esa-sceva/satcom-chunk-collection huggingface dataset:
# Genomic and Metabolic Characteristics of the Pathogenicity in Pseudomonas aeruginosa
## 2 Pseudomonas aeruginosa
### Clinical Impact of Pseudomonas aeruginosa  
_P. aeruginosa_ is an opportunistic pathogen. It lives as a commensal and environmental organism, but can occasionally change to a pathogenic state, causing infections that are difficult to treat [24]. For example, _P. aeruginosa_ is the major cause of infections such as ventilator-associated pneumonia, urinary tract, bloodstream, and chronic infections [25; 26; 27]. In immunocompromised hosts, like AIDS or diabetes mellitus patients, bacteremia caused by this bacterium is a common complication [26]. In animals, _P. aeruginosa_ causes diseases in both livestock and pets. For example, it causes otitis and urinary tract infections in dogs, mastitis in dairy cows, hemorrhagic pneumoniae in fur-bearing animals such as milk or foxes, and endometritis in horses [28].  
This bacterium displays a natural resistance to many classes of antibiotics and its capacity to rapidly develop new ones during treatment represents one of the most common reasons for therapeutic failures [29]. _P. aeruginosa_ is one of the six \"ESKAPE\" pathogens belonging to the World Health Organization \"priority pathogens\" list of antibiotic resistance [1]. The resistance rate of _P. aeruginosa_ to antibiotics is increasing worldwide and that is why it is necessary to increase monitorization of infections in animals and humans in order to minimize possible transfers between hosts [28].  
_P. aeruginosa_ can also develop and grow in a sessile community structure that provides protection against antibiotics, host defense mechanisms, desiccation, ultraviolet light, and disinfectants [30]. Given its resistance to antibiotics, the opportunistic, and resilient character, it has become one of the most concerning issues of pathogenesis nowadays and has drawn the attention of many researchers in the quest to obtain more knowledge and understanding [7; 31; 32].
# Detection Method of Partial Discharge on Transformer and Gas-Insulated Switchgear: A Review
## 3 Type of Sensor Used to Detect PD Signals
### Optical Sensor  
The prevailing technique to detect (PD) occurs in HV transformers and GIS equipment is by optical detection technique utilizing a fiber intrinsic sensor coil. Figure 10a presents a depiction of the sensor coil, which is created by coiling 8 m of fiber around a former, resulting in a coil with a 25-millimeter diameter. This intrinsic sensor is based on Mach-Zehnder fiber interferometers and operates using single-mode optical fiber typically submerged inside oil along the transformer being tested to measure its PD characteristics [113]. Meanwhile, Figure 10b illustrates the multimode optical fiber sensor specifically designed for PD detection in GIS [114].  
Figure 8: (**a**) Disc-type, monopole-type, and spiral-type UHF sensors. (**b**) Capacitive coupler schematic. Reprinted by [32; 110].  
Figure 10: Intrinsic optical sensors; (**a**) Mach–Zehnder fiber interferometer-based optical fiber sensor and (**b**) multimode optical fiber sensor. Reprinted by [113; 114].  
Figure 9: (**a**) A connectorized piezoelectric film sensor and (**b**) conventional piezoelectric [112].  
Furthermore, Figure 11a,b showcase the extrinsic Fabry-Perot interferometer sensor and extrinsic microelectron-mechanical system sensor, respectively, both utilized for measuring PD in oil-cooled high-voltage transformers [115].
## 1 Introduction  
Heat-shock proteins (HSPs) have recently been extensively studied in the context of anticancer properties, especially their extracellular form. Serum HSPs have been found to elicit antitumor immunity by acting as tumour-specific antigens, and adjuvants that facilitate uptake, processing, and presentation [1, 2].  
According to many reports, HSPs demonstrate a strong association with gamma-delta (\(\gamma\)s) T cells by different mechanisms including direct recognition of specific epitopes in their free form or as peptide-HSPs complexes [3].  
Human \(\gamma\)s T cells represent a small subset of CD3+ T lymphocytes (1-10%), however, these cells have been gaining the interest of scientists' and clinicians' as they demonstrate both innate and adaptive immune properties. Their primary functions include phagocytosis and the presentation of soluble antigens to alpha-beta (\(\alpha\)s) T cells, induction of dendritic cells (DC), maturation and the production of cytokines [4]. The key advantage of \(\gamma\delta\) T cells is their ability to identify antigens out of the context of the classical major histocompatibility complex (MHC) and the natural tropism of \(\gamma\delta\) T cells for the tumour microenvironment [5].  
Numerous reports have confirmed the safety of using \(\gamma\delta\) T cells in adoptive immunotherapy [6]. Unfortunately, the efficacy of \(\gamma\delta\) T cell immunotherapy has been limited. This is hypothesized to be due to the ambiguous effects of specific \(\gamma\delta\) T cell subsets on cancer cells. Furthermore, energy or exhaustion of the effector \(\gamma\delta\) T cells has been observed after induction by ligands such as n-aminobisphosphonates or phosphorylated antigens [7]. The targeting of \(\gamma\delta\) T cells by HSPs seems to be a forgotten direction.  
Most of the studies regarding interactions between \(\gamma\delta\) T cells and HSPs were conducted in the 1990s but, despite promising results, the concept was abandoned. Currently, with better technical capabilities, greater knowledge of lymphocyte diversity, and more accurate diagnostic methods we decided to study the correlation once again in the neoplastic condition.  
Due to the limited reports concerning extracellular HSP90--one of the most investigated proteins of the HSP family and the correlation with the frequency of specific subunits of \(\gamma\delta\) T cells in cancers, we examined the relationship between these parameters in the peripheral blood of 21 paediatric patients with B-cell acute lymphoblastic leukaemia (ALL)--the most common cancer in children. In the past, the prognosis was distressingly poor with only a 31% chance of a five-year survival. New diagnostic and treatment modalities have contributed to a drastic improvement in patient outcomes [8; 9]. However, despite the relatively satisfying results of conventional chemotherapy, leukaemia remains the leading cause of cancer-related death among children [10].
# What is 5G? Emerging 5G Mobile Services and Network Requirements
## 4 5G Mobile Services
### Public 5G Services
#### 4.5.2 Private Security and Public Safety  
In disaster situations, networks may be reconstructed as quickly as possible with mobile base stations and wireless core networks, as first-aid activities are carried out. In such situations, infrastructureless networks should be established only with mobile devices to provide the minimal telecommunication services needed. In addition, network-connected CCTVs can monitor city safety. These examples are shown in Figure 11. The spatial limitations can be overcome by using drones and unmanned robots in cases where the rescuers cannot easily access the disaster area. These robots will be able to inform about field status to the rescue workers and the command center and also rescue and restore the site remotely. Moreover, a service to inform about the location of people to rescue can be provided by using relay transmissions between public safety- enabled (PS-enabled) terminals even without a network infrastructure [29]. In addition, 5G can provide advanced security mechanisms. The most important feature of 5G to facilitate such security and safety services is to classify public safety service data according to priority. Additionally, public safety services may be provided with PS-enabled terminals that can broadcast the message and perform direct relay communication via their terminals. For public safety networks or military strategy networks, features regarding higher security and reliability may be provided. Energy efficiency of PS-enabled terminals shall be secured for longer operating times in emergency situations. Furthermore, mission critical services requiring very high reliability and global coverage will be supported by the 5G infrastructure.
# Real-Time Image Detection for Edge Devices: A Peak Fruit Detection Application
## 2 Materials and Methods
### Hardware for Inference  
The hardware platform (edge device) used to perform inferences consists of the following parts, as shown in Figure 1: (1) A microcontroller development kit--Raspberry Pi 4 [33]; (2) a Coral TPU accelerator [29]; (3) a Raspberry Pi Camera Module 2 [34]; (4) a DC-to-DC converter [35]; (5) three Li-ion batteries [36]. Note: The battery in Figure 1 is only an illustration for the application, as the capacity of the battery used depends on the application. The Raspberry had a quad-core Cortex A72 processor and 8 GB of RAM, and it used the Linux operating system with a Python interpreter and the TensorFlow Lite library. The Coral TPU accelerator, which was connected to the Raspberry Pi via USB, was an integrated edge TPU coprocessor designed to perform machine learning operations in an optimized manner (e.g., four Tera operations per second).
# Improved Video Anomaly Detection with Dual Generators and Channel Attention
## 4 Experiments and Results  
### Datasets and Train Details  
To verify the effectiveness and accuracy of DGGAN, we conduct exhaustive experiments on two datasets, i.e., **UCSD Ped1 and Ped2** and **CUHK Avenue**.  
**UCSD Ped1 and Ped2**[28]. The scene of the UCSD dataset is a sidewalk, and abnormal events in the scene include bicycles and wheelchairs on the sidewalk, pedestrians running, skateboards, and cars. **Ped1** is the scene of pedestrians moving away and approaching the camera with a resolution of 238 \(\times\) 158. **Ped2** is a scene in which pedestrians move parallel to the camera. The abnormal events in the scene include bicycles and wheelchairs on the sidewalk, pedestrians running, skateboards and cars, etc. The resolution is 360 \(\times\) 240.  
**CUHK Avenue**[29]. The scene of the CUHK **Avenue** dataset is Campus Avenue, which contains abnormal events: pedestrians running, walking in the wrong direction, trucks, bicycles, suspicious objects, etc. The resolution is 640 \(\times\) 360.  
All experiments are conducted on the PyTorch deep learning framework with a single RTX 3080 Ti (12 GB GPU Memory). In both datasets, the training set contains only normal events, while the testing set contains both normal and abnormal events.  
The experimental results were evaluated using the area under the curve (AUC) criteria. AUC is an indicator used to evaluate the performance of a binary classifier. It represents the area under the line graph (ROC curve) between the probability of a classifier correctly classifying a positive sample and the probability of misclassifying a negative sample at a certain probability threshold. The larger the AUC value, the better the performance of the classifier, and a value of 1 is a perfect classification. In the video anomaly detection algorithm, the higher the AUC value, the more accurate the algorithm identified the anomaly. Note that the false positive rate (FPR) refers to the ratio of the number of videos that are misjudged as abnormal to the total number of non-abnormal videos among all non-abnormal videos, and the true positive rate (TPR) refers to the ratio of all abnormal videos. The ratio of the number of correctly detected videos to the total number of abnormal videos. The ROC curve is a two-dimensional graph consisting of the TPR and FPR of the classifier at different thresholds.
# First-Principles Study for Gas Sensing of Defective SnSe\({}_{2}\) Monolayers
###### Abstract  
We report the interaction between gas molecules (NO\({}_{2}\) and NH\({}_{3}\)) and the SnSe\({}_{2}\) monolayers with vacancy and dopants (O and N) for potential applications as gas sensors. Compared with the gas molecular adsorbed on pristine SnSe\({}_{2}\) monolayer, the Se-vacancy SnSe\({}_{2}\) monolayer obviously enhances sensitivity to NO\({}_{2}\) adsorption. The O-doped SnSe\({}_{2}\) monolayer shows similar sensitivity to the pristine SnSe\({}_{2}\) monolayer when adsorbing NO\({}_{2}\) molecule. However, only the N-doped SnSe\({}_{2}\) monolayer represents a visible enhancement for NO\({}_{2}\) and NH\({}_{3}\) adsorption. This work reveals that the selectivity and sensitivity of SnSe\({}_{2}\)-based gas sensors could be improved by introducing the vacancy or dopants.  
SnSe\({}_{2}\); defect; O-doped; N-doped; gas sensors; first-principle study Article
## 4 Case Study  
### Basic System Parameters  
To illustrate the validity of the proposed model, case research was conducted on the IEEE 9-node system [24] and the network structure of the system is shown in the Figure 3. The system includes three generator sets and three large users. Among them, G1 is a thermal power unit belonging to the thermal power generator (unit capacity of 1500 MW), unit G2 belongs to the wind power generator (unit capacity of 500 MW), and unit G3 belongs to the photovoltaic power generator (unit capacity of 300 MW). L1, L2, and L3 are the three large users in the system. Similarly to study [45], the daily forecast output curves of wind and solar power producers are shown in Figure 4a,b. The specific parameters of the power generation cost coefficients, quotation, and carbon emission intensity of the generators are shown in Tables 1-3[46; 47; 48; 49], respectively. According to the carbon price survey report and other data [50], the annual carbon price is set at 205/tCO\({}_{2}\). The optimization period was one day and the time step was set to 1 h.  
Figure 3: IEEE9-node system structure diagram.  
In addition, as a powerful simulation software, MATLAB R2019a has built in a variety of matrix operation function libraries with high computational efficiency. In the implementation of the algorithm, the relatively simple M language is used for programming so it is widely used in power system optimization scheduling [51]. Moreover, Gurobi solver can solve large-scale linear problems, quadratic problems, and mixed-integer linear problems and supports multi-objective optimization with a faster solution speed and accuracy than other solvers. Therefore, based on MATLAB platform, this paper uses its built-in application toolbox Yalmip cooperating Gurobi solver to solve the two-layer model.
## 4 Simulations and Discussion  
In this section, signal tracking and sorting performance are analyzed by numerical simulations. The intermediate result of TOA estimation and dynamic programming are also discussed. The simulation settings are shown in Table 1.  
Without loss of generality, a differential quadrature reference phase shift keying (DQPSK) modulated multiple signal is applied in this section. The carrier frequency of source signals is transformed to [0, 5 GHz] as the input of our system and the sub-band number is \(L=195\). Thus, the Nyquist sampling rate is \(f_{nyq}=10\) GHz. The periodic frequency is \(f_{p}=f_{nyq}/L\approx 51.3\) MHz. The baseband sampling rate is \(f_{s}=f_{p}=51.3\) MHz, which is the theoretical minimum. The total sampling rates of our system are \(f_{s}\times 32=1.6416\) GHz for \(m=32\) and \(f_{s}\times 50=2.565\) GHz for \(m=50\), which is much lower than the Nyquist sampling rate. The number of sampling points for a single hop is \(f_{s}/1\times 10^{4}=5130\), which is enough for further processing. Each signal is shaped by the raised cosine finite impulse filter (FIR), which has been widely used in practice.
## 4 Optimization Design of Acceleration Control Plan
### Optimization Scheme of Control Plan Based on Target Shooting Method
Then we have \(m\) initial value problems (IVP): \[\dot{x}_{i}(S_{i},u_{i},T,\tau)=F(x_{i}(S_{i},u_{i},T,\tau),u_{i}(\tau),T), \tau\in[\tau_{i},\tau_{i+1}],\] (26) where the initial value is: \[x_{i}(S_{i},u_{i},T,\tau_{i})=S_{i},i=0,1,\ldots,m-1.\] (27)(4) Constituting Nonlinear Programming (NLP). The optimization objective is:  
\[\min f_{1}(S_{m}^{*},T), \tag{28}\]  
where \(S_{m}^{*}\) is the estimate of \(x_{l+1}(1)\). The endpoints of each interval meet the matching conditions:  
\[x(S_{i},u_{i},T,\tau_{i})-S_{i+1}=0,\ i=0,1,\ldots,m-1. \tag{29}\]  
The starting and terminal states meet the boundary conditions:  
\[\left\{ \begin{array}{l}S_{0}=x_{0}\\ S_{m}=x_{T}\end{array} \right.. \tag{30}\]  
Constraints on state quantity, control quantity and terminal time in the process are transformed into constraints on parameters at the end of the interval:  
\[\left\{ \begin{array}{l}l_{1}\leq P_{i}\leq h_{1}\\ l_{2}\leq S_{i}\leq h_{2},\ \ i=0,1,\ldots,m\\ l_{3}\leq T\leq h_{3}\end{array} \right.. \tag{31}\]  
Finally, the Mayer optimization problem is transformed into a general NLP problem as shown in the following Equation. \[\left\{ \begin{array}{l}\min F_{1}(y)\\ F_{2}(y)=0,\\ F_{3}(y)\leq 0\end{array} \right. \tag{32}\]  
where \(F_{1}\), \(F_{2}\) and \(F_{3}\) respectively represent the objective, equality constraint and inequality constraint function, and the optimization variable \(y\) is:  
\[y=\left(S_{0}^{T},S_{1}^{T},\ldots,S_{m}^{T},P_{0}^{T},P_{1}^{T},\ldots,P_{m}^ {T},T\right)^{T}. \tag{33}\]  
For the solution of the NLP problem, any NLP algorithm can be used, such as sequential quadratic programming, active set, interior point method, etc. In this paper, the Quasi-Newton method is used. The optimization variables are iteratively updated by the following formula:  
\[y^{k+1}=y^{k}+t_{k}\Delta y^{k},\ t_{k}\in[t_{\min},t_{\max}], \tag{34}\]  
where the increment \(\Delta y^{k}\) is determined by the Kuhn-Tucker condition of the quadratic approximation problem below:  
\[\left\{ \begin{array}{l}\min\frac{1}{2}\Delta y^{T}b^{k}\Delta y+\
abla F_{1 }^{k}\Delta y\\ F_{2}^{k}+\
abla F_{2}^{k}\Delta y=0\\ F_{3}^{k}+\
abla F_{3}^{k}\Delta y\geq 0\end{array} \right., \tag{35}\]  
where \(F_{i}^{k}\) and \(\
abla F_{i}^{k}(i=1,2,3)\) are the values and gradients of the function at \(y^{k}\), and \(B^{k}\) is the approximation of the Hessian matrix (\(\
abla_{y}^{2}L\)) of the following Lagrange equation:  
\[L(y,\lambda)=F_{1}(y)-\lambda_{1}^{T}F_{2}(y)-\lambda_{2}^{T}F_{3}(y). \tag{36}\]The above parameters can be updated by the following formula:  
\[\left\{ \begin{array}{l}B^{k+1}=B^{k}+U\Big{(}B^{k},\Delta y^{k},\gamma^{k}\Big{)} \\ \gamma^{k}=\
abla L\Big{(}y^{k+1},\lambda_{1}^{k},\lambda_{2}^{k}\Big{)}-\
abla L \Big{(}y^{k},\lambda_{1}^{k},\lambda_{2}^{k}\Big{)}\end{array} \right., \tag{37}\]  
where \(U\Big{(}B^{k},\Delta y^{k},\gamma^{k}\Big{)}\) represents some correction method. The more commonly used is BFGS correction.
## 3 Results
### APP Function Verification  
APP function verification was another important experimental result in this paper. Here, it showed the data querying, remote setting, feedback messages, and voice prompts based on the role of login user. Firstly, everyone needed to register an account to login, as shown in Figure 7. The role of the registered user will be determined based on the registered information. While the role of user is the patient, the login function was shown in Figure 8. While the simulated patient clicked the \"Start rehabilitation\" function, the action of rehabilitation began. After the number of rehabilitation (\(N_{r}\)) was larger than the number of recommended rehabilitation (\(N_{rr}\)), the rehabilitation was completed. The data of rehabilitation was then transmitted to the cloud database in real time, as shown in Figure 9. The rehabilitation record could be queried based on the setting date, as shown in Figure 10. Finally, the simulated patient could get the feedback the recommendation in \"Doctor's recommendation\", as shown in Figure 11. The designed wearable device actually worn by the user and the developed application on the smartphone during the real user rehabilitation activities was shown in Figure 12.  
While the role of user is the medical staff, the login function was shown in Figure 13. In this paper, the setting of elbow joint rehabilitation was not judged by the simulated patients. The setting of elbow joint rehabilitation was judged by medical staff based on the status of the simulated patient's rehabilitation remotely. Since the simulated patients had no medical professional ability, the setting of elbow joint rehabilitation judged by medical staff could have better efficiency of rehabilitation. Hence, the setting of elbow joint rehabilitation was judged by medical staff was shown in Figure 14. The medical staff could track the rehabilitation status of each simulated patient based on the name and date. Moreover, the medical staff could also send the recommendation to the simulated patient while tracking this simulated patient's rehabilitation status, as shown in Figure 15. Finally, the simulated patient could feedback the reminder to the simulated patient in any time, as shown in Figure 16.  
Figure 7: Registration.  
Figure 8: Login for patients.  
Figure 9: Start rehabilitation.  
Figure 11: Recommendation by doctor.  
Figure 10: Record query of rehabilitation.  
Figure 12: Designed wearable device actually worn by the user and the developed application on the smartphone.  
Figure 13: Login for medical staff.  
Figure 14: Setting of elbow joint rehabilitation was judged by medical staff.  
Figure 15: Status tracking of rehabilitation and recommendation for patients by medical staff.
# A Cloud Microservices Architecture for Data Integrity Verifiability Based on Blockchain
## 2 Preliminaries and Related Work
### Data Integrity in Cloud Systems  
Cloud computing can be perceived as a collection of computing resources providing shareable infrastructure through the internet,which are accessible and available everywhere, reference [24]. The name was coined by Mr. [PERSON] in 1997, reference [25], which agree with [24; 26].  
Cloud computing is defined by the National Institute of Standards and Technology (NIST) as a model for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction, reference [27]. Applications of cloud computing are mailing services to storage, document processing, hosting services, image processing, and video streaming [26].  
The reality is that cloud computing right now is one of the most trending topics in the field of information technology (IT) in recent years [1]. Companies, such as Amazon, Google, and Microsoft, have enhanced their services to provide cloud environments for their customers. Thus, every day, many organizations and companies are migrating their services over the cloud, since the benefits of cloud computing encompass improved efficiency, flexibility, and reduced infrastructure costs, reference [2].  
However, there exist some concerns when uploading systems to the cloud, such as security, privacy, power efficiency, compliance, and integrity [1,28]. Preserving confidentiality and integrity in distributed systems and cloud services is also a key problem. The importance of confidentiality and data integrity in cloud systems is imminent. Confidentiality refers to restricting access to information, at least, it is authorized to access it. Data integrity aims to protect information against intruder modifications or software bugs.  
Traditional techniques used for data integrity and verification for cloud storage are already provided in the literature [29]. [PERSON] et al. [30] argue that traditional systems (only off-chain) remain inefficient, antiquated, leading to critical data omissions, security vulnerabilities, and even corruption. We explore a newer technique, the combination of data integrity with a blockchain platform.