Retrieval Validation  
Figure 9 presents the accuracy of FVC retrieval using various methods for angular correction. The performance of FVC retrieval surpassed that of LAI retrieval, with the coefficient of R\({}^{2}\) for FVC retrieval from the original reflectance already exceeding 0.8. Notably, the Fixed-Jilin-1 method significantly enhanced the accuracy of FVC retrieval when compared to the original reflectance. Specifically, the R\({}^{2}\) value rose from 0.8 to 0.87, the RMSE decreased from 9.54% to 7.58%, and the MRE reduced by over 35%. However, an underestimation of FVC was observed with the other four methods, despite an improvement in the R\({}^{2}\) value. Particularly, the AFX method exhibited the most pronounced underestimation, with an RMSE reaching 11.55%. When the crop coverage is low (FVC \(<\) 50%), the influence of the angular effect on FVC retrieval is small, and the application of the five angular correction methods did not lead to a significant improvement in the retrieval accuracy of FVC. Conversely, for higher crop coverage (FVC \(>\) 50%), the accuracy of FVC retrieval was markedly enhanced following angular correction.
# Multi-User Linear Equalizer and Precoder Scheme for Hybrid Sub-Connected Wideband Systems
## 1 Introduction  
Mobile data traffic has increased over the years and the next generation (5G) is a response to this demand for higher data rates [1, 2]. It is expected that 5G will achieve a minimum data rate of 1 Gb/s, 5 Gb/s for high mobility users, and 50 Gb/s for pedestrian users [3, 4]. The frequency spectrum used by the mobile communications is currently saturated, so it is necessary to find another spectrum band for mobile applications. In this context the millimeter wave (mmW) band, with its huge available bandwidth [5], with wavelengths from 1 to 10 millimeters [6], can be an interesting solution. Other key technology for future generation communications is massive multiple-input-multiple-output (mMIMO), allowing achievement of higher data rates and better energy efficiency (EE) when compared to the previous generation, 4G-Long Term Evolution (LTE) [7].  
The combination of mmW with mMIMO systems enables the use of a large antenna array at the base station (BS) and user terminals (UT) [8]. Massive MIMO, also known as large-scale antenna systems (LSAS), beyond improving EE, can also improve the spectrum efficiency (SE) of the mobile communications systems [9]. SE is independent of the number of antennas employed at the BS andgrows with the increase of the number of radio frequency (RF) chains, as discussed in [10]. It is well known that when we have a large number of antennas, it is not feasible to have one dedicated RF chain per antenna, and consequently, a full digital beamforming (BF) architecture is not realistic due to the higher costs and power consumption [9; 11]. On the other hand, a system that works only in the analog domain, by employing full analog BF, is not feasible due to the availability of only quantized phase shifters and the constraints on the amplitudes of these analog phase shifters. As a result, the fully analog architecture is normally limited to a single-stream transmission [12]. One possible solution to overcome these limitations is to consider hybrid digital and analog BF where the signal is processed at both analog and digital levels. When this hybrid architecture is compared with the fully digital one, the performance of the hybrid solution is limited by the number of RF chains, but it is possible to design efficient signal processing schemes to achieve a performance close to the fully digital counterpart [13].
# Assessment of Deep Learning Methodology for Self-Organizing 5G Networks
## 4 Enabling 5G with SON and Deep Learning
### Deep Learning-Based Anomaly Detection
#### 4.2.2 Experimental Setup  
A simulation study has been performed in ns-3 simulator whereby 105 mobile users were uniformly spread around 7 base stations. The base stations were spread regularly on the grid where each circle represents a base station. The users were mobile and spread uniformly across the grid. The periodic (once every 0.2 ms) MDT report generated by each of the users were recorded for about 50 s. The data has been hand labeled so that each MDT report has been assigned a label from the set {0, 1, 2, 3}. Labels 0, 1 and 2 represent normal network conditions with distinct transmit power levels. An MDT report with label 3 indicates that the corresponding user was previously being served by a BTS that has failed. Only one BTS failure is simulated. The main parameters used in the system simulation are summarized in Table 1.  
In order to use the autoencoder model for cell outage detection, a machine learning framework was set up. We used L1 type regularization at the input layer and the dropout at the first hidden layer with a 50% dropout rate. This helped to overcome any potential over-fitting problems. The model was trained for 100 epochs with a mini-batch size of 22 samples. The Adam training algorithm was used, and the cost function was the mean squared error. We used the Keras library in Python for the implementation of the model.  
There were two phases: the detection of anomalous cases in the test KPIs and finding the nearest base stations for each test sample. After finding the nearest base-stations, it was possible to identify the base station with the most anomalous cases.  
Table 2 gives four data sets that are used to test the cell outage framework. These have been made publicly available by the authors for the readers to do their own analysis [68].  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Cell Layout & Hexagonal grid \\ \hline Number of cells & 57 cells (19 eNodeBs, 3 sectors per eNodeB) \\ \hline Number of sectors for each base station & 3 \\ \hline Inter-site Distance & 500 m \\ \hline eNodeB default Tx Power & 46 dBm \\ \hline Number of UEs in the scenario & 105 \\ \hline UE Distribution & Uniform \\ \hline Traffic Type & Downlink Full Buffer \\ \hline Simulation lengths & 150 s \\ \hline Cell Outage Tx Power & \(-\)50 dBm \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Data Set** & **Number of Users** & **Number of Cells** \\ \hline Data set 1 & 105 & 21 \\ \hline Data set 2 & 210 & 57 \\ \hline Data set 3 & 105 & 57 \\ \hline Data set 4 & 105 & 57 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation data set parameters.
## 4 System Design and Goals
### Message Sequence
#### 4.2.3 HELLO Message State  
Nodes use this state for the following:  
* To send a HELLO broadcast. HELLO broadcasts can be sent if any of the following conditions are met:
* At the expiration of DB_SELF and before the LISTEN state if the EMERGENCY_HELLO constant is greater than zero.  
Nodes that emitted the HELLO message must immediately set EMERGENCY_HELLO to zero (default value is \(-1\)) and await a HI response or wait for the expiration of DB_NEIGHBORS_RCVD (see flowchart in Figure 5).
## 2 Material and Methods
### Inversion  
The proposed inverse method is based on the use of attenuated backscatter profiles derived from micro-lidar measurements. After a series of pre-processing and denoising techniques, the lidar signals are radiometrically calibrated using the instrument lidar constant \(K_{0}^{*}\) of the system. It is recommended to regularly measure this calibration constant over time depending on the laser stability. In our case, a rapid measurement of the lidar constant is carried out before every lidar campaign. Hence, the attenuated backscatter profile U, is calculated as:  
\[\mathrm{U(r)}=\frac{\mathrm{RCS(r)}}{\mathrm{K_{o}^{*}\,O(r)}}\qquad[\mathrm{m }^{-1}\;\mathrm{sr}^{-1}] \tag{15}\]  
The idea to obtain the reconstruction method is, from (3), to introduce the two-way transmittance T linked to the total backscattering and extinction coefficients, \(\beta\) and \(\alpha\) respectively, by the relation:  
\[\mathrm{T(r)}:=\frac{\mathrm{U(r)}}{\beta(\mathrm{r})},\quad\mathrm{and\; conversely}\quad\beta(\mathrm{r})=\frac{\mathrm{U(r)}}{\mathrm{T(r)}}. \tag{16}\]  
It thus follows:  
\[\mathrm{T(r)}=\exp\left(-2\int_{0}^{\tau}\alpha(r^{\prime})\mathrm{d}r^{ \prime}\right), \tag{17}\]  
which is positive, decreasing, continuous, and differentiable since \(\alpha\) (and thus \(\beta\)) is a piecewise non-negative continuous and bounded function. Moreover, under the assumption of calibrated signal, one can write:  
\[\mathrm{U(r=0)}=\beta(\mathrm{r=0}),\quad\mathrm{and\;thus}\quad\mathrm{T(r=0 )}=1. \tag{18}\]  
By evaluating the derivative of \(T\), it follows:  
\[\frac{\mathrm{d}}{\mathrm{d}r}\mathrm{T(r)}=-2\alpha(\mathrm{r})\mathrm{T(r)} =-2\,\mathrm{LR\,U(r)}, \tag{19}\]  
Figure 3: Radiometric lidar calibration. Measurements and analytical model are presented in plain dots and blue curve, respectively. Error bars represent standard deviation over 50,000 measurements.  
and integrating over \([0,\mathrm{r}]\), using (19) we get:  
\[\mathrm{T}(\mathrm{r})=1-2\,\mathrm{LR}\,\int_{0}^{\mathrm{r}}\mathrm{U}(\mathrm{r }^{\prime})dr^{\prime}. \tag{20}\]  
The integration in (20) has to be performed numerically so finally the backscattering coefficient \(\beta\) is obtained from the relation (16). An analytical solution for \(\beta\) with an explicit form is given by the following equation:  
\[\beta(\mathrm{r})=\frac{\mathrm{U}(\mathrm{r})}{1-2\,\mathrm{LR}\,\int_{0}^{ \mathrm{r}}\mathrm{U}(\mathrm{r}^{\prime})\mathrm{dr}^{\prime}}\qquad[m^{-1} sr^{-1}] \tag{21}\]  
Equation (21) does not involve the necessity of a reference zone or clean aerosol-free zone in the lidar signal, which is required for short-range elastic micro-lidar. Its denominator can be straightforwardly evaluated as long as calibrated lidar signals are provided. We note that the expression is also a solution of the well-known differential Bernoulli-Riccati equation of the lidar problem expressed in terms of attenuated backscatter profile:  
\[\mathrm{LR}\,\frac{\mathrm{d}}{\mathrm{dr}}\,\mathrm{ln}\,\mathrm{U}(\mathrm{r })=\frac{1}{\alpha(\mathrm{r})}\frac{\mathrm{d}\alpha(\mathrm{r})}{\mathrm{ dr}}-2\alpha(\mathrm{r}) \tag{22}\]
# An Intelligent Parking Management System for Urban Areas
## 3 Description of the System
### Communications System  
Communication between the sensor nodes and the gateway node will be performed using the 6 LoWPAN protocol on the Contiki operating system. This protocol allows the use of IPv6 over wireless networks implemented using the IEEE 802.15.4 standard. In the proposed system each sensor node is assigned an IP address.  
The transmission of packets using the IPv6 protocol requires an adaptation layer between IPv6 and the IEEE 802.15.4 standard. One of the functions of this adaptation layer is to compress the IP header, as this header is too large for 802.15.4. It also handles the fragmentation and reconstruction of packets because IPv6 supports a maximum transfer unit of 1280 bytes, while the maximum for the IEEE 802.15.4 standard is 127 bytes. It also handles the routing of packets within the local IP network, routing to external IP networks, readings of the status of neighbouring nodes, and support for multicast transmission. In the IEEE 802.15.4 standard, node consumption during transmission varies between 10 and 30 mA, depending on the power level, and communication is performed with a maximum transfer rate of 250 kbit/s. IEEE 802.15.4 uses the ISM band for industrial, scientific, and medical uses: 868 MHz in Europe, 915 MHz in the US, and 2.4 GHz worldwide. However, when designing devices for wireless sensor networks, the 2.4 GHz band is usually chosen since it is not assigned.  
It should be noted that the sensor network will be implemented following the ad hoc topology. This network topology describes networks as flexible mesh. In these networks all of the nodes provide routing services, so, in addition to functioning as end devices, they also perform packet retransmissionmulti-hop functions. This way it is possible to transmit packets to nodes with which there is no direct coverage. Thanks to this feature, the network can cover large areas. However, this topology requires routing protocols to calculate the most efficient route to favour lower energy consumption in the nodes. Figure 6 shows the payload of a 6 LoWPAN packet structure used in the system for communicating with the nodes. The payload consists of a custom header, IDs for each specific sensor node, the data length and the data itself (a message determining whether a vehicle went in or out). These messages are sent using 6 LoWPAN protocol under Contiki.
# Publish/Subscribe-Middleware-Based Intelligent Transportation Systems: Applications and Challenges
## 4 Application of Publish/Subscribe Middleware in ITS
### Autonomous Vehicles
#### 4.2.3 Air Transportation  
Air transportation is a complex system and a mode of transportation that moves passengers and goods by aircraft, such as airplanes [88]. It is considered one of the most crucial components of the global transportation system, which aids in traveling long distances for different countries in the shortest time possible and with a high safety level and convenience. It is worth mentioning that this type of transportation is essential for international commerce or trade, business, and tourism. [PERSON] et al. [89] emphasized that air transportation struggles with fragmented data sharing, inconsistent interfaces, and delayed or inefficient data exchange across diverse stakeholders and heterogeneous systems. A publish/subscribe middleware can perfectly address these problems by providing a decoupled, event-driven framework that ensures real-time, secure, and scalable distribution of critical information among all relevant parties.  
[PERSON] et al. [90] proposed an MQTT-based publish/subscribe middleware to manage multiple drones and services that operate with a high level of autonomy yet must collectively share congested airspace. The authors proposed Pop-up Drone Zones (PuDZs), i.e., temporary operational environments where small Uncrewed Aerial Systems (sUAS) can be rapidly deployed for tasks like emergency response. Traditional air traffic control methods are ill-suited for these transient, fast-changing scenarios since each sUAS and related service (e.g., weather updates, no-fly zones) has its local objectives and might not directly coordinate with other actors. Without a cohesive communication fabric, there is a real risk of conflicting airspace allocations or inconsistent responses to critical events such as erratic drone flights or sudden adverse weather. The authors developed the MQTT-based publish/subscribe architecture in which each system (e.g., airspace \"air leaser\", digital shadow, drones) runs a local self-adaptive loop and shares events (faults, no-fly updates, weather changes) in real time. This method ensures loosely coupled but coordinated behavior. A Policy Manager enforces global rules, such as keeping only one active air traffic control service. Experiments show that the UAVs safely shared the airspace, avoided collisions, and responded consistently to unexpected events such as off-course flight, loss of ground control, or new no-fly zones. However, MQTT is not optimized for time-sensitive or safety-critical applications, and reliance on this lightweight protocol may limit system robustness in high-density or mission-critical drone operations [26].
# Linear Approximation Signal Detection Scheme in MIMO-OFDM Systems
## 4 Proposed Detection Scheme
### DFE Stage  
The conventional DFE in Section 3 is applied to the two systems in Equations (18) and (20), respectively. So, the complexity of the DFE stage is lower than the conventional DFE, which is simply applied in original \(4\times 4\) MIMO-OFDM system. After the DFE stage, all of the transmit symbols from \(\hat{X}_{1}\) to \(\hat{X}_{4}\) are initially estimated.
## 3 Optimization, Considering the Cogging Torque and Torque Ripple
### The Comprehensive Optimization of the Rotor's Key Parameters  
#### 3.3.1 The Three Key Rotor Parameters  
The above analysis gives the relationship between the key parameters in the rotor with the torque performances, respectively. In this part, Figure 9 shows the variation of torque performances considering the \(w_{rib}\), \(h_{rib}\), and \(w_{b}\) comprehensively, when \(R_{2}\) was 47.5 mm. As shown in Figure 9a, the average torque varied from 858.7 Nm to 894 Nm when the \(h_{rib}\) changed; and \(h_{rib}\) showed greater influence on torque output than the other two parameters. It can be clearly seen from Figure 9b that when the \(w_{rib}\) changed, the torque ripple varied from 64.6 Nm to 123.8 Nm. The torque ripple coefficient \(k_{rip}\) could be considered more sensitive to \(w_{rib}\) than to \(h_{rib}\) and \(w_{b}\). Overall, Figure 9 provides a reference for optimizing the design of IPMSM with V-type magnets. If the torque ripple is taken as the optimization objective, the minimum torque ripple is 46.4 Nm with a torque ripple coefficient of 3.84%, and the torque waveforms are compared by Figure 10. Table 2 summarizes the influences of the key parameters on the average torque and torque ripple. Table 2 shows the optimization priority of the three key parameters: \(h_{rib}\) should be optimized firstly to get high torque output capability; then \(w_{rib}\) is optimized aiming at low torque ripple; finally, \(w_{b}\) is optimized to get overall better torque output and lower torque ripple.  
Figure 8: Then-influences of magnet position on average torque and torque ripple. (**a**) Influence of \(R_{2}\); (**b**) influence of \(w_{rib}\); (**c**) influence of \(h_{rib}\); (**d**) influence of \(w_{b}\).
## 1 Introduction
In addition, [PERSON] et al. [34] assessed the applicability of InSAR for slope monitoring, discussing its practical applications in geotechnical engineering through several case studies, but without providing a comprehensive review of recent advancements in InSAR techniques for landslide studies. With regard to LSM research, existing reviews primarily examine from two perspectives: methodological advancements and conditioning factors. For instance, [PERSON] et al. reviewed LSM frameworks based on machine learning methods, comparing commonly used algorithms and evaluating their performance via case studies [35]. [PERSON] et al., on the other hand, explored LSM from the perspective of conditioning factors, particularly the influence of land use and land cover (LULC) on susceptibility assessments [36]. Other reviews have also provided broad statistical analyses, summarizing commonly used LSM methodologies and conditioning factors [37; 38]. However, considering the rapid development of InSAR-based landslide research in recent years, these reviews have not specifically examined InSAR's role in LSM or its potential impact on LSM methodologies. The unprecedented expansion of spaceborne InSAR-based landslide research has led to significant advancements and novel approaches in both landslide monitoring and susceptibility mapping. Consequently, there is an urgent need to systematically review and synthesize this evolving body of knowledge, thereby identifying emerging research gaps and future directions. In this study, we provide a systematic review of the research trend, landscape, and future research directions in spaceborne InSAR-based landslide research. This review is structured as follows: Section 2 outlines the article selection principles for this review, as well as the publication trends; Section 3 analyzes the geographic distribution characteristics of published landslide articles regarding InSAR and summarizes the major SAR platforms and their applications; Section 4 offers a thorough examination of the application and advancements of InSAR in landslide monitoring studies. Section 5 involves a detailed exploration of the InSAR's role in LSM. Section 6 outlines promising research directions for future landslide monitoring and susceptibility mapping studies.