esa-sceva/satcom-chunk-collection huggingface dataset:
# Robust Text-to-Cypher Using Combination of BERT, GraphSAGE, and Transformer (CoBGT) Model
## 3 Proposed Model
### Key Value Extraction Module  
We utilized a BERT-based model for key information extraction. Bidirectional encoder representations from Transformers (BERT) was a powerful pretrained language model known for its effectiveness in various NLP tasks. This module aims to identify the key value in the question. These key elements often include the name of entities, number value, or properties value relevant to the nodes in Neo4j graph.  
Method: We fine-tune a pretrained BERT model using a Begin, Inside, Outside (BIO) tagging strategy. This strategy labels each token in the input text as the beginning of a key entity (B), inside a key entity (I), or outside any key entity (O). This fine-tuning process enables the model to accurately extract useful information from input questions.  
We created a training dataset where each token in the input questions is annotated with BIO tags corresponding to the entities, attributes which had a high chance of being used in the WHERE clause in the corresponded Cypher query.  
For example, in the question _\"Can you provide a list of actors who appeared in movies directed by [PERSON]?\"_, the Cypher query for this question is _\"MATCH (a:Actor)-[:ACTED_IN]>(:Movie),(:Movie)<[:DIRECTED]-(:Director) WHERE d.name = \"[PERSON]\" RETURN DISTINCT a.name\"_. We can see that, in the WHERE clause, _\"[PERSON]\"_ is the value used for searching the director's name, so the tokens _\"[PERSON]\"_ would be tagged as key information (Figure 4).  
The detailed process of key-value extraction is illustrated in Figure 5. Firstly, the input question undergoes tokenization, where the question Q is split into individual word terms.  
Figure 4: Example BIO tagger for value extraction module.  
Figure 3: Diagram of the proposed model.  
These terms are then mapped to unique token ID numbers (a necessary step because the BERT model requires a numeric input). Next, the sequence of token IDs is fed into the BERT model, which generates corresponding embedded vectors (E\({}_{1}\), E\({}_{2}\),..., E\({}_{n}\)) for each token. Finally, to classify which tokens represent key values, these embedded vectors are fed into a classification layer (a fully connected neural network). Tokens labeled O indicate non-key values, B indicates the beginning of a key value, and I indicates tokens inside a key value.
# Tunnel-Free Distributed Mobility Management (DMM) Support Protocol for Future Mobile Networks
## 3 Analytical Modeling and Performance Evaluation  
A mathematical analysis was performed to evaluate the performance of tunnel-free MIPv6 with MIPv6, PMIPv6, FMIPv6, and HMIPv6 in terms of the handover latency, handover blocking probability, and data packet loss.
## 1 Introduction  
Many cities find it challenging to set up a high-efficiency city logistics system to increase freight efficiency and decrease the impacts of city distribution on city living conditions [1]. [PERSON] et al. [2] pointed out that urban goods distribution (UGD) has an important impact on the sustainable development of cities. It is necessary to find new solutions for the management of freight distribution in order to reach a higher level of efficiency. So a city logistics model and some urban freight traffic policies have been studied by many researchers such as [PERSON] et al. [3], [PERSON] et al. [4], [PERSON] et al. [5], [PERSON] et al. [6], [PERSON] et al. [7]. [PERSON] et al. [8] showed that the use of urban consolidation centers (UCC) is presumed to provide more efficient distribution in an urban area, and it can decrease energy use and environmental impact. A UCC can be described as a logistics facility located in relatively close proximity to the geographic area that it serves, and with the range of terms used to refer to the UCC concept main including a public distribution depot, urban trans-shipment center, freight platforms, cooperative delivery system, urban distribution center, consolidation center (sometimes specific, e.g., retail, construction), pick-up drop-off location, offsite logistics support concept and so on [9]. UCCs are one of the most frequently implemented and studied city logistics initiatives, and according to [PERSON] al. [10] many case studies have been presented in literature [11; 12]. One of the most efficient and typical ways to implement goods consolidation is to adopt multi-stage distribution systems, especially two-stage distribution system, where the delivery from distribution center to customers is managed by routing and consolidating the freight through intermediate depots called transfer stations [13]. Therefore, selection of the locations of the transfer stations and planning of the two-phase distribution routes are key problems in city logistics system optimization.  
With the development of the economy and the continuous improvement of people's living standards in China, demand for cold-chain products has also increased to a large extent, which promotes rapid development of the cold-chain logistics. The cold-chain logistics has become an important part of the urban distribution system.  
The remainder of this paper is organised as follows. Firstly, the location-routing problem is described in a literature review. Then a two-stage location-routing model is constructed and a metaheuristic algorithm is implemented to solve the model. The algorithm is included as a hybrid genetic algorithm [14]. Finally, the feasibility and validity of the model and algorithm are demonstrated through a test example, and results of the study are summarised.
## 5 Experiment Results and Discussion
### Analysis of the Calculated Settings for Individual Users  
In this paper, special emphasis is placed on the analysis of the calculated settings for each user, that is, the interaction mechanisms adapted to them in SG2 based on objective and subjective measures collected by tracking user interactions during the play of SG1. Since users rated their satisfaction with interaction mechanisms in both serious games, SG1 and SG2, it is possible to compare the results and user satisfaction with them. The aim of the result analysis performed as a part of this paper is to show that tracking user behaviour in terms of their performance and interaction during gameplay can be useful in revealing potential accessibility issues as well as have a positive impact on the user experience by adapting critical elements of the game. Critical elements in this case are types of interaction mechanisms used for tasks in an AR-based serious game related to the virtual object transformation.  
In this section, the distribution of interaction mechanisms assigned to participants is analysed to explore how interaction mechanisms are assigned based on collected metrics rather than formally comparing groups or making predictive inferences. Therefore, descriptive statistics were used to present distributions and trends without applying inferential tests. In addition, while we present the distribution of interaction mechanisms per participant group, some subgroups have relatively small sample sizes, which may limit the reliability of inferential statistical tests.  
The method for determining the interaction mechanism for a specific task of transforming a virtual object is described in Section 4.2.4. Figure 12 shows the distribution of interaction mechanisms assigned to the participants (N = 108). For the _Translation_ task, both interaction mechanisms (touch gestures and indirect control) are equally represented (indirect control is assigned to 51.85% (56) of the participants, while touch gestures are assigned to 48.15% (52) of the participants). For the _Rotation_ and _Scaling_ tasks, however, indirect control is assigned to a significantly higher proportion of participants than touch gestures. More specifically, 85.19% (92) of the participants are assigned indirect control for the _Rotation_ task, while the rest of the participants, i.e., 14.81% (16), are assigned touch gestures. Similarly, for the _Scaling_ task, 83.33% (90) of the participants are assigned indirect control, and 16.67% (18) of the participants are assigned touch gestures.  
Figure 12: Distribution of interaction mechanisms assigned to participants (N = 108) for transformation tasks (Translation, Scaling, and Rotation). The figure shows the proportion of participants assigned to each interaction mechanism: Touch Gestures and Indirect Control.  
Results of applied interaction mechanisms in SG2 for tasks of translation, rotation and scaling the virtual object for participants by disability and age groups are summarized in Table 10. More elaboration on the results for each task follows below.
# Mechanisms of Noise Transmission in Velocity Broad-Band Seismometers: Modeling and Analysis
## 3 Methodology
### Noise Transfer Model in Feedback Path  
#### 3.2.1 Derivation of Noise Transfer Functions  
When analyzing the noise transfer function in the feedback path, we take the integrator noise as a representative example. Electronic noise generates a voltage at point C based on the voltage division principle, causing a voltage differential across the force transducer coil. This results in a current \(I_{coil}(t)\) flowing through the coil.  
To calculate the voltage generated at point C by noise in the feedback path, we model the feedback path as shown in Figure 4[23].  
When calculating the noise transfer function for the integrator, we neglect the influence of other noise sources in the loop. The transfer function of the integrator noise to the current flowing through the force transducer is given by the following:  
\[H_{init}^{I_{coil}}(s)=H_{I}(s)\cdot\left|\frac{Z_{1}(s)}{Z_{1}(s)+R_{2}} \right|\cdot\frac{1}{R_{coil}} \tag{30}\]where \(Z_{1}(s)\) represents the impedance of \(R_{1}\), \(C_{1}\), and \(R_{coil}\) in parallel, expressed as follows:  
\[Z_{1}(s)=\frac{1}{\frac{1}{R_{1}}+C_{1}s+\frac{1}{R_{coil}}}. \tag{31}\]  
Next, the transfer function of the force transducer current to the EIN in acceleration is expressed as follows:  
\[H_{l_{coil}}^{a_{i}}(s)=-\frac{E\cdot K_{f}}{K_{m}}. \tag{32}\]  
After Equations (30) and (32) are combined, the noise transfer function of the integrator noise to the EIN in acceleration is derived as follows:  
\[H_{n_{int}}^{a_{i}}(s)=H_{n_{int}}^{Coil}(s)H_{l_{coil}}^{a_{i}}(s)=-H_{I}(s) \cdot\left|\frac{Z_{1}(s)}{Z_{1}(s)+R_{2}}\right|\cdot\frac{EK_{f}}{R_{coil} K_{m}}. \tag{33}\]  
Similarly, using this approach, the noise transfer functions for the resistor thermal noise of the proportional controller and the force transducer's resistor thermal noise can be expressed as follows:  
\[H_{n_{R1}}^{a_{i}}(s)=-\left|\frac{Z_{2}(s)}{Z_{2}(s)+R_{1}}\right|\cdot \frac{EK_{f}}{R_{coil}K_{m}}, \tag{34}\]  
\[H_{n_{fl}}^{a_{i}}(s)=-\left|\frac{1}{Z_{3}(s)+R_{coil}}\right|\cdot\frac{EK_ {f}}{K_{m}} \tag{35}\]  
where the following applies:  
\[Z_{2}(s)=\frac{1}{\frac{1}{R_{2}}+C_{1}s+\frac{1}{R_{coil}}}, \tag{36}\]  
\[Z_{3}(s)=\frac{1}{\frac{1}{R_{1}}+C_{1}s+\frac{1}{R_{2}}}. \tag{37}\]  
Here, \(Z_{2}(s)\) represents the impedance of the parallel combination of \(R_{2}\), \(C_{1}\), and \(R_{coil}\), while \(Z_{3}(s)\) represents the impedance of the parallel combination of \(R_{1}\), \(C_{1}\), and \(R_{2}\), both in the s-domain.  
Generally, the thermal noise generated via resistors \(R_{1}\) can be neglected compared to other noise sources. Here, the noise transfer function of resistor \(R_{1}\)'s thermal noise is provided for reference only. A subsequent analysis related to the thermal noise of resistor \(R_{1}\) will not be conducted.
# DTN and Non-DTN Routing Protocols for Inter-CubeSat Communications: A comprehensive survey
## 4 DTN Routing Protocols in Disjointed Satellite Networks
### Discussion  
Flooding based routing algorithms are considered to provide the highest delivery rates and the minimum end-to-end delay when compared with other quota-based protocols; however, these protocols exhaust the network with large overheads. In quota-based routing protocols, the focus was on how an algorithm can mitigate the high overheads that are produced by flooding-based algorithms, enhance the network performance by increasing the delivery rate, and minimise the end-to-end delay. Most of these algorithms build a complete knowledge about the networks using historical information that is collected over time from exchanging data and acknowledgments between nodes. Resultantly, these algorithms still yield high overheads for networks with limited resources, such as the CubeSat network. However, no link recovery techniques have been proposed in these algorithms, and the lack of consideration for regular link failure that is caused by congestion and node failure might lead to network degradation, especially in networks with restricted resources. Furthermore, most of these protocols are proposed for unpredictable mobile networks, losing the advantage of predictable or semi-predictable satellite movement in developing routing tables. It has been proven through some of the aforementioned research that networks with global knowledge tend to have better performance, while, on another hand, forwarding-based routing that is based on the space-time graph guarantees the delivery of a message if there is a complete path between a source and its corresponding destination. The forwarding-based routing protocols also save network resources due to no message replication, although this is at the cost of high delay and low delivery rates.
## 2 Effect of Floating DC Bus on System Stability
### DC Microgrid Topology  
The simplified power system shown in Figure 1 was chosen as a case study to analyze the voltage stability in a floating-bus DC microgrid in presence of a destabilizing CPL. The islanded microgrid used in this study is based on the following elements:  
* DC power-generating system, composed of an internal combustion engine (IC), an alternator (G), and a controlled rectifier (P), to supply loads and/or recharge batteries;
* Energy storage system, i.e., an electrochemical battery (B);
* LC input filter (F), to assure proper voltage and current quality on the load bus;
* Generic static DC load (L), fed by a DC-DC converter (C);
* Generic rotating load (M), supplied by means of a controlled inverter (I).  
In order to save space, the system avoids using a battery charger unit, relying upon the controlled rectifier (P) to regulate battery charging. Thus, the DC bus voltage is floating when the power generating system is offline, depending on the battery SoC (the battery is the only source of power). As already shown in Reference [33], in this case voltage stability issues may affect the DC bus. Particular attention has to be paid to the loads; depending on the control bandwidth of the converters used to supply loads from the DC bus, it is possible to classify them into two classes: (i) conventional loads (CLs), and (ii) constant power loads (CPLs). The former category represents loads that do not require strict voltage regulation for proper operation (e.g., resistive heaters, loads with an integrated input conversion stage, etc.). Therefore, their global effect can be modeled through an equivalent linear resistance \(R_{L}\). Conversely, the latter category is made up of loads requiring tight control by their input power converter (C or I in Figure 1), to either provide a constant voltage (for static loads) or a constant speed/torque (for rotating loads). The final effect is a constant power delivery from the DC bus to the loads. These loads can be modeled through a single equivalent nonlinear current \(I_{L}=P/V\), where \(P\) is the CPL power. It has to be noticed that both static and rotating loads can be classified as CL or CPL, depending on their operating characteristics. The effect of several CLs and CPLs can be modeled using two equivalent aggregated loads. Given the interest in evaluating voltage stability in a floating-bus system, the simplified microgrid shown in Figure 1 was considered as being supplied by batteries only (power system section depicted in black in Figure 1). Thus, it is possible to model the overall power system in battery-only operation using the equivalent circuit of Figure 2, where \(E\) is the battery voltage, \(V\) is the voltage on the CPL, \(L\) and \(C\) are the filtering stage components, and \(R\) is the inductor physical resistance.  
Figure 1: Proposed direct current (DC) microgrid; the power system section in battery-only operation is shown in black.
## Appendix A General Considerations in RFE Design
### RFE-Bandwidth  
The usable bandwidth, or applicable data rate \(r=2B\), of an RFE is defined by the APD-bandwidth, the TIA-APD combination, the LP, and the limiter. Since only the LP dominates \(B\), the other components must be chosen so as to not conflict with this requirement. This is, however, partly contradictive since, often, the APD-size is as large as possible to ease the optical spot requirements, as well as the TIA needing to bear the highest possible transimpedance to exhibit the lowest noise-density, while both requirements limit the bandwidth. The following summarizes some rules for optimization of the signal processing chain:  
Manufactures often state a bandwidth for their photodiodes, although this would require further data for its implementation [58]. However, for an appraisal of bandwidth potential, the photodetector's capacitance (as well as the multiplied dark current) can be assumed linear in relation to its area, leading to the following rough estimation for typical InGaAs-APDs based on bandwidth values from APD data sheets (\(D_{APD}\) is detector diameter):  
\[B_{APD}\approx\ 40\ Hz\cdot m^{2}/\ D_{APD}{}^{2} \tag{10}\]  
The usable bandwidth of the combination of the APD and TIA will be further limited by the input capacitance of the TIA (the APDDs capacitance plus further capacitance from TIA and the signal traces on the circuit board). The TIAs data sheet might state the expected bandwidth under certain input capacitance assumptions. Since this capacitance is mainly given through the APDs size, its diameter reduction-if applicable-might increase the \(B\) of the RFE significantly.  
Further, the limiter shall exhibit high sensitivity to the signal level from TIA and LP but will still support a high \(r\). Again, both parameters limit each other, requiring accordant optimization.
## 3 Experimental Setup
The outdoor experimental scenario is a parking lot allowing LoS, minimum Wi-Fi, and Bluetooth interference. For two-hop communication, the RSSI values are measured by placing one router node at an equidistance from the end node and the coordinator. For further illustration, the two-hop measurement setup for any distance is depicted in Figure 3. For multi-hop communication, Zigbee routing protocol is employed which is a modified version of ad hoc on demand distance vector (AODV). With this protocol, the node that wants to be connected initiated the communication and routes are maintained as long they are in use by the source node. Further details on Zigbee routing algorithms can be studied from [48; 49; 50]. For this work, the two-hop tests are conducted alongside single hop with only an RSSI measurement and round-trip latency analysis. This is because multi-hopping primarily affects these two parameters. Moreover, based on these two metrics, other metrics, such as PDR, throughput, overall energy consumption of the network, are also affected. For other tests, only single hop communication is taken into consideration. For two-hop communications, the network is configured to always choose the multi-hop for all the different distances. However, AODV routing protocol might not choose multi-hops for all the transmission distances based on network performances at that point. However, for two-hop tests with RSSI and round-trip latency analysis, multi-hopping is forced at all distances for fair comparison of single and two-hop communication. Moreover, these two tests are not focused on evaluating the routing protocol of Zigbee, rather they focus on how the performances may vary if the end node chooses two-hop instead of single hop at any given distance. For indoor testing, lab space is taken into consideration. The RSSI values are measured by placing the coordinator at one and end nodes at a distance ranging from 0 to 40 m with NLoS. The indoor facility is divided into several rooms and a hallway with usual building blocks and metallic doors, as depicted in Figure 4. Here, E\({}_{5}\), E\({}_{10}\), E\({}_{15}\), E\({}_{20}\), E\({}_{25}\), E\({}_{30}\), E\({}_{35}\), E\({}_{40}\), E\({}_{45}\), E\({}_{50}\) are the different positions of end nodes at 5, 10, 15, 20, 25, 30, 35, 40, 45, and 50 m, respectively, from the coordinator (C\({}_{1}\)). We should also note that the building has extensive Wi-Fi coverage along with a large amount of Bluetooth devices, affecting the Zigbee performances in comparison to the outdoor scenario. The latency and throughput  
Figure 3: Single hop and two-hop setup for QoS measurements. analysis are conducted by placing the coordinator and end node at 30 m from each other. These analyses evaluate how different baud rates, data encryption, number of hops, and deployment scenario (indoor/outdoor) affect QoS metrics.
# Research on Network Attack Traffic Detection HybridAlgorithm Based on UMAP-RF
###### Abstract  
Network attack traffic detection plays a crucial role in protecting network operations and services. To accurately detect malicious traffic on the internet, this paper designs a hybrid algorithm UMAP-RF for both binary and multiclassification network attack detection tasks. First, the network traffic data are dimensioned down with UMAP algorithm. The random forest algorithm is improved based on parameter optimization, and the improved random forest algorithm is used to classify the network traffic data, distinguishing normal data from abnormal data and classifying nine different types of network attacks from the abnormal data. Experimental results on the UNSW-NB15 dataset, which are significant improvements compared to traditional machine-learning methods, show that the UMAP-RF hybrid model can perform network attack traffic detection effectively, with accuracy and recall rates of 92.6% and 91%, respectively.  
internet; cyber attack; random forest; UMAP; machine learning +
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# Analysing the Performance of a Trust-Based AODV in the Presence of a Flooding Attack
## 7 Simulation and Evaluation
### Performance Based on Number of RREQs Varying the Flooding Rate  
The simulation was run varying the flooding rate from 50 to 100 packets per second with a constant number of 200 nodes and 5 malicious nodes. The flooding rate is the number of fake packets sent by a malicious node per second. Figure 3 shows that in AODV, increasing the flooding rate leads to a rapid increase in the number of RREQs, whereas the number of RREQs in TAODV seems more stable. The reason for this is that any node reaching the limit of permitted RREQs will be isolated regardless of the flooding rate.