esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Elderly people are particularly vulnerable to respiratory tract infections, so natural strategies to ameliorate the duration and severity of these infections are of great interest in this population. The objective of this study is to evaluate the efficacy of the consumption of a combination of elderberry and reishi extracts on the incidence, severity, and duration of respiratory tract infections in a group of healthy elderly volunteers. A randomized, double-blind, placebo-controlled pilot study was performed during the winter season. A group of 60 nursing home residents \(\geq\)65 years of age was randomly assigned to receive a combination of 1.5 g of elderberry +0.5 g of reishi or a placebo daily for 14 weeks. Data about the health conditions of the volunteers were evaluated and recorded by a medical doctor every 2 weeks. The incidence of respiratory infections was similar in both groups. However, volunteers in the extract group presented a significantly lower duration of common cold events (2.5 vs. 4.8 days, \(p=0.033\)).and a significantly lower probability of having a high severity influenza-like illness event (\(p=0.039\)). Moreover, the incidence of sleep disturbances was significantly lower in the extract group (\(p=0.049\)). Therefore, the administration of a combination of elderberry and reishi extracts to the elderly population during the winter season might be used as a natural strategy to reduce the duration and severity of respiratory tract infections.  
elderberry; reishi; respiratory tract infections; common cold; influenza-like illness; respiratory infection symptoms; randomized controlled trial +
Footnote †: journal: Applied Sciences
## 5 Numerical Evaluation Results
### Separated Peak Demands
#### 5.2.3 Impact of Spacing between Operator Traffic Bursts  
While Figures 3 and 4 considered a fixed 40 s separation of the starting time instants of the data bursts (of 10 s duration) at the two operators, we consider a range of burst separations in Figure 5. We observe from Figure 5 that a burst separation of zero, which corresponds to the scenario in Figure 3a,b does not permit queue reductions through backhaul resource sharing. In contrast, the 40 s separation of the data bursts corresponding to the scenario in Figure 3c,d as well as Figure 4, does permit the sharing of the backhaul resources of the two operators. Thus, with the large \(V=1000\) flexibility parameter setting, substantial reductions of the average eNB queue lengths can be achieved for both operators for large separations of the data bursts.  
In contrast, for the short separation times of 5 s and 10 s, we observe from Figure 5 that operator \(o=1\) achieves queue length reductions for the large \(V=1000\) setting, whereas the queue lengths for operator \(o=2\) increase. The eNBs at operator \(o=1\), which receives the earlier data burst, can still achieve queue length reductions by using some of the backhaul capacity of operator \(o=2\) to serve the data burst arriving to the eNBs at operator \(o=1\). However, the use of the \(o=2\) capacity by the \(o=1\) burst when the data burst to the eNBs at operator \(o=2\) arrives, slows down the service for the \(o=2\) burst, resulting in the \(o=2\) queue length increases observed in Figure 5. However, we observe from  
Figure 5: Queue length in kB at an eNB averaged over time and over the eNBs at a given operator \(o\) as a function of separation of \(o=1\) and \(o=2\) data bursts in s; Figures 3 and 4 consider a burst separation of 40 s.  
Figure 5 that the average of the curves for \(o=1\) and \(o=2\) for the \(V=1000\) setting is slightly below the corresponding queue length averages for operation without SDN or with the inflexible \(V=10\). Thus, the flexible sharing of backhaul capacity with \(V=1000\) does not \"harm\" the overall system compared to operation without sharing. The QMW benchmark gives yet lower queue length as QMW shares the bandwidths of the two operators without any constraints, i.e., corresponds to \(V\) approaching infinity (which would not enforce fair bandwidth allocations to operators).
# A Survey on Congestion Control for RPL-Based Wireless Sensor Networks
## 4 Congestion Control Schemes for RPL
### Schemes Exploiting Path Diversity
#### 4.2.7 Bird Flocking Congestion Control Algorithm  
A flock-based congestion control approach for wireless sensor networks was proposed by [PERSON] et al [54]. The approach is based on the idea of guiding packets towards a global attractor (the sink in WSNs), while trying to avoid obstacles (failing nodes and congested regions). The movement direction of a packet group is determined using repulsion and attraction forces between packets, the field of view and the artificial magnetic field in the direction of the artificial magnetic pole (sink). The field of view (FoV) which limits the range of potential next hops is used to keep packets from being trapped in loops and to prevent the same node from always being considered.  
In the bird flocking model, each packet is supposed to be repelled from neighboring packets located on nodes one hop away. The repulsion force is proportional to the number of packets on nodes one hop away, which is obtained using broadcast control packets. In contrast, each packet is attracted to neighboring packets located on nodes two hops away and the attraction force is proportional to the number of packets on nodes two hops away. Since nodes two hops away are outside the transmission range, the number of packets on nodes two hops away is inferred by measuring the number of packets transmitted successfully from nodes one hop away to nodes two hops away. The repulsion and attraction forces are synthesized using the desirability function. A node within the FoV with the highest desirability function value is selected as a next hop. In this way, packets are 'flying' through the network while being attracted to nodes with low wireless channel loading, and being repelled from nodes with high buffer occupancy.  
[PERSON] et al. apply a similar bird flocking model to control congestion in CoAP/RPL/6 LoWPAN networks [39]. In their approach, the ZoR (zone of repulsion) of a node is the set of its parents and children and the ZoA (zone of attraction) the set of direct ascendants of its parents and direct descendants of its children. A packet tries to avoid more congested nodes in ZoR and to follow less congested nodes in ZoA. The congestion level of a node is defined as the buffer filling ratio. To estimate the buffer filling ratios of other nodes accurately without incurring high overhead, the authors propose eavesdropping data messages instead of using control messages. For each packet, the next hop is selected satisfying two conditions: (1) the next hop should be the least congested in ZoR, (2) the successors of the next hop should be the least congested in ZoA.  
Through Cooja simulation, performance of the proposed scheme was evaluated. However, the authors only showed the effect of tuning the retransmission-related parameters, but did not clarify how effectivethe bird flocking approach to next hop selection in RPL was in mitigating congestion compared to the existing RPL approaches.
# A Multicast Routing Scheme for the Internet: Simulation and Experimentation in Large-Scale Networks
## 2 Related Work and Our Contribution
### Brief Overview of Multicast Routing in the Internet
During the last decade, only PIM-SM and PIM-SSM, have been really deployed and only for the intra-domain multicast case, mostly for the IPTV service within some Internet Service Provider (ISP) network [5; 34]. However, multicast has failed to be adopted and does not reach the acceptance level required for its wide deployment, especially for the inter-domain case.
## 7 Conclusions  
The main controlling factors affecting the deformation of overlying strata and surface subsidence during UCG are found through numerical simulation, an innovatively established UCG surface subsidence prediction method based on continuous-discrete medium theory. The following conclusions are obtained:  
1. Through numerical simulation, the high-temperature thermal stress and coal-rock burnt caused by UCG will increase the strata movement and surface subsidence. However, high-temperature thermal stress has little influence. The coal-rock burnt is the main factor that changes the strata movement and surface subsidence characteristics of UCG. The research results can provide an essential foundation for studying control methods for UCG rock strata in the later stage.
2. The prediction model of the surface subsidence of UCG is innovatively established using the continuous-discrete medium theory. The co-subsidence space of the gasifier roof-pillar-floor is obtained by using continuum mechanics. The roof subsidence space is regarded as mining space and the surface subsidence is predicted using the probability integral method. This method is verified by using the Ulanqab UCG experimental station. The difference between the predicted and measured results is only 5 mm. This dramatically improves prediction accuracy and provides an essential reference for assessing surface environmental damage caused by UCG.  
Conceptualization, X.L. and L.X.; Software, X.L.; Formal analysis, X.L.; Writing--original draft, X.L.; Writing--review & editing, L.X. and K.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Anhui Provincial Natural Science Foundation (Grant No. 2108085 QE207, 2008085 QD179), the National Natural Science Foundation of China (Grant No. 52104172), the University-level Key Projects of Anhui University of Science and Technology (Grant No. kjzd2020-13).  
Not applicable.  
The data used to support this study are available from the corresponding author upon request.  
The authors also thank [PERSON], for his support and guidance in preparing and revising this paper.  
Figure 11: UCG surface subsidence No. 5 monitoring station.
## 3 Experimental Section
### Preliminary Experiment with Doppler Vibrometry
Substitution of Equation (7) into Equation (6) leads to the following condition regarding the amplitude and phase of the oscillation of the film displacement:  
\[A = \frac{f/m}{\sqrt{(\omega_{0}^{2}-\omega^{2})^{2}+4\beta^{2}\omega^{2}}} \tag{8}\] \[\tan\delta = \frac{2\beta\omega}{(\omega_{0}^{2}-\omega^{2})} \tag{9}\]  
Here, \(\omega_{0}=\sqrt{k/m}\) is the natural frequency of the oscillatory system, and \(2\beta=b/m\). \(f/m\) in Equation (8) is the acceleration of the transducer surface. The amplitude of the transducer surface \(A_{0}\) when the transducer is operated at \(\omega\) can be related to the acceleration as \((f/m)=A_{0}\omega^{2}\). Using this relation, we can rewrite Equations (8) and (9) in the following forms. \[\frac{A}{A_{0}} = \frac{\xi^{2}}{\sqrt{(1-\xi^{2})^{2}+4\gamma^{2}\xi^{2}}} \tag{10}\] \[\tan\delta = -\frac{2\gamma\xi}{(1-\xi^{2})} \tag{11}\]  
where the frequency and damping coefficient are normalized by the natural frequency as \(\xi=\omega/\omega_{0}\) and \(\gamma=\beta/\omega_{0}\). Since the left-hand sides of Equations (10) and (11) can be experimentally determined from Figure 5, we can estimate the natural frequency and decay constant for a given driving frequency by finding the combination of \(\omega_{0}\) and \(\gamma\) that yields \(A/A_{0}\) and \(\tan\delta\) closest to the experimental values. Figure 6 plots theoretical \(A/A_{0}\) and \(\tan\delta\) as a function of \(\xi\) (solid lines) and the experimental values determined from Figure 5 (square and circle markers) for the driving frequency of 50 kHz. For the untreated/treated specimen, when \(\omega_{0}\) and \(\gamma\) are as shown in Table 1, the experimental values agree with the theory the best. It is seen that the untreated specimen shows 18% higher resonant frequency than the treated specimen and that the surface treatment makes the elastic dynamics more energy dissipative. This 18% ratio of the resonant frequency of the untreated specimen to the treated specimen observed in the polystyrene films is approximately the same as the case of the other films tested in the study (see Section 3.2 below for more details). This agreement can be simply a coincidence, but is interesting to note. The higher energy dissipation in the treated specimen can be understood by considering that the oxygen plasma bombardment makes the substrate surface rougher and causes more friction when the layers undergo oscillatory motions. Figure 5: Signal from Doppler vibrometry.
## 5 Experimental Results
### Benchmark Instances  
We tested our algorithm on 15 instances derived from five power systems. Table 1 gives the information of these power systems, where the numbers of edges, power nodes and load nodes are displayed in the columns \"# Edges\", \"# Generation nodes\" and \"# Load nodes\", respectively. The names of the test instances are _ieee118-2_, _ieee118-3-a_, _ieee118-3-b_, _sop2737-2_, _sop2737-3_, _sop2737-4_, _wop2746-2_, _wop2746-3_, _wop2746-4_, _wp3012-2_, _wp3012-3_, _wp3012-4_, _sp3120-2_, _sp3120-3_ and _sp3120-4_.  
The power system _ieee118_ represents a portion of the American electric power system (in the Midwestern USA) in 1962. [PERSON] _et al._[26] divided the generators of _ieee118_ into two and three groups and accordingly introduced two instances, namely _ieee118-2_ and _ieee118-2-a_, where the first part of the  
Figure 9: The procedure of the islanding approach.  
instance name denotes the name of the power system and the second part is the number of generator groups. Moreover, [PERSON] _et al._[32] also generated a three-island instance that differs from that of [26]; we call this instance _ieee118-3-b_. The power systems _sop2737_, _wop2746_, _wp3012_ and _sp3120_ are the Polish grid in 2007-2008 with different loading scenarios, which are summer off-peak season, winter off-peak season, winter peak season and summer peak season [33]. We randomly divided each of these power systems into two to four parts and specify that the generators in each part belong to the same coherent generator group, thereby generating 12 instances. The generators in each group may not be truly coherent, and these instances are only used to evaluate our algorithm. All instances and detailed solutions are available in the online supplement to this paper at [34].
# Insider Threat Detection Using Machine Learning Approach
## 3 Materials and Methods
### Insider Threat Models
#### 3.4.1 Anomaly Detection Models  
The method employed to deal with the insider threat problem is determined by data availability. Previous insider attacks in the organisation would consist of only normal instances rather than a mixture of normal and abnormal instances. This section will look at cases where an organisation's data availability maturity is medium (i.e., only normal behaviour data are available).  
The suggested framework detects harmful insider threats using one-class support vector machine (OCSVM) and isolation Forest (iForest) as base approaches. Because the data are highly skewed (30:1000), we want to make a model that treats the 30 instances as anomalies or outliers. The basic aim of OCSVM and iForest training is to identify a function, f, that returns a positive (+) result when applied to a point within f and a negative (-) result otherwise.  
We take 70% of the typical data for the training phase after labelling it to (-1, 1) to make it appropriate to the output of the OCSVM and iForest. We add the remaining 30% to the threat instances for the testing phase. For OCSVM, we use gamma = 0.0001 and nu = 0.001 with RBF kernel, and for iForest, we used the following: number of estimators = 100, maximum number samples = 256, and contamination = 0.5.
## 4 Discussion
### Canopy Heterogeneity and Impact of Scale on LAI Retrieval Accuracies  
Our high-resolution data confirmed that LAI was highly heterogeneous at scales below the satellite resolution (Table 2, Figure 5). The linear fit between the UAV and ground LAI was significantly better when sampled at two metres when compared to that at 20 m. The performance of GPR LAI retrieval model when calibrated with the 2-m resolution data (R\({}^{2}\) = 0.8 and NRMSE = 11%) is also comparable to other studies with this sensor on wheat trials plots [26], where [PERSON] et al. reported an R\({}^{2}\) of 0.84 and NRMSE of 9%. These results together suggest that a resolution of 2 m is required to capture the variability in LAI measurements within the fields sampled. At coarser resolutions, the UAV retrieval of LAI became more biased. When investigating the retrieval of winter wheat LAI from EO data, [PERSON] et al. [41] showed that even averaging data at a spatial resolution of 5 m can significantly reduce the variability in LAI when compared to ground measurements.
# A Survey on Secure WiFi Sensing Technology: Attacks and Defenses
## 3 Attacks Against WiFi Sensing
### Passive Attack  
Passive attacks primarily focus on monitoring or eavesdropping on the wireless channel and the sensing results without altering or interfering with them [52; 55; 66; 67]. In these attacks, the attacker remains undetected while collecting valuable information from the system. The objective is to silently gather data on the WiFi sensing environment, such as user movements, device activity, or other sensitive information, without directly interacting with or affecting the system's operations. These attacks are particularly concerning in scenarios where sensitive data are being transmitted or where long-term monitoring can reveal patterns and behaviors without the knowledge of the system's users.  
[PERSON] et al. [66] conducted a passive attack on public WiFi sensing datasets and investigated the leakage of private attributes. They found that public WiFi sensing datasets (e.g., gesture recognition dataset WiDar [75] and activity recognition dataset WiAR [76]) will leak users' private attributes (e.g., height, weight, and gender) when attacked by a specially designed deep learning model.  
[PERSON] et al. [55] studied the feasibility of monitoring the victims via through-wall WiFi sensing. They demonstrated that with a proper analysis of CSI collected by the behind-wall attacker, the attacker can recognize both the presence of targets as well as their moving direction in a room. This private information may be leveraged to track and count the flow of traffic throughout a building.  
ActListener [67] demonstrates the vulnerability of WiFi sensing where an eavesdropper can eavesdrop on user activities imperceptibly using a WiFi infrastructure in any location of the user sensing area. As shown in Figure 4, this attack can be used to monitor the confidential information of the victims. ActListener can not only estimate the locations of legitimate devices and the victim users but also convert the eavesdropped signals to those of legitimate devices based on the models. Moreover, ActListener is robust to noises in dynamic wireless channels.  
WiPeep [52] uncovers loopholes in the 802.11 protocol, which can be exploited to steal the location information of WiFi devices. Specifically, WiPeep first generates fake WiFi traffic from the attack device (e.g., a drone), which will cause the victim device to respond. With this response, WiPeep can use a ToF scheme to locate the victim device even when the device is non-cooperative and in a building.  
Figure 4: Illustration of the scenario of ActListener [67].
# LaRoAwan and Urban Waste Management--A Trial
## 4 Trials
### Sensors  
Considering the aim and purpose of the tests, it is intended that they are based on sensors with different characteristics, allowing meaningful conclusions. For this purpose, two types of filling level monitoring sensors for waste containers were purchased, both commercial, but from different manufacturers and different cost ranges:  
* IoTsens Waste Sensor, high-end sensor, with quality specifications and higher costs (\(\upxi 300\));
* Dingtek DF702, sensor with lower specifications, also corresponding to lower costs (\(\upxi 70\)).  
Both sensors use the general principle of ultrasound measurement, in which the sensor is placed on top of the container and measures the distance between the sensor and the waste. Thus, a lower distance entails a higher filling level. This form of measurement has as its main advantages, the ease of use and low cost and as disadvantages, its inability to deal with situations of nonhomogeneous filling levels and false positives in situations of low-density waste (such as cardboard). This implies that the position of the sensor placement and its processing capacity greatly influence the reported level.
# Ras Isoforms from Lab Benches to Lives--What Are We Missing and How Far Are We?
## 5 Missing Pieces in the Ras Puzzle
### One Ras and Many Locations  
#### 5.6.1 Ras in Golgi  
The plasma membrane is the hotspot for Ras signaling; however, Ras is also present in the Golgi, Endoplasmic reticulum, mitochondria, and endosomes. Do these organelles also have a distinct pool of Ras activators and effectors? Growth factor-induced delayed and sustained activation of Golgi resident Ras has been visualized through fluorescent studies [137]. Moreover, the presence of Ras GEFs, GAPs Scaffold proteins (regulates the kinetics of signaling) Set, and a Ras effector Rain1 [138] have been discovered in Golgi. Different GEFs are involved in the activation of Ras in this organelle. For instance, the activation of Ras on the Golgi is mediated by calcium/diacylglycerol dependent Ras guanyl release protein \(1\) (Ras GRP) [139], whereas in the ER, the activation of H-Ras is by Ras-specific guanine nucleotide-releasing factor \(1\) (Ras GRF1) and factor \(2\)[140]. Receptor endocytosis post-stimulation and interaction of the tail of the receptor with the cytosol account for the activation of Ras isoforms in endosomes. There also operates a pathway independent of receptor endocytosis that activates Ras in the Golgi through Src family kinases and phospholipase C-\(\gamma\) (PLC-\(\gamma\)). By the generation of second messengers required for the activation of Ras GRP, Ras activation occurs [141]. The activation kinetics of Ras in the Golgi is delayed but sustained unlike on plasma membrane which is rapid and transient. H-Ras, as well as N-Ras, are activated in Golgi. As mentioned earlier, palmitoylation of H and N-Ras affords hydrophobicity to it for its membrane attachment. A dynamic equilibrium exists between acylated and deacylated H-/N-Ras between the plasma membrane and the Golgi complex [129]. As mentioned earlier, two enzymes that take part in maintaining this equilibrium are Golgi resident palmitoyl transferase and acyl protein thioesterase. One proposed mechanism of receptor-induced activation of H-/N-Ras in Golgi is that on activation of these isoforms, APTs depalmitoylates Ras and hence mislocalizes it from the plasma membrane. This depalmitoylated H-/N-Ras reaches the Golgi again to be re-palmitoylated, contributing to the pool of active H-/N-Ras in the Golgi. N-Ras as it is singly palmitoylated has faster recycling and kinetics of activation when compared to H-Ras which has dual palmitoylation. Blocking the process of palmitoylation using inhibitors like 2-bromopalmitate, abrogated the pool of active Ras in the Golgi [142]. Raf kinase trapping to Golgi (RKTG), a Raf binding protein, and Set both localize to Golgi. RKTG sequesters Raf in the Golgi in an inactive conformation, whereas Set retains ERK in the cytosol [143].