esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results
### OrtT is an Orphan Toxin
Three predicted promoters (_i.e._, \(\sigma^{\text{E}}\), \(\sigma^{\text{D}}\), and \(\sigma^{\text{H}}\)) for _ortT_ and one predicted promoter (_i.e._, \(\sigma^{\text{D}}\)) for _ydcY_ are shown by dashed arrows; (**B**) Growth of BW25113 wild-type cells harboring empty plasmid, plasmid carrying _ortT_ only or _ortT_ with _ydcY_, _ghoS_, or the intervening region upstream and downstream of _ortT_ (UTR) in LB medium with chloramphenicol (30 \(\mu\)g/mL) with 1 mM IPTG introduced at 1 h. Two independent cultures were evaluated. Error bar represents standard deviation; (**C**) Growth on LB plates with chloramphenicol (30 \(\mu\)g/mL) with or without 1 mM IPTG. Plasmid abbreviations: Empty, pCA24N; OrtT, pCA24N-_ortT_; OrtT-YdcY, pCA24N-_ortT-ydcY_; OrtT-GhoS, pCA24N-_ortT-ghoS_; OrtT-UTR, pCA24N-_ortT-UTR_. Two independent cultures were evaluated. (Figure S2C,D). We found that RNA both upstream and downstream of _ortT_ did not reduce its toxicity (Figure 3B). Note that previously such a construct has led us to identify the RNA antitoxin partner RalA for the toxin RalR [13]. To verify that an antitoxin for OrtT is not coded elsewhere within the genome, we performed an extensive search for potential antioxins by constructing a genomic library by partially digesting BW25113 genomic DNA and cloning the DNA fragments downstream of _ortT_ gene in the pCA24N-_ortT_-B construct. The library of cloned plasmids was then transformed into BW25113 cells and screened for an antitoxin by growing the transformants in presence of IPTG to induce _ortT_ expression. With about 3-fold coverage of the genome and an average insert size of 622 bp, we found that the constructs that were able to survive OrtT toxin expression were those with defective plasmids (_i.e._, those lacking a functional _ortT_ gene). Therefore, no antitoxin partner for OrtT was identified from these four sets of experiments which confirm that OrtT is an orphan toxin.
## 2 Methodology
### Data Evaluation  
A key problem with ML algorithms is their complexity. Thus, according to different proposals, an algorithm is assessed based on three criteria, namely accuracy, computational efficiency, and interpretability. To ensure accuracy, metrics such as the Mean Absolute Error (MAE), the Root Mean Square Error (RMSE), and the determination Coefficient (R\({}^{2}\)) are considered [37; 38]. For both the MAE and the RMSE, the smaller or closer they are to zero, the better the accuracy of the predictive model. In the case of R\({}^{2}\), the closer it is to one, the better the accuracy of the model. The MAE, RMSE and \(R^{2}\) are calculated as follows:  
\[\text{MAE}=\frac{1}{n}\sum_{i=1}^{n}|y_{i}-x_{i}|, \tag{1}\]  
\[\text{RMSE}=\sqrt{\sum_{i=1}^{n}\frac{\left(y_{i}-x_{i}\right)^{2}}{n}}, \tag{2}\]  
\[R^{2}=1-\frac{SSR}{SST}, \tag{3}\]  
where \(y_{i}\) and \(x_{i}\) are the predicted and observed values, respectively, whereas \(n\) denotes the total number of data points. \(SSR\) is the sum of squared regression, while \(SST\) is the total sum of squares.  
Figure 1: Definitions and hyperparameter for each ML algorithm.  
Moreover, [PERSON] and [PERSON] [41] introduced the regression error characteristic curve (REC), which is used here to represent the errors of the ML algorithms. The REC curve plots the error tolerance on the \(x\)-axis against the percentage of points predicted within the tolerance on the \(y\)-axis. The error on the \(x\)-axis can be defined as the absolute deviation. The REC for a perfect model is one, which is located at the top-left corner. The area above the curve represents a global error measure, which should be small as possible. Accordingly, this plot provides for a quick and easy comparison of the different regression models.  
Computational efficiency also refers to the estimated time it takes to execute an algorithm. Here, the four ML algorithms were observed according to computational efficiency.  
Next, the interpretability of an algorithm is another important criterion. In this work, interpretability is based on the sensitivity analysis of the four ML algorithms. As part of the data-driven approach, the sensitivity analyses were recorded in the form of a global sensitivity response (GSA) after training the model. The GSA response is then used to determine the relative importance of the input variables in relation to the output variable \(\text{E}_{\text{u}.50}\).
# Consistency Models of NoSQL Databases
## 4 NoSQL Databases Background
### Neo4j  
Neo4j is a graph NoSQL database system. Its data model prioritizes relationships between entities in the form of graphs [28; 29].  
In the RDBMS world, despite the normalization forms, first introduced in 1970 by [PERSON] [30], database architects tend to put some extra information into some tables to prevent joins, ending up with several replications of the same data and many consistency problems by having multiple versions of this data. MongoDB also tries to avoid joins by nesting objects which cause the same duplication problem as RDBMS [28].  
In Neo4j, a graph is defined by a node and a relationship. As shown in Figure 3, a node represents an entity (i.e., the entity Person). It can have several node attributes. (i.e., the Person with the name \"[PERSON]\"). Two entities can be linked by a relationship (i.e., the Person with name \"[PERSON]\" likes the Person with name \"[PERSON]\"). Relationships can also have properties [31].  
Internally, Neo4j uses linked lists of fixed size record on disk [32]. Properties are stored as a linked list of property records. Each property record holds a key/value. Each node or relationship references its first property record. Relationships are stored in a doubly linked list. A node references its first relationship.  
Neo4j is schema optional. It is not necessary to create indexes and constraints. Nodes, relationships, and properties can be created without defining a schema.  
Labels define domains by grouping nodes into sets. Nodes that have the same label belongs to the same set. For example, all nodes representing cars could be labeled with the same label: Car. This allows Neo4j to perform operations only within a specific label, such as finding all cars with a given brand.
## 3 Related Work on Protection against Sybil Attacks
### B&SEBP&F Algorithm  
_Solution against Bogus Attack_--Monitoring Node Behavior and Interaction with Other Nodes  
When a vehicle is in its range, it checks if the trust level needs to be decreased or increased. Car is considered as untrusted when its trust level (TL) drops below 0.5. The second approach, the Bogus & Sybil Enhanced Behavior Processing & Footprint (B&SEBP&F) algorithm, improved the previous one, the Bogus & Sybil Trust Level & Timestamp (B&STL&T), and more events were added to the system.  
The first method listed in Bogus & Sybil (2018) was the antibogus mechanism that relies on the car's trust level (TL) so the car confidence. The confidence level for each vehicle varies from 0.0 up to 1.0. This level increases (by 0.1) only if the incident reported by the car is confirmed by another three. This level could be lowered by 0.4 if not enough cars confirm that event within 30 s. The stationary network points maintain the whole grading system. Cars are deemed untrustworthy and removed from the network when their TL falls below 0.5. The current algorithm was expanded and more events were applied to the method in the second approach.  
In order to protect from massive bogus attacks, the following events were looked at in greater detail:  
* accident,
* heavy traffic,
* speed control.  
Each of these events is characterized by different vehicle behavior on the road and different expected information from the vehicle. Data provided by the vehicle (i.e., \(X\) and \(Y\) coordinates, speed, confidence level, and direction) are analyzed each time to determine the truthfulness of the reported road incident (Algorithm 1 and Figure 4). Each incident or event is sent by the car to the network where is further verified by the antibogus module. Then, if the message was verified correctly, it could appear for the all nodes in the network.
## 1 Introduction
[20] evaluated a unique tri-generation system for simultaneously generating electrical power, air conditioning, and desalinated water. The proposed system includes an ORC, a Humidification and Dehumidification (HDH) water desalination system, a Desiccant Cooling System (DCS), a solar system (including evacuated tube collectors and a thermal energy storage unit), and additional components. The proposed trigenation system has the greatest electrical power, freshwater capacity, space cooling capacity, and Energy Utilization Factor (EUF) of 104.5 kW, 72.37 kg/h, 25.48 kW, and 0.266%, respectively. According to the literature review, few tri/multi-generation systems for power, fresh water, and cooling load combined with MED/SRC/ARS, HDH/ORC/ECC, and HDH/ORC/DCS operated with solar, biomass, biogas, and geothermal energies. As a result, multi-generation systems have not been thoroughly investigated to date. Therefore, a new multi-generation set-up based on an ORC, DCS, and HDH unit to generate power, space cooling/domestic heating, and fresh water is aimed to small/medium-scale buildings. The low operating temperatures are suitable for ORC power generation [21]. HDH water desalination has several advantages, including low-temperature energy utilization (e.g., waste energy, solar energy), simplicity, and inexpensive operation and installation costs [22]. Desiccant air conditioners do not produce polluting gases such as chlorofluorocarbons, which are produced by traditional air conditioning systems. Therefore, these constraints motivate the authors to research the design, operation, and performance assessment of multi-generation systems capable of providing continuous daylight electrical power, fresh water, and space cooling/domestic heating using solar energy for small and medium-scale buildings. To the authors' knowledge, no previous research into such advanced systems has been done. Therefore, a more efficient multi-generation system, including ORC, HDH, DCS, and with heat recovery systems is proposed, thermodynamically evaluated and compared with the basic system [20] in this study. The impact on the system's productivity and performance parameters is investigated using thermodynamics analysis and parametric study. The systems' cycles are thermodynamically simulated and analyzed using the EES (Engineering Equation Solver) software. The following points will be fulfilled: (i) extensive energy mathematical modeling, (ii) effects of various operating parameters on the system's productivity and performance, and (iii) assessing and evaluating the proposed system compared with basic system. It is expected that the findings of the proposed integrated ORC, HDH, and DCS multi-generation systems will attract attention and benefit researchers, solar multi-generation developers, power plant designers, and investors because of theor economic and environmental feasibility for producing electricity, space cooling, domestic heating, and fresh water for end users, especially for small and medium-scale buildings.
# An Entity-Relation Extraction Method Based on the Mixture-of-Experts Model and Dependency Parsing
## 3 Methodology  
The entity-relation extraction model proposed in this paper consists of four main components: an encoding layer, a feature fusion layer, a relation classification layer, and a decoding layer. The detailed architecture of the model is illustrated in Figure 2.  
In the encoding layer, the BERT model is initially employed to encode the input text sequence and generate vector representations for each character. To compensate for BERT's limitations in capturing syntactic information, we introduce an additional dependency syntax encoding module, as detailed in Section 3.1.2. The feature fusion layer integrates character-level and word-level information, enabling the model to learn more comprehensive feature representations and understand the relationships between entities in a sentence more accurately, as explained in Section 3.2. Notably, we innovatively introduce the concept of a mixture-of-experts architecture in the relation classification layer, incorporating a gating network to dynamically select the appropriate expert based on the input sample's features, as discussed in Section 3.3. The decoding layer adopts the approach of the OneRel [15] model, utilizing a specific relation-tagging strategy to define  
Figure 2: The overall architecture of the proposed model.  
the boundaries of the head and tail entities and decode entities and relations from the output matrix. This method effectively minimizes redundant information and prevents cascading errors.
## 2 Materials and Methods  
### Data and Preprocessing  
The present study used GPP, ET, and LAI data with a 0.05\({}^{\circ}\) spatial resolution for 1982-2018 obtained from Global Land Surface Satellite (GLASS) products [31; 32]. To ensure the reliability of the data, FLUXNET tower data were used to verify the GPP and ET values in the STC (Figure S1). Meteorological data were obtained from the National Tibetan Plateau Data Center (2019). This dataset, with a temporal and spatial resolution of 3 h and 0.1\({}^{\circ}\), respectively, was compiled through a combination of remote sensing data, field station data, and data reanalysis. Included in the dataset are data for seven near-surface climate variables, namely surface pressure, air temperature, specific humidity, downward shortwave radiation, wind speed, downward long-wave radiation, and rate of precipitation [33; 34]. In this study, we used average annual temperature (MAT), annual accumulated precipitation (MAP), and annual solar radiation (Rg) data to analyze the impact of climate change on WUE dynamics. VPD data were derived from the Google Earth Engine (GEE) Terra Climate, which provides a climate water balance for terrestrial surfaces at a global scale and monthly resolution [25]. Soil water content (SWC) was obtained from the GLDAS Noah Land Surface Model L4 monthly 0.25 \(\times\) 0.25\({}^{\circ}\) V2.0. Atmospheric CO\({}_{2}\) concentration data were sourced from the Center for Global Environmental Research. Climate zone and landscape data were sourced from the Resource Environmental Science and Data Center. Climate zoning data were used to determine the extent of the STC. For convenience of research, the landscape was divided into evergreeniferous forest (ECF), evergreen broadleaf forest (EBF), deciduous broadleaf forest (DBF), shrub, grassland, and cropland (Figure S1). All datasets were resampled to 0.05 \(\times\) 0.05\({}^{\circ}\). To facilitate comparisons between the variables of different units and magnitudes, we normalized all variables based on the time series.
The difference between the GBD and MGBD is that the MGBD is optimized with a modified minimum mean square error (MMSE) criterion in order to improve bit error rate (BER) gain. The modified MMSE criterion is designed to minimize the Frobenius norm for non-diagonal components of equivalent channel that causes IUI. Since MGBD uses the effective channel for making the precoding matrix, MGBD can achieve performance enhancement compared to GBD. However, MGBD needs one more SVD operation to make effective channel compared to RBD. Therefore, MGBD has high computational complexity at the base station compared to RBD and GBD. In this paper, proposed scheme that combines RBD precoding method with partial nulling concept achieves performance enhancement due to the transmit diversity gain compared to RBD. The proposed scheme utilizes partial nulling concept by suppressing IUI imperfectly and decreasing the number of data streams like GBD. Diversity gain is obtained by leaving IUI. The intended IUI is made by choosing the row vectors of the channel matrix that are not nullified. Since the criterion for choosing the row vectors of the channel is the power of the channel, the number of selected row vectors of the channel for each device can be unfair. The proposed scheme exploits the simpler way intended IUI is removed at the receiver compared to GBD and MGBD. It is unnecessary to form and multiply post-coder at the receiver. Furthermore, the proposed scheme has better performance compared to the GBD and MGBD when the number of reduced data streams is not enough for most devices to achieve diversity gain. When the proposed scheme is used, the better performances of the bit error rate (BER) and throughput can be achieved with low complexity. The complexity of the proposed scheme at the receiver is lower than GBD and MGBD. In addition, the performance of the proposed scheme is better than GBD and MGBD when the number of data stream is enough. The low complexity at the receiver is the great benefit since the operation of the receiver is heavier burden than the operation at the base station. Therefore, the proposed scheme is more practical than GBD and MGBD in terms of the complexity at the receiver. The computational complexity of the proposed scheme at the transmitter is also lower than RBD and MGBD. However, the proposed scheme can achieve the better performance with low complexity at the transmitter. Thus, the proposed scheme is highly efficient compared to conventional schemes in respect of performance and complexity.
## 3 Dataset Preparation  
### Training Data  
Fine-tuning pre-trained models using STS (Semantic Sentence Similarity) datasets is a popular method for training Sentence BERT models. These datasets typically contain pairs of sentences, along with their corresponding semantic similarity scores ranging from 0 to 5. Table 1 provides a summary of the datasets that were utilized to train our proposed models.  
To the best of our knowledge, among these datasets, the STSb (STS Benchmark) [12], STS 2012-2017 [12; 13; 14; 15; 16; 17], and SICK-R datasets are the most popular STS datasets. Thus, we consider them for training English sentence embedding models. For training Korean sentence embedding models, we consider three types of datasets: (1) KLUE-STS [8], which is one of the most popular Korean STS datasets, consisting of Policy News (articles produced by ministries, etc.), ParaKQC (conversational sentences about smart home devices), and Airbnb Reviews. (2) KorSTS [18] is a translated version of STSb, but the translated sentences were refined. (3) KSTS and KSICKR are the translated versions of STS (STS 2012-2017) and SICK-R, respectively, using gpt-3.5-turbo. When translating using gpt-3.5-turbo, we exclude some of the sentences that are not correctly translated for a number of reasons. Note we use the notation \"SICKR\" instead of \"SICK-R\" and \"KLUE\" instead of \"KLUE-STS\" to express the dataset names concisely.  
Note that, in the experiments described in the following sections, we used all of the training, development, and test sets for training. For instance, although KorSTS includes  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Language** & **Dataset Name** & **\# of Sentences** \\ \hline \multirow{4}{*}{Korean} & KLUE & 24,374 \\  & KorSTS & 17,250 \\  & KSTS (STS 2012–2017, Translated) & 18,874 \\  & KSICKR (Translated) & 15,612 \\ \hline \multirow{2}{*}{English} & STS (STS Benchmark) & 16,080 \\  & STS (STS 2012–2017) & 35,804 \\  & SICKR & 19,854 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of the data used for training our proposed models.  
separate datasets for training, development, and testing, we trained our models on all of the KorSTS datasets (including the training, development, and test sets) when reporting our results using the KorSTS dataset.
## 4 Analysis of Reservation Frame Slotted-ALOHA
### Probabilities of Success and Release  
According to [27], the probability \(P_{s}(S_{ij},c_{i},f_{i})\) that \(S_{ij}\) contending end-devices succeed in transmitting their first data packet in a frame, having \(f_{i}\) free slots and \(c_{i}\) contending end-devices, can be formulated as:  
\[P_{s}\left(S_{ij},c_{i},f_{i}\right)=\frac{\left(\begin{array}{c}f_{i}\\ S_{ij}\end{array}\right)\prod\limits_{k=0}^{S_{ij}-1}\left(c_{i}-k\right)G \left(f_{i}-S_{ij},c_{i}-S_{ij}\right)}{f_{i}^{c_{i}}} \tag{6}\]  
where:  
\[G\left(T,t\right)=T^{t}+\sum\limits_{k=1}^{t}\left\{\left(-1\right)^{k}\cdot \prod\limits_{j=0}^{k-1}\left\{\left(t-j\right)\left(T-j\right)\right\}\left( T-k\right)^{t-k}\frac{1}{k!}\right\} \tag{7}\]  
with \(T=f_{i}-S_{ij}\) and \(t=c_{i}-S_{ij}\).  
A reserved slot becomes free when one end-device has transmitted all of its \(L\) data packets to the coordinator. Then, since \(L\) is an exponentially-distributed random variable, the probability that a reserved slot is released in a given frame can be expressed as:  
\[p_{r}=\frac{1}{\overline{L}} \tag{8}\]  
where \(\overline{L}\) is the mean of the exponential distribution of \(L\).
# Lessons Learnt from COVID-19: Computational Strategies for Facing Present and Future Pandemics
## 1 The COVID-19 Pandemic
### Convalescent Plasma and Monoclonal Antibodies  
With the first round of spontaneously healed patients, doctors started flanking standard treatment with the use of convalescent plasma (CP), i.e., the plasma derived from recently recovered donors with a sufficiently high neutralizing antibody titer [52]. A similar protocol was previously adopted to face Ebola [53] and MERS [54] outbreaks, justifying its emergency use in the first stages of the COVID-19 pandemic. Unfortunately, despite promising observational data from the first studies performed on small-size patient cohorts [55], more thorough investigations from more extensive clinical trials demonstrated the inefficacy of this treatment [56; 57], leading to its dismission from routine clinical practices. Despite this failure, CP inspired the design of safer and more targeted immunological treatments in the form of monoclonal antibodies (mAbs) [58; 59]. Since the beginning of the pandemic, several mAbs directed against COVID-19 have been developed, with some obtaining approval from regulatory agencies [60]. Multiple of these mAbs are often used in conjunction to combine their neutralizing power and boost their therapeutic efficiency, exploiting their ability to bind at different epitopes [61].  
The list of approved ones contains the therapeutic combinations of casirivimab and imdevimab (Regeneron/Roche), redanvimab (Celltrion Healthcare), sotrovimab (GSK), and the combination of taxagevimab and cilgavimab [62; 63]. Furthermore, the association of bamlanivimab and etesevimab is nearly approved, despite the previous failure of trials investigating bamlanivimab on its own [63].