esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Theoretical Case with 68 Sensors  
The trained models were deployed to make predictions using the testing feature data. These data were generated by conducting the second FEA and reducing the Young's modulus of the academic structure's material by 5%. As can be seen in Figure 7b, the NARX model makes inaccurate predictions of the first three frequency steps. These steps were used as delays for training. The remaining frequency steps are predicted accurately. The ANN with biLSTM layer was the most delicate to train, and it makes more or less accurate predictions. It wrongly predicts the heights of some peaks, for instance, the peaks at frequency steps 40 and 90, as can be seen in Figure 7b. The frequency-dependent ANN predicts the heights of the peaks correctly (see Figure 7a), whereas the form of the peak at frequency step 50 is poorly predicted. The pretrained ANN (Figure 7d) makes slightly inaccurate predictions about the height and the form of the peak at frequency step 50 as well as the peak at step 90, corresponding to the peaks that are shifted the most in the testing data set compared to the training data set.  
Figure 7: Actual and predicted element force over frequency steps for the academic case with 68 sensors: (**a**) frequency-dependent ANN, (**b**) NARX with more frequency steps, (**c**) ANN with bidirectional long short-term memory layer (biLSTM) layer, and (**d**) pretrained ANN.  
This evaluation can be illustrated by a regression analysis. Therefore, the predicted values, called output in Figure 8, are plotted against the calculated base force by FEA, referred to as targets, and a regression line is computed. Figure 8 shows the resulting regression plots, where the black dots represent the data points and the blue line represents the regression line.  
An overview of the respective regression coefficient of the models, i.e., the slope of the regression line in Figure 8 and the root mean square error (RMSE) between the target and the predicted base force, allows a quantitative comparison of the models. Table 2 summarizes these metrics for the prediction on the test data. The second values (R = 0.9644 and RMSE = 0.0305 N) for the NARX model are the regression coefficient and the RMSE, respectively, for the data without the first three values representing the delays. It can be determined that the pretrained model has the lowest performance, whereas the frequency-dependent ANN makes the most accurate predictions.  
Figure 8: Regression plots of the predictions for the academic case with 68 sensors: (**a**) frequency-dependent ANN, (**b**) NARX, (**c**) ANN with biLSTM layer, and (**d**) pretrained ANN.  
The evaluation of the first theoretical case leads to the conclusion that the method has proven to be successful, even though these first predictions were made with an unrealistically large number of sensors.
### \"Airborne and Terrestrial Situational Awareness\"--A Modular System by DLR
#### 1.2.2 Traffic Management by KeepOperational  
KeepOperational is a web-based portal for emergency managers to support decision making in traffic management and rescue logistics. It is based on the DLR traffic data platform and provides traffic analysis and route planning capabilities [14]. Two features determine its core functionality: A dynamic route planning feature and an isochrones router. The first can be used to select routes especially for convoys. Convoys of emergency units can consist of heterogeneous vehicle types including special vehicles. These may have special requirements and restrictions, e.g., vehicle size or turning radius, that require attention whilst route planning. The second component of the routing algorithm, the isochrones router, is implemented to analyze the accessibility of a certain area. Isochrone maps are useful to visualize areas of similar travel times and are an established tool in transportation planning. In times of crisis, this is of importance, as inaccessible areas considerably impair the route guidance and thus the travel time.  
A peculiarity of KeepOperational is that it can incorporate crisis related information layers derived from aerial or satellite imagery by ZKI together with real-time information from terrestrial traffic sensors, such as loop detectors or floating car data, to analyze the traffic situation. This means that it does not only display the affected area by crisis on its route planning interface, but automatically considers it as a barrier in the proposed routing. Equivalent to the flood information, damage layers  
Figure 1: German Aerospace Center’s (DLR’s) “Airborne and Terrestrial Situational Awareness” system for rapid decision support composed of an interconnected (**1**) remotely piloted platform; (**2**) 3K camera system; (**3**) disaster analysis and visualization component; (**4**) traffic management platform.  
could be obtained from ZKI, or additional restrictions from other data sources with influence on the transport infrastructure can be imported by using standardized formats.  
Both modules of the \"Airborne and terrestrial situational awareness\" application system were successfully integrated into the TTI (cf. 2.3.1) to be tested and evaluated by an interdisciplinary practitioner group within the trial \"The Netherlands\" in the context of severe flooding.
# mmS-TCP: Scalable TCP for Improving Throughput and Fairness in 5G mmWave Networks
## 4 Evaluations  
In this section, we describe the implementation of several 5G mmWave network simulation scenarios to evaluate the performance of the redesigned mmS-TCP. We focus on throughput, number of duplicate ACKs, and TCP fairness.
# Experimental Evolution of Magnetic Nanoparticle Resistance in _Escherichia coli_
## 4 Discussion
Resistance to magnetite NP conferred resistance to all the antibiotics tested similarly to the iron (II)- and iron (III)-resistant populations from our previous studies [13; 14]. These antibiotics have different modes of action due to the nature of their structures and degree of affinity to certain target sites within bacterial cells. Ampicillin exerts bactericidal activity through inhibition of bacterial cell wall synthesis by binding to penicillin binding proteins (PBPs) and by inhibiting certain PBPs related to the activation of a bacterial autolytic process [58]. Tetracycline enters the cells and binds reversibly to the 30S arresting translation, thus inhibiting protein synthesis which ultimately leads to a bacteriostatic effect [59]. Sulfanilamide prevents bacterial replication by inhibiting dihydroprotezo synthetase [60]. Chloramphenicol diffuses through the bacteria cell wall and reversibly bind to bacterial 50S ribosomal subunit interfering peptidyl transferase activity and block peptide bond formation impeding bacterial cell proliferation [61]. Rifampin specifically inhibits bacterial RNA polymerase arresting DNA-directed RNA synthesis of bacteria [62]. Resistance to these antibiotics may involve efflux systems that transport the antibiotics from inside to outside the bacterial cells or a ribosomal protection protein remove antibiotics from ribosomes. Resistance to rifampicin has been linked to a variety of mutations in _rpoB_ and _rpoC_[63; 64; 65]. Given that the FeNP populations have a general lack of variants in genes traditionally associated with iron (II), iron (III), or antibiotic resistance, we propose that this resistance may result from changes in gene expression. Finally, while we did not measure changes in gene expression in this study, our past study of iron (II) resistance in _E. coli_ K-12 MG1655 found that there were large changes in gene expression associated with general metal resistance and iron metabolism in general [19]. We suspect that this was the case here as well and our future studies will examine this possibility.
An illustrative, yet realistic, case study of an IoT enabled bridge, being subjected to cyber-physical attack (i.e., energy depletion attack) against its IoT based WSN is used to demonstrate the application and usefulness of the approach. The cyber-physical attack is composed of four scenarios, based on the application of different control barriers, which can prevent and detect cyber-physical attacks. Control barriers can operate in the cyber (e.g., intrusion detection systems) and physical space (e.g., motion detectors), separately or at the same time (i.e., integrated control barriers). Monte Carlo simulations are performed to vary the quantitative scores assigned to vulnerabilities, threat sources and physical impacts, and conduct a sensitivity analysis to illustrate the impact of the scores on the cyber-physical risk. This paper contributes to existing knowledge in the following ways. First, cyber-physical characteristics, that have not been previously considered in the aspect of threat source profile (e.g., terrorism experience), all associated with an IoT enabled transportation infrastructure, are integrated in the cyber-physical risk assessment approach, minimizing the knowledge gap between civil and security engineering domain. Second, this paper describes and uses a realistic case study of a cyber-physical attack scenario that takes place in the sensing area of an IoT enabled transportation infrastructure. Third, the role of control barriers, which could be activated in the sensing layer, is introduced, and considered in the approach, to inform assessors towards the cyber-physical risk reduction. The remainder of the paper is as follows: Section 2 presents the related work; Section 3 describes the proposed cyber-physical risk assessment approach; Section 4 illustrates the case study and presents the results; Section 5 includes a description of the results and limitations of the approach; and Section 6 concludes the paper.
# Practical 3-D Beam Pattern Based Channel Modeling for Multi-Polarized Massive MIMO Systems +
## 2 3-D Antenna Configuration
### 3-D AES and Antenna Element's Positioning
#### 2.1.2 Receive Antenna Configuration
Furthermore, vector \(\widetilde{\mathbf{a}}_{lk}(\phi_{i},\theta_{i})\), \(\widetilde{\mathbf{a}}_{l^{\prime}k^{\prime}}(\varphi_{i},\theta_{i})\) and \(\widetilde{\mathbf{a}}_{lk}(\phi_{LOS},\theta_{LOS})\), \(\widetilde{\mathbf{a}}_{l^{\prime}k^{\prime}}(\varphi_{LOS},\theta_{LOS})\) are the array responses of the TX and RX antennas for LOS component and NLOS components, respectively, by expressing the response vector \(\chi\) of \(X_{Pol}\), V, H and V/H antenna polarizations in Equations (2), (4), (6) and (8) whose entries are given by  
\[\widetilde{\mathbf{a}}_{lk}(\phi,\theta)_{X_{Pol}}=\left[\chi^{\theta}_ {lk}\chi^{\phi}_{lk}\right]\exp\left(j\mathbf{s}_{lk}(lk-1).d^{BS}_{X_{Pol}}\right) \tag{12}\] \[[\mathbf{H}]_{lk,l^{\prime}k^{\prime}}(t,\tau)=\alpha_{LOS}\sqrt{gt(\phi_{LOS}, \theta_{LOS},\theta^{tilt})}\sqrt{gr(\varphi_{LOS},\phi_{LOS},\theta^{tilt})} \mathbf{\tilde{a}}_{l^{\prime}k^{\prime}}(\varphi_{LOS},\vartheta_{LOS})\times \tag{11}\]0 (13) \[[\mathbf{H}]_{lk,l^{\prime}k^{\prime}}(t,\tau)=\alpha_{LOS}\sqrt{gt(\phi_{LOS}, \theta_{LOS},\theta^{tilt})}\sqrt{gr(\varphi_{LOS},\phi_{LOS},\theta^{tilt})} \mathbf{\tilde{a}}_{l^{\prime}k^{\prime}}(\varphi_{LOS},\vartheta_{LOS})\times \tag{11}\]1 (14) \[\widetilde{\mathbf{a}}_{l^{\prime}k^{\prime}}(\varphi,\theta)_{V/H}= \left[ \begin{matrix}\chi^{\varphi}_{l^{\prime}k^{\prime}}\\ \chi^{\varphi}_{l^{\prime}k^{\prime}}\end{matrix} \right]\exp\left(j\mathbf{s}_{l^{ \prime}k^{\prime}}(l^{\prime}k^{\prime}-1).d^{UE}_{V/H}\right) \tag{15}\]  
where (.) is the scalar product, \(d^{BS}_{X_{Pol}}\) is the location of \(lk_{th}\) antenna element at the TX, and \((d^{UE}_{V},d^{UE}_{H},d^{UE}_{V/H})\) are the locations of \(l^{\prime}k^{\prime}_{th}\) antenna element at the RX. Also \(\mathbf{s}_{lk}\) and \(\mathbf{s}_{l^{\prime}k^{\prime}}\) are the TX and RX wave vectors, respectively, where \(\mathbf{s}_{lk}=\omega\frac{2\pi}{3\iota_{lk}}\mathbf{g}\) and \(\mathbf{s}_{l^{\prime}k^{\prime}}=\frac{2\pi}{\lambda_{l^{\prime}k^{\prime}}}\mathbf{ \hat{y}}^{\prime}\) with \(\lambda\) is the wavelength of the carrier frequency and outgoing wave \(\hat{y}\) is equal to \((\sin\theta\cos\phi_{l}\sin\theta\sin\phi\cos\theta)\) and incoming wave \(\hat{y}^{\prime}\) is equal to \((\sin\theta\cos\varphi_{l}\sin\theta\sin\theta\cos\theta)\) which are the directions of wave propagation in the response of the TX and RX antennas, respectively. Elevation angles \((\theta,\theta)\) are defined between \(90^{\circ}\) and \(-35^{\circ}\)azimuth angles \((\phi,\varphi)\) are defined between 30\({}^{\circ}\) and 150\({}^{\circ}\) at the TX and RX, respectively. \(\omega\) is the beam weight with \(lk_{th}\) antenna element.
## 3 Design of the IoT-Edge-Cloud Platform
### System Architecture Design  
The diagram in Figure 2 illustrates a high-level system architecture of a flexible, hyper-distributed IoT-Edge-Cloud platform designed to be deployed in two different sites. It can be scaled based on the number of connected IoT devices, the geographical distribution of Edge nodes, and the specific industrial needs, ensuring the smooth functioning of real-time Digital Twin applications. This level of scalability is essential to meeting the demanding requirements of modern logistics and industrial environments, where operational efficiency, low latency, and high reliability are paramount. The architecture includes several layers: the IoT, Edge, Cloud, and orchestration layers.  
In the IoT-Device layer, a diverse set of devices--including sensors, cameras, industrial robots, and vehicles--continuously gather data from the physical environment. The number and type of devices are variable, allowing the architecture to accommodate different scenarios, from a few localized devices to hundreds distributed over larger areas. The IoT devices are connected to the Edge layer via a private 5G network, divided into the radio access network (5G RAN) and core network (5 GC). The private 5G network supports slicing at the RAN, transport, and core levels, which allows the creation of multiple virtual networks that can be tailored to the different requirements of each IoT application, such as bandwidth, latency, or security needs.  
In the Edge computing layer, powerful multi-core processors and memory in geographically distributed Edge nodes are strategically deployed to manage latency-sensitive data processing. These nodes, located wherever needed, handle real-time responses locally, significantly reducing the need for data transfer to the Cloud. The Edge nodes can vary in number and capacity, depending on the specific application, and can be scaled as needed to ensure low-latency performance.  
The Cloud computing layer, with its vast computational resources, serves as a central hub for more resource-intensive tasks, such as long-term data storage and in-depth analysis, allowing the system to offload non-latency-sensitive workloads.  
Finally, the orchestration layer is critical in managing resources, applications, and services across the whole distributed platform. It dynamically allocates tasks to the most appropriate computing resources based on real-time conditions, performance needs, and service-level agreements (SLAs). It plays a critical role in managing the lifecycle of services and applications across the IoT, Edge, and Cloud infrastructure. A crucial aspect of this layer is the dynamic placement of network functions (NFs) that comprise the 5G core, which can be distributed between the Cloud and Edge servers based on the specific requirements of the use case. For example, the User Plane Function (UPF) can be moved closer to the Edge to reduce latency for real-time applications like Digital Twins.
## 2 Network Model
### Network Architecture  
In this paper, there are 2 kinds of nodes including anchors and ordinary nodes.  
(1) Anchors are nodes which positions are accurately known before the localization process;(2) Ordinary nodes are nodes which should get their positions by communicating with anchors directly or indirectly. Ordinary nodes can be divided into 2 types: the reference nodes and the unknown ordinary nodes. Reference nodes are nodes which are selected by anchors to assist in the localization process, while unknown ordinary nodes are nodes which should get their positions by communicating with anchors and selected reference nodes.  
Figure 1 shows the network architecture. In this paper, we assume that only one reference node is needed for localization. Other conditions can be extended from this assumption.
# Effect of Seawater Irrigation on the Sugars, Organic Acids, and Volatiles in 'Reliance' Grape
## 3 Results  
### Effect of seawater Irrigation on Average Weight and Texture Parameters of the Berry  
The average berry weight of the berries showed an increasing trend during the entire growing stage and plateaued after the EL 37 stage. No significant differences were found between the average berry weight of berries in the 10% seawater treatment and the control at different developmental phases (Figure 1).  
The berries in stages EL 31 and 33 were too small for texture detection; thus, only EL 35 to EL 38 were used to evaluate the skin hardness, frangibility, firmness, and average hardness of the berry flesh (Figure 2). No significant differences in skin hardness were detected between the control and 10% seawater treatments (Figure 2a), while S1 and S2 treatments significantly increased the average hardness of flesh at EL 38 (Figure 2d). This finding could be attributed to the skin of \"Reliance\" berries being so thick that the slight differences in the skin hardness of different groups were covered. However, the S1 treatment significantly reduced the berry frangibility at EL 35 and EL 36, although the difference disappeared at EL 37 and EL 38 (Figure 2b). This result shows that S1 treatment promotes berry softening earlier than other treatments, thereby reducing the frangibility of the berry in the early stages of fruit development. Seawater treatment improved the berry firmness at EL 38 (Figure 2c). The S1 and S2 treatments had the most significant effect on improving the fruit firmness of \"Reliance\" berries, which were 26.60% and 23.69% higher  
Figure 1: Effect of seawater irrigation on the average berry weight of ‘Reliance’ grapes. Values are the means of three replicates, and error bars denote the SD. Different lowercase letters indicate significant differences of samples in one developmental phase, according to [PERSON] multiple comparison post-hoc test at \(p<0.05\).  
than the control berries. Furthermore, the berry firmness of the S3 treatment was increased by 18.75% compared to the control berries.
## 3 The DELTA Platform
### Inertial Measurement Unit (IMU)  
The dataset includes high-resolution inertial and environmental parameters such as tri-axial acceleration, gyroscope readings, angular orientation, magnetometer readings, and ambient temperature, all timestamped for precise data logging. Integrating this IMU data with GNSS and GIS is essential for advanced navigation systems used in mobile robotics and micro-mobility services [23]. GNSS provides accurate positional data, while GIS offers digital maps with essential spatial environmental information.  
Figure 5: Georegistered trajectory points mapped on OpenStreetMap.  
Gyroscopes in the IMU ensure the accurate orientation of maps, critical for safe navigation. Magnetometers offer crucial directional information, particularly useful in areas with complex magnetic fields that might disrupt traditional compass readings [24]. Additionally, synchronizing IMU data with GNSS coordinates enhances the precision of the location data used for creating maps within GIS systems. The use of advanced data fusion techniques such as the Kalman filter [25], extended Kalman Filter [26], and fuzzy logic systems [27] in conjunction with accelerometer data significantly improves the mapping system's functionality and accuracy. These techniques allow for a more reliable understanding of the robot's position and orientation, as captured by GIS. By leveraging GNSS, GIS, and IMU data with these sensor fusion techniques, navigation systems can achieve high levels of accuracy and robustness, enabling the safe and efficient operation of mobile robots and micro-mobility services in complex urban settings.
## 2 The Proposed SAR ADC
### Comparator  
Factors such as comparator misalignment, noise, and speed can directly affect the overall performance of SAR ADCs. Comparators are classified into static and dynamic comparators based on whether a clock signal is required to switch on and reset the comparator. Static comparators do not require a clock signal to control them, and these comparators are always in an operating state waiting for a signal to be input, thus consuming more power.  
Dynamic comparators have no quiescent current, have the advantages of speed and low power consumption, and are widely used in ADCs. Usually, in comparator applications, NMOS is used as the input port of dynamic comparators [17; 18; 19], but in this work, PMOS is used as the input port to improve accuracy, as shown in Figure 9a. Dynamic comparators have two operating states, the reset state in the first stage and the comparison state in the second stage. In the first stage, the Sample is constant and high, so the CLK_comp, generated by the OR gate, is high. At this time, M1 is off; M6, M7, M10, and M11 are ON; A and B voltages are grounded; and OUTP and OUTN are high. In the second stage, the CLK_comp is low when the Sample is constant and low, and the CLK drops; M6, M7, M10, and M11 are OFF; VDD of A is charged via M1, M2, and M4; and VDD of B is charged via M1, M3, and M5.  
Figure 8: (**a**) Transient simulation of sampling switch; (**b**) FFT of sampling switch.  
Figure 9: (**a**) Dynamic comparator; (**b**) transient simulation of dynamic comparator.  
If IP \(>\) IN, the A point voltage is greater than the B point voltage. M4, M5, M8, and M9 collectively constitute a positive-feedback latch circuit. Subsequently, A and B point voltages decrease and increase, respectively; OUTP increases; and OUTN decreases. If IP \(<\) IN, then eventually A and B point voltages increase and decrease, respectively, and OUTP and OUTN decrease and increase, respectively. During the comparator operation, the change from VDD to ground does not form a DC circuit; therefore, it only consumes dynamic power.  
In Figure 9b, the transient simulation of the comparator is illustrated. When the CLK decreases, the voltages at A and B increase and then separate at the same time, with one increasing and the other decreasing. Ultimately, OUTP increases and OUTN decreases. The output of the comparison result is obtained by the comparator.