esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 CubeSats and Inter-Satellite Links (ISLs)
### Laserm Terminals Payload Capacity  
The analysis of commercially available and custom-designed laser communication (lasermom) systems suitable for CubeSats reveals a diverse landscape of technological capabilities, catering to the increasing demand for global satellite constellations. This paper surveyed leading corporations and research institutions involved in the development and deployment of CubeSat-compatible lasercom terminals, presenting a comprehensive overview of their characteristics and parameters. In Table 3, a variety of commercial lasercom terminals from notable entities, such as TESAT, Mynaric, Thales Alenia Space, and Hyperion Technology, among others, are listed. These terminals exhibit a wide range of features including data rates, link types, size, power consumption, and release times. For instance, TESAT's TOSIRIS offers data  
Figure 2: Distribution of link types in satellite communication systems by country. rates ranging from 1.25 to 10 Gbps with a compact volume of 28.0 \(\times\) 20.0 \(\times\) 15.0 cm\({}^{3}\) and a power consumption of 40 W. Similarly, Mynaric's CONDOR Mk2 and Mk3 support data rates from 0.1 to 1.25 Gbps and 0.1 to 10 Gbps, respectively, showcasing the flexibility in bandwidth options. Moreover, Table 4 presents customized (R&D) lasercom systems developed for CubeSats by research and development institutions like TESAT, NEC/JAXA, NASA/MIT/University of Florida, and DLR/University of Stuttgart. These systems offer unique transmission rates and link types tailored to specific mission requirements. For instance, TESAT's EDRS-A LCT provides a transmission rate of 1.8 Gbps for GEO-GND links, whereas NASA/MIT/University of Florida's CLICK-A focuses on LEO-LEO communication at a modest 10 Mbps. The significant progress and innovation in CubeSat laser communication technology has offered a spectrum of solutions, encompassing varying data rates, link types, and spectral wavelengths. These advancements not only meet the increasing demand for global satellite constellations but also drive the miniaturization and efficiency enhancement of CubeSat payloads, ushering in a new era of space-based communication and exploration. However, despite these technological advancements, significant challenges remain. The earlier generation of terminals accentuates the pressing need for continued efforts towards miniaturization and decreased power consumption. Furthermore, factors including reliability, security, and the impact of atmospheric conditions necessitate careful
## 3 Results
### Analyses of SPECTRAL indices of Vegetation
#### 3.4.3 Nitrogen-Content Index and Other Vegetation Indices  
The PLSDA analysis performed on all the selected indices (18) exhibited 4 hits with VIP scores >1 (Supplementary Figure S3): NPQI, NDVI(N), PRI and SIFb. The two-way ANOVA carried out with the nitrogen-content index, NDVI(N), showed a significant interaction between the assemblage and the water availability (F= 6.921, _p_! 0.001). For each assemblage, the nitrogen levels were compared between the control and exclusion  
Figure 8: Histograms of photosynthesis and fluorescence index (NIRvP) of assemblages in control (C) and rain-exclusion (E) conditions. _Quercus cccifera_ (Qc), _Cistus albidus_ (Ca) and _Rosmarinus officinalis_ (Ro) and _Ulex parifforus_ (Up). The first two letters of each assemblage indicate the species measured. NS, not significant; *_p_! 0.05; **_p_! 0.01; ***_p_! 0.001. \(n\) = 27.  
Figure 7: Histograms of drought index (R975/R900) of assemblages in control (C) and rain-exclusion (E) conditions. _Quercus cccifera_ (Qc), _Cistus albidus_ (Ca) and _Rosmarinus officinalis_ (Ro) and _Ulex parifforus_ (Up). The first two letters of each assemblage indicate the species measured. NS, not significant; *_p_! 0.05; **_p_! 0.01; ***_p_! 0.001. \(n\) = 27.  
treatments. In the monospecific assemblages (Figure 9), the decrease in precipitation did not have a significant effect on the nitrogen content compared to the controls. However, in the bispecific assemblage, Up improved only the nitrogen content of Ca; in addition, it probably did so in the trispecific (CaQcUp) and tetraspecific (CaQcRoUp) assemblages. It should be noted that the nitrogen content was also increased under the drought conditions for QcCaUp and RoQcUp.
## 3 Experiment Results and Observations
### Simulation and HILS Experiment Results
#### 3.2.4 The Lateral Driving Simulation and HILS Experiment Results  
Figure 11 shows the control output results of the power distribution system controller during DLC driving when the power distribution ratio instantaneously changed from 90:10 to 70:30 while the RPM of the drive motor was 20 and 100 rpm. Accordingly, the CarSim controller outputted a relatively fixed value without any significant variations, and the results for the drive conditions of 20 and 100 rpm both exhibited similar trends with an error of \(\pm 0.01\), thus confirming proper adherence to the power distribution ratio.  
Figure 12 illustrates the lateral acceleration and yaw rate of the vehicle during DLC driving. When the vehicle made a left turn, the lateral acceleration and yaw rate increased rapidly, and the lateral acceleration was \(+0.38\) g\({}^{\prime}\) and the yaw rate was \(+10.07\) deg/s. When the vehicle made a right turn, there was a decreasing trend in which the lateral acceleration was \(-0.51\) g\({}^{\prime}\), and the yaw rate was \(-13.2\) deg/s. Notably, identical results were also observed at 20 and 100 rpm, respectively.  
Figure 10: Power distribution ratio: (a) 20 rpm. (b) 100 rpm.  
Figure 9: The wheel’s longitudinal slip ratio: (a) 20 rpm. (b) 100 rpm.
# An Efficient Framework for Securing the Smart City Communication Networks
## 4 Simulation Network and Setup
### Network Simulator  
A network simulator is a program that simulates networks with a variety of nodes. To create real-time network experiments, simulation tools are practically required. Several simulation tools, such as the OPNET modeler, and other open source simulators, such as NS-2, GNS2, GNS3, and OMNET, are available.  
One of the most widely used data network modeling and network simulation technologies is the OPNET modeler. It is a comprehensive network modeling tool with a slew of useful features. The OPNET modeler, in particular, allows for the simulation of heterogeneous networks using various scenarios, as well as the simulation of specifically planned network architecture and analysis in order to compare different types of traffic and situations [25]. It is also used to support a variety of operating systems, including Linux and Microsoft. Because of its real-time configuration and operation capabilities, the fundamental advantage of using OPNET is the ability to evaluate and analyze networks in realistic experiments. As a result, OPNET allows for the comparison of network im  
Figure 5: Implementing and applying a man-in-the-middle (MITM) attack and network intrusion detection system (NIDS) in our topology.  
Figure 6: How a man-in-the-middle attack intercepted messages passing via the smart city communication network.  
provements based on multiple protocols, as well as the development of new protocols and technologies. OPNET also supports the following simulation technologies: Discreet Event Simulator (DES), Flow Analysis Simulation, ACE Quick Predict Simulation, and Hybrid Simulation [26]. Therefore, DES will be the simulation technology used in this study.  
As shown in Table 2, we use the following parameters for both authentication and encryption tasks. Each has two phases, as mentioned above. In the first phase of the authentication task (Smart Meter to Control Center), we set up the requests to be in initialization time = 0 second, request count = 1 (constant), request packet size = 3 Kbytes (constant). For the response of this phase, the processing time = 0.5 s, number of replies = 1 (constant), request packet size = 5 kbytes (constant). In the second phase of the authentication task (Verify Control Center), we also set the requests in the initialization time = 0.2 s, request count = 0 (constant), whereas there is no response in this phase.  
For the second task (encryption), the first phase (Smart Meter to Control Center) includes the following in the request field: initialization time = 0.2 s, request count = 1000 (constant), inter-request time = 1.2 s (exponential), request packet size = 1-10 kbytes (uniform_int). The request of the second phase (Control Centre to Smart Meter) is exactly the same as that in the first phase of the encryption task. Now, both requests in both phases have no responses.  
All the results are averaged over 50 runs for randomly generated topologies, while the simulation time for each run is set to 3600 s.
# Research Progress on Synergistic Technologies of Agricultural Multi-Robots
## 3 Research Progress on Synergistic Technologies of Agricultural Multi-Robots
### Communication
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Communication Technology** & **WI-FI** & **Bluetooth** & **ZigBee** \\ \hline \multirow{4}{*}{Communication protocol} & IEEE 802.11 a;1 & \multirow{4}{*}{IEEE 802.11 b 2;} \\  & IEEE 802.11 b 2; & \\  & IEEE 802.11 g 3; & \\  & IEEE 802.11 p 4; & \\ \hline \multirow{4}{*}{Advantage} & Fast transfer speed; & High transmission rate; & Low power consumption; \\  & & Strong anti-interference & Low cost; \\  & & ability; & Large transmission range; \\  & Long effective distance; & Flexible networking; & Good scalability \\  & Reliable connection & Low power consumption; & Strong anti-interference \\  & Wide coverage; & Small volume; & Good security; \\ \hline \multirow{4}{*}{Disadvantages} & High power consumption; & Slow; & Low transmission rate; \\  & Expensive; & Short distance; & Short distance; \\  & Protocol coding complexity; & Weak networking ability & The mobile phone cannot \\  & Protocol coding complexity; & Poor security and & communicate directly; \\ \hline \multirow{4}{*}{Application} & Plowed in wheat fields or & & Monitored vineyards [28]; \\  & orchards [126; 127]; & & Monitored and weeded \\  & Plowed [44; 48]; & & farmland [49]; \\  & & Plowed [46]; \\ \hline \hline \end{tabular}  
* IEEE802.11a standard, operating in the 5 GHz band, has a data transmission rate of 54 Mb/s. 2 IEEE802.11b standard, operating in the 2.4 GHz band, has a data transmission rate of 11 Mb/s, and is not compatible with IEEE 802.11 a. 3 IEEE802.11g is a standard that increases the transmission speed of 802.11b from 11 Mb/s to 54 Mb/s. 4 IEEE 802.11p standard is a communication protocol expanded by IEEE 802.11 standard, which is mainly used in wireless communication of automotive electronics.  
\end{table} 
Table 7: Cont. direction to select suitable multi-robot communication technology according to the characteristics of crops and to carry out research on multi-robot communication patterns based on crop shape characteristics.
# Near-Optimal Active Learning for Multilingual Grapheme-to-Phoneme Conversion
## 3 Multilingual G2P Model
### Word Sequence Encoding-Decoding Process  
The fundamental concept of converting a sequence of words into a sequence of pronunciations involves encoding an LSTM that progressively reads each grapheme of a word and maps the sequence to a fixed-dimensional vector representation. The decoding LSTM is essentially an LSTM language model that takes input sequences; combines vectors, hidden layer states, and phonemes from the previous moment; predicts phonemes one by one; and outputs pronunciation sequences.  
For instance, taking the English word \"cat\" [kk aa tt] as an example, Figure 4 illustrates an example of the LSTM encoding\(-\)decoding model. The neural network in Figure 4 is composed of two layers, where the encoding LSTM is located on the left side of the dashed line, and the decoding LSTM is on the right side. The encoded LSTM reads the input sequence \"\(<\)s\(>\) t a c\" in reverse chronological order and represents the sequence \"c a t\" as a vector v of fixed dimensions based on the state of the last hidden layer. After encountering the onset \(<\)os\(>\), the decoding LSTM is activated, and the vector v is utilizedas the initial state of the hidden layer to compute the probability of the next phoneme step by step, resulting in the final phoneme sequence \"kk aa tt</os>\" through the cluster search algorithm. During this process, 
 indicates the beginning of the input sequence, and <os> and </os> signify the start and stop of the output phonemes, respectively. The start and stop characters enable the model to encode and decode sequences of arbitrary lengths, and the decoding LSTM stops predicting after </os>. Additionally, the encoder reads the graphemes in reverse order, which can introduce short-term dependencies in the data and simplify the training optimization process.
## 1 Introduction
### Basic Information  
Although several studies have dealt with turbomachinery analysis, studies on compressible turbomachinery with OpenFOAM are relatively few. Among them, the studies of [PERSON] [5], [PERSON] [6], [PERSON] [7], and so on researched compressible MRF solvers and interfaces for turbomachinery, and most of the issues they tackled were solved using the foam-extend version. In particular, the axial turbine problem was dealt with in the study of [PERSON] [8]. Therefore, in this study, a gas turbine analysis was performed using the steadyUniversalMREFFoam solver from foam-extend-4.1, as utilized in the study by [PERSON]. The solver and libraries were modified to correct issues that arose during calculations, allowing for a more accurate analysis.  
The steadyUniversalMREFFoam solver is based on the PISO solver, so PISO-related coefficients must be set in the fvSolution file. In this study, nCorrectors 2 and nNonOrthogonalCorrectors 1 were used as the default settings. In addition, 0.5 was set for all parameters when setting up relaxationFactors.  
For this study, the Korea Institute of Science and Technology Information (KISTI)'s fifth super-computer Nurion [9] was used. In the foam-extend-4.1 version, there is an issue whereby parallel nodes cannot be calculated. Therefore, this calculation was performed using up to 68 cores at one node.  
Mesh generation was performed using ANSYS TurboGrid. The mesh was then converted to a Fluent mesh format and subsequently transformed into an OpennFOAM mesh using the utility \"fluentMeshToFoam\".  
Two geometries were used for this study: one uncooled turbine stage developed by the Korea Aerospace Research Institute, KARI, and two or five stages of the NASA/GE  
Figure 1: An example of the turbomachinery flow domain for analysis: E\({}^{3}\) LPT’s five stages.  
Energy Efficient Engine (E\({}^{3}\)) Low-Pressure Turbine (LPT) [10; 11; 12]. The comparison of the two geometries is described in Table 1 and shown in Figure 2.  
The energy equation of the steadyUniversalMRFFoam solver of the original code contains a viscous heating term. However, in this paper, the calculation results are shown excluding this term, except for several specific cases.
# A Channel Allocation Mechanism for Cellular Networks
## 2 Related Work
### Channel Allocation Mechanisms
#### 2.2.1 Static Channel Allocation  
The mechanism of channel allocation in PCS adopts the Static Channel Allocation (SCA) mechanism; when the channels which are allocated, the BS can only use these channels to provide the service of communication. In this case, there is a definite relationship between the channels and the cells that can be used at any time. The obvious disadvantage of using the SCA mechanism can be explained using an example. Imagine two adjacent cells with their allocated channels. At any time, one of the cells may have all of its channels occupied, and another new call requests a free channel in this cell. This call will be blocked even if the adjacent cell has a free channel at this moment. The situation will result in the customer satisfaction and economic loss.  
Channel reuse is an essential feature of cellular system. However, the discussion of directional cell sites explained the economic incentive for minimizing the ratio of \(\sigma\), and the parameter of \(\sigma\) can be expressed as a ratio of the physical distance between the center of cells (\(D\)) divided by the radius (\(R\)) of cells. The co-channel reuse ratio has an impact on both the communication quality and the ender customer capacity of the system [19]. Because the co-channel ratio actually has impact on the communication quality, the ratio will decide the number of channels in each channel sets. The ratio also can limit the communication capacity of cells by deciding the number of channels in each channel set. In the SCA mechanism, when the number of channel (\(N\)) is assigned to each cell, the number of channels (\(N\)) is a fixed number and permanently allocated to each cells [20]. When the number of channel is decided, the BS only can use these channels to provide service of communication. In general, the number of channels (\(N\)) in each cell can be expressed as Formula (1) [6; 10; 21]:  
\[N=\bigg{(}\ \frac{1}{3}\ \bigg{)}\sigma 2. \tag{1}\]  
Here, we define \(\sigma\) as \(D/R\), where \(D\) is the physical distance between the two cell centers and the \(R\) is the radius of the cell. Therefore, the number of \(N\) can only be an integer value. For example, \(N\) is 3, 4, etc.  
In the SCA mechanism, because the number of channels in each cell is permanent, the solution is suitable for the stable situation of traffic information. The CBP will increase with the traffic flow increases.
# Leveraging Retrieval-Augmented Generation for Swahili Language Conversation Systems
## 4 Experimentation
### Experimental Results
#### 4.3.9 Perplexity Score Analysis  
Figure 17 presents the perplexity score comparison for four models--mT5, mBART, GPT-2, and GPT-Neo--evaluated under fine-tuned and Retrieval-Augmented Generation (RAG) conditions. Perplexity is a metric commonly used to assess the performance of language models, with lower values indicating better performance and greater alignment with the reference data. As illustrated in the graph, the RAG models consistently achieved lower perplexity scores compared to their fine-tuned counterparts across all tested models. This highlights the effectiveness of incorporating retrieval mechanisms in improving the quality of language model outputs. Among the models, GPT-Neo demonstrated the most significant reduction in perplexity under the RAG condition, followed by GPT-2  
\begin{table}
\begin{tabular}{l l} \hline
**Swahili Query** & **English Query** \\ \hline Ni nija zipi bora za kujiking na magnojwa? & What are the best ways to protect yourself from diseases? \\ Ni vyakula gani bora kwa afya? & What are the best foods for health? \\ Jinsi ya kuboresha afya yako? & How to improve your health? \\ Jinsi ya kupika chakula cha asubuhi? & How to cook breakfast? \\ \hline \end{tabular}
\end{table}
Table 10: Swahili query and corresponding English translation for Figure 15.  
Figure 15: Performance comparison between fine-tuned and RAG models on general Swahili queries. The graph compares the performance of four models, mT5, mBART, GPT-2, and GPT-Neo, under both fine-tuned and Retrieval-Augmented Generation (RAG) conditions.  
and mT5. On the other hand, mBART exhibited a moderate decrease in perplexity with RAG integration, indicating that the baseline performance of the model may influence the extent of improvement. These results emphasize the advantage of the RAG approach in enhancing the generative capabilities of language models, particularly in tasks involving Swahili language processing. The lower perplexity scores achieved by RAG models suggest more accurate and contextually relevant responses, addressing challenges associated with low-resource languages.  
Figure 16: Comparison of inference times across models for Fine-Tune and RAG techniques.  
Figure 17: Perplexity score comparison by model for fine-tuned and RAG conditions. The graph shows perplexity scores for mT5, mBART, GPT-2, and GPT-Neo under both fine-tuned and Retrieval-Augmented Generation (RAG) conditions. Lower perplexity scores indicate better performance.
## 4 Experiments and Results
### Comparison with Baselines  
Following previous studies [3,11], we conducted our experiments in a held-out evaluation instead of costly human evaluation. Held-out evaluation compares predicted relational facts in text with existing facts in Freebase. To verify the effort of our model, we adopted precision-recall (PR) curves as the evaluation metrics, which can show the trade-off between precision and recall. To further quantify PR curves, we also report precision at recall (P@Recall) and the area under the curves (AUC). P@Recall indicates the precision values at concrete recall rates, and AUC evaluates the overall performance of PR curves. We selected the following baselines to compare with our model, the NDRE:  
* Mintz: A multi-class logistic regression model based on text syntactic and lexical features proposed by [PERSON] et al. [3];
* MultiR: A probabilistic graphical model in a multi-instance learning framework proposed by [PERSON] et al. [12];* PCNN+ATT: A CNN-based model combined with piecewise max pooling and selective attention over sentences proposed by [PERSON] et al. [6];
* PCNN+ATT+SL: An entity-pair level model with soft labels that exploits semantic/syntactic information from correctly labeled instances, proposed by [PERSON] et al. [15];
* BGWA: A BiGRU-based model with word-level attention proposed by [PERSON] et al. [16];
* RESIDE: A GCN model based on Graph Convolutional Network (GCN) with utilizing syntactic dependency, entity type, and relation alias information, proposed by [PERSON] et al. [8];
* Direct+Dis: This model, by ([PERSON] et al., employs directly supervised data as supervision for the attention weights of DS data [9];
* REDS2: A two-hop distant supervision model fusing data from NYT and web tables proposed by [PERSON] et al. [10];
* PCNN+PU: A bag-level reinforcement-learning-based method utilizing relation embedding and unlabeled instances proposed by [PERSON] et al. [18];