esa-sceva/satcom-chunk-collection huggingface dataset:
# Green Cloud Software Engineering for Big Data Processing
## 3 Research Methodology
### Openstack NEAT  
In addition to the Openstack controller components, the NEAT Global Manager [95] also ran in the controller node. Figure 4 shows the components of Openstack NEAT. NEAT Global Manager makes decisions about mapping virtual machines to compute hosts and initiating migration of the selected VMs [83]. A local manager ran on each compute host which made decisions on underload or overload situations and VM selection for migration. A data collector ran on compute nodes locally to collect resource utilization data from hypervisor and send the data to the central database in the controller.  
Figure 4: Components of Openstack NEAT [42].  
A VM consolidation workflow to test and compare the 6 combinations of algorithms is presented in Figure 5. Local managers collected resource utilization data from 'Ceilometer' [96] Openstack service periodically.  
Underloaded hosts were identified by invoking an underload detection algorithm (i.e., THR--Threshold based Heuristic). When the host was under-utilized, the local manager requested the global manager to migrate the VMs from the host using Openstack VM migration API [97] and put the host to sleep mode. On the other hand, if the host was not under-utilized, an overload detection algorithm (i.e., THR-threshold-based Heuristic; MAD-median absolute deviation; LRR-local regression robust) was invoked. If the host was not overloaded, the resource monitoring processes continued. If the host was overloaded, VMs to be relocated were selected by invoking the VM selection algorithm (i.e., RC-random choice; MMT-minimum migration time) and placed in a suitable host via VM placement algorithm (i.e., BFD-best fit decreasing). The status of the destination host was checked before the global manager migrates the VMs. If the host was in sleep mode, the host was awakened by sending magic packets using WakeOnLAN standard [98].
# A Trustworthy Communication Hub for Cyber-Physical Systems
## 5 Solutions of the CPS Communication Hub
### Solutions for Data Plane Negotiation  
The data plane negotiation is performed utilizing the XMPP protocol and services. This implies that the physical resource that is going to participate in the data plane negotiation must deploy the CPS hub connector with the XMPP client that implements the required tasks. In the following sections, the description of data plane negotiation is divided into three parts, of which the two first are preliminary steps and prerequisite for successful data plane negotiation.
## Materials and Methods
### Experimental Measurements  
The EIS technique was used to investigate the dielectric properties of the test samples at different concentrations. Although this measurement technique is very powerful and popular, it is sensitive to humidity and temperature. Therefore, all the experiments were conducted in a controlled laboratory under the same humidity and temperature level. An impedance analyzer (HIOKI IM3536, Hioki, Japan) was used to determine the changes taking place for different experimental samples and different frequencies. The bonding pads of the electrodes of the interdigital sensor were attached to wires with silver epoxy glue for connection purposes. These wires were connected to the impedance analyzer via Kelvin probes and the setup was kept constant during the entire experimental procedure.  
As shown in Figure 5, the impedance analyzer was connected to a computer using a USB cable to collect the data in the Microsoft Office Suite(r) using an automated data acquisition algorithm. The sensors on the electrochemical microfluidics system were connected to the LCR meter and a 10 Hz-100 kHz signal with 1 V amplitude was given to the electrodes. Five different concentrations of prepared calcium solution were used to examine the system. Finally, the optimal frequency for the EIS measurements was chosen. It is noteworthy that the measurement was taken five times and averaged results were used for analysis.
# Multi-Path Routing Algorithm Based on Deep Reinforcement Learning for SDN
## 5 Results and Validation
### Performance Comparison
#### 5.3.7 Node Diversity Analysis  
In Figure 8, we performed an extensive examination of node diversity across different routing algorithms. Specifically, when observing the NSFNet-14 (see Figure 8a), the Dijkstra algorithm consistently maintains a fixed path for the traffic, resulting in a constant node diversity value of 11. In contrast, the TBPPO algorithm displays an intriguing behavior, as it gradually converges to a node diversity of 8 over approximately 600-episode iterations, demonstrating a clear convergence trend. The PPO algorithm also exhibits a commendable convergence performance but stabilizes at a node diversity value of 15. Conversely, the TBDRSIR and DRSIR algorithms both reach a node diversity of 13, albeit with relatively significant fluctuations. These findings indicate that the TBPPO algorithm places greater emphasis on optimizing the composition of routing nodes compared to the other algorithms. Moreover, the TBPPO algorithm distinguishes itself by offering notable advantages over traditional Dijkstra and PPO approaches concerning security. Additionally, it outperforms the DRSIR algorithm and TBDRSIR in terms of stability and various performance metrics. The algorithm excels in providing multiple dependable routing options in network topologies with consistent structures dynamically changing the link conditions, striking a balance between link delay and security.  
In Figure 8b of the Germany-50 data, the Dijkstra algorithm consistently follows a fixed path, maintaining a constant node diversity value of 38. On the other hand, the TBPPO algorithm begins to converge towards a node diversity of 23 after about 1500-episode iterations, displaying a clear convergence pattern. PPO, which does not take into account node types, shows a performance similar to Dijkstra. Both TBDRSIR and DRSIR algorithms still exhibit relatively large fluctuations in node diversity. These findings imply that the TBPPO algorithm places greater emphasis on the composition of routing nodes, thereby improving its ability to prevent failures in similar nodes when sudden events occur from operators.  
Figure 8: Performance comparison of diversity of nodes of five algorithms: (**a**) NSFNet-14, and (**b**) Germany-50.
# Photonics-Based Microwave Image-Reject Mixer
###### Abstract  
Recent developments in photonics-based microwave image-reject mixers (IRMs) are reviewed with an emphasis on the pre-filtering method, which applies an optical or electrical filter to remove the undesired image, and the phase cancellation method, which is realized by introducing an additional phase to the converted image and cancelling it through coherent combination without phase shift. Applications of photonics-based microwave IRM in electronic warfare, radar systems and satellite payloads are described. The inherent challenges of implementing photonics-based microwave IRM to meet specific requirements of the radio frequency (RF) system are discussed. Developmental trends of the photonics-based microwave IRM are also discussed.  
microwave photonics; frequency conversion; image reject; photonic signal processing
# LoRaWAN Communication Protocols: A Comprehensive Survey under an Energy Efficiency Perspective
## 3 Communication Protocols
### Routing Protocols
#### 3.3.1 Clustering Approaches (CA)
Specifically, each node sends a packet with its ID at a specific time slot, and all devices within range of that node can receive and archive the packet. Depending on the number of nodes and the distribution of the nodes in the network, this process is repeated for a predefined number of iterations. Then, every node sends its own ID, as well as a list of RSSIs from other nodes. Each RSSI packet contains, as additional information, the ID of its associated node. As a result, the base station schedules and broadcasts the determined routing path and configuration of the nodes to the network. Note that optimal LoRa parameters, i.e., BW, SF, and TP, were predefined in end-devices in order to optimize the energy efficiency of the protocol. The scheme was studied in large scale LoRaWANs, and it was tested in both simulation and experimental environments. The results revealed that the proposed communication protocol improves both network coverage and the energy consumption of the entire network significantly. Additionally, the authors presented analytical comparative studies with star and tree LoRaWANs. The internet engineering task force (IETF) has standardized the IPv6 routing protocols for low-power and lossy network (LLN) called RPL, extending networks' lifetime. RPLs aim to find multi-hop routes in order to reach every destination in LLN. Furthermore, it constructs a destination-oriented directed acyclic graph (DODAG) where nodes have one of the following roles: root, parent, or leaf. These protocols fall into proactive category (i.e., the topology information is periodically exchanged between all the network nodes), with the difference that an external mechanism called objective function is running to find the optimal path. Additionally, note that RPL can be used both in uplink and downlink communication. Specifically, in [72], the authors reported a tree routing solution based on RPL aiming to select the routing path that has the lowest ToA. To achieve this, they selected the optimal SF per link. This process was divided into two phases: neighbor discovery and SF selection. To implement RPL for LoRaWAN, a new MAC protocol called RPL plus LoRa MAC (RLMAC) was developed.
# Evaluation of a Novel Condition Indicator with Comparative Application to Hypoid Gear Failures
## 3 Condition Monitoring
### ASD Indicator  
Another approach (also described in [23]) is to look at the absolute difference per band \(S_{diff}^{abs}(i)\) for calculating the Absolute Difference Spectrum (\(ASD\)) condition indicator,  
\[S_{diff}^{abs}(i)=\begin{cases}S(i)-S_{ref}^{upper}(i)&\text{ if }S(i)>S_{ ref}^{upper}(i)\\ 0&\text{ if }S_{ref}^{lower}(i)\leq S(i)\geq S_{ref}^{upper}(i)\\ S_{ref}^{lower}(i)-S(i)&\text{ if }S(i)<S_{ref}^{lower}(i)\end{cases} \tag{9}\]  
\[ASD_{abs}=\sum_{i=1}^{n}S_{diff}^{abs}(i) \tag{10}\]  
\[ASD_{rel}=\frac{1}{n}\sum_{i=1}^{n}S_{diff}^{abs}(i) \tag{11}\]  
In this application, the \(RSD_{abs}\) was used, since the \(ASD\) is mainly depending on the dominant frequency bands and lowers the influence of changes in the other areas of the spectrum.
# A Comprehensive Review and Assessment of Cybersecurity Vulnerability Detection Methodologies
## 5 Challenges and Potential Solutions for Automating Vulnerability Detection
### Data Challenges  
Cybersecurity professionals often deal with data from a variety of sources, including vulnerability databases, network logs, mobile devices, edge systems, telemetry, sensor data, and threat intelligence feeds. These data can vary significantly in format, accuracy, and detail. Inconsistent, low-quality, redundancy, noise, volatility state, lack of historical, heterogeneous sources, lack of labeling and incomplete data, device resource constraints, data fragmentation, and imbalanced data, among others, complicate the automation of vulnerability detection, leading to unreliable results [138; 139; 140; 11].  
Potential Solutions. To enhance data quality, cybersecurity teams can implement robust data preprocessing techniques. These include data cleaning, normalization, and enrichment processes to standardize and improve the quality of the data before they are fed into automated systems. Moreover, adding middleware layers and distributed data aggregation, within IoT and mobile environments, unifies data from different communication protocols and collects data at the edge. Additionally, integrating machine learning models can help predict and fill in missing data, improving the completeness and reliability of the datasets [141; 142; 9; 143].
# Individual Differences in Psychological Stress Associated with Data Breach Experiences
## 3 Results
### Hierarchical Multiple Regression Predicting IES-R Scores
\begin{table}
\begin{tabular}{c c c c c c} \hline  & \(B\) & \(SE\) & \(Beta\) & \(t\) & \(p\) \\ \hline \multicolumn{2}{c}{**Model 1**} & & & & \\ DBSI & 2.286 & 0.406 & 0.240 & 5.64 & \(<\)0.001 \\
**Model 2** & & & & & \\ DBSI & 2.076 & 0.424 & 0.218 & 4.90 & \(<\)0.001 \\ Gender & 3.732 & 1.227 & 0.135 & 3.04 & 0.002 \\ Sample & 2.038 & 1.902 & 0.071 & 1.07 & 0.285 \\ Age & 0.069 & 0.062 & 0.070 & 1.11 & 0.267 \\ Years of education & \(-\)0.018 & 0.268 & \(-\)0.003 & 0.07 & 0.948 \\
**Model 3** & & & & & \\ DBSI & 2.218 & 0.404 & 0.233 & 5.49 & \(<\)0.001 \\ Gender & 2.429 & 1.187 & 0.088 & 2.05 & 0.041 \\ Sample & \(-\)0.238 & 1.839 & \(-\)0.008 & 0.13 & 0.897 \\ Age & 0.100 & 0.059 & 0.103 & 1.70 & 0.089 \\ Years of education & 0.035 & 0.256 & 0.006 & 0.14 & 0.890 \\ Negative emotionality & 0.254 & 0.150 & 0.069 & 1.69 & 0.092 \\ PSWQ score & 0.106 & 0.088 & 0.077 & 1.20 & 0.230 \\ GAD-7 score & 0.594 & 0.152 & 0.250 & 3.91 & \(<\)0.001 \\
**Model 4** & & & & & \\ DBSI & 2.169 & 0.406 & 0.227 & 5.34 & \(<\)0.001 \\ Gender & 2.131 & 1.197 & 0.077 & 1.78 & 0.076 \\ Sample & \(-\)1.069 & 1.881 & \(-\)0.037 & 0.57 & 0.570 \\ Age & 0.120 & 0.060 & 0.123 & 2.00 & 0.046 \\ Years of education & 0.018 & 0.256 & 0.003 & 0.07 & 0.944 \\ Negative emotionality & 0.232 & 0.150 & 0.063 & 1.54 & 0.124 \\ PSWQ score & 0.105 & 0.088 & 0.077 & 1.19 & 0.235 \\ GAD-7 score & 0.564 & 0.152 & 0.237 & 3.70 & \(<\)0.001 \\ Smartphone use & 0.721 & 0.670 & 0.053 & 1.08 & 0.282 \\ Social media use & 1.257 & 0.883 & 0.071 & 1.42 & 0.155 \\ Browser use & \(-\)0.013 & 0.573 & \(-\)0.001 & 0.02 & 0.982 \\
**Model 5** & & & & & \\ DBSI & 1.923 & 0.411 & 0.202 & 4.68 & \(<\)0.001 \\ Gender & 1.587 & 1.220 & 0.057 & 1.30 & 0.194 \\ Sample & \(-\)1.359 & 1.906 & \(-\)0.048 & 0.71 & 0.476 \\ Age & 0.090 & 0.061 & 0.093 & 1.49 & 0.137 \\ Years of education & 0.100 & 0.255 & 0.016 & 0.39 & 0.696 \\ Negative emotionality & 0.248 & 0.149 & 0.067 & 1.66 & 0.098 \\ PSWQ score & 0.100 & 0.087 & 0.073 & 1.14 & 0.255 \\ GAD-7 score & 0.541 & 0.151 & 0.228 & 3.58 & \(<\)0.001 \\ Smartphone use & 0.585 & 0.665 & 0.043 & 0.88 & 0.379 \\ Social media use & 1.222 & 0.876 & 0.069 & 1.40 & 0.164 \\ Browser use & \(-\)0.004 & 0.574 & 0.000 & 0.01 & 0.994 \\ Digital security expertise & \(-\)0.727 & 0.545 & \(-\)0.061 & 1.33 & 0.183 \\ Post-data breach security & 0.274 & 0.080 & 0.145 & 3.44 & \(<\)0.001 \\ \hline \end{tabular}
\end{table} 
Table 9: Hierarchical multiple regression predicting IES-R scores. To summarize, the analysis of the participants with high (>24) IES-R scores revealed several notable differences from the analysis of the full sample, namely, (1) the GAD-7
## 3 System Design
### Multicast Tree Generation Procedure
#### 3.5.2 Root Election  
Root election is performed before SPT calculation. During the multicast packets transmission, the source first sends packets to root, and then the root forwards packets along the SPT. When network is small and multicast group is few, one root can satisfy the forwarding requirement. In this case, the root can be designated by MMC and then notifies all the routers in core network. The designation principle should consider the application scenarios and topology characters. For example, MMC can select the root based on node centrality such as degree centrality. When the network is large, there cannot be only one root in the core network for that the traffic is too heavy. In this case, MMC can configure multiple roots for different multicast groups through root bootstrapping mechanism. Besides, CCSM also considers the root update caused by root fault and changes of multicast membership and network topology.  
Root bootstrapping mechanism  
Figure 7: Overview procedure of MMC.  
The candidate root (ASR or CR) sends the assert message periodically which carries its RID, priority and serving MSI range. MMC collects these assert messages, and adopts the following rules to determine the root for specific MSI, and then generates the mapping between root and MSI, and finally notifies the routers in the core network.  
The decision rules are shown as follows:  
1. Compare the priorities of candidate roots, and select the root with highest priority;
2. If the priority is equal, then compare the hash value according to Equation (1), and select the root with highest hash value;
3. In the other case, select the candidate root with large RID.  
\[Value(G,M,C_{i})=(1103515245\cdot((1103515245\cdot(G\&M)+12345)\textbf{ OR }C_{i})+12345)\textbf{ mod }2^{31} \tag{1}\]  
In Equation (1), \(Value\) represents hash value, \(G\) represents multicast address, \(M\) represents the hash mask length, \(C_{i}\) represents the RID of candidate root, and represents the logic \(and\) operator, \(XOR\) represents the logical \(xor\) operator, and \(mod\) represents the \(modulus\) operator.  
* Root update mechanism  
When root is failure, MMC will re-elect root and notifies the other routers, and then triggers the SPT reconstruction. When a specific group membership or network topology is changed, the root does not to be updated.
# A Review of Hybrid VLC/RF Networks: Features, Applications, and Future Directions
## 2 Hybrid VLC/RF Networks in Indoor Scenarios under Ideal Conditions
### Load Balancing
The degree of rationality of each decision was calculated by combining the results of the analytical hierarchy process and a two-person cooperative game model. Recently, the authors of [58] proposed an automated vertical handover algorithm for VLC-WiFi networks using the received signal strength values. This algorithm selects the next most likely base station to transmit based on the hidden Markov model. Horizontal handover is a fundamental technique in hybrid VLC/RF networks. If a user is moving from one location to another connected to one of the two networks of the system (VLC or RF), it is necessary to perform a horizontal handover to move the mobile load to the next access point of the same technology. In [59], an adaptive horizontal and vertical handover mechanism for hybrid VLC/MMW systems was proposed. The mechanism was based on a centralized system architecture and used the signal-to-interference plus noise ratio levels of the APs as a performance metric. The authors of [60] proposed a solution to the horizontal and vertical handover of a VLC/WiFi network to address problems due to user mobility, obstacles, and shadowing. Table 4 shows a summary of studies carried out in the literature on load balancing in hybrid VLC/RF networks in the last ten years. Although this topic has been extensively studied in recent years, the constant evolution and dynamics of cellular networks make it necessary to constantly update load-balancing algorithms.
# Lab-on-Chip Culturing System for Fungi--Towards Nanosatellite Missions
###### Abstract  
In this paper, a lab-on-chip system dedicated to fungi cultivation in Earth's gravity and simulated microgravity, being a solution that could be used in future nanosatellite missions, is shown. For the first time, a fully glass lab-on-chip structure enabling the proper environment for cultivation of fungi species--_Fusarium calumorum_--is presented. Apart from the biological validation of the fungi cultures with the use of the lab-on-chip system, tests were carried out under induced microgravity utilising a Rotary Wall Vessel. Correct functioning of the lab-on-chip system was obtained, enabling the growth of fungi spores both in ground and in simulated microgravity conditions. Interestingly, culturing tests have shown that microgravity stimulates the growth of fungi notably, compared to the ground-based experimentation performed simultaneously. The findings of this study can provide substantial new knowledge on microscopic fungi cultivation in lab-on-chip devices, other soil organisms, as well as a potential behavior of these species in microgravity conditions. Culturing system shown in this work can help mycologists to provide better understanding of microscopic fungi nature and their development mechanisms at a single spore level. This opens the way towards regular usage of microfluidic tools in agriculture and horticulture fields and more importantly, in future research on microscopic fungi in space, e.g., as a part of nanosatellite missions.  
lab-on-chip; microfluidics; cell culturing; astrobiology; fungi research; RWV microgravity simulator
# Scalable Name Lookup for NDN Using Hierarchical Hashing and Patricia Trie
## 4 Proposed Name Lookup Scheme  
Scalable name lookup is challenging for several reasons. First, the name space is unbounded, and the length of a name is variable. An FIB should accommodate variable-length name prefixes. Second,it is expected that the number of name prefixes is much larger than network prefixes in an IP network. Lastly, the lookup process should find the longest matching name prefix.  
Hashing usually provides fast key lookup at a constant time if the key length is not very large. However, key length is variable, and we need to find the longest matching name prefix during NDN lookup instead of the exact matching name prefix. Thus, hashing needs to be modified or enhanced if used.  
The set of the name prefixes is hierarchically structured in NDN, so the upper-level component strings may be stored redundantly if each full name prefix string is stored in the flat-style structure. For instance, if two name prefixes /com/google and /com/facebook are stored in the flat structure, the first level component string/com would be stored twice. Additionally, hashing time is redundant while finding the longest matching name prefix. For a given search name/com/google, we need to compute hash codes, h(\"/com\") and h(\"/com/google\") because we do not know which one is the longest matching prefix in advance. Thus, the hashing time for \"/com\" is doubled in this instance. To eliminate component redundancy and support the longest matching efficiently, we adopted a hierarchical structure of hash tables, each of which was basically used for a component.