esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Explainable and Interpretable Fall Detection
The study's findings illuminate the critical gait characteristics linked to these musculoskeletal conditions, offering potential for early detection and intervention. By understanding which aspects of gait are most predictive of health issues, healthcare providers can develop more effective treatments and preventive measures to manage or mitigate these conditions. The authors of [27] developed a sophisticated vision-based human-fall-detection system employing 3D multistream CNNs coupled with an innovative image-fusion technique. This system processes video data from surveillance cameras to detect fall events with high accuracy and speed. The utilization of 3D CNNs allows the system to understand and interpret complex movements dynamically, enhancing its ability to distinguish between normal activities and falls. The image-fusion technique improves the system's robustness against varying lighting conditions and obstructions, ensuring reliable fall detection across different environments. Their method demonstrates significant potential for deployment in settings such as elderly care facilities, where timely fall detection can prevent severe injuries and improve overall patient care. While most research in explainable fall detection focuses on sensor data, a limited amount of work is conducted on video datasets. This gap highlights the need to explore and develop explainable deep learning approaches leveraging video data for accurate fall detection. Incorporating video datasets can provide additional visual cues and context,potentially enhancing the performance and interpretability of fall-detection systems in real-world scenarios.
# Integration of _Salmonella_ into Combination Cancer Therapy
### Intrinsic Oncolytic Capacity  
Several studies have claimed that _Salmonella_-induced tumor inhibition is in part mediated by the intrinsic oncolytic activity of _Salmonella_ itself. A variety of mechanisms underlie _Salmonella_'s intrinsic anti-tumor activity, all of which are mediated through the induction of tumor cell apoptosis. _In vitro_ and _in vivo_ studies utilized Annexin V detection, caspase-3 activity, and TUNEL assays to report on increased tumor cell death following infection with _Salmonella_ and subsequent invasion [12; 49; 50]. It was suggested that nutrient deprivation and release of bacterial toxins could underlie apoptosis promotion [51; 52]. In addition, the ability of _Salmonella_ to induce autophagy was correlated to its intrinsic anti-tumor effect [13; 53]. In one study, _Salmonella_ induced autophagy in melanoma tumor cells in a dose- and time-dependent manner through downregulation of the AKT/mTOR pathway [13]. Other studies reported that _Salmonella_ could delay tumor progression by inhibiting tumor angiogenesis [54; 55; 56]. The anti-angiogenic ability of _Salmonella_ is derived from its capacity to downregulate the expression of HIF-\(\alpha\) and vascular endothelial growth factor (VEGF) through the AKT/mTOR pathway [54]. The intrinsic oncolytic activity can be also attributed to nitrate reductase enzyme released from lysed _Salmonella_ and its role in converting nitrate and nitrite into nitric oxide, which induces apoptosis in tumor cells [57; 58]. Moreover, _Salmonella_ was documented to downregulate the expression of certain oncoproteins in tumor cells including p-glycoprotein (p-gp) [59] and matrix metalloproteinase 9 (MMP-9) [60], inhibiting drug resistance and tumor metastasis, respectively.
## 2 Microwave Liquid Crystal Technology
### Next Generation of Microwave Liquid Crystals for Electronically Steerable Antennas
Beyond microwave properties, parameter improvements related to the usage of LC in a real product aim (1) to increase the temperature range, (2) to reduce the tuning voltage, and (3) to increase the switch-off response time [58; 59]. (1.) Important improvements have been made to the low-temperature stability of the third generation of mixture classes by lowering the crystallization temperature \(T_{c}\) down to below \(-20\) \({}^{\circ}\)C and \(-30\) \({}^{\circ}\)C, respectively. 2. The dielectric anisotropy at low frequencies \(\Delta\varepsilon_{1\text{ kHz}}\) could be increased significantly to values of 10 to 20 and above, leading to much lower threshold voltages, and hence, lower tuning voltages. 3. Response times are proportional to the rotational viscosity. This parameter has been decreased to around 0.3 Pa-s for the third-generation LCs compared to 2100 Pa-s for second-generation LC TUD-566, thus enabling considerable improvements in response time, as can be seen from Figure 8.  
These improved performance metrics of the third-generation LC mixtures combined with the parallel progress in the processing, manufacturing, and assembly technology will bring MLC-based components and devices, in particular passive LC-based antenna technology, a step closer toward becoming a part of the ground and user terminals of future LEO/MEO-satellite constellations and hubs of 5G mm-wave systems. A comprehensive survey of commercial mm-wave phased array companies focused on SATCOM and 5G applications is given in [2; 246; 247]. Thus, Kymeta has demonstrated a flat LC-metamaterial surface antenna in a satellite communication setup [248; 249; 250; 251], where a first lab-scale demonstrator was already presented in 2010 by [PERSON] et al. [158]. ALCAN Systems also demonstrated recently a first flat passive beam-steering phased array antenna with an LC-tunable phase shifter stack [252; 253; 254; 255; 256]. Both antennas have been fabricated in a partnership with display manufacturing companies for producing and commercializing cost-efficient large-scale Electronically Steerable Antennas (ESAs). All of them aim for low-profile and light ESAs with a modular concept to be scaled to any requirement and a thickness of a few centimeters only. Moreover, all work on the form factor of their ESAs, for compact stand-alone terminals with aesthetic appearance and to integrate them smartly or nearly seamlessly into the structure or body of a carrier object such as into the skin of an airplane or into the rooftop of a vehicle.
# Plant Height Estimation in Corn Fields Based on Column Space Segmentation Algorithm
## 2 Materials and Methods
### Point Cloud Segmentation for Corn Populations  
#### 2.5.1 Region Growing Algorithm  
In order to create a full region, the region growth algorithm first identifies the seed points and then progressively combines the points in the neighborhood with comparable characteristics. Normals and curvatures are calculated and sorted in ascending order of curvature. The neighboring points are compared, and the lowest curvature point is chosen as the initial seed. The normal angle threshold is set, and the eligible neighboring points are added. The curvature threshold is set, and the growth continues until all the points have been processed. The enveloping box algorithm uses simple geometries (such as AABB, enveloping sphere, and OBB) to approximate complex objects. The minimal hexahedron used to represent AABB has a simple structure, but it is inapplicable to complex shapes, particularly when it must be recalculated during rotation.
# Predicting Topology Propagation Messages in Mobile Ad Hoc Networks: The Value of History
## 4 Implementation of the OLSR-HBP
### Implementation Aspects
When the TP message (received or predicted) is forwarded, the extra delay causes it to not combine with other pending OLSR messages in the same communication packet; it increases the number of packets transmitted (with just one TP message) and also the energy consumption in the network. To alleviate this problem we tuned the jitter parameter. The jitter is the additional time to wait before transmitting the TP message, in order to combine other later messages into the same packet. OLSR uses jitter values of 0-0.5 s. We have extended the OLSR-HBP jitter to increase opportunities to combine several messages into the same packet. Despite the potential benefits of the OLSR-HBP, there are also a couple of potential limitations: (1) if a TP message that had been broadcast is lost during transmission, OLSR-HBP at the destination will wrongly assume that it was a _global hit_ and thus, a wrong TP message will be generated; and (2) the predictor is not 100% accurate due to the delay in updating the routing information at the destination--the delay is caused by the extra time required for a new TP message to arrive from the origin node before a prediction can be made.
## 5 Simulating CMLs for Computational Experiments
### The CMLs Simulation Algorithm  
Pre-set variables (i.e., variables that are constant throughout the run):  
1. Region Area: This is a physical parameter, e.g., 10 \(\times\) 10 (km\({}^{2}\)).
2. Spatial Resolution--N: N is the number of pixels the area is being partitioned to.
3. Environment Type--Mean CML Length: As established in Section 3, simulating a different environment corresponds to choosing a different mean CML length. Values typically range from 1.5 to 10 (km).  
Figure 6: A discrete model of a CML. (**a**)—a single CML of length 4.8 pixels, (**b**)—its discrete representation.  
The Number of CMLs to Generate \(n_{set}\): Given the region area and the maximum spatial density desired for the experiment, let \(n_{set}\) be sufficiently high so to allow for multiple random draws of the maximal number of CMLs. The computation of the CMLs set:  
1. Generate an Array of \(n_{set}\) Objects, Each is a CML Length Value: Each length is drawn randomly from an exponential distribution with the above pre-set mean length.
2. Assign Each Object an Orientation: Each of the generated objects is assigned an angle drawn uniformly from \([0,\pi)\).
3. Assign Each Object a Position: Each object's beginning point is drawn uniformly in a square of dimensions \(\sqrt{N}\times\sqrt{N}\). Then, the end point is defined by drawing a line based on the length and orientation of the object. Note that in this step, each CML object is defined by \"continuous\", i.e. not discrete, measures.
4. Calculate Quantized Pixels Values: In order to suit the discrete model, the CML is being represented as an array. For each CML object, partition the \(\sqrt{N}\times\sqrt{N}\) area to \(\sqrt{N}\times\sqrt{N}\) squares, each represents a pixel. Set 0 to a pixel that does not have the CML pass through it. For a pixel that the CML does pass through, assign a positive value equal to the physical length of the CML's overlap with the pixel's region (i.e., overlapping with the square). See Figure 6 for a graphical description. Figure 7 portrays the density, lengths, and orientations of a synthetic CML set.
## 4 Security
### Authenticity of Communication Peers  
Packet manipulation can be one sign that one or several malicious nodes have succeeded in integrating itself with the network. Such intrusions widely open the doors to further attacks like Sybil [40] and node replication [41] attacks. Both attacks profit from weaknesses of the authentication mechanisms to insert malicious nodes. In the former case, these nodes take illegitimately multiple identifiers; while in the latter, they capture and use existing device IDs. The identifier manipulation allows the attacker to modify the content of the traffic exchanged between the devices as well as control messages such as routing messages. These attacks can therefore be the basis of further routing attacks like wormhole [42] or black hole [43] attacks, where the attackers are able to disconnect part of the network or make it totally inoperable.
###### Abstract  
This study investigates a green intermodal routing problem considering carbon tax regulation and a mixed (combined soft and hard) time window to improve cost- and time-effectiveness and promote carbon emission reduction in intermodal transportation. To enhance the feasibility of problem optimization, we model the uncertainty of both the carbon tax rate and the intermodal network capacity in the routing problem. By using interval fuzzy numbers to formulate the twofold uncertainty, an interval fuzzy linear optimization model is established to address the problem optimization, in which the optimization objective of the model is to minimize the total costs (consisting of transportation, time, and carbon emission costs). Furthermore, we conduct crisp processing of the proposed model to make the problem solvable, in which the optimization level, a parameter whose value is determined by the receiver before solving the problem, is introduced to represent the receiver's attitude towards the reliability of transportation. We present a numerical experiment to verify the feasibility of the optimization model. The sensitivity analysis shows that the economics and environmental sustainability of the intermodal routing optimization conflict with its reliability. Improving the reliability of transportation increases both the total costs and the carbon emissions of the intermodal route. Furthermore, through comparison with deterministic modeling, the numerical experiment shows that modeling the twofold uncertainty can cover the different decision-making attitudes of the receiver, provide intermodal routes that are sensitive to the optimization level, enable flexible route decision-making, and avoid unreliable transportation. Through comparison with hard and soft time windows, the numerical experiment proves that the mixed time window is more applicable for problem optimization, since it can obtain the intermodal route that yields improved economics and environmental sustainability and simultaneously satisfies the receiver's requirement for timeliness. Through comparison with the green intermodal route aiming at minimum carbon emissions, the numerical experiment indicates that carbon tax regulation under an interval fuzzy carbon tax rate is not feasible in all decision-making scenarios where the receivers have different attitudes regarding the reliability of transportation. When carbon tax regulation is infeasible, bi-objective optimization can provide Pareto solutions to balance the objectives of reduced costs and lowered carbon emissions. Finally, the numerical experiment reveals the influence of the release time of the transportation order at the origin and the stability of the interval fuzzy capacity on the routing optimization in the scenario in which the receiver prefers highly reliable transportation.  
intermodal transportation; green routing; uncertain capacity; uncertain carbon tax rate; mixed time window; interval fuzzy linear optimization model +
Footnote †: journal:  
Article
# Resilient Green Cellular IoT for Landslide Monitoring Using Voice Channels
## 5 Conclusions  
We propose a novel method called VRTTHI for the Green Industrial Internet of Things with ultra-low resource hardware (as less than 2 KB RAM) where nodes are directly exposed to Internet without any intermediate nodes, such as gateway. VRTTHI employs dual-channel communication mechanism where a voice channel is used for control signals followed by a data channel for the actual communications. This paper discusses the design of VRTTHI in terms of detail communication sequence using voice channel and data channel. Being an interrupt-based technique, VRTTHI achieves increased energy efficiency and improved real-time communications with respect to other prominent IoT protocols in the Industry. A thorough analysis of the state of the art in IoT reflects that the domain lacks such a solution for ultra-low memory hardware. Evaluation of VRTTHI demonstrates that it is able to provide increased protection against DoS attacks, efficient real-time communications, and reduced energy consumption. The proposed solution is applicable to any IoT scenarios with tiny ultra-low memory nodes, such as smart rings, smart watches, etc. In the future, we would like to provide information coding techniques on the tones in control channels used in VRTTHI. This will enable sending additional control information among the nodes and help in further energy saving. Secondly, in the current scenario, we assume that the core network is a trustful carrier network. In the future, a complete protocol and key exchange handshake between the nodes could be implemented. This helps to protect the data from spoofing by a core network and provide an end-to-end protection between the nodes.  
Figure 11: Energy consumptions using VRTTHI. (**a**) Power of Data and Voice Channels, (**b**) Comparison with IoT Protocols.
## 3 Results
### DSC Measurements  
Table 2 shows the \(T_{g}\) values at different testing conditions for the in-bulk specimens. In the initial state, a drastic reduction of \(T_{g}\) was observed when comparing the non-doped with the doped adhesive. This means that CNTs accelerated the curing process, leading to the maximum conversion point of the system (\(T_{g}\sim 150\)\({}^{\circ}\)C). This affirmation was supported by the measurements of the second scanning, where the \(T_{g}\) of all the samples were close to 150 \({}^{\circ}\)C, indicating the point of the maximum conversion of the resin. In addition, by observing the \(T_{g}\) of the aged samples, a significant decrease in comparison to the non-aged specimens was observed when adding CNTs, resulting in a similar glass transition temperature than for the neat adhesive. However, by observing the \(T_{g}\) obtained in a second scanning, it reaches the point of the maximum conversion in every case. This indicates that there was a plasticization effect caused by the water absorption in the case of CNT-doped samples. In the case of the neat adhesive, no significant differences were found when comparing aged and non-aged samples, so the plasticization effect was similar for the non-aged and aged samples. This affirmation was given by the fact that the network of the neat adhesive was not initially totally cured, with the plasticization effect less prevalent due to water absorption. It is important to note that the selected curing cycle was the same as that given by the supplier.  
Alternatively, when comparing the CNT-doped samples, it was observed that the addition of surfactant results in a more drastic reduction of the \(T_{g}\), implying, thus, a higher plasticization effect. In order to better explain the possible effects that can take place in the material, it was necessary to focus on the mechanical testing of the SLS joints.
# GF-1 Satellite Imagery Data Service and Application Based on Open Data Cube
## 3 Experiment and Results  
### Experiment Design and Environment  
The proposed framework was applied to the GF-1 imagery data of the Miyun Reservoir pilot area in Beijing spanning from 2014 to 2021 to verify its usability and advantages in terms of data storage and management and application analysis. The experiments were conducted on a PC with an Intel Core i7-8700 processor, 16 GB RAM, and a 13 TB hard disk running the Ubuntu 20.04 operating system, with PostgreSQL 12.4 installed to store the location information and auxiliary information of metadata and imagery data.
## 5 Experimental Results  
### Dataset  
CICDDoS2019 is one of the latest datasets collected for the Distributed Denial of Service (DDoS) attack and has been widely used for intrusion detection research [42] since its publication. In a DDoS attack, the idea is to attack a single network or a machine from different locations. Various attackers can cause this attack from a different location, or a single attacker can take control of multiple machines in different locations and use them to attack victims simultaneously.  
Eight different traffic types are used in the training set, namely, Normal, PortMap, NetBIOS, LDAP, MSSQL, UDP, UDP-Lag, and SYN. After the image embedding, a sample image for each of the eight classes is shown in Figure 7. The test set contains 12 types of traffic. In addition to the eight types collected in the training set, the other four types, including NTP, DNS, WebDDoS, and TFTP, are included in the test set. These additional traffic types help in the generality of the IDS because they are blind to IDS at the training stage. In the proposed work, an anomaly detection model detects these four additional attacks by considering them as an anomaly class.
## 2 The Experimental Data
### The Ricker Wavelet  
The Ricker wavelet is a theoretical waveform obtained by solving the Stokes differential equation [60; 61]. This type of wavelet is representative of seismic waves propagating through viscoelastic homogeneous media because it matches the effects of Newtonian viscosity. Mathematically, the Ricker wavelet is the second order derivative of a Gaussian function. Being zero-phase, it is symmetric in the time domain. In reality, seismic signals are usually minimum-phase, and are therefore similar to the first or one-and-a-half derivatives of a Gaussian function [61; 62]. Notwithstanding, various derivatives of a Gaussian function have similar amplitude spectra that are different from a Gaussian distribution. The Ricker wavelet is commonly used in seismic analysis, for instance, in wave field simulations or reflectivity inversions [63].  
The Ricker wavelet is defined in the time domain as  
\[r(\tau)=\left(1-\frac{1}{2}\omega_{p}^{2}\tau^{2}\right)\exp\left(-\frac{1}{4 }\omega_{p}^{2}\tau^{2}\right), \tag{8}\]  
where \(\tau\) is time (in seconds) and \(\omega_{p}\) is the most energetic frequency (in radians per second). It is symmetric in the time domain and has a zero mean, as \(\int_{-\infty}^{\infty}r(\tau)d\tau=0\). The Fourier transform of the Ricker wavelet may be expressed as  
\[R(\omega)=\frac{2\omega^{2}}{\sqrt{\pi}\;\omega_{p}^{3}}\exp\left(-\frac{ \omega^{2}}{\omega_{p}^{2}}\right), \tag{9}\]  
where \(\omega=2\pi f\) is the angular frequency and \(f\) is the frequency in Hz.  
For our modeling of a synthetic source gather we selected a Ricker wavelet (Figure 9) with similar spectral characteristics as the experimental data (Figure 3). Both have a frequency content of 20 to 70 Hz and a peak frequency at 35 Hz.  
Figure 9: **(a)** The Ricker wavelet used in the modeling of a synthetic source gather and (**b**) its corresponding frequency spectrum. The peak frequency of this wavelet is 35 Hz.
## 3 Results and Discussion
### Automated Column Sorption System
#### 3.2.2. Acidity Levels
The structural stability of the sorbent should not be compromised; for once the pH level reaches below 1, the structure of clinoptilolite breaks down in a process termed 'dealumination'. Precipitation should be avoided, for once the ions of interest have precipitated they cannot be sorbed [43]. It should be noted that while low pH levels prevent precipitation, the competitive H\({}^{+}\) ions present would hinder the sorption of HMIs. Therefore, it is to be expected that future field installations for the treatment of AMD (with typical pH range of 2 to 5 [40]) should potentially demonstrate even higher removal efficiencies. However, care should be taken in the design of industrial applications to incorporate pre-treatment processes to reduce particulates prior to applying the waste to any sorption system, to avoid flow obstruction in the sorption columns.