esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background
### Nonlinear Mixed-Effects Models  
A generic representation of a nonlinear model,  
\[y_{ij}=f(\Phi_{i},x_{ij})+e_{ij} \tag{1}\]  
describes the \(j\)th response of the \(i\)th individual, expressed as a nonlinear function, \(f\), of the predictor vector, \(x_{ij}\), and the r-parameter vector, \(\Phi_{i}\), and with a measurement or process error given by \(e_{ij}\), usually assumed to be independent and identically normally distributed, \(\mathcal{N}(0,\sigma^{2})\), where \(\sigma^{2}\) represents the variance of the residual. The peculiarity of the ME lies in the parameter vector \(\Phi_{i}\), expressed as the combination of two types of effects (see [7; 12; 13]):  
* Fixed effects--the terms, in the parameter expressions, that remain constant over all the individuals of the analyzed population. They represent the set of the population parameters.
* Random effects--the random components of the model parameters, assumed to be different among individuals or groups of individuals; Therefore, we can rewrite the vector of individual parameters \(\Phi_{i}\) as follows: \[\Phi_{i}=A_{i}\beta+B_{i}\omega_{i}\] (2) where \(A_{i}\) and \(B_{i}\) represent the covariate (or design) matrices for the \(i-th\) subject for the fixed and random effects, respectively; \(\beta\) is the p vector of the fixed population parameters; and \(\omega_{i}\sim(0,\sigma^{2}D)\) is a q vector of random effects with covariance matrix equal to \(\sigma^{2}D\)[7].
# Multi-View Masked Autoencoder for General Image Representation
## 3 Method
### Training Objectives
#### 3.2.2 Contrastive Loss  
For contrastive loss, we use NT-Xent (the normalized temperature-scaled cross-entropy loss) proposed in [13]. This loss operates cosine similarity between given pairs, thus computing mutual information between them. In a mini-batch of \(N\) samples, images augmented from the same image are regarded as a positive pair and the rest of the samples, \(2(N-1)\), are treated as negative samples. Contrastive loss is defined as follows:  
\[\ell(i,j)=-log\frac{exp(sim(z_{i},z_{j})/\tau)}{\sum_{k=1}^{2N}1_{[k\
eq i]}exp (sim(z_{i},z_{k})/\tau)} \tag{4}\]  
\[sim(i,j)=z_{i}^{\top}z_{j}/(\|z_{i}\|\|z_{j}\|) \tag{5}\]  
\[\mathcal{L}_{c}=\frac{1}{2N}\sum_{k=1}^{N}\{\ell(2k-1,2k)+\ell(2k,2k-1)\} \tag{6}\]  
where \(1_{[k\
eq i]}\in\{0,1\}\) is an indicator representing \(1\) if \(k\
eq i\) and \(\tau\) denotes temperature constant. \(\tau\) is set to 0.07, thus following the default setting of [13]. The denominator of \(\ell(i,j)\) computes similarity over a positive pair, and the final contrastive loss function, \(\mathcal{L}_{c}\), is computed across all positive pairs. By doing so, different views from the same image, which we call positive samples, are pulled together while pushing away negative samples in embedding space.  
The total loss \(\mathcal{L}\) is a weighted sum of reconstruction loss \(\mathcal{L}_{r}\) and contrastive loss \(\mathcal{L}_{c}\), which is formulated as follows:  
\[\mathcal{L}=\mathcal{L}_{r}+\lambda\mathcal{L}_{c} \tag{7}\]  
where \(\lambda\) is a hyperparameter deciding loss weight. The combination of two losses, reconstruction loss, and contrastive loss, contributes to learning image representation in a  
Figure 7: A flowchart of an decoder. Decoder takes latent representation \(z_{i}\), masked patches \(x_{i}^{m}\), and positional embeddings as input, and it outputs reconstructed image \(y_{i}\).  
different way: reconstruction loss computes over the reconstructed images (i.e., model prediction), and the aimed target images learn local patterns of an image by reconstructing original information from corrupted ones. Contrastive loss computes over latent representations generated from the encoder and learns to capture instance discriminative representations of an image. By jointly using them, the model learns rich representations considering both global and local patterns of an image.
# Assessing Cyber Challenges of Maritime Navigation
## 3 Findings
### Cyber Trends and Challenges
#### 3.3.3 Offices Onshore  
After the devastating cyber incident in June 2017 when the NotPetya malware attack, originating in Ukraine, infected the IT systems of the shipping giant Maersk and forced the company to shut down all devices and handle all operations manually, shipping offices onshore have realized that the shipping industry is not immune to cybercrime. Maersk was not explicitly targeted, and thus it was rather collateral damage. The attack resulted in significant interruptions to Maersk's operations and terminals worldwide, costing them up to USD 300 million [6; 77; 79; 103].  
Another wake-up call for the maritime industry were two major cyber incidents reported in 2018. The first happened in July 2018 when COSCO Shipping Lines fell victim to a cyberattack. The company's internet connection was disrupted within its offices in the Americas. After activation of COSCO's contingency plans, operations were back to normal after five days. Being aware of what happened to Maersk, they had taken proactive steps to minimize their risk of a cyberattack. The second incident occurred in October 2018 when the Australia-based ferry and defense shipbuilder Austal was hit by a cyberattack that penetrated their data management systems. The attackers managed to steal internal data and offered some of it for sale on the dark web in an apparent extortion attempt [103].  
Carnival Corporation was the latest to fall victim to a ransomware attack on its IT systems in August 2020. The cybercriminals managed to download certain data files related to guests and employees' personal data, which could result in potential claims from guests, employees, shareholders, or regulatory agencies [104].  
According to [PERSON] [103], there are three key takeaways from these three cyberattacks. The first is related to IT hygiene, which is key to fighting cybercrime--a need to shift people's mind-set towards IT security. Second, every shipping manager needs to approach cybersecurity as an integral part of overall safety management. Response and recovery plans should be in place, updated, and tested frequently. Third, there is no zero cyber risk environment today, since new cyber threats and vulnerabilities are constantly emerging. Despite all precautions, vulnerabilities remain in the systems and networks, and attackers are constantly trying to find new tools to break through cyber defenses.
# Synthesis and Preliminary Screening of the Biological Activity of Sulindac Sulfoximine Derivatives
## 3 Results and Discussion
### COX Inhibition Studies  
Sulindac sulfide, the active metabolite of the indene prodrug sulindac, shows a slow, tight-binding inhibition of both COX-1 and COX-2 (COX-1 IC\({}_{50}\) = 115 nM and COX-2 IC\({}_{50}\) = 140 nM) [22; 23], and it is well known that sulindac sulfone and sulindac sulfoxide constitute, respectively, the inactive metabolite and the inactive prodrug of the correspond ing sulindac sulfide. Since the novel compounds are sulindac derivatives, we decided to test their ability to inhibit cyclooxygenase.  
Only sulindac-OMe (**1a**) (Figure 3) has a very slight inhibitory activity (15% at 50 uM) against the COX-2 isoform. All other sulfoximine derivatives were found to be inactive (around 1% at 50 uM) in inhibiting both cyclooxygenase isoforms, but this does not hinder the activity of sulindac sulfide, since sulfoximines are \"in vivo\" metabolized by CytP450, and thus may also be considered prodrugs of sulindac sulfide itself.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**IC\({}_{\text{50}}\) (uM) or \% Cell Viability**} \\ \hline
**Compound** & **HepG-2** & **MCF7** & **U87** & **CaCo-2** & **HeLa** \\ \hline
**1b** & \(58\pm 4\) & \(145\pm 9\) & \(130\pm 10\) & \(120\pm 9\) & \(105\pm 6\) \\
**2a** & \(93\pm 4\) & \({}^{1}107\pm 8\%\) & \({}^{1}114\pm 7\%\) & \({}^{1}115\pm 7\%\) & \(120\pm 5\) \\
**2b** & \(71\pm 3\) & \(145\pm 6\) & \({}^{1}99\pm 5\%\) & \({}^{1}72\pm 6\%\) & \(83\pm 6\) \\
**1a** & \(70\pm 2\) & \(80\pm 6\) & \(102\pm 8\) & \(100\pm 5\) & \(75\pm 2\) \\ \hline \hline \end{tabular} \({}^{\text{1}}\) % Cell viability tested at 150 uM.  
\end{table}
Table 1: IC\({}_{\text{50}}\) values for cell viability of HepG-2, MCF7, U87, Caco-2 and HeLa cell lines after treatments with **1b**, **2b**, sulindac (**2a**) and sulindac methyl ester (**1a**).  
Figure 3: Percentage (%) of COX-1 inhibition of **1a–1b** and **2a–2b** at 50 uM final compound concentration. All compounds were tested at different concentrations (0.1–50 uM), but only the percentage of inhibition at the higher dose used is reported.  
Figure 2: Dose (μM)–effect plots of the mean of three different cell viability experiments, conducted in various human cancer cell lines incubated for 72 h with compounds.
# Performance Analysis of Internet of Things Interactions via Simulation-Based Queueing Models
## 3 Queueing Models
### Additional Features
#### 3.7.2 Queues of Finite Capacity  
According to this feature, a specific buffer size is considered for each queue that prevents the system from handling more than \(K\) messages (in both queue and server). Such a feature prevents the system from storing messages for a long period of time in devices of limited capacity. More specifically, consider Figure 4b, where messages arrive in the queue with a rate \(\lambda\). If condition _new_queue_size_ + _message_in_service_ \(>K\) is true when a new message arrives in the system, then the message is dropped. Otherwise, the message joins the queue and waits to be served. According to the literature, an M/M/1 queue with a limited buffer size is notated as M/M/1/\(K\)[8]. In the case of our model, we incorporated system size \(K\) in the M/M/1 and the ON/OFF queues by adding it to corresponding Tuples (1),(3), (5), and (7).
## 1 Introduction  
Contemporary dental Computer Aided Design/Computer Aided Manufacturing (CAD/CAM) technology is used to manufacture dental prostheses. According to recent studies reports [1; 2; 3], the global market for Dental CAD/CAM is booming and expected to achieve prominence by 2024-2027, with an expected growth rate of about 5.6% to 8.1% per year.  
However, the dental CAD/CAM process requires several steps to produce a prosthesis, with the last step the milling process performed with a Numerical Controlled (NC) milling machine. This NC milling step plays a crucial role as it generates a characteristic \"signature\" on the prosthesis shape called surface integrity (SI), not sufficiently understood at present in restorative dentistry [4]. These SI properties (roughness, hardness, marginal chipping, porosity, internal stress, etc.) of the milled prosthesis are mainly affected by the milling process [5]. In addition, several requirements of the prosthesis surface such as aesthetics, biological response (dental plaque retaining) and mechanical behavior (micromechanical retention of the cement or/and adhesion on the intaglio surface) and restoration lifetime are highly dependent on residual roughness after CAD/CAM milling [6; 7; 8; 9; 10; 11; 12; 13].  
Prosthetic surface roughness is a crucial parameter and a specific roughness can be manufactured for each clinical requirement. For example, restoration lifetime, aesthetics and biological response require a minimal surface roughness currently ensured by polishing after CAD/CAM [14]. Such surface roughness should be obtained directly after milling by optimizing milling parameters. On the contrary, the surface to be bonded must be sufficiently rough to ensure good bonding [15].  
To fulfill these clinical requirements, several classes of biomaterials are available on the marketplace, such as ceramics, composite resins and PICN (Polymer-Infiltrated Ceramic-Network) [16; 17; 18; 19]. These biomaterial classes differ considerably in their mechanical, chemical and machinability properties. However, few data on their properties after milling are provided in the literature [20; 21]. This article focuses on the properties of biomaterial SI after milling. In addition, biomaterial SI properties also depend on the tool-biomaterial couple used [22]. The tool-biomaterial couple is composed of the tool (diamond rotary cutting instrument) used, the milled biomaterial, and the milling parameters (such as feed rate) programmed [23; 24]. To the best of our knowledge the interactions between the tool-biomaterial couple and SI, in dentistry, have not yet been studied. The aim of this study is to assess these interactions in the end milling mode (use of the tool tip for milling). The SI properties are mainly described through roughness parameters [25; 26; 27; 28].  
The first null hypothesis of this study is that roughness does not vary depending on the tool-biomaterial couple while the second null hypothesis is that roughness does not vary as a function of the feed rate.
## 3 Results and Discussion
### Color Characteristics of Pasteurized Orange Juice
#### 3.3.1 Lightness (L*)  
The logistic 3P model proved to fit data slightly better than the Gompertz 3P model, as Table 4 indicates (lower AICc and BIC values and higher AICc weight and R2 values):  
\[Y=\frac{c}{1+e^{-a*(day-b)}} \tag{3}\]  
In Table 5, a general decreasing trend of L* is observed, based on the negative values of the growth rate in all temperature levels, almost all significant, in a decreasing order per level. Although the reduction in the L* value below 48 initiates at \(\sim\)30 days of storage at 4 \({}^{\circ}\)C, at the higher temperature levels (15, 20, and 25 \({}^{\circ}\)C), the reduction initiates at \(\sim\)25, \(\sim\)20, and \(<\)10 days, respectively (Figure 4). These results follow the trend in the literature, and, more specifically, the study performed by [PERSON] et al. [21] and [PERSON] et al. [4], where a decrease in the L* values of juice during storage is apparent, particularly at higher storage temperatures. Of course, the L* value indicates the lightness of orange juice, and, expectedly, during the storage duration, oxidative and enzymatic browning reactions lead to a change in the juice color from brighter to darker [24; 25]. However, since most of the browning reactions are intensified at higher temperatures, according to the Arrhenius laws, it is reasonable for a premature and more intense reduction in the L* values to occur as the temperature rises [26]. Another explanation for the browning intensity at higher storage temperatures is the rapid degradation of ascorbic acid at higher temperature conditions. The ascorbic acid acts protectively on the juice color because of its high antioxidant activity; thus, it inhibits the oxidative and non-oxidative reactions that cause browning [27].  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **AICc** & **AICc** & **BIC** & **SSE** & **MSE** & **RMSE** & **R-Square** \\ \hline Logistic 3P & 465.22 & 0.556 & 502.81 & 142.06 & 0.953 & 0.9764 & 0.7498 \\ Gompertz & 465.67 & 0.444 & 503.26 & 142.46 & 0.956 & 0.9773 & 0.7491 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Statistical information of the two candidate models used for describing the L* value.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Temperature** & **Growth Rate** & **Inflection Point** & **Asymptote** & _p-Value_ \\ (\({}^{\circ}\)**C**) & **Growth Rate** & **Inflection Point** & **Asymptote** & _p-Value_ \\ \hline
4 & \(-\)0.241786 & 51.705908 & 47.720108 & 0.0569 \\
15 & \(-\)0.141779 & 58.235922 & 47.872129 & 0.0134 \\
20 & \(-\)0.135923 & 55.134761 & 47.808139 & \textless{}0.0001 \\
25 & \(-\)0.068845 & 63.301295 & 48.350857 & \textless{}0.0001 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Statistical information on L* during different aging conditions.
# 2D/3D Multimode Medical Image Alignment Based on Spatial Histograms
## 4 Discussion  
Comparing the different images of the three groups of experiments, the difference images of the WHGD in the three experiments were smoother. The difference images of the WSHGD in Experiment 2 and Experiment 3 were smooth, but there was a significant deviation in Experiment 1. Therefore, the WHGD features have better stability in the registration process. In contrast, the WSHGD features cannot guarantee consistent registration accuracy, but the WSHGD features can still achieve successful registration.  
As shown in Table 2, compared with the WSHGD proposed in this paper, the parameter-decoupling registration method based on WHGD features performs better in terms of MAE and ME, reflecting that the registration accuracy based on WHGD features is higher. Compared with Figure 7, the standard deviation of the WSHGD method is more significant than that of the WHGD method, indicating that the registration performance based on WHGD is more stable. Sometimes, WSHGD registration may have a more significant deviation, consistent with qualitative analysis. However, from the perspective of MAE, the registration accuracy of the WHGD method and the WSHGD method is not very different. Based on WSHGD features, CT and X-ray image registration can also be achieved within an acceptable accuracy range. When the sampling space of the translation parameters of the initial point of the rigid-body transformation is expanded, the DRR image's foreground will exceed the image's field of view. It is challenging to ensure consistent registration accuracy using the WHGD method, and sometimes it cannot even be registered. In contrast, the WSHGD method can still achieve registration, but its accuracy is reduced; however, it is still within the acceptable range.  
Based on the above analysis, the pixel coordinates' mean and variance information can be introduced through a spatial histogram to ensure sensitivity to rotation transformation. This ensures that the synchronous optimization of translation and rotation parameters sacrifices a certain amount of precision to expand the convergence range of translation  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{2}{c}{**Rotation/\({}^{c}\)**} & \multicolumn{2}{c}{**Translation/mm**} \\ \hline \multirow{3}{*}{WHGD} & MAE & 0.483 & 0.503 \\  & SDE & 0.670 & 0.237 \\  & ME & \(-\)0.093 & \(-\)0.417 \\ \hline \multirow{3}{*}{WSHGD} & MAE & 0.563 & 0.877 \\  & SDE & 1.593 & 1.027 \\ \cline{1-1}  & ME & \(-\)0.193 & \(-\)0.632 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Registration results of the WSHGD and WHGD.  
Figure 7: Registration error and mean values of the weighted histogram of gradient directions (WHGD) and the weighted spatial histogram of gradient directions (WSHGD).  
transformation. However, the weighted histogram of gradient directions is only suitable for the foreground--one of the limitations of small image registration.
## 2 Constellation Model
### Collision Avoidance  
To avoid collisions within the constellation, we adopt the approach of [18]. For each orbit \(k\), we assume that the satellites are placed so that  
\[\theta_{k}(0)=(k-1)\frac{2\pi}{N}F, \tag{8}\]  
where \(N\) is the total number of satellites in the constellation \(N=N_{\text{sat}}\times N_{\text{orb}}\). The parameter \(F\) is an integer in the range of \(0\leq F\leq N_{\text{orb}}-1\). This scheme is illustrated in Figure 3.  
To choose the optimal value for \(F\), we can run simulations considering various possible values of \(F\) to determine the minimum satellite-to-satellite distance \(r_{\text{min}}\) over an orbital period, \(0\leq t<T\). We can significantly speed up the computations by leveraging the uniformity of the constellation. Thus, instead of considering all possible combinations of satellites, we can focus on one particular satellite \((k,m)\) and calculate the distances to all other satellites \((p,q)\) within one orbital period [19; 20].  
\[r_{\text{min}}=\min_{pq,t}|\mathbf{r}_{km}(t)-\mathbf{r}_{pq}(t)|. \tag{9}\]  
The optimal value of \(F\), as determined in our model (Table 1), was calculated using the Pyminisat module (as referenced in Appendix A) considering [6] and selected for the remaining simulations of this constellation.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Parameter** & **Symbol** & **Value** \\ \hline Number of orbits & \(N_{\text{orb}}\) & 24 \\ Number of satellites per orbit & \(N_{\text{sat}}\) & 66 \\ Total number of satellites & \(N\) & 1584 \\ Altitude & \(a\) & 550 km \\ Distance from Earth’s center & \(r\) & 6921 km \\ Orbital period & \(T\) & 5730.11 s \\ Angular momentum & \(h\) & 52,523.60 km\({}^{2}\)/s \\ Inclination & \(i\) & 53\({}^{\circ}\) \\ Optimal phasing parameter & \(F\) & 13 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Orbital parameters.  
Figure 2: Satellite trajectories (colored in blue) for the Walker-delta constellation of Table 1. The equator is shown in red.
## 4 Research Methodology
### System Architecture Design  
This research study explores two distinct approaches for the multi-class classification of hotel reviews using advanced natural language processing models: fine-tuning and prompt engineering. Each approach utilizes different models and techniques to tackle the task at hand.  
In the fine-tuning approach, we select pre-trained models and further train them on a specific downstream task, in this case, multi-class classification of hotel reviews. The models chosen for this approach are BERT, RoBERTa, and DeBERTa. BERT uses the Transformer architecture with bidirectional encoders, simultaneously analyzing the context from both sides of a word or phrase, making it powerful in understanding context [6]. RoBERTa, while similar to BERT, uses optimized hyperparameters and eliminates some of BERT's components, like next-sentence prediction, resulting in better performance [11]. DeBERTa, another variant of BERT, employs disentangled attention and enhanced training techniques to improve performance.  
These models' implementation, training, and evaluation are performed using the Grouphug library [32], which extends the Hugging Face Transformers platform. Grouphug allows for the flexible integration of various machine learning models and attaches a classification head for each task, enabling simultaneous fine-tuning of the model on multiple  
Figure 2: Distribution of sentiments across categories.  
tasks. The proposed system architecture for the fine-tuning approach is illustrated in Figure 3.  
On the other hand, the prompt engineering approach focuses on utilizing the generative capabilities of large language models, specifically GPT-3.5 (ChatGPT) and GPT-4. These models use the Transformer architecture emphasizing the decoder side, allowing for sequential text generation [22]. ChatGPT, optimized for conversational text, uses a similar approach to GPT-2 but is further tailored to generate conversational responses through prompts [17].  
Due to the inherent generative nature of GPT models, we employ prompt engineering techniques to direct the model towards the desired classification task [33]. For this purpose, we use the LangChain library, which allows for the dynamic generation of prompts tailored to the specific task. This library, in combination with the OpenAI API, gives us access to the \"chatgpt-3.5-turbo\" and \"gpt-4\" models, key components of the ChatGPT and GPT-4 platforms. The proposed system architecture for the prompt engineering approach is depicted in Figure 4.  
By comparing these two approaches, we aim to determine the most effective method for multi-class classification of hotel reviews, considering factors such as accuracy, efficiency, and computational requirements. These models are selected based on their unique architectures and proven performance in various NLP tasks, and our goal is to adapt, fine-tune, and optimize each model to achieve optimal performance in the multi-task classification of sentiment and categories of hotel reviews.