esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Specified User Tasks  
We iteratively consulted domain experts in the medical, statistical, and informatics fields to determine the most appropriate user tasks ML-MEDIC needed to address to aid in facilitating the adoption of machine learning in medicine. We consulted a cardiologist with extensive predictive analytics experience as well as a data science expert with experience in clinically applying contemporary statistical and machine learning methods. Additional cross-domain experts were consulted as needed, including an applied behavior analyst, a human-computer interaction (Graphical user interface/visualization) specialist, an epidemiologist, and a clinical psychologist.  
In collaboration with the domain experts, we identified the following user-specified tasks that ML-MEDIC needed to facilitate the clinical application and adoption of machine learning:  
1. Task 1: A tool that facilitates interpretable machine learning, through multiple model comparison, possible understanding of variables input into a model, and validation of machine learning methods with traditional statistical models. The clinical experts we spoke with all expressed concerns regarding the \"black-box\" nature of machine learning algorithms and the potential for spurious applications and conclusions. They expressed the need to be able to understand what input variables were most important to a model's predictive output and the ability to compare and validate this with other commonly used statistical methods. When asked to consider trade-offs such as improved performance with \"black-box\" methods vs. worse performing methods with clearer interpretability, the domain experts suggested a preference for understandability initially and suggested that they would be more likely to use less interpretable methods only after evaluating more familiar, explicable methods.
2. Task 2: Ability to implement machine learning analyses without learning new coding languages, accessing new software, and collaborating on platforms without worrying about data security. The medical, data science, and statistical experts we interviewed expressed that software and pipelines designed for machine learning and statistical modeling require collaboration from multiple domains to be employed successfully and to meet data security standards. The collaborative nature of these analyses often means installing and running computational pipelines on multiple computers. Accessing software and pipelines in multiple environments often requires permission and assistance from one or multiple institutions and can often lead to challenges with accessibility, interoperability, and reusability. Even with correct versioning and identical data, differences in local hardware and operating systems can prevent interoperability and reproducibility.
# Context-Aware End-User Development Review
## 5 What Is behind the Tool? Implementation Approaches and Challenges
### Discussion and Challenges
#### 5.6.1 Context-Aware End-User Development  
The presented approaches facilitate the specification of context-aware applications, which involves (1) integration of required context, e.g., reading from sensors, (2) how to evaluate context values, e.g., two similar contexts, check event, and (3) definition of desired actions (i.e., reaction, proaction) that the system must perform when evaluating matches. Furthermore, rule-based, natural language, programming by demonstration, and trigger-action (based on events) are EUD approaches suitable for the IoT [11]. However, the reviewed approaches reveal challenges to target non-technical users. For example, using a model for tailoring applications requires end-users to learn the model's components. Combining metaphors for improved UI composition [80] and introducing artificial intelligence and machine learning is complicated for non-technical users. Likewise, we found technicalities for programming with an augmented UI interaction, e.g., tangible, wearable, and embodied interaction. This aspect is relevant because the final product (end-user program) uses things through a tangible/embodied [87] or wearable [93] interaction. Non-technical users should learn technical aspects such as linking smart tags and wearables (e.g., rings [94]).  
Mobile end-user tools use available device resources that users can access for building applications (e.g., available services and actions, mobile sensors). We found eight out of 37 projects that enable EUD on mobile devices. From those projects, seven out of eight also run on mobile devices. In addition to those seven projects, three out of 37 projects run on a mobile device, but the EUD is on a PC/Web. However, in mobile EUD, it is challenging to include functionalities and improve the expressiveness because of the limitation in resources and screen dimensions. For example, eliciting space/geographical relations and enabling complex composition blocks (e.g., computing math) is still performed in a PC.
# Time-Dependent Vehicle Routing Problem with Drones Under Vehicle Restricted Zones and No-Fly Zones
## 4 Mathematical Model
### Vehicle-Related Constraints  
\[\sum_{i\in V}x_{kij}=\sum_{i\in V}x_{kji}\leq 1,\forall k\in K,\forall j\in V/0 \tag{10}\]  
\[\sum_{i\in V/0}x_{k0i}=\sum_{i\in V/0}x_{ki0}\leq 1,\forall k\in K \tag{11}\]  
\[B^{1}-\sum_{i\in V}\sum_{j\in V}e_{kij}x_{kij}\geq B^{1}\epsilon,\forall k\in K \tag{12}\]  
\[T_{ki}^{d}+\frac{l_{kij}}{v(t)}-M\Big{(}1-x_{kij}\Big{)}\leq T_{kj}^{a},\forall i \in V,\forall j\in V,\forall k\in K \tag{13}\]  
\[T_{ki}^{d}+\frac{l_{kij}}{v(t)}+M\Big{(}1-x_{kij}\Big{)}\geq T_{kj}^{a},\forall i \in V,\forall j\in V,\forall k\in K \tag{14}\]  
\[n_{ki}-n_{kj}+1\leq M\Big{(}1-x_{kij}\Big{)},\forall i\in V/0,\forall j\in V /0,\forall k\in K,i\
eq j \tag{15}\]  
\[T_{s}\leq T_{ki}^{d},T_{ki}^{a}\leq T_{f},\forall i\in V,\forall k\in K \tag{16}\]  
\[\text{max}\Big{\{}T_{h_{1}}^{f}-T_{i}^{a},0\Big{\}}\leq M\Big{(}1-x_{kij} \Big{)},\forall i\in V,\forall j\in V_{1},\forall k\in K,\forall h_{1}\in H_{1} \tag{17}\]  
Constraint (10) represents the balance of vehicle arrivals and departures at the customer service points. Constraint (11) represents the balance of vehicle arrivals and departures at the distribution center. Constraint (12) indicates that the remaining battery charge of the vehicle upon returning to the distribution center must meet the minimum state of charge requirement. Constraints (13) and (14) describe the relationship between the departure and arrival times of the vehicle from \(i\) to node \(j\), and restrict the sub-cycles of the vehicle. Constraint (15) eliminates sub-cycles for the vehicle visiting node \(i\). Constraint (16) ensures that the vehicle deliveries fall within the depot time window. Constraint (17) ensures that the vehicle can only serve customers in vehicle-restricted zones after the restricted time period ends.
# Influence of Noise-Limited Censored Path Loss on Model Fitting and Path Loss-Based Positioning +
###### Abstract  
Positioning is considered one of the key features in various novel industry verticals in future radio systems. Since path loss (PL) or received signal strength-based measurements are widely available in the majority of wireless standards, PL-based positioning has an important role among positioning technologies. Conventionally, PL-based positioning has two phases--fitting a PL model to training data and positioning based on the link distance estimates. However, in both phases, the maximum measurable PL is limited by measurement noise. Such immeasurable samples are called censored PL data and such noisy data are commonly neglected in both the model fitting and in the positioning phase. In the case of censored PL, the loss is known to be above a known threshold level and that information can be used in model fitting and in the positioning phase. In this paper, we examine and propose how to use censored PL data in PL model-based positioning. Additionally, we demonstrate with several simulations the potential of the proposed approach for considerable improvements in positioning accuracy (23-57%) and improved robustness against PL model fitting errors.  
ostioning; path loss; path loss model; maximum-likelihood estimation; censored data; localization; shadow fading; wireless networks; probabilistic modeling +
Footnote †: This paper is an extended version of our paper published in [PERSON]; [PERSON]; [PERSON] Positioning Based on Noise-Limited Censored Path Loss Data. In Proceedings of the 2020 International Conference on Localization and GNSS (ICL-GNSS), Tampere, Finland, 2-4 June 2020.
# A Comparison of an Adaptive Self-Guarded Honeypot with Conventional Honeypots
## 2 Background
### Attacker's Behaviour  
[PERSON] et al. examined attack data from a Linux honeypot and built a state machine from the collected Linux commands to profile the attacker's behaviour [15]. To construct the state machine, they defined seven states as follows:  
* **CheckSW--**Check software configuration\({}^{\prime}\): this describes commands that the attacker uses to gather more information about the system's software and its users. The commands in this state are w, id, whoami, last, ps, cat /etc/*, history, cat.bash_history,php-v.
* **Install--**Install a program\({}^{\prime}\): this describes the software installed or the process of installing a new software by an attacker. For example, an attacker can unarchive a downloaded file, followed by other filesystem related commands, such as copying, moving and deleting files. These commands can be tar, unzip, mv, rm, cp, chmod, mkdir.
* **Download--**Download a file\({}^{\prime}\): this describes the commands used to download a remote file. These commands can be wget, ftp, curl, lwp-download.
* **Run--**Run a rogue program\({}^{\prime}\): this describes when the attacker runs a program that was downloaded. These kinds of commands can be detected when the attacker precedes './' in front of the program name.
* **Password--**Change the account password\({}^{\prime}\): this describes when the attacker wants to change the password of an account.
* **CheckHW--**Check the hardware configuration\({}^{\prime}\): this describes commands that enable an attacker to gather more information about the system's hardware.
* **ChangeConf--**Change the system configuration\({}^{\prime}\): this describes the operation of attackers that alters the system state by changing the environment variable, terminating running programs, modifying files, etc. The commands included in this state are export, PATH=, kill, nano, pico, vi, vim, sshd, useradd, useradd.  
[PERSON] et al. also suggested the state **no-op**, which corresponds to the commands that have no effects on the system, and they are cd, ls, bash, exit, logout, cat. The rest of the other commands are classified as unmatched, due to a typographical error.
###### Abstract  
Hydrothermal carbonization (HTC) is a promising thermochemical process to convert residues into hydrocarbon. While conventional HTC utilizes one type of residue as raw material only, Co-HTC generally combines two. By mixing dry and wet wastes, Co-HTC can advantageously avoid water addition. Therefore, this work investigated the potential of hydrocarbon derived from the Co-HTC of sawdust and non-dewatered sewage sludge as a dye (methylene blue) adsorbent and evaluated the toxicity of the resulting Co-HTC process water (PW) on _Daphnia magna_. Three hydrochars were produced by Co-HTC at 180, 215, and 250 \({}^{\circ}\)C and named H-180, H-215, and H-250, respectively. For methylene blue adsorption, H-180 and H-215 heated a better performance than H-250. Both H-180 and H-215 presented a maximum adsorption capacity of approximately 70 mg\(\cdot\)g\({}^{-1}\), which was superior compared with the adsorption of methylene blue by other hydrocarbons in the literature. Moreover, the removal percentage obtained with H-180 remained satisfactory even after five cycles. Regarding the toxicological assays of the PWs, raising the Co-HTC temperature increased the variety of substances in the PW composition, resulting in higher toxicity to _D. magna_. The EC\({}_{50}\) values of PW-180, PW-215, and PW-250 were 1.13%, 0.97%, and 0.51%, respectively. This highlights the importance of searching for the treatment and valorization of the PW. Instead of viewing this by-product as an effluent to be treated and disposed of, it is imperative to assess the potential of PWs for obtaining other higher added-value products.  
hydrothermal carbonization; adsorption; methylene blue; ecotoxicity; _Daphnia magna_; waste valorization; biochar
# ACapMed: Automatic Captioning for Medical Imaging
## 3 Materials and Methods
### Semantic Feature Encoding  
#### 3.2.1 Text Pre-Processing  
We pre-processed the captions to clean the text and keep only significant words. Specifically, we tokenized each caption, converted all characters to lowercase, removed stop-words, filtered out the punctuation, and calculated the stems of the identified words. Two other words were added to each caption to identify the beginning (_<start>_) and the end (_<end>_) of the sentence. This pre-processing process was performed using the NLTK package ([[https://www.nltk.org](https://www.nltk.org)]([https://www.nltk.org](https://www.nltk.org)), accessed on 2 October 2022). The maximum length of one caption for the whole training set was 50 words, which was used to pad the remaining captions at the end. Further, embeddings were calculated from these captions to capture the semantic meaning of each sentence. Finally, each image was represented with a vector of size 50, encoding the corresponding caption.  
As previously noted, we used both visual features and semantic features as joint features for the process of caption generation. So, semantic features were obtained from the processing of the medical concepts associated with the images. We processed them in the same manner as we did for the captions. Medical concepts are provided as concepts unique identifiers (CUIs), e.g., C1306645 refers to 'Plain X-ray'. The first step was to substitute each CUI with its corresponding text (UMLS--unified medical language system), which is possible through the National Library of Medicine ([[https://www.nlm.nih.gov/research/umls/index.html](https://www.nlm.nih.gov/research/umls/index.html)]([https://www.nlm.nih.gov/research/umls/index.html](https://www.nlm.nih.gov/research/umls/index.html)), accessed on 2 October 2022). In general, the UMLS constitutes different words; we tokenized, converted to lowercase, stemmed, and removed the stop words among them. Consequently, each image is represented with a vector (of a shape of 10x9), where ten concepts were considered and each concept was encoded with a vector of size 9 (composed of 9 words). The maximum number of CUIs per image was found to be 10; after tokenization, the maximum length of one CUI was 9 words for each concept of the training set. If the concept had fewer words, we padded the sequences at the end. Similarly, if the image had fewer associated CUIs, we repeated the vector of the last CUI for the remaining positions (to reach 10 CUIs, i.e., 10 vectors). Embeddings were calculated as well from medical concepts and constituted the semantic features.  
Figure 3: The model for visual feature extraction. The model inputs were medical images and the outputs were the visual feature vectors (size: 4096).
# Smartphone-Based Task Scheduling in UAV Networks for Disaster Relief
## 3 System Model
### Model of the Disaster Relief Center  
Since different disaster magnitudes cause different degrees of damage to surface buildings, in this study, we took the earthquake disaster as an example and established two different levels, namely, the mild disaster and the severe disaster scenarios.  
Mild disaster: If the magnitude of the earthquake is \(l_{e}<D\), the earthquake naturally causes damage to some infrastructure. However, the damage to buildings may be relatively light, and people can still live in their homes. In this situation, DRCs only need to distribute survival supplies to people in the disaster area, which means that people can dynamically enter and exit the DRCs.  
Let the probability of people entering DRCs follow the Poisson process with the average arrival rate of \(\lambda\)[11; 19]. Let us suppose that each DRC has only one service window and that the service speed is \(\mu\). Then, the process of people arriving at a DRC follows the M/M/1 queuing model.  
According to this model, the average waiting time for people in a DRC is  
\[\overline{T_{w}} =T_{q}+T_{s} \tag{1}\] \[=\frac{\lambda}{\mu(\mu-\lambda)}+\frac{1}{\mu}\] \[=\frac{1}{\mu-\lambda}\]  
where \(\overline{T_{w}}\) is the average waiting time for people in a DRC, and \(T_{q}\) and \(T_{s}\) denote the average queuing time and the average service time, respectively.  
The remaining waiting time (\(T_{m}\)) of SP m at a DRC is  
\[T_{m}=\overline{T_{m}}-t_{m} \tag{2}\]  
where \(t_{m}\) is the time that SP \(m\) has already spent at the DRC.  
Severe disaster: When the earthquake's magnitude is \(l_{e}\geq D\), the earthquake causes serious damage to the city. In this circumstance, the DRCs not only need to distribute necessary survival supplies to people nearby but also provide residential accommodation for them. In this case, it is considered that the SPs are located within the DRCs, and their positions are relatively fixed.  
The remaining waiting time of SP \(m\) at the DRC is then given as  
\[T_{m}=\infty \tag{3}\]  
Figure 1: System model.  
After obtaining the remaining waiting time of the SPs at a DRC, we select the SP whose remaining waiting time is greater than the tolerance delay of at least one task in the queue of tasks that are candidates for task execution, where the task maximum tolerance delay is the maximum execution delay required for executing the task.
## 1 Introduction
Contextual information may be lost when focusing on local corrections in the image domain, thus affecting the correction quality [18]. Addressing this problem, we propose an innovative joint deep learning framework that synergistically harnesses the UNet and ResNet architectures through a dual-domain correction process [19; 20]. Initially, UNet is utilized to directly handle the raw projection data containing noise and artifacts, obtaining pre-corrected data; then, these corrected data are used for image reconstruction with the FBP algorithm, and ResNet is responsible for further refining the CT images in the image domain to eliminate any remaining artifacts. A dual-domain approach that can utilize complementary information across multiple domains while simultaneously optimizing the entire system has stronger performance potential compared to single-domain correction methods. Notably, the combined UNet and ResNet architecture can extract multi-scale and in-depth features while validating and correcting the information across projection and image domains. This dual-domain method is more adept at processing complex artifacts as it considers the physical properties of the data acquisition process and utilizes rich image content information, providing a new pathway for obtaining high-quality, artifact-free industrial CT images with high efficiency and effectiveness.
Sodium hypochlorite (NaOCI) is commonly used as a root canal irrigant; however, it is corrosive towards metals and causes micro-pitting due to removal of nickel. Similarly use of erosive and acidic irrigants can cause 81 microstructural defects leading to crack formation and stress concentrations [18]. In order to overcome this problem, herbal alternatives, i.e., _Sapindus mukorossi_ (_S. mukorossi_), have gained the researchers attention for their antifungal, antimicrobial, and anti-inflammatory properties. The major ingredients of the pericarp of its fruits are sugars (10%), saponins (10-11.5%), and mucilage [19]. The surfactant action is because of the higher content of saponins. The detergent-like properties of _S. mukorossi_ fruit reduces the surface tension of the irrigant, which leads to the dissolution of water-insoluble substances [20]. When used as an irrigant, it helps eliminate debris, which is components of the smear layer, pulp canal tissues, as well as microbes. [PERSON] et al. and [PERSON] et al. reported its effectiveness for dental caries management [21; 22]. _S. mukorossi_ can be a potential root canal irrigant due to its emulsification and surfactant properties in removing and dissolving debris and smear layers [23]. However, its role as a final irrigant on the surface properties of the NiTi rotary file is still not known and needs investigation. Atomic force microscopy (AFM) has been employed in assessment of metallic alloy-based instruments in endodontics [12]. AFM is based on the principle that a probe scans the investigated surface and its motion is captured and used to create a three-dimensional image. AFM offers versatility as most surfaces, including ceramics, composites, polymers, and biological samples can be assessed [24]. The ProTaper Gold is comparatively a newer rotary system and enough evidence is available related to the effect of irrigants such as EDTA on the surface characteristics of ProTaper Gold [24]. However, the effect of the new irrigant (_S. mukorossi_) on ProTaper Gold rotary system surface properties is still not determined. Therefore, it is hypothesized that there will be no significant differences in Ra and RMS of the ProTaper Gold file after using ethanolic extract of _S. mukorossi_ and 17% EDTA solution as compared to unused control files. Moreover, it was also hypothesized that 17% EDTA and ethanolic extract of _S. mukorossi_ treated files will not demonstrate any significant difference in Ra and RMS of ProTaper Gold files. Therefore, the aim of the present study is to evaluate the surface roughness (Ra and RMS) of ProTaper Gold file after interaction with 17% EDTA and ethanolic extract of _S. mukorossi_ in combination with root canal shaping in root canal treatment.
# A Review of Machine Learning and IoT in Smart Transportation
## 2 Machine Learning and IoT in Smart Transportation
### ML and IoT Applications in Smart Transportation
#### 2.2.2 Parking
In the ASPIRE system, users can set up their parking preferences, like maximum walking time, preferred location, etc, through a 'Local Agent' and the system will take all these data into consideration when a parking reservation request is made. A cloud based software agent will then decide list with most suitable parking spots for the user. The user is notified through the Local Agent and chooses the preferred parking slot. The parking recommendation algorithm is based on the Analytic Hierarchy Process (AHP), a structured mathematical technique to analyze complex decisions. Parking lots are equipped with RFID readers at the entrance that will track and identify cars entering and leaving the parking. The cars must also be equipped with an RFID tag to make the identification possible. A reliability analysis of a smart parking system is conducted in [68], resulting in a success rate greater than 96%. The system uses ultrasonic sensors controlled by an ESP8266 controller with embedded WiFi. The sensors at each parking spot communicate with a private cloud server using a REST API. The sensor registration and their availability status is managed by the Orion Broker which is part of the FIWARE IoT platform. On top of the described setup resides an application layer that offers a user friendly interface for the users to access through their mobile devices.