esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Dependability for MCAs in 6G
### Dependability Analysis of 6G MCAs
#### 6.2.3 Safety  
The reliability and availability of MCAs each have a direct relationship with safety, especially in use cases that involve direct human contact. Since MCAs are usually the backbones of their respective use cases, the consequences of their failures can go from vast economical losses to putting human lives at danger. For example, in an autonomous driving scenario, if the vehicle is not capable of properly performing computations that help it determine whether or not to change lanes, the risk of an accident, and as an extension, human losses, is high. Therefore, MCAs need to re-focus, becoming more autonomous, so human operators are less involved and thus less prone to being affected in cases of faulty behavior. Additionally, a distributed architecture would benefit MCAs, since it increases their availability and reliability, making it easier to update or make changes to the application.
## 5 Numerical Studies
### Sensitivity Analysis
#### 5.4.1 Impact of Parameter \(e\)  
This experiment seeks to explore how the initial supply of empty containers (\(e\)) at the depot affects the total cost. To this end, four instances are selected as representatives for testing, as listed in Table 9. For each instance, we incrementally raise the parameter \(e\) from 0 to 16 in steps of 2. Table 9 summarizes the results. Furthermore, to offer a clearer visual representation, the variations in total cost concerning the depot's initial empty container supply are graphically presented in Figure 7.  
Table 9 and Figure 7 show that the total cost generally decreases with the increase in \(e\). More specifically, the fulfillment of requests such as Ins-30-5, Ins-45-2, and Ins-55-2 is impossible if there is not an adequate number of empty containers. When the quantity of empty containers gradually increases, there is an evident trend of decreasing the total cost. Notably, for instance Ins-45-2, a 4.78% drop in the total cost is observed when the initial supply of empty containers at the depot rises from 2 to 10. Nevertheless, it is also  
\begin{table}
\begin{tabular}{l r r r r r r r r r} \hline \hline
**Instance** & \(e\) & **0** & **2** & **4** & **6** & **8** & **10** & **12** & **14** & **16** \\ \hline Ins-25-1 & 183.607 & 159.264 & 147.927 & 147.611 & 147.611 & 147.611 & 147.611 & 147.611 \\ Ins-30-5 & / & 190.610 & 190.072 & 189.997 & 189.997 & 189.997 & 189.997 & 189.997 & 189.997 \\ Ins-45-2 & / & 297.458 & 293.326 & 289.433 & 286.215 & 283.874 & 283.874 & 283.874 \\ Ins-55-2 & / & 388.353 & 385.467 & 385.182 & 385.182 & 385.182 & 385.182 & 385.182 & 385.182 \\ \hline \end{tabular}
\end{table}
Table 9: Impact of \(e\) on the total cost.  
Figure 7: Variations of the total cost with the increase of \(e\).  
worthwhile to mention that the decrease in the total cost eventually evens out once the number of empty containers surpasses a specific threshold. That is to say, after reaching this point, stocking more empty containers at the depot does not further reduce the total cost. This finding indicates that from an economic standpoint, maintaining an excessively abundant inventory of empty containers might not be financially advantageous. It is crucial for operators to determine the optimal container inventory level based on various factors like daily volume of inland container transportation requests, container costs, and lifecycle.
# Video Super Resolution Using a Selective Edge Aggregation Network
## 2 Related Work
### Video Super Resolution  
A video dataset is a set of consecutive frames, and information between neighboring frames is similar. Therefore, the VSR model attempted to combine the spatio-temporal information of the target frame and the neighboring frames.  
Ref. [4] tried reconstruction without aligning the feature. Temporal information on the neighbor frame feature was combined with the target frame feature through TM-CAM, and spatial information according to the multi-scale was combined with the target frame feature through CN-CAM.  
Refs. [9; 10] combined spatio-temporal information by aligning the neighbor image feature using deformable convolution networks [11]. In particular, EDVR [10] showed more sophisticated alignment using PCD.  
Refs. [1; 2; 12] applied motion estimation and motion compensation. Motion estimation is a process of extracting motion information between neighboring frames and has an important influence on VSR model performance. However, motion estimation does not guarantee accuracy because it is vulnerable to large-scale motion. Therefore, our model adds structure information and additional modules to improve this vulnerability.
###### Abstract  
The fraction of open Photosystem II (PSII) reaction centers (\(q_{\mathrm{L}}\)) is critical for connecting broadband PSII fluorescence (ChlFPSII) with the actual electron transport from PSII to Photosystem I. Accurately estimating \(q_{\mathrm{L}}\) is fundamental for determining ChlFPSII, which, in turn, is vital for mechanistically estimating the actual electron transport rate and photosynthetic CO\({}_{2}\) assimilation. Chlorophyll fluorescence provides direct physiological insights, offering a robust foundation for \(q_{\mathrm{L}}\) estimation. However, uncertainties in the ChlFPSII-\(q_{\mathrm{L}}\) relationship across different plant functional types (PFTs) limit its broader application at large spatial scales. To address this issue, we developed a leaf-level instrument capable of simultaneously measuring actively and passively induced chlorophyll fluorescence. Using this system, we measured light response, CO\({}_{2}\) response, and temperature response curves across 52 species representing seven PFTs. Our findings reveal the following: (1) a strong linear correlation between ChlFPSII derived from passively induced fluorescence and that from actively induced fluorescence (\(R^{2}=0.85\)), and (2) while the parameters of the ChlFPSII-\(q_{\mathrm{L}}\) relationship varied among PFTs, ChlFPSII reliably modeled \(q_{\mathrm{L}}\) within each PFT, with the \(R^{2}\) ranging from 0.85 to 0.96. This study establishes quantitative ChlFPSII-\(q_{\mathrm{L}}\) relationships for various PFTs by utilizing passively induced fluorescence to calculate ChlFPSII. The results demonstrate the potential for remotely sensed chlorophyll fluorescence data to estimate \(q_{\mathrm{L}}\) and strengthen the use of fluorescence-based approaches for mechanistic GPP estimation at large spatial scales.  
broadband chlorophyll fluorescence spectra; fraction of open PSII reaction centers (\(q_{\mathrm{L}}\)); pulse-amplitude modulation (PAM); photosystem II (PSII); leaf level +
Footnote †: journal: Physics Letters B
# Radio Frequency Electromagnetic Fields Exposure Assessment in Indoor Environments: A Review
## 3 Results
### Sources
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Location**} & \multirow{2}{*}{**RF-EMF Source**} & \multirow{2}{*}{**Communication Standard**} & \multirow{2}{*}{**Frequency** **(MHz)**} \\ \hline Out & Base Station for radio & FM & Frequency Modulation & 100 \\ Out & Base Station for radio & DAB & Digital Audio Broadcasting & 220 \\ Out & Base Station for television & TETRA & Terrestrial Trunked Radio & 390 \\ Out & Base Station for television & Analogue TV & Analogue TV & 174-223 \\ Out & Base Station for television & DVB-T/TV & Digital Video Broadcasting–Terrestrial & 470-830 \\ Out & Base Station for television & UHF & Ultra-high frequency Television & 470-860 \\ Out & BS for mobile telecommunications & GSM900 LL & Global System for Mobile Communications & 900 \\ Out & BS for mobile telecommunications & GSM1800 DL & Global System for Mobile Communications & 1800 \\ Out & BS for mobile telecommunications & DCS1800 DL & Digital Communication System & 1800 \\ Out & Base station/Small cell & UMIs DL & Universal Mobile Telecommunications System & 2100 \\ Out & Base station/Small cell & LTE & Long Term Evolution & 2600 \\ In & Femtocell & UMTS DL & Universal Mobile Telecommunications System & 2100 \\ In & Femtocell & LTE DL & Long Term Evolution & 2600 \\ In & Access point & WIFI 2G & Wireless Local Area Networks & 2400 \\ In & Access point & WIFI 4G & Wireless Local Area Networks & 2400 \\ In & Access point & WMAX & Worldwide Interoperability for Microwave Access & 3500 \\ In & Access point & WIFI 5G & Wireless Local Area Networks & 5500 \\ In & Mobile phone/Tablet & GSM900 UL & Global System for Mobile Communications & 900 \\ In & Mobile phone/Tablet & GSM1800 UL & Global System for Mobile Communications & 1800 \\ In & Mobile phone/Tablet & DCS1800 UL & Digital Communication System & 1800 \\ In & Mobile phone/Tablet & UMTS UL & Universal Mobile Telecommunications System & 2100 \\ In & Mobile phone/Tablet & LTE UL & Long Term Evolution & 2600 \\ In & Cordless phone & DECT & Digital enhanced cordless telecommunications & 1880 \\ In & Headphones, computer peripherals & Bluetooth & \\ In & Babyphones & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: RF-EMF sources influencing the indoor exposure. or a conventional macrocell base station while in an office environment considering both near and far field exposure, finding that, in average macrocell coverage and mobile phone use-time conditions and for UMTS technology, the total exposure can be reduced by a factor 20 to 40 by using a femtocell, mostly due to the significant decrease in the output power of the mobile phone. As reported in Table 1, most of the sources for which the assessment of the exposure levels accounted for in literature showed working frequencies higher than 100 MHz. No one of the papers considered in this study reported the exposure levels for sources, such as induction cookers, wireless alarm systems or Radio-Frequency Identification devices, with slightly lower working frequency.
# State-of-the-Art Mobile Radiation Detection Systems for Different Scenarios
## 3 Mobile Platforms
### Ground-Based Platforms
#### 3.1.4 Non-Wheeled/Bio-Inspired Robots  
Despite the fact that wheeled robots are normally chosen for search and rescue tasks, it is expected that bio-inspired robots starts to be used soon [18].  
Many bio-inspired robots have now been developed, and some of them are already commercially available:  
* Quadruped robots--can carry significant payload and may cross terrain with loose gravel or grass, as well as climb/descend stairs. Some examples are the SPOT robot from Boston Dynamics (up to 14 kg payload) [50], the ANYmal from ANYbotics (Figure 2a) [51], and the models Laikago/Aliengo/A1 from Unitree [52].
* Multi-legged robots--compared to quadruped robots, they have enhanced stability to walk in difficult and rough terrain. Examples of these robots are the small DLR Crawler from the Institute of Robotics and Mechatronics (Figure 2b) [53]; the Lauron series from FZI Research Center for Information Technology [54], which has adaptable behavior-based control and can also use the two front legs for manipulation purposes; and PhantomX AX Metal Hexapod MK-III from Trossen Robotics (Figure 2c) [55].
* Snake-like robots--due to their long and flexible body, they can move through complex environments and enter very confined spaces (e.g., pipes) (Figure 2d) [18; 56].  
Figure 2: Examples of bio-inspired robots: (**a**) Anymal C from ANYbotics. Reproduced, with permission, from Reference [51] copyrighted 2021 ANYbotics [[https://www.anybotics.com](https://www.anybotics.com)]([https://www.anybotics.com](https://www.anybotics.com)) (**b**) DLR (CC BY 3.0) crawler. Reproduced, with permission, from Reference [53]. (**c**) Hexapod MK-III. Reproduced, with permission, from Reference [55]. (**d**) Snake-like robot. Reproduced, with permission, from Reference [56]; published by the Royal Society, 2020, under the terms of the [[https://creativecommons.org/licenses/by/4.0/CC](https://creativecommons.org/licenses/by/4.0/CC)]([https://creativecommons.org/licenses/by/4.0/CC](https://creativecommons.org/licenses/by/4.0/CC)) BY 4.0 license.  
Humanoid robots are also included in this category; however, they are still under development. They have the advantage of being able to manipulate objects and tools like humans (e.g., important for search and rescue situations) [18].
## 1 Introduction
In contrast, the SPEI, especially in arid zones, builds upon the SPI by incorporating heat impacts on potential evapotranspiration and demonstrates greater flexibility than the PDSI in accommodating drought monitoring at different timescales [32]. Different levels of drought can directly or indirectly inhibit vegetation photosynthesis, thereby reducing vegetation growth rates. Therefore, changes in the GPP can serve as an indicator of drought development. In northwestern China, Xinjiang is one of the most arid and semi-arid areas, distinguished by its delicate vegetation ecosystems and climate, and marked by complexity and diversity [33]. Here, we selected the \(\mathrm{GPP_{LUE}}\) model along with two machine learning approaches, \(\mathrm{GPP_{XGBoost}}\) and \(\mathrm{GPP_{RF}}\), using multi-source data, including solar-induced fluorescence (SIF), near-infrared reflectance of vegetation (NIRv), and the leaf area index (LAI). We aimed to address the following questions: (1) Which model or method can most effectively simulate the GPP of vegetation in arid regions of Xinjiang? (2) Is there an optimal timescale for the response of vegetation GPP to the SPEI in arid zones? (3) How do different natural factors affect the GPP of vegetation in arid zones? These results provide a scientific rationale for vegetation conservation and management practices in the drought-stressed environments of Xinjiang, with marked implications for understanding the ecological impacts of drought and devising adaptive strategies to mitigate their effects.
# A Reinforcement Learning Model of Multiple UAVs for Transporting Emergency Relief Supplies
## 4 Proposed Method  
### Q-Learning  
RL is a framework for an agent to learn appropriate strategies by trial and error in an environment by obtaining rewards from that environment. RL is a machine learning method in which an agent learns by itself to determine which action to choose to maximize the total reward. Figure 4 shows the interaction between the agent and environment in RL. When the agent observes the state \(S_{t}\) of the environment at a certain instant \(t\), the agent chooses an action \(A_{t}\) among all possible actions. The chosen action will have repercussions in the environment, and, consequently, it will influence the state at the instant \(t+1\), \(S_{t+1}\). Whether the new state \(S_{t+1}\) is positive or negative to the agent's main objective, it is quantified with a reward \(r_{t+1}\). The RL approach searches for the policy that gives the highest long-term reward \(R_{t}\). In this study, we use QL [30], which is a typical RL method.  
In QL, the agent has an action-value matrix, \(Q\) matrix, which represents the value of being in a specific state \(S_{t}\) while choosing an action \(A_{t}\) at instant \(t\). By trying different actions in different states (exploration) but also by choosing the best possible action that gives the highest reward value based on its past experience (exploitation), QL is shown to converge for any type of policy being followed [50]. The agent updates a value of the \(Q\) matrix, which is represented as \(Q(state,action)\), for each action. It is defined as follows:  
\[Q(s_{t},a_{t})\gets Q(s_{t},a_{t})+\alpha\Big{[}r_{t+1}+\gamma\underset {a}{\max}Q(s_{t+1},a)-Q(s_{t},a_{t})\Big{]} \tag{16}\]  
where \(Q(s_{t},a_{t})\) is the current \(Q\) matrix, \(\alpha\) is the learning rate (\(0\leq\alpha\leq 1\)) and \(\gamma\) is the discount factor (\(0\leq\gamma\leq 1\)). \(r_{t+1}\) is the expected reward at instant \(t\), and \(\underset{a}{\max}Q(s_{t+1},a)\) is the maximum action value among all possible actions in state \(s_{t+1}\). In this study, the initial value of the \(Q\) matrix was set to \(0\) for all actions in all states of the environment.
# Research on Three-Dimensional Scanning Path Planning of Casing Parts Based on Industrial Robot
## 3 Scan Path Planning
### Mathematical Modeling of the Circular Scan Path
The scanner continued to move up along the outer wall of the casing to reach point E, and the distance between the two points is \(K_{1}\). Move up \(m\) times in a turn, obtain the final point F, and complete \(m\)-cycle scanning:  
\[m=\frac{H}{\cos\beta\times 27.5} \tag{11}\]  
\[K_{1}=\frac{H_{i}-H_{i-1}}{\cos\beta};i\geq 2 \tag{12}\]  
where 27.5 cm is the effective scanning area of the scanner in the vertical direction. Assuming that the total distance of the robot moving upward is \(K\), according to Equations (11) and (12) above, the mathematical expression of the upward translation parallel to the outer wall of the casing is as follows:  
\[K=mk_{1}=\frac{H(H_{i}-H_{i-1})}{27.5{(\cos\beta)}^{2}} \tag{13}\]According to Equations (2) and (13), the mathematical expression of the total distance \(Q\) of the circular scanning is as follows:  
\[Q=n\sum_{i=1}^{m}\tan\gamma(R_{i}+AW)+\frac{H(H_{i}-H_{i-1})}{27.5(\cos\beta)^{2}} \tag{14}\]  
The total number of posture adjustments of the annular scanning method is \(n\):  
\[n=\sum_{i=1}^{m}\frac{360^{\lx@math@degree}}{\gamma_{i}} \tag{15}\]
# Detection of Denial of Service Attack in Cloud Based Kubernetes Using eBPF
## 2 Related Work

Table 1: Summary of related studies. element of eBPF was experienced while creating complex networks. Use of eBPF at the kernel, which filtered a sizable number of network packets using a matching rule set of the Snort approach, as the solution for the system's intrusion detection. In [16; 17], the objective is to develop a processing pipeline that is effective and efficient, which will aid in preventing DDoS attacks. By employing a specific ruleset, the authors have boosted the server-end mitigation strength, decreasing the DDoS. eBPF-based solutions essentially bypass the kernel and filter all network packets in userspace, avoiding the Linux network stack's overhead in the process. The performance problem that occurs when packets are filtered and examined in the kernel space has been resolved by the suggested fix. In [15; 18], a non-intrusive, protocol-independent intelligent analytics system that operates on eBPF is more effective, accurate, and offers a point-to-point observability solution. Additionally, it makes the packet transparency for the microservices possible. In addition, it enhances its capability for in-kernel packet processing applications, which provides flexibility dynamically in the run-time environment. In [14], the advantages of XDP and eBPF utilizing the eBPF-based offloading method from the userspace to the kernel space have been noted. It is ideal to use the offloading-based approach for packet processing. Unlike earlier studies, our proposed solution stands out for its lightweight monitoring and its scalability. Unlike the previous studies, this work applied eBPF and XDP which can significantly contribute to the safety and stability of Kubernetes clusters, to detect and observe the attacks on the server, and therefore determine and prevent disruption or flooding of the target's service with network packets. Therefore, utilizing a Denial-of-Service attack against Kubernetes using eBPF and XDP, we suggested a detection and observation mechanism in this study to filter out the harmful information. By using the eBPF and XDP programs, malicious packets can be rejected based on the eBPF program's defined routines. To the best of our knowledge, no existing work has been done on the detection and observation of DDOS. The technique that is used for the cluster's nodes is very effective and the script does not overburden the nodes working on different VMs. Our proposed solution is a lightweight, efficient, and scalable solution.
# Mobility Management Based on Beam-Level Measurement Report in 5G Massive MIMO Cellular Networks
## 2 System Model
### Beam Shaping Model for Massive MIMO Networks
#### 2.1.3 Beam Pattern Model Based on Antenna Array Radiation Pattern  
We consider the \(S\) sectors (i.e., cells) in a single gNB. Furthermore, each sector activates at least 1 horizontal main beams from the set of main beam candidates, \(\mathcal{L}=\{0,\cdots,L-1\}\). To guarantee the equal angular distance of each main-beam, we defined the horizontal main-beam factor as follows:  
\[\begin{split} G_{t}(l)&=\quad\tau A_{A}(\theta_{s},\phi_{s},\theta,\phi),\\ \phi_{s}&=\quad\frac{360^{\circ}}{S}(\frac{l}{L-1}), \quad\quad\quad\quad\forall l\in\mathcal{L},\end{split} \tag{7}\]  
where \(l\) is a beam-index in the sector, \(L\) is the maximum number of the beam candidates, and \(\tau\) is an on-off factor. In addition, \(\theta_{s}\) and \(\theta\) are \(90^{\circ}\) because we consider the environment that vertical angle is always \(90^{\circ}\). We represent the beam pattern of each beam-index in case of \(S=3\) and \(L=3\). i.e., \(\phi_{s}=-40^{\circ},0^{\circ},40^{\circ}\) cases as shown in Figure 1. We can confirm that the degree of the main beam is the same as the degree where we obtain the maximum-gain points.
## 5 Conclusions  
The results point to two different perception strategies when listeners are asked to judge separateness and roughness of familiar and unfamiliar sounds. With familiar sounds a strong coincidence detection seems to take place, where listeners concentrate on only a small number of spikes representing merely pitch. With unfamiliar sounds they use the raw input much more strongly, without the coincidence detection reduction, so on a more elaborated pitch, on timbre. This corresponds to the overall experience listeners reported for the unfamiliar sounds, namely, that they could often not tell if the sounds consisted of two pitches or only one, or could not clearly identify one of the pitches. Therefore, they needed to rely on timbre rather than on extracted pitches.  
A second perception strategy seems to happen when listeners are asked to judge separateness or roughness of sounds. With roughness, timbre is the part listeners concentrate on; with separateness perception the strategy changed between familiar and unfamiliar sounds. With unfamiliar sounds separateness is correlated positively with a complex sound; with familiar sounds timbre is correlated negatively. So in unfamiliar multi-pitch sounds the pitches are perceived separately when the timbre is complex, while with familiar sounds the pitches are found separately when the timbre is simpler. It might be that listeners find it easier to separate pitches with familiar sounds when the timbre is simpler. Still, if they are not familiar with the sound and not able to extract pitches anyway, they find complex spectra more separate than fused.  
In all cases, judgements based on timbre are much more inconsistent between subjects than those based on pitch.  
In terms of neural correlates of roughness and separateness perception, the entropy of the ISI histogram is much better able to explain perception compared to the number of different amplitudes or, even worse, the weighted amplitude sum. This is expected, as entropy is a more complex way of summing a sensation into a single judgement or separateness or roughness than the other methods, as entropy gives an estimation of the distribution of amplitudes in the spectrum, and therefore, their relation. Other methods might even be better suited, which is beyond the scope of the present paper to discuss.  
Overall, it appears that many judgements of subjects are based on low-level parts of the auditory pathway, before extended coincidence detection. This points to the necessity of perceiving timbre and pitch as a field of neural activity.  
This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.