esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Electrical Conductivity and \(pH\)  
Electrical conductivity (EC) and pH values, measured over time in the feed, the vermifilter effluent, and the hydroponic unit effluent, are presented in Table 2. An increase in EC was observed, most noticeably from the feed to the effluent of the vermifilter on day 21. EC has been previously reported to increase in vermifiltered wastewaters due to organic matter mineralization [15; 22]. The reason for a much lower EC in the feed than in the other samples of that day is unclear. Subsequent measurements showed relatively high EC values till day 42. pH showed neutral to slightly alkaline values, higher in the vermifilter effluent and hydroponic effluent.  
According to a 2016 technical report produced at Oklahoma State University, EC ranging from 0.25 S/m to 0.30 S/m should be optimal for cabbage (unspecified variety); optimal pH was reported to be between 6.5 and 7.0 [44]. On the other hand, increasing salinity has been reported to impair nitrogen, potassium, calcium and magnesium leaf concentrations in Chinese cabbage, with nitrogen and potassium remaining stable at EC from 0.22 to 0.52 S/m [45], so some metabolic damage on the crops might still result. For _Eisenia fetida_, negative effects of high EC on survival and growth were reported, the 50% lethal dosage after 42 days being 0.183 S/m [46]. Alkaline conditions promote the deprotonation of ammonia to its toxic form NH\({}_{3}\), which might also affect the earthworms; however, a study of both ammonia and pH increase on a vermifiltration system reported little to no negative effects on the operation [47]. In this study, the measured pH ranged from 6.5 to 7.8. These values could benefit nitrification activity since optimal ammonia and nitrite oxidation in polluted waters have been reported to occur at pH between 6.5 and 7.5 [48]. According to Portuguese law, the final pH values fell within the limit values for wastewater discharge (6 to 9) and the recommended values for irrigation (6.5 to 8.4); however, EC values in all system samples exceeded the recommended for irrigation (0.1 S/m) [49].
## 2 State of the Art
### Deep Learning for Text Classification  
The Transformer architecture ([PERSON], 2016) has changed the landscape of NLP, as almost all the classic NLP tasks (e.g., classification, dependency parsing, sentiment analysis, namedentity recognition, or question answering) can be implemented with it. Transformer-based language models (e.g., BERT [28], DistilBERT [29], or RoBERTa [30]) employ a self-attention mechanism to capture the context and relationships among words. This self-attention mechanism enables them to assess the significance of individual words within a sentence, prioritizing semantically meaningful tokens while filtering out irrelevant noise [31]. [PERSON] [32] even names pre-trained Transformer foundation models because most NLP tasks can be designed around them. Still, they also require adaptation to domain-specific tasks (e.g., text classification, sentiment analysis, etc.).  
A large-scale survey by [PERSON] et al. [33] presents most of the deep learning architectures widely used for text classification, from LSTMs to Transformers. A taxonomy of Transformers can be found in [27] and includes most models used for classification until late 2021. Some specialized surveys are also available. One survey [34] examines the role of embeddings in text classification. The last few years have also seen the rise of hybrid architectures that combine sequence-to-sequence or graph neural networks with Transformers, as described in [PERSON] et al. [35]. Another recent survey [36] examines text classification models in the context of designing spam filters.  
Large Language Models (LLMs) exceed Transformers in size (i.e., over 10 billion parameters [37]) and apply training strategies such as instruction tuning and adaptation tuning to enable instruction following and zero-shot capabilities. The GPT 3/4 models ([[https://chat.openai.com](https://chat.openai.com)]([https://chat.openai.com](https://chat.openai.com)), accessed on 1 March 2024) [38] inhibit so-called emerging capabilities which further improve their capability to correctly interpret human language and, therefore, pave the way for even more advanced text classification systems [37].  
Using AI tools for classification or related generative processes requires considering issues such as transparency and accountability. Since generative tools can easily reuse or remix text, code, or images on demand, it is important to know more details about the training process so that the generated artifacts can be traced back to the training corpora. An early review of LLM transparency and accountability can be found in [39].
## 4 Discussion
### Limitation and Recommendation for Future Research  
Although the multivariate integration index showed a consistent pattern with in situ richness data, it faced two known limitations. Firstly, the limited availability of in situ richness data constrained the comparison and integration of remote sensing metrics. Three dramatic declines of DHI's explained variance in 40\({}^{\circ}\)N zones, 10\({}^{\circ}\)S zones, and 40\({}^{\circ}\)S zones were found. It appeared that the in situ data of mammal richness were insufficient, because of the detail loss in these regions (Figure 5). Once higher resolution species richness data are available, the multivariate integration index should exhibit higher accuracy. Secondly, relationship patterns between energy/productivity, climate and ecosystem texture metrics, and animal diversity were limited by many other factors, namely scale, resolution, latitude, and so on.  
Future research will consider both scale and resolution when comparing remote sensing metrics. Because the focus is taking full advantage of long time-series remote sensing data for species monitoring, some important metrics (e.g., precipitation and air temperature) which are not available from remote sensing data globally, were excluded in this study. To make biodiversity monitoring and assessment more globally accurate, future research will consider more datasets from both remote sensing data and model simulation data in integration index development. In addition, with the collection of more species information of both plants and animals, future research will develop the integration index for predicting plant species richness, as well as animal species richness.
## 5 Results  
The simulation results obtained in the TRNSYS environment for the investigated buildings are discussed in this section. TRNSYS software allows for the calculation of various parameter trends over different time bases (yearly, monthly, weekly, daily, hourly). In this work, a time step of 0.05 h is selected. The trend of the total heat transfer rate for a typical winter day will be analyzed by summing up all the heat transfer rates related to the considered building geometries. Monthly results for the thermal energy demand and primary energy of all buildings obtained from dynamic simulations will be described for both the reference and proposed energy refurbishment scenarios.  
Yearly results from the dynamic simulations will compare the reference system and the proposed energy refurbishment scenario in terms of CO\({}_{2}\) emissions, primary energy, and natural gas cost.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Thermal Insulations** & **Thickness [m]** & \begin{tabular}{c} **Conductivity** \\ **[kJ/hmK]** \\ \end{tabular} & \begin{tabular}{c} **Specific Heat** \\ **[kJ/kgK]** \\ \end{tabular} & \begin{tabular}{c} **Density** \\ **[kg/m\({}^{3}\)]** \\ \end{tabular} & \
\begin{tabular}{c} **Cost** \\ **[€/m\({}^{2}\)]** \\ \end{tabular} \\ \hline
\begin{tabular}{c} Rock wool \\ Rigid polyurethane foam \\ \end{tabular} & 0.100 & 0.126 & 1.03 & 100 & 66.34 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Thermophysical properties of the thermal insulations in the proposed system.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Solution Proposed** & **Technical Requirements for the Solution Proposed** \\ \hline \begin{tabular}{c} Thermal insulation of the walls \\ \end{tabular} & \
\begin{tabular}{c} Weather zone C \\ Weather zone D \\ Weather zone E \\ \end{tabular} &
\begin{tabular}{c} \(U\leq 0.30\) W/m\({}^{2}\)K \\ \(U\leq 0.26\) W/m\({}^{2}\)K \\ \(U\leq 0.23\) W/m\({}^{2}\)K \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 9: Thermal conductance limits for the building’s external walls according to the Italian Legislation.  
The results pertain to each type of building, i.e., individual buildings with geometries/shapes representing types A to G. However, the analysis will also cover the entire building stock, i.e., all 2940 buildings representing types A to G in the Naples municipality, to evaluate the effect of the proposed energy measures according to two penetration scenarios.
# A Simulation of Tire Hydroplaning Based on Laser Scanning of Road Surfaces
## 4 Result Analysis
### Changes in Critical Water Sliding Velocity  
The critical water sliding speed on the road surface can be considered to be the speed that causes the dynamic water pressure to be equal to the tire in a certain water film thickness, in which case the road surface and the tire just slip. The boundary water sliding speed on different roads is shown in Figure 19.  
As can be seen from the figure above, the overall trend of the critical water slip velocity under the three kinds of road surfaces is to decrease with the increase of water film thickness. The reduction rate of the critical water slip velocity is faster in the range of 2-10 mm, and the reduction trend of the critical water slip velocity becomes slower after 10 mm.  
In addition, there are the following rules:  
1. Under the same water film thickness conditions, the critical water skid speed for different road surfaces is OGFC > SMA > AC, indicating that OGFC pavement has better anti-skid performance compared to other pavements. However, the SMA pavement does not show a significant improvement in anti-skid performance compared to the AC pavement, especially when the water film thickness is relatively high. The water skid curves of both are very close, which does not correspond to the actual measured anti-skid indicators.  
Referring to the critical water speed marked in the figure, the critical water film thickness corresponding to the three kinds of road surface is 16 mm for the SMA road surface and 13 mm for the AC road surface, while the OGFC road surface can pass through the road surface with a 20 mm water film thickness at the speed of 85 km/h under the simulated condition.
3. As analyzed above, the critical water skid speed begins to decrease sharply when the water film thickness reaches around 8 mm. Essentially, the 8 mm longitudinal trend pattern on tires can no longer promptly drain surface water, leading to a sharp reduction in the critical water skid speed. To ensure that more vehicles have sufficient safe driving speeds, it is necessary to establish lower safe driving speeds for rainy weather. To guarantee the safety of most vehicles, it is recommended to set a safe water film thickness of 4 to 5 mm.
# A New Under-Sampling Method to Face Class Overlap and Imbalance
On the other hand, despite the fact that over-sampling has been shown to be apparently superior to under-sampling [27, 28], the under-sampling approach can be especially useful when dealing with big data applications [29] since these algorithms will lead to a reduction of the data set size and also a decrease in the computational requirements of the subsequent classification process. Bearing both these issues in mind, the present paper introduces a new under-sampling technique (here called DBMIST-US) to face not only the class imbalance but also the overlapping between classes. It deals with the noisy instances in the majority class via a noise filter based on the DBSCAN clustering algorithm [30] combined with the use of a minimum spanning tree (MST) algorithm to reduce the size of the negative class. The reason to combine the DBSCAN clustering with the MST approach is because DBSCAN has demonstrated to be a powerful tool for identifying and removing noisy instances and cleaning the overlapping between classes [31], but it does not produce a well-balanced class distribution. By viewing the data set as a weighted complete graph, the MST algorithm allows for discovering the core of the majority class, which is further used to remove the amount of redundant negative instances needed to balance both classes. Hereafter, the remainder of this paper is organized as follows. Section 2 introduces several well-known methods to tackle the class imbalance problem. Details of the DBMIST-US algorithm here proposed are described in Section 3. The experimental setup is reported in Section 4. In Section 5, the results are analyzed and discussed. Finally, Section 6 presents the main conclusions and outlines some open lines to be addressed in the future.
# Revisiting the Design of Parallel Stream Joins on Trusted Execution Environments
## 6 Towards More Efficient Parallel Stream Join on Multicore Processors with TEEs
### Put It All Together  
A good design for parallel stream join processing on SGX is one that benefits the data from all the security guarantees that TEE offers whilst minimizing the performance overheard introduced in comparison to running the same join operation in a non-SGX environment (referred to in what follows as a Native join). Currently, the performance gap between a Native join and its SGX implementation is quite large and further increases exponentially as the number of tuples in the input stream increases. This is impractical for large-scale secure join operations. Hence, considering the current scale-up limitations of SGX hardware, a model-guided scale-out solution is required to enable the optimal use of hardware resources available given the metadata provided by the user (See Section 6).  
For simplicity, we refer to our custom solution as _Secjoin_. To investigate the effectiveness of our model-driven parametrization of _Secjoin_, we first write an SGX implementation of the Symmetric Hash Join algorithm following the Join-Matrix partitioning scheme and execute a join operation over two columns. The experiment is repeated over streams of incrementally equal sizes. As expected, we notice in Figure 8 that the SGX implementation matches the Native code in performance at first, then develops a performance gap that increases exponentially as we increase the number of tuples until the program is eventually killed by the kernel around the 5000 input mark due to lack of memory. Meanwhile, the SecJoin implementation consistently delivered a quasi-native performance with negligible overhead and reliable scalability potential. Analysing the sample run utilising 5000 tuples, we realise that over four-fifths of the end-to-end program latency is dedicated to SGX-induced overheads such as memory page swaps. On the other hand, SecJoin limits such overheads to a ratio of <5% as illustrated in Figure 9. Although introducing a new communication overhead due to the use of MPI for data orchestration, SecJoin still achieves up to 4-folds of improvement in both latency and throughput.  
Figure 8: _Cont._  
Figure 8: Performance Evaluation of SecJoin Compared to Native and SGX Joins.  
Figure 9: Breakdown of End-to-End Program Execution Time.
## 3 Fuzzy-Logic Topology Control (FTC)  
In this section, we first present the design of Fuzzy-logic Topology Control (FTC), and then propose a protocol that can run on sensor nodes. We utilize topology control based on fuzzy-logic to achieve the desired node degree, in turn improving the network connectivity. Because WSNs are large-scale and distributed networks, the algorithm/protocol running in a sensor node is localized, only depending on the node itself and its one-hop neighbors' information.
## 6 Summary and Conclusions  
Though considerable effort has been expended on agricultural DSSs, their uptake has been limited. In part, this has been caused by a \"technology/science push\" approach to DSS development. This paper suggests that this problem can be improved considerably using a consultative systems-based approach to DSS design.  
Such an approach was employed to design EASY--an agricultural DSS that would support the effective and user-driven discovery, publishing and access to a range of information, primarily spatial in nature. This information needs to be easily accessed by individuals involved in the delivery of spatial information in broadly defined \"agricultural extension\" activities. A logical way to achieve this is by developing a computer-based delivery mechanism reliant on spatial technology that has been designed through an assessment of needs of information providers (researchers and government organisations) and end-users--_i.e._, extension officers and landholders. The EASY prototype has been developed as a nationally applicable conceptual prototype and related demonstration of such an information system based on representational user requirements analysis. The design was also developed after a survey had been done of existing software systems to eliminate duplication and ensure compatibility with those that are the most useful. This survey also built upon the results of previous desktop scans of farm software [33,34].  
The system envisaged enables the ingestion of a large range of information, potentially including vector spatial data, geo-referenced imagery, raster spatial datasets, output from modelling processes, and reports. An operational version of the system would be developed based on a Web 2.0 paradigm allowing for a two way flow of information. Web 2.0 collaboration tools are reported in [35] as a way forward in realising more informed and participatory land use decision-making.  
Such a Web 2.0 system would be accessible via a number of means including an API (Application Programming Interface) for automated integration with other software applications and also via a web-delivered user interface that allows extension staff and public agency land management planners to easily discover and extract information for relevant areas and in formats that are directly useable. Such a system would ultimately be compatible with mobile computing devices to support data collection, and more sophisticated GIS tools to support advanced spatial analysis and data visualisation. Extracted data may also be made available to landholders who may use it in existing desktop software tools such as ifarmTM. The next steps in Australia are to provide better access to the growing digital datasets that can inform more sustainable and profitable farming practices, and to develop a suitable business model for the long-term support of EASY. The EASY prototype presented in this paper provides a critical next step in spatially enabling Australia's agricultural sector.
# EDDA: An Efficient Distributed Data Replication Algorithm in VANETs
## 5 Performance Evaluation
### Simulation Setup  
To evaluate the performance of the proposed replication algorithm, we have conducted extensive simulations. In the simulations, the following default settings are used.  
Compromised by the complexity of simulations, we select a bounded area on the map of Los Angeles for the simulations. We extract a 2000 m \(\times\) 2000 m rectangle street area through OpenStreetMap [57], the satellite map of which is presented in Figure 2(a). Then, we use Simulation of Urban Mobility (SUMO) [58] to convert the extracted area to the road networks. Figure 2(b) shows the converted road network of the selected area in Los Angeles. The realistic mobility trace of vehicles is generated by the open-source microscopic space-continuous and time-discrete vehicular traffic generator package SUMO. SUMO uses a collision-free car-following model to determine the speeds and the positions of the vehicles. The output from SUMO is converted into input files for the movement of nodes in the NS-3 simulator. As for the V2V communication, the Nakagami-\(m\) channel model is applied. We set the value of \(m\) according to the communication distance. When the distance is less than 50 m, \(m\) is set as one. When the distance is in the range of [50; 150], \(m\) is set as 1.5. When the distance is larger than 150 m, \(m\) is set as two.  
The coverage of V2V communications is set as 300 m. The transmission frame duration is set as 1 ms. The average encounter duration is related to the vehicle's velocity and density. The MAC layer protocol follows 802.11p, with the distributed coordination function enabled. We refer to [38] and set the simulation time as one hour.  
We conduct two sets of simulations, which are implemented in the urban and highway environment. We set up different numbers of vehicles to indicate normal traffic conditions and sparse traffic conditions, rather than a static number of vehicles. In each set, the replication limit of message \(M\) is indicated by parameter \(n\); in other words, the number of copies that can be spread is \(n\). As for the value of parameter \(n\), we apply the method in [11] for calculation. We also vary \(n\) to see  
Figure 3: Los Angeles.  
the effect of different numbers of message copies on network performance. Note that in the tradeoff analysis, all metrics are plotted as functions of the number of copies and the number of vehicles, respectively. We repeat the simulations and provide the results plotted with the confidence interval. The main experimental parameters are shown in Table 1.
# Identifying At-Risk Students for Early Intervention--A Probabilistic Machine Learning Approach
## 4 Results
This prediction demonstrates how the PLR model can incorporate the possibility of a student being either at-risk or not-at-risk, rather than simply providing a point estimate of 61%, as a standard logistic regression (LR) model would do without a credible interval. Therefore, the PLR \begin{table}
\begin{tabular}{l l l l l l l l l l} \hline \multirow{2}{*}{**Stages**} & \multirow{2}{*}{**Weeks**} & \multirow{2}{*}{**Accuracy**} & \multicolumn{2}{c}{**F1-Score**} & \multicolumn{2}{c}{**Precision**} & \multicolumn{2}{c}{**Recall**} \\ \cline{3-10}  & & **LR** & **PLR** & **LR** & **PLR** & **LR** & **PLR** & **LR** & **PLR** \\ \hline Stage 1 & Week 2 & 62.98 & 62.98 & 60.73 & 60.73 & 64.66 & 64.66 & 57.25 & 57.25 \\ Stage 2 & Week 4 & **67.43** & 67.30 & **66.67** & 66.49 & **68.27** & 68.18 & 65.14 & **64.89** \\ Stage 3 & Week 6 & 92.60 & **92.81** & 92.63 & **92.83** & 92.34 & **92.55** & **92.92** & 93.13 \\ Stage 4 & Week 7 & 82.77 & 82.77 & 83.83 & 83.83 & 79.00 & 79.00 & 89.29 & 89.29 \\ Stage 5 & Week 10 & 78.29 & 78.29 & **79.79** & 79.75 & 74.61 & **74.71** & **85.75** & 85.52 \\ \hline \end{tabular}  
Numbers are in bold to denote instances where one model outperformed the other.  
\end{table} 
Table 4: Probabilistic logistic regression (PLR) and standard logistic regression (LR) model’s performance. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline  & **Stage 1** & **Stage 2** & **Stage 3** & **Stage 4** & **Stage 5** \\ \hline
**Variables** & \(\overline{x}(1\_{\mathbf{x_{d}}1})\) & \(\overline{x}(1\_{\mathbf{x_{d}}1})\) & \(\overline{x}(1\_{\mathbf{x_{d}}1})\) & \(\overline{x}(1\_{\mathbf{x_{d}}1})\) & \(\overline{x}(1\_{\mathbf{x_{d}}1})\) \\ \hline TITLE & 0.47 (0.58) & 0.54 (0.60) & 1.10 (1.12) & 1.20 (0.74) & 0.62 (0.68) \\ TIME ON COURSES & 0.70 (0.35) & 0.93 (0.36) & 3.5 (1.30) & 0.80 (0.59) & 0.71 (0.48) \\ AS 1 & & & 1.60 (0.60) & 0.63 (0.41) & 0.71 (0.39) \\ TM 1 & & & & & 0.88 (0.47) & \(-0.17\) (0.50) \\ TM 2 & & & & & 1.1 (0.52) \\ \hline \end{tabular}  
\end{table} 
Table 5: Posterior plot values: 94% HDI values for the model’s parameters. model provides a more comprehensive view of the prediction's uncertainty, making it a valuable tool in decision-making scenarios.