esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Act
### Network and Node Model  
We consider a densely deployed wireless sensor network such that each sensor node has one or more sensing devices. A sensor node frequently transfers the sensed data to a sink node or gateway. Sensor nodes should control the data transmission interval in an inverse proportion to the variation in a phenomenon. A tree-based routing protocol is used to construct the path from sensor nodes to the sink node. The path to the sink node can be changed whenever the link states to the upper nodes are unreliable due to node failure or obstacles. The lower nodes in a routing tree have to compete with other nodes for an upper node in a routing tree. The errors and losses that occur in the air are corrected by an error-correcting code such as FEC or ACK, which are based on a retransmission technique.  
Figure 2 presents the queuing model in a sensor node. A sensor node has two queues, a relaying queue for transit traffic and a sending queue for transferring data. The packets generated by the sensor node itself are inserted directly into the sending queue. A sensor node could generate a data packet for sensed data in an application layer or a routing packet for routing information in a network layer. Therefore, the packets received from the network also can be one of two packet types, a data packet and a routing packet. If the destination address of a data packet is the receiving node, the packet is transferred to an upper application layer. If the destination address is not the receiving node, the packet is inserted into the relaying queue and then into the sending queue. A routing packet is used by the routing protocol in a network layer.  
Figure 2: Packet flow in a sensor node.  
A sensor network is a type of _ad-hoc_ network that is deployed without any infrastructure. Therefore, a sensor node should not only act as a data source but also as a relaying node. Packets in the sending queue of a sensor node can be classified into a transit packet received from another node and a source packet generated by the sensor node itself. The routing protocol should reduce the insertion rate of not only the transit packet but also the source packet. Reducing the transit traffic is called as the back pressure, and reducing the source traffic is referred to as the up pressure.
# Bit Error Rate Closed-Form Expressions for LoRa Systems under Nakagami and Rice Fading Channels
## 4 Numerical Results  
In this section, we will evaluate the numerical expressions and compare the results. Figure 2a presents comparisons of the derived Nakagami BER given in (23) and (33) versus the theoretical BER performance solved numerically using the integrals in (18) and (26), respectively. We choose \(SF\in\{6,12\}\) as the lowest and highest spread factor values to produce two sets of curves and each group with \(m\in\{1,2,10\}\). As shown in Figure 2a, the expressions in (23) and (33) are indistinguishable from (18) and (26). Also, note that as \(m\) increases, the bit error rate curve gets very close to the AWGN curve, as expected. With \(\mathrm{SF}=12\) and \(m=10\), there is a gain of almost 30 dB for a BER of \(10^{-4}\) in a multipath environment.  
Figure 2b shows the percentage error of the difference between the Rayleigh BER approximation in [11] and (33). The difference can vary from 6% (when \(SF=12\)) to 15% (when \(SF=4\)).  
Next, we focus on the accuracy of the results given by numerical methods considering integrals and a finite series. We calculated the percentage error between on (18) and (23). The data is summarized in the Table 1. The data was generated such that parameters assume \(\mathrm{SNR}(\mathrm{dB})\in\{-30,-10,10\}\), \(SF\in\{12,10,8,6,4\}\), and \(m\in\{1,3,5\}\). The numbers in the table are tiny, and therefore, it is necessary to multiply by a factor of \(10^{-6}\) to get the percentage error.  
Figure 3 presents the derived BER in (38) versus simulation for Rician environments. Again, we choose \(SF\in\{6,12\}\) as the lowest and highest spread factor values to produce two sets of curves and each group with \(\kappa\in\{0,5,\infty\}\). As shown in the figure, the expression in (38) superposes with the discrete values found through simulation. Note that simulated and theoretical curves are almost indistinguishable. Also, note that as \(\kappa\) tends to infinity, the bit error rate curve gets very close to the AWGN curve, as expected.  
Figure 2: (a) Bit Error Rate for Nakagami-\(m\) and Rayleigh cases. The continuous blue curve is given by (26) (Rayleigh Integral) and the blue stars are computed using (33) (Rayleigh Exact). The green and red curves are the BER for the Nakagami-\(m\) case, the continuous and star lines are given by (18) (Nakagami-\(m\) Integral) and (23) (Nakagami-\(m\) Exact), respectively. Analytic AWGN curve is provided for reference. (b) Percentage error between the Rayleigh BER approximation in [11] and our _exact_ analytical solution given in (33).
## 1 Introduction
This is not the case of SIFA. Third, the re-emitted photon is scattered around in the canopy, which is wavelength-specific behaviour [15]. Another remote sensing indicator that is linked to the plant biochemistry is the photochemical reflectance (PRI; [16]), which is inversely correlated to the NPQ. Therefore, SIF and PRI are expected to provide similar, yet complementary information on the plant's photosynthetic activity. Within the plant kingdom, various drought survival strategies exist. These strategies break down into two broad categories, isohydric and anisohydric, which exist alongside each other on a spectrum [17]. Isohydric plants tend to have a risk-avoidant strategy, closing their stomata immediately in cases of a water shortage. This leads to a reduced growth in case of a drought stress. The anisohydric strategy is a higher risk strategy, in which a plant keeps its stomata open, maintaining its growth rate while suffering high water losses through transpiration [18], thereby showing larger variations in turgor pressure [17]. Plants following a more anisohydric strategy are expected to show a fierce reaction in their canopy structure compared to more isohydric plants. Because of its mechanistic link to photosynthesis, canopy-scale SIF is typically linearly linked to ecosystem-scale photosynthesis, quantified by the Gross Primary Productivity (GPP; [19; 20]), but the link between SIF and GPP is less straightforward in cases of drought stress. A field-scale SIF-GPP relationship could be finetuned during drought periods using PRI data [21]. Field-scale studies have pointed out drought stress at both the structural and biochemical level [22]. Both stress-induced changes in the leaf biochemistry and in the canopy structure affect both SIFA and SIFB [23]. Due to the sensitivity of the biochemical component to drought stress, SIF proved to be an interesting input for a crop growth model [24]. This paper describes the reaction of SIFA and SIFB to increasing drought stress conditions by monitoring the SIF emission and canopy reflectance of a yellow mustard (Sinapsis alba) and a common lettuce (Lactuca sativa) canopy continuously over their growing season, and thus capturing a variety of stress conditions. In addition, the near-infrared reflectance is monitored to estimate short-term variations in canopy structure. Yellow mustard and common lettuce differ greatly in their degree of isohydicity, in which the mustard is more isohydric than the lettuce. We expect the lettuce to show a more expressed structural reaction because it tends to lose more turgor pressure given its anisohydric nature. The mustard's more isohydric nature should force its stomata to close more quickly, and thereby causing a reaction at the level of the leaf biochemistry.
## 4 Conclusions  
In conclusion, we have presented a facile method for synthesizing hierarchical micro/nanostructured SERS active substrates using simple steps and inexpensive costs. The SERS active substrates were fabricated through wet etching to create a pyramid-shaped large structure on a silicon wafer and then used electrochemical treatments to create gold nanostructure on the pyramid surface. From the relationship between electrochemical oxidation peaks and SERS signal intensity, we optimized the electrochemical treatment for SERS substrate preparation. Simulation has shown that many strong hot spots can be formed between the nano-gap of gold nanostructures. According to the SERS experiments, the micro/nano hierarchical structured SERS substrate showed a strong SERS enhancement factor up to \(1\times 10^{7}\). The developed SERS substrates were further applied to the detection of malachite green solutions. The results showed that the substrate has high sensitivity and can measure very low concentrations of malachite green (0.01 nM), indicating its practical application potential in food contamination detection. Therefore, the developed SERS active substrate is highly promising for use in rapid chemical and biomolecular detection applications, including medicine, food safety, and biotechnology.  
Conceptualization, C.-Y.H.; methodology, C.-Y.H.; investigation, C.-H.C.; data curation, C.-H.C.; writing--original draft preparation, C.-Y.H.; writing--review and editing, C.-Y.H.; supervision, C.-Y.H.; project administration, C.-Y.H.; funding acquisition, C.-Y.H.  
This research was funded by Ministry of Science and Technology, Taiwan, grant number MOST 107-2221-E-005-045.  
The authors would like to thank the Ministry of Science and Technology of the Republic of China, Taiwan, for financially supporting this research under Contract No. Grant MOST 107-2221-E-005-045.  
The authors declare no conflict of interest.
# Cascaded Reinforcement Learning Agents for Large Action Spaces in Autonomous Penetration Testing
## 6 Results
### CybOrg  
We tested the proposed algorithm in multiple CybORG scenarios with different numbers of hosts to see how well the CRLA learned given action spaces of increasing size. Each performance in Figure 7 was repeated five times with different random seeds to ensure reproducibility. We examined two indicators to verify performance: the maximum score attainable by the agents (shown in the left panel) and the number of steps to capture the flag (shown in the right panel). For each scenario, the maximum score the agent could receive was approximately 20 points (minus some small negative rewards for invalid actions taken to reach the assets).  
We tested the CRLA algorithm in a variety of scenarios with different configurations of hosts and action spaces, as shown in Table 1. In terms of scenario complexity, we varied the number of hosts from 6 to 100 and increased the action space from 49 to 4646 actions. However, we only added two more agents to train. In all the scenarios tested, CRLA's convergence results were either similar to or superior to the DDQN agent, with the dependent factors being the complexity of the scenario and the size of the action space.  
Figure 7 presents the algorithms' performance on four scenarios where the complexity was significant enough to showcase the superiority of the algorithm while not being too computationally expensive for repeated training. In scenarios where both DDQN and CRLA were able to learn the optimal policy, CRLA showed faster and more stable convergence than DDQN. DDQN's performance on scenarios with 60 and 70 hosts was unstable as it only successfully learned the optimal policy in one out of four runs. In contrast, CRLA held up its performance on the scenario with 100 hosts where DDQN failed to achieve any progress during training.  
For the 100 hosts scenario, DDQN was not able to explore the action space at all, nor could it learn the policy, while CRLA took approximately 4000 episodes to grasp the scenario and begin to converge on the optimal policy. Further, the policy learned by CRLA in each of the tested scenarios was optimal in terms of having a minimum number of actions taken.  
Figure 7: Results of CRLA and single DDQN on different CybORG scenarios. Sub-figures from left to right, top to bottom: (**a**) 50-hosts scenario, (**b**) 60-hosts scenario, (**c**) 70-hosts scenario, (**d**) 100-hosts scenario.  
This simulation serves as the first demonstration of applying deep reinforcement learning to an automatic penetration-testing scenario with such a large action space [25].
# Regularized Within-Class Precision Matrix Based PLDA in Text-Dependent Speaker Verification
## 3 Graphical Least Absolute Shrinking and Selection Operator (GLASSO)  
### Gaussian Markov Random Field  
Consider a \(D\)-dimensional random vector \(\mathbf{x}=[x_{1},\ldots,x_{D}]^{T}\) (corresponding to the embedding in our case), which is a set of \(D\) random variables \(x_{i}\). The random vector \(\mathbf{x}\) is a Markov random field (MRF, an undirected graphical model) if it satisfies Markov properties [47]. An undirected graph \(G=(V,E)\), where \(V\) is a set of vertices and \(E\) is a set of edges, can describe the random vector. Each vertex in \(V\) corresponds to one of the random variables in \(\mathbf{x}\), that is, \(V=\{x_{1},\ldots,x_{D}\}\). Each edge \(e_{ij}\) in \(E\) represents the dependency between \(x_{i}\) and \(x_{j}\) such that \(i\
eq j\). In undirected graphical models, all edges \(e_{ij}\in E\) are unordered pairs, that is, \(e_{ij}=e_{ji}\). The Markov property relates to conditional independence, and three kinds of Markov properties exist: the pairwise, local, and global Markov properties. In this study, we focus on the pairwise Markov property. The pairwise Markov property is that variables \(x_{i}\) and \(x_{j}\) are conditionally independent given all the other variables \(\mathbf{x}_{-ij}\), \(x_{i}\bot x_{j}|\mathbf{x}_{-ij}\), which is equivalent to stating that no edge \(e_{ij}\) exists in \(E\)[48].  
The random vector \(\mathbf{x}\) is a Gaussian Markov random field (GMRF; a Gaussian undirected graphical model) if it satisfies the Markov property and follows a multivariate normal distribution \(N(\mathbf{\mu},\ \mathbf{\Sigma})\) with mean \(\mathbf{\mu}\in\mathbb{R}^{D}\) and covariance matrix \(\mathbf{\Sigma}\in\mathbb{R}^{D\times D}\). In other words, the GMRF is the MRF following a multivariate normal distribution. In the GMRF, the set of edges \(E\) is represented by the precision matrix \(\mathbf{\Sigma}^{-1}\). The edge \(e_{ij}\) corresponds to the element in the \(i\)-th row and the \(j\)-th column of the precision matrix \(\mathbf{\Sigma}_{ij}^{-1}\), and no edge \(e_{ij}\) exists if and only if \(\mathbf{\Sigma}_{ij}^{-1}=0\). Therefore, the following three statements are equivalent to each other: (i) there is no edge \(e_{ij}\), (ii) \(\mathbf{\Sigma}_{ij}^{-1}=0\), and (iii) \(x_{i}\bot x_{j}|\mathbf{x}_{-ij}\). To summarize, we can reflect the conditional independence structure to the variables by making \(\mathbf{\Sigma}^{-1}\) sparse [47].
## 2 The \({}^{3}\)ColStar Satellite Mission
### 3 ColStar Main Requirements  
The 3 ColStar mission success criteria were developed to delineate both the high-level needs and the specific requirements necessary for mission success. In this context, needs refer to the overarching objectives the mission aims to achieve, such as advancing scientific knowledge, promoting technological innovation, and generating societal benefits. Conversely, requirements are the precise, measurable conditions derived from these needs that guide the design, development, operation, and evaluation of the satellite system.  
For example, one high-level need is to enhance Colombia's capabilities in space technology. To address this need, the corresponding requirements include the development and integration of locally manufactured space components and subsystems. These components encompass the satellite's structure, Electrical Power System (EPS), On-Board Computer (OBC), Attitude Determination and Control System (ADCS), and associated electronics. Each requirement specifies detailed performance criteria to ensure that these components fulfill their intended functions effectively.  
Another critical need is to foster collaboration among academia, industry, and government entities. The associated requirements entail formalizing partnerships with 13 institutions and implementing structured mechanisms for communication and coordination. These mechanisms include regular meetings, the establishment of shared documentation protocols, and the synchronization of project timelines. Such requirements are designed to ensure that collaborative efforts are systematically managed and contribute to the mission's overall success. Additionally, the need to deliver educational and social benefits is translated into requirements such as the installation of IoT connection points in remote areas and the integration of mission results into educational programs. These requirements ensure that the mission's outcomes are leveraged for community and educational advancements, aligning with the mission's broader objectives. By clearly defining these needs and translating them into specific, quantifiable requirements, the 3 ColStar mission ensures that each aspect of the project is aligned with its strategic goals. This approach facilitates a systematic and rigorous process for designing, developing, and evaluating the satellite system, as detailed in Table A1 (Appendix A).
## 5 Key Technologies Supporting Deliberate Attack Defense for IECPS
### Cyber Security of IECPS
#### 5.4.4 Attack Attribution and Countermeasures  
Attack tracing technology can extract and analyze deliberate attack events occurring in IECPS, providing technical support for countermeasures. In the field of cyber security,methods such as IP tracing, log and traffic analysis, domain name system tracing, link tracing, and routing analysis are commonly used for attack tracing. However, due to the complex cyber-physical interaction mechanisms and massive operational data in IECPS, traditional traffic analysis techniques face issues of low efficiency and high analytical difficulty. At this point, big data technology and AI can be used to extract and analyze attack characteristics of the system, thereby speeding up the efficiency of attack tracing.  
Attack countermeasure technology serves as a preemptive defense technique. With the increasing number of new types of attacks, passive defense technologies such as planning, detection, and scheduling struggle to provide effective protection. To address this flaw, the academic community has developed attack countermeasure technologies, such as honeypot technology. Figure 17 illustrates a schematic diagram of a honeypot technique, which depletes an attacker's attack resources by arranging a number of hosts, network services, or information that serve as decoys and entice the attacker to attack them. In addition, honeypot technology can capture the attacker's behavior, infer their intentions and motivations, and subsequently strengthen the system accordingly.
## 4 System Implementation and Simulation Results
### Physical Implementation
Figure 4: Layout of the implemented design for the three-channel Power MOSFETs and their gate drivers. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Transistor** & **Length (L)** & **Width (W)** & \(M_{1}\)**G.Driver Mult** & \(M_{2}\)**G.Driver Mult** \\ \hline \(M_{D1}\), \(M_{D3}\), \(M_{D9}\) & 6 μm & 10 μm & 36 & 6 \\ \(M_{D2}\), \(M_{D4}\), \(M_{D10}\) & 2 μm & 20 μm & 18 & 4 \\ \(M_{D5}\) & 6 μm & 100 μm & 36 & 6 \\ \(M_{D6}\) & 2 μm & 20 μm & 18 & 4 \\ \(M_{D7}\) & 6 μm & 100 μm & 72 & 12 \\ \(M_{D8}\) & 2 μm & 20 μm & 36 & 8 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Transistors sizing for \(M_{1}\) and \(M_{2}\) gate drivers. are designed to achieve an output voltage of \(V_{o}=12\) V, a per channel output current of \(I_{L}=100\) mA, i.e., \(n_{c}=3\) is the number of channels, while also targeting a current ripple of \(r_{I}=0.474\) (\(47.4\%\)) and a voltage ripple of \(r_{V}=0.084\) (\(8.4\%\)).
# Analysis of Lower Limb Performance Determinants in Sport Climbing
## 3 Results
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multicolumn{1}{c}{**Test**} & **All (_n = 24_)**} & **Female (_n = 7_)** & **Male (_n = 17_)** & **Difference (\%)** & _p-Value_ \\ \hline SJ (cm) & 28.5 \(\pm\) 5.6 & 23.9 \(\pm\) 3.1 & 30.4 \(\pm\) 5.3 & 21.3 & 0.003 \\ CMJ (cm) & 31.9 \(\pm\) 5.6 & 26.2 \(\pm\) 2.5 & 34.2 \(\pm\) 4.8 & 23.5 & \(<\)0.001 \\ DJ (cm) & 30.7 \(\pm\) 5.8 & 25.3 \(\pm\) 3.1 & 33.0 \(\pm\) 5.2 & 23.3 & \(<\)0.001 \\ Power (W) & 2902.7 \(\pm\) 638.2 & 2093.4 \(\pm\) 257.7 & 3236.0 \(\pm\) 394.9 & 35.3 & \(<\)0.001 \\ Power (W/kg) & 43.3 \(\pm\) 5.1 & 37.3 \(\pm\) 2.5 & 45.7 \(\pm\) 3.6 & 18.4 & \(<\)0.001 \\ Power (W/FFM) & 50.5 \(\pm\) 5.1 & 45.6 \(\pm\) 3.0 & 52.6 \(\pm\) 4.3 & 13.3 & \(<\)0.001 \\ DS left (\({}^{\circ}\)) & 45.8 \(\pm\) 7.5 & 42.4 \(\pm\) 7.7 & 47.2 \(\pm\) 7.2 & 10.2 & 0.081 \\ DS right (\({}^{\circ}\)) & 46.2 \(\pm\) 6.5 & 43.8 \(\pm\) 7.4 & 47.3 \(\pm\) 6.1 & 7.4 & 0.121 \\ Sit and Reach (cm) & 9.4 \(\pm\) 9.0 & 12.6 \(\pm\) 6.6 & 8.1 \(\pm\) 9.7 & \(-\)57.2 & 0.133 \\ LFM (cm) & 73.5 \(\pm\) 4.0 & 79.4 \(\pm\) 8.5 & 75.2 \(\pm\) 5.4 & \(-\)5.5 & 0.083 \\ RFM (cm) & 74.1 \(\pm\) 3.8 & 79.0 \(\pm\) 10.4 & 77.1 \(\pm\) 5.5 & \(-\)2.4 & 0.283 \\ Hop Test Left (m) & 1.6 \(\pm\) 0.2 & 1.4 \(\pm\) 0.1 & 1.7 \(\pm\) 0.2 & 15.9 & 0.003 \\ Hop Test Right (m) & 1.6 \(\pm\) 0.2 & 1.4 \(\pm\) 0.1 & 1.7 \(\pm\) 0.2 & 16.8 & 0.002 \\ H3 JL (m) & 5.4 \(\pm\) 0.8 & 4.6 \(\pm\) 0.5 & 5.7 \(\pm\) 0.7 & 18.5 & \(<\)0.001 \\ H3 JR (m) & 5.4 \(\pm\) 0.7 & 4.7 \(\pm\) 0.3 & 5.7 \(\pm\) 0.7 & 16.7 & \(<\)0.001 \\ Pull-ups (rep.) & 11.0 \(\pm\) 5.4 & 7.4 \(\pm\) 3.9 & 12.5 \(\pm\) 5.4 & 40.8 & 0.018 \\ Handgrip strength (kgf) & 44.4 \(\pm\) 9.1 & 32.8 \(\pm\) 2.3 & 49.1 \(\pm\) 5.8 & 33.2 & \(<\)0.001 \\ Relative handgrip strength (kgf/body mass) & 0.66 \(\pm\) 0.09 & 0.60 \(\pm\) 0.08 & 0.69 \(\pm\) 0.09 & 13.0 & 0.015 \\ Relative handgrip strength (kgf/fat-free mass) & 0.78 \(\pm\) 0.79 & 0.72 \(\pm\) 0.77 & 0.80 \(\pm\) 0.70 & 9.9 & 0.012 \\ \hline \end{tabular} Source: values expressed as mean \(\pm\) standard deviation; SJ, squat jump; CMJ, counter movement jump; DJ, drop jump; FFM, fat-free mass; DS, dorsification; LFM, left foot mobility; RFM, right foot mobility; H3 JL, triple Hop Test left; H3 JR, triple Hop Test right. Kgf, force in kilograms.  
\end{table} 
Table 2: Comparison of performance variables between males and females.
# Practical Performance Analysis of Interference in DSS System
## 4 DSS Interference and Solutions  
As mentioned in the previous section, DSS means that the same time-frequency resources can be dynamically and flexibly provided for inter-system in the same frequency band. The current 4G and 5G frequencies may be refarmed, which may cause intra-frequency interference between networks.
# The Hyperloop System and Stakeholders: A Review and Future Directions
## 3 Analysis and Results
### Communication System  
Several types of communication systems are required, including (1) the communication of the pod's sensor data to and from a centralized data processor and (2) the communication related to the pod's location between the pod and the tube [20]. Certain challenges exist related to the pod communication and the collection of data, as well as the high-speed connection between the pods and the infrastructure [16; 20; 94; 124; 125; 126; 127; 128].  
A new generation of optical fibers allows the wireless communication between antennas with radio waves, [9; 10; 19; 20]. The GSM-R (Global System for Mobile Communications-Railway) is the primary communication system [20] for HSR; however, due to certain limitations [128; 129], the LTE-R (Long-Term Evolution-Railway) may be used in communication systems. The LTE-R provides capabilities for data transmission, Internet access, and high-quality voice or mobile video broadcasting [130]. Innovative systems have been also developed, such as radio and fiber networks, with dedicated antennas placed at intervals along the hyperloop system and hardware installed on the pod using the latest 802.11 Wi-Fi standards [131].