esa-sceva/satcom-chunk-collection huggingface dataset:
# Implementation of a Cryogenic Facility for Space Debris Analysis
## 4 Prototype Implementation
### User LabVIEW Interface  
The user interface was developed in LabVIEW to display the information gathered from the sensors mentioned above and the status of the actuators, i.e., the patch heaters and pneumatic actuators. Figure 19 shows a complete view of the control panel [12].  
1. This panel displays the current temperature in each of the five PT100 sensors installed.
2. Actuators' control panel. This panel consists of an Override button and a matrix of buttons that allows one to control and know the state (ON/OFF) of an actuator. The matrix is divided into three columns: the first with red buttons, the second with blue buttons, and the third with green ones, representing, respectively: 1. User manual input that can override the automatic control if the Override button is toggled ON 2. Automatic control input 3. The state of the output to the control board
3. Displays the temperature over time.
4. Stage control panel. As mentioned before, there are four stages in a normal thermal cycle, although due to functionality reasons, two more were added: an OFF stage, where all automatic controls are disabled, and a warm-up stage (up to \(-45\)\({}^{\circ}\)C), which is used to ensure a secure temperature for opening the system without the formation of ice inside the chamber. These two stages are only assessable through the Stage Override button. In this panel, it is also possible to define the upper and lower temperature limits, as well as the dwelling times for each one individually.
5. Displays the current pressure inside the chamber.
6. Displays the data captured from the cryocooler and cryocooler monitor.
7. System alert panel. This advises the user on whether some parameters, i.e., the cryocooler temperature, water flow, cold-finger temperature, and vacuum pressure, are outside normal values. This warning is represented by the red light present in this panel, as well by sending a text message to a pre-defined cell phone number, and it also displays the number of cycles already done.
8. Reading from an ultraviolet (UV) sensor.  
Figure 19: Interface of the control panel.
## 2 Materials and Methods
### Outcome Measures
#### 2.3.2 Secondary Outcomes  
The secondary outcomes were proprioception, muscle force output, and dynamic balance. For the proprioception test, the Biodex isokinetic dynamometer (Biodex System, Shirley, NY, USA) was used to assess knee active joint proprioception. Each participant sat in an upright position, and the dominant leg was on the test arm of the Biodex dynamometer. The thigh was fixed with a strap, and the calf was fixed to the movable arm to reduce compensatory body movements. Participants were asked to wear an eye mask to isolate their visual input. Subsequently, each participant actively moved the limb to the target angle of 45\({}^{\circ}\) of flexion and then returned to 90\({}^{\circ}\) of knee flexion [25]. The test was assessed three times and the results averaged. The joint reposition angle error, which is the difference between the setting and the perceived angle, was recorded as an index of knee proprioception [26].  
The Biodex isokinetic dynamometer was also used in the knee isokinetic muscle strength test. The isokinetic quadriceps and hamstring muscles were measured at 60\({}^{\circ}\)/s using the Biodex system, which has high ICC values. [24]. The posture setting was the same as that for the proprioception test, but participants removed the eye mask. The dominant leg was tested. The participants were asked to actively perform knee extension and immediate knee flexion as rapidly as they could three times at a set angular velocity of 60\({}^{\circ}\)/s. The highest peak torque value was recorded by means of the Biodex software, used in the statistical analysis, and further normalized by participants' body weight [27].  
To assess the dynamic balance of the lower limbs, the YBT had a high ICC of 0.91 [23]. In this test, participants were required to maintain single-leg balance in their dominant leg. They were asked to be barefoot to eliminate the influence of shoes on the stability and balance of the lower limbs. First, participants stood on the central standing board and maintained balance, and then they moved the nondominant foot (moving foot) as far as possible from the origin to the front side. After returning the foot to the center point, participants extended in three directions (i.e., anterior, posterolateral, and posteromedial) (Figure 1). Six trial practices of the YBT were allowed for their dominant leg in the familiarization session to eliminate the learning effect [28]. When the participants performed the test, they let their toes slide with ease, extend to the farthest point, and return to the original position while maintaining balance. The measurement for each direction was obtained three times, and the data were averaged. The YBT composite score was calculated by averaging the three distances reached and normalizing the outcomes according to the lower limb length (measured from the anterior superior iliac spine to the medial malleolus in the supine position) and multiplied by 100.
# The Next Generation Platform as A Service: Composition and Deployment of Platforms and Services
## 4 Virtual Network Function as A Service, Overview
### Supported Virtual Network Functions (VNFs) and Value Added Services (VAS)
#### 4.4.2 Value Added Services: Monitoring, Alerting, and Healing  
In the Telco PaaS, monitoring utilises the ElasticSearch-Logstash-Kibana (ELK) stack [33]. This stack consists of three main components: (1) Elastic Search: A distributed search and analytics engine based on REST APIs; (2) Logstash: A server-side data processing tool that can ingest data from various probes and manipulate it before sending it to ElasticSearch; (3) Kibana: A visualisation tool provided with ElasticSearch. As noted, in the Telco PaaS prototype the selected VNFs are based on proprietary VMs, thus deploying monitoring probes internal to the VNFs is not feasible. To overcome this limitation the probes in the Telco PaaS are deployed in a side-car fashion, externally to the target service; but can poll the target service for the required using the available interfaces. For this prototype, we rely on the SNMP protocol and REST interface to monitor the router VNF and ONOS SDNC respectively. More specifically, SNMP is used to poll the virtual router for its CPU utilisation, while REST is used to poll ONOS for network traffic information regarding the virtual interfaces of the deployed VNFs.  
As an enhancement to baseline monitoring, this prototype also provides Alerting and Healing capabilities. The ELK stack can map trends in the CPU utilisation of monitored VNFs to predefined anomalous behaviours. Once such an anomaly is detected, a Healing function is called, which redeploys the anomalous VNF with more resources (e.g., more virtual CPUs or RAM). All healing actions are performed, by the Healing rule via the RDCL 3D tool, thus ensuring alignment between the state of the deployment and the view of the tool. Figure 9 shows the operation of the monitoring VAS in detail. As mentioned before, monitoring, alerting, and healing are complementary services provided selectively to VPs. As a result, upon deployment of a VNF, the VSP can set a flag in the RDCL 3D metadata to trigger or not trigger the monitoring capabilities for this VNF.  
Figure 10 illustrates the capability for live network traffic information for each interface of the provisioned VNFs. As mentioned, this information is collected by the monitoring probe through the ONOS REST API. On the other hand, Figure 11 shows how live information is reported about the CPU utilisation of all monitored VNFs. In addition, this segment is configured with three predefined  
Figure 9: Operation of the Monitoring Value Added Service.  
thresholds (50%, 80%, and 90%)--when any of these thresholds is reached a corresponding alert is shown to the administrator. In the provided example, the critical threshold of 90% has been exceeded (94%); exceeding this threshold triggers an automated healing workflow that will re-provision the VNF with more virtual resources.
## 1 Introduction
Commonly used demodulation methods include the waveform detection method and the laser pulse response method. When these two methods are used for signal demodulation, they have relatively high requirements for the detection intensity of light. Especially in ultraviolet optical communication, signals are prone to being affected by atmospheric attenuation and noise. Therefore, a highly sensitive detection system is required to ensure good signal quality. This study proposes a Poisson distribution-based codeword-level maximum likelihood decoding method for four-frequency shift keying (4 FSK) modulation in UV communication systems, incorporating channel coding for system optimization. The method integrates key technologies including frame header statistical modeling, photon pulse counting, frequency correlation detection, and clock synchronization/tracking to enhance decoding reliability in low-light environments. To evaluate algorithm performance, computer simulations were conducted, and the algorithm was successfully implemented in hardware. Experimental results demonstrate that the UV communication system achieves a maximum communication range of 900 m. When LDPC coding is added, the system achieves a BER as low as \(10^{-5}\) at a communication distance of 500 m.  
The second section analyzes the discrete Poisson channel, pulse frequency slicing counting, frequency correlation detection, and clock synchronization/following techniques. In the third section, the link model of UV LOS communication and the single scattering model of NLOS communication are analyzed; the computer simulation results and experimental results are provided. Finally, the fourth section summarizes the entire paper.
## 3 Simulation Experiments
### Model Implementation
This approach gradually approximates the global optimum by leveraging feedback from the target function, overcoming the inefficiencies associated with traditional grid search or random search methods. * Evaluation and Validation: For every set of hyperparameter configurations, the resulting model was evaluated on the validation set to ensure robust performance not only on the training set but also on unseen data. Cross-validation techniques were employed to further mitigate the risks of overfitting or underfitting. The key hyperparameters for the internal structure of the TCN-BiLSTM model are summarized in Table 4. Batch size refers to the number of training examples processed in a single forward and backward pass. It is a crucial hyperparameter that affects training efficiency, convergence speed, and model generalization. Larger batch sizes may accelerate computation but could reduce model generalization, while smaller batch sizes may improve generalization at the cost of slower training.
## 3 Results and Discussion  
### Effect of HPP and Storage Time on the pH Value of Beef  
The results of the beef quality characteristics are presented in Figure 1. On day 1 of storage, the pH of the meat was 5.6-5.7. This was a typical pH level for good-quality beef [5; 6; 9]. There was no significant (\(p\) > 0.05) effect of HPP treatment on the pH of skin-packed beef until day 21 of storage. From day 28 to day 56 of storage, HPP500 meat was characterized by a significantly (\(p\) \(\leq\) 0.05) higher pH than the control meat. Furthermore, the HPP400 meat had a higher pH than the control meat, but significant differences were observed only on the 28 th and 49 th day of storage (Figure 1). The observed differences were probably caused by different HPP treatment conditions (including temperature and pressure holding time). In the study by [PERSON] et al. [16], the pH of beef did not change immediately after HPP treatment (450 MPa for 15 min or 600 MPa for 10 min) compared to control samples and remained at a level of approx. 5.9. [PERSON] et al. [10] stated that applying HPP at 450 and 600 MPa for 90 s (12-16 \({}^{\circ}\)C) increased the pH of beef compared to the control and meat treated at 300 MPa.  
Figure 1: Changes in pH of beef during 56 days of storage (average \(\pm\) standard deviation; \(n\) = 3). The symbols identifying the treatments are as follows: control—not treated by HPP; HPP300, HPP400, or HPP500—HPP-treated at 300, 400, or 500 MPa. Different uppercase letters (A,B) within the same column indicate that average values are significantly different (\(p\) \(\leq\) 0.05)—differences between treatments for a particular day of storage. Different lowercase letters (a,b) within the same row indicate that average values are significantly different (\(p\)\(\leq\) 0.05)—differences between days of storage for a particular treatment.  
During storage, a gradual decrease in pH of the control meat to 5.3 was observed on day 56. Compared to days 1-42, the pH of the control meat was lower (\(p\leq 0.05\)) on days 49 and 56. Throughout the storage time, the HPP400 and HPP500 meat samples were characterized by a stable pH which was maintained at the level of 5.6-5.7 (Figure 1). A decrease in the pH of meat that was not subjected to HPP treatment and meat pressurized with 350 MPa (10 min, 10 \({}^{\circ}\)C) and stored for 45 days was also observed by [PERSON] and [PERSON] [9]. The probable reason for the decreased pH of the control meat observed in our study may be the increasing number of lactic acid bacteria during storage. It has been shown that the use of HPP, especially 400 MPa and 500 MPa, is very effective in limiting the growth of this group of bacteria in skin-packed beef [31].
# Targeting Type II Toxin-Antitoxin Systems as Antibacterial Strategies
## 3 Strategies for the Artificial Activation of Toxin-Antitoxin Systems
### Direct Activation of TA Systems  
#### 3.1.1 Disruption or Preventing the Formation of TA Complexes  
The most straightforward strategy for direct activation of the antibacterial effect of type II TA systems requires disruption of the protein toxin and antitoxin complexes, leading to toxin liberation (Figure 2a). This strategy has been validated by several research groups who focused their efforts on identifying high affinity peptide inhibitors that can efficiently displace toxins from their cognate antiotoxins.  
[PERSON] and co-workers selected the \(\varepsilon\)-\(\zeta\) TA system from _Streptococcus pyogenes_ as their research model because the free \(\zeta\) toxin was shown to trigger a loss of cell proliferation similar to that caused by known antimicrobials [56]. Using high-throughput methods, they screened several oligopeptide libraries for the ability to impair the assembly of \(\varepsilon\)-\(\zeta\) complexes. This led to the identification of a library containing a mixture of 17-amino acid-long oligopeptides that interfered with the TA interaction. However, further subfractionation of this library resulted in a diminished effect. The authors suggested that the disruption of \(\varepsilon\)-\(\zeta\) complexes that was originally observed might have been a consequence of the concerted action of several weak-binding oligopeptides. Nevertheless, this study provided a proof-of-concept for the antimicrobial potential of this strategy [56].  
A similar approach was applied in the case of two related TA systems--_pemIK_ and _maxXT_ of _Bacillus anthracis_. The former module encodes the toxin PemK, a ribonuclease whose overexpression exerts a toxic effect in _B. anthracis_ cells characterized by the drastic inhibition of protein synthesis [57]. Based on in silico protein structural modeling, several peptides were designed to mimic the _C_-terminal domain of the antitoxin PemI, which is involved in toxin binding. In vitro experiments indicated the effectiveness of the designed molecules and demonstrated the feasibility of disrupting the TA interaction using octapeptides [58]. Similar results were obtained in studies on the related TA system _maxXT_. Based on the crystal structure of the MoxX-MoxT complex, [PERSON] and colleagues [59] designed a series of peptides that effectively disturbed the interaction of MoxT with antitoxin MoxX and stimulated MoxT ribonuclease activity [60].  
Analogously, following a detailed analysis of _vapBC_ TA systems of _Mycobacterium tuberculosis_, [PERSON] and colleagues [61] designed VapB- and VapC-based peptides that effectively disrupted the TA complexes, causing activation of the VapC toxin ribonuclease [61].
# Benchmarking Deep Learning Models for Instance Segmentation
## 3 Background
### Quantization  
Most of the interior of the neural network model is composed of weights and activation functions, and it is represented using 32-bit floating-point numbers to improve the accuracy of the model. In mobile, AI-device, and embedded environments, inference using deep learning models is difficult because memory, performance, and storage space are limited. Therefore, in recent years, many studies related to model weight reduction have been conducted in deep learning. Quantization refers to the process of converting a floating-point type into an integer or fixed-point number. Reducing the number of bits by mapping the 32-bit floating-point data type to a certain range leads to reductions in memory and model size as well as increases in inference speed and power consumption efficiency. There are two types of quantization techniques: (1) post-training quantization, which is learning with a floating-point model and then quantizing the resulting weight values to minimize quantization errors, and (2) quantization-aware training, which performs quantization during weight optimization in training. In the post-training quantization method, both dynamic quantization and fusion quantization are performed. Dynamic quantization quantizes only the weight of the model and dynamically quantizes activations during inference, whereas static quantization combines the activation and its layer to reduce the context-switching overhead of the model. Quantization-aware training performs clamping and rounding tasks by placing a fake quantization module in the model during training, which can be changed to the integrated model with values such as scale and zero points stored in the module when training is completed.
## 4 Results and Discussion
### Temperature-Based Damage Identification
#### 4.2.1 Demonstration of LDR Concept for Face Layer Debonding Detection
In both temperature profiles the gray shaded areas indicate the location of the surface temperature sensors. Due to the fact that the increased temperature is not uniformly distributed over the whole debonded area (cf. Figure 7a,b,d), a comparison of the absolute temperature values is not meaningful. Moreover, the IR results show a very high noise level. Nonetheless, it should be noted that the absolute temperature increase of the IR-camera results \(\Delta\overline{T}_{k}\) is of a similar order of magnitude compared to the temperature increase of approximately 1 K displayed by the temperature difference measurements using the surface sensors. The normalized temperature difference results for both surface sensors and spatially averaged IR-images correlate very well, as shown in Figure 8a. Both temperature profiles measured at the debonded face layer clearly shows heating and cooling phases, depending on whether the structure, particularly the damage, is being excited or not. Due to the fact that most of the noise and structural temperature drifts caused by the environment are equally distributed over the whole structure during the experiment the subtracting step in this presented procedure reduces the noise in the IR-camera results significantly. Furthermore, it can be seen hat due to the slower cooling compared to the heating phases in a cycle, the structure heats up continuously over the subsequent cycles.
## 3 Mathematical Formulation of the Reactive Power Supply
### Cost of Reactive Power Generation of the Compensators
#### 3.4.2 Tcsc  
This device is modeled as a reactance in series with the transmission line and can operate in both capacitive and inductive ranges. The capacity of the TCSC depends on the reactance of the transmission line on which it is installed [12]. As mentioned in Equation (9), the equivalent impedance is the sum of the line impedance and the TCSC impedance. In most cases, the TCSC is in capacitive mode and its reactance is negative. Therefore, the equivalent impedance is less than the transmission line impedance. Figure 3 shows the TCSC model.  
\[X_{ij}=X_{line}+X_{TCSC}=(1+r_{TCSC})X_{line} \tag{9}\]  
In the above equation, \(X_{line}\) is the reactance of the transmission line and \(r_{TCSC}\) is the compensation factor. The compensation factor usually varies between 0.2 and \(-0.8\). The cost of the TCSC, similar to the SVC, is a second-order function of its reactive power injection.  
The cost function [13] is as follows:  
\[C_{TCSC}=0.0015s^{2}-0.7130S+153.75\bigg{(}\frac{USS}{K}var\bigg{)} \tag{10}\]  
In this equation, \(S\) is the reactive power flowing through the transmission line before and after installing the TCSC.
## 3 Methodology and Experimental Set-Up  
We have carried out two experiments to find out the detection rate and precision of three SIDS with predefined off-the-shelf configurations. The first experiment is aimed at finding the detection rate (recall) scored against various public and in-house datasets of URI attacks. The second one is aimed at finding the precision and rate of false alarms obtained against a real-life large dataset.