esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Payload Design  
The design of the payload was centered on the first generation of RSONAR [10]. There were two distinct imaging systems, referred to as subpayloads, that were connected to a singular Power Distribution Unit (PDU). Subpayload 1 was dedicated to gathering a dataset through continuous acquisition of RSO images at varying resolutions for the purpose of testing and improving existing SSA algorithms. Subpayload 2 consisted of real-time attitude determination and RSO detection systems.
# Object-Based Mapping of Coral Reef Habitats Using Planet Dove Satellites
## 3 Results
### Accuracy Assessment  
Confusion matrix results were calculated using field measurement data. Table 2a,b shows the results of the accuracy assessment and corresponding error matrix comparing the observed and predicted classes for the 11 different benthic habitat types. Overall accuracy was 82%. When considering the users' accuracy, deep water without benthic signals (88%), deep and shallow sand (87%) and both dense and sparse seagrass (86%) have high reported accuracy. Shallow sand has a higher accuracy (92%) than deep sand (81%). Dense seagrass was slightly easier to predict (92%) than sparse seagrass (84%). For the coral reef classes, coral fore-reef classification performed better (77%) than the coral back-reef classification (69%). Coral reef crest (58%) and gorgonian/soft coral (49%) had the lowest reported classification accuracy.  
Figure 5: Benthic habitat classification results covering three study sites within the Arrecifes del Sureste marine sanctuary in southeastern Dominican Republic: Catalina Island, Saona Island, and eastern Dominican Republic.
# Ultrahigh-Strain-Rate Mechanical Properties of Polystyrene near the Glass Transition Temperature
## 1 Introduction
extended this study to the CS deposition of various polymer \(\upmu\)Ps on polymeric substrates and developed deposition windows over the impacting particle's velocity-temperature space [6]. The velocity of the impacting \(\upmu\)Ps was calculated using a one-dimensional inviscid gas and particle dynamics model developed by [PERSON] et al. [17]. The results showed that deposition temperatures closer to the \(T_{\text{g}}\) of polymers were advantageous for higher deposition efficiencies and wider deposition windows. [PERSON] et al. studied the effect of pre-heating or pre-cooling the feedstock nylon 6 polymer powders. However, the authors did not observe any meaningful improvement in the deposition efficiency with the pre-heating/-cooling feed powder but reported that in a low-pressure CS system, pre-heating resulted in 28% higher mechanical strength in the CS deposits in comparison to the deposits from room temperature and pre-cooled feedstock powders [18]. Meanwhile, [PERSON] et al. reported that the deposition efficiency was inversely proportional to the degree of crystallinity of the feedstock polymer (polyetherketoneketone; PEKK) [19]. The previous CS studies suggested that several significant factors influence the CS consolidation process of the polymer \(\upmu\)Ps via their complex interplay. This challenge in understanding the complicated effects of the major factors should be addressed through parametric experimental approaches with the decoupling of the factors. In this aspect,temperature-controlled single \(\upmu\)P collision experiments with amorphous polymers can be effective. Due to their noncrystalline nature, amorphous polymers display temperature-dependent characteristics primarily through glass transition. As a model noncrystalline polymer, polystyrene (PS) is investigated in this study. PS is a durable thermoplastic polymer used in a wide range of products owing to its versatile properties [20]. The temperature-dependent mechanical characteristics of PS are well documented in the low-strain-rate regime. However, very few studies have focused on the ultrahigh-strain-rate (UHSR) behavior of PS, regardless of its cruciality in understanding the CS-relevant characteristics. The current research focuses on the temperature-dependent extreme collision characteristics of PS \(\upmu\)Ps in normal and tilted impact experiments of PS \(\upmu\)Ps conducted at different temperatures in a highly controlled single-particle impact method called laser-induced particle impact testing (LIPIT) [21].
## 3 Results and Discussion  
### Descriptive Statistics of Soil Heavy Metals and Physicochemical Properties  
The soil heavy metal content and related properties in the study area are shown in Table 1. The content of different heavy metals in the topsoil was quite different. Compared with the background level of soil heavy metals in Sichuan Province, the ratio between the level of 6 heavy metals in the study area and their background level in Sichuan Province was As (61.90%) > Cr (36.51%) > Ni (28.57%) > Zn (11.11%) > Pb (6.35%) > Cu (0.00%), indicating that these heavy metals (particularly As element) were accumulated with varying degrees in the study area. The highest contents of Pb, As, Cu, Zn, Ni, and Cr were 1.18, 5.29, 0.91, 1.34, 1.90, and 2.91 times of the background level, respectively. The contents of NH\({}_{4}^{+}\)-N, NO\({}_{3}^{-}\)-N, AP, AK, EC, CEC, and SWC in the topsoil of the study area were 5.56-118.10 mg kg\({}^{-1}\), 28.16-381.60 mg kg\({}^{-1}\), 4.92-281.20 mg kg\({}^{-1}\), 30.28-867.50 mg kg\({}^{-1}\), 1.43-25.04 mS m\({}^{-1}\), 0.10-12.90 cmol\({}^{+}\) kg\({}^{-1}\), and 4.47-47.49%, respectively. The pH of soil samples in this area ranged from 3.48 to 7.06, with an average value of 5.12. In addition, the samples with pH value less than 7.0 accounted for 98.41% of the total samples, indicating that the soil is generally weakly acidic, and the soil heavy metal activity is relatively strong. Soil pH controls nearly all aspects of physical, chemical, and biological processes in soils. These processes can alter metal availability, including dissolution and precipitation of solid phases of metals, complexation of metal species, and acid-based reactions [14,15] Since lower pH generally reduces the adsorption of metal ions on soil particles, the competition between free metal ions and other cations in solution increases, thereby increasing the metal concentration in soil solution [16].  
The coefficient of variation (CV) is an important measure to characterize the degree of sample variation, and can reflect the degree of artificial influences on the sample. CV < 0.10 represents weak variation, 0.10 \(\leq\) CV \(\leq\) 0.50 represents moderate variation, and CV > 0.50 represents high variation [17]. The degree of variation for the six heavy metals (from high to low) was As > Pb > Cu > Zn > Cr > Ni. Except for Ni with moderate variation, the content of other heavy metals had high level of variation, indicating that As, Pb, Cu, Zn, and Cr in the soil of the study area are significantly affected by external factors. This could be due to human activities, e.g., industry, transportation, and chemical fertilizer application, which have a great impact on the health of local residents and the ecological environment.
###### Abstract  
The use of non-cryogenic certified commercial electronics for cryogenic applications may be attractive due to their cost and availability, but it also carries risks related to reliability, performance and thermal compatibility. The decision to use commercial components that are not certified for cryogenics instead of components specifically designed for such applications must be carefully weighed based on specific project needs and risk tolerances. This work presents the characterisation of an attenuator circuit at cryogenic temperatures used in a microwave kinetic inductance detector (MKID) readout system. In order to characterise the operation of the attenuator at cryogenic temperatures and because the circuit works at frequencies up to 40 GHz, a specific microwave PCB has been designed. The cooling system used consists of a cryostat, all the connectors, cables, a vacuum pump, a compressor, pressure and temperature sensors, a temperature control system and a cold head operating in a closed helium gas cycle according to the Gifford-McMahon principle. The circuit was tested and characterised at temperatures ranging from 296.5 K to 83.6 K.  
cryogenic; vacuum; astrophysical instrumentation; integrated circuit (IC); attenuator; microwaves; non-cryogenic certified components; MKIDs +
Footnote †: journal: Sensors
# Defense and Security Mechanisms in the Internet of Things: A Review
## 1 Introduction
We can approach defense and security mechanisms in IoT environments from two perspectives. The first perspective is connected with the primary goal of these mechanisms, which is to protect against network attacks (defense mechanisms). Some more significant tools incorporate these methods, such as the Intrusion Detection System (IDS) or Intrusion Prevention System (IPS). IDS and IPS are high-end security measures to prevent different kinds of threats. An IDS is a device that analyses the network traffic and searches for any abnormal behaviour that could indicate an attack. The IDS system notifies administrators about the incident when it detects a potential threat. However, it does not take any automatic action to stop the attack. IPS, on the other hand, works similarly to IDS. Still, in addition to detecting threats, it can also take steps to block them, such as blocking network packets, resetting connections, or deactivating infected user accounts. IPS offers preventive measures and reactions at the moment. This is very important in preventing threats from becoming bigger [21; 22; 23]. From the second perspective point of view, we can also look at different techniques that can be used to safeguard users, devices, data, or even networks, referred to as security mechanisms. In this category of security mechanisms, communication security protocols implement various cryptographic methods such as encryption, anonymisation, or hash ing for the messages to be exchanged. Thus, they are considered the primary security mechanism in IoT systems [24; 25; 26].
## 1 Introduction
The ACMFI tool assists users in conducting fault injection simulations by allowing them to select the target area for fault injection and automatically generating statistical fault injection test scenarios. The tool offers several advantages. Firstly, it effectively addresses the side effect problem by restricting the fault injection to specific, predefined areas within the device under test (DUT). This method ensures that the faults are contained within their designated regions and do not introduce unnecessary errors into other external areas designated for fault injection. Additionally, as an API-based fault injection tool, it facilitates the swift creation and update of test environments. These advantages enhance the accessibility of the testing process and support user-friendly testing. Furthermore, it allows for an accurate analysis without requiring modifications or additions to the testing circuit. The soft error rate (SER) of four types of security circuits was analyzed using Digilent's Zybo-Z7 20 board as a case study of the ACMFI tool. The security circuits analyzed were as follows: the Data Encryption Standard (DES); Advanced Encryption Standard (AES); Secure Hash Algorithm (SHA); and [PERSON], [PERSON], and [PERSON] (RSA). For statistical fault injection testing, ATALANTA, an open-source Automatic Test Pattern Generator (ATPG) tool [15], was utilized. A literature review was also conducted to gather the necessary data for calculating the \(SER_{est}\). A linear relationship analysis was carried out to analyze the AVF and SER results presented in this study. Through this linear correlation analysis, side effect minimization was proposed, highlighting a strong coefficient of determination for DUT-coverage AVF. In addition, exponential distribution reliability modeling was applied to evaluate system-level reliability in terms of the soft error rate (SER). To compare the reliability assessments, different emulation and beam test results were applied to the reliability model and compared. The results confirmed that the ACMFI tool alleviated the problem of reliability overestimation due to the existing side effects and provided estimates similar to the beam test results. The document is structured as follows: Section 2 explains the failures caused by the radiation effects in SRAM-based FPGAs and various methodologies to analyze them. Section 3 describes the ACMFI tool developed in this paper, including the statistical fault injection test procedure and SER estimation method. In Section 4, the proposed tool is used to analyze and discuss the vulnerability and reliability of four types of target security circuits. Finally, Section 5 provides a summary of the conclusions reached in this paper.
# Linked Coupling Faults Detection by Multirun March Tests
## 1 Introduction
```
1:for\(cell=0\) to \(N-1\)do
2: Memory[cell] = 0;
3:endfor
4:for\(cell=0\) to \(N-1\)do
5: value = Memory[cell];
6:if\(value<>0\)then
7: goto error;
8:endif
9: Memory[cell] = 1;
10:endfor
11:for\(cell=N-1\) downto 0 do
12: value = Memory[cell];
13:if\(value<>1\)then
14: goto error;
15:endif
16: Memory[cell] = 0;
17:endfor
```  
**Algorithm 1** MATS+ march test algorithm  
However, these studies are limited to the detection of linked coupling memory faults. The proposed research aims to establish the necessary and sufficient conditions for detecting such faults in modern memory devices. Currently, there are no one-run march  
Figure 1: Flowchart of simple march element. test approaches for detecting undetectable linked coupling memory faults. Most of the recent research in this area focuses on developing and enhancing march tests to detect new faults introduced by Very Deep SubMicron (VDSM) memory devices, such as \(CF_{ir}\), \(CF_{drd}\), \(CF_{rad}\), and \(CF_{ir}\). The problem of detecting linked coupling memory faults has been tackled for neighborhood pattern-sensitive faults (_NPSFs_). Several test algorithms have been developed to detect _NPSFs_, but they come with very high test complexities. As mentioned in [12], a 68\(N\) arch test algorithm can be used for _ANPSFs_ and _PNPSFs_, while it requires a 96\(N\) march test to detect all _ANPSFs_, _PNPSFs_, and _SNPSFs_. Detecting very complex _NPSFs_ is not only time-consuming but also does not guarantee the detection of undetectable faults, including _CF_s. The approach proposed in this paper enables the identification of previously undetectable linked coupling faults. Additionally, the proposed mathematical model for describing linked coupling memory faults may be applicable to other memory faults involving more than two cells. Nonetheless, this necessitates further rigorous and time-consuming research.
# Computer-Vision-Based Product Quality Inspection and Novel Counting System
## 2 Related Works
### Automated Inventory Management and Product Counting System
#### 2.3.2 Existing Product Counting Methods and Problems  
The problem with existing load cells is that accurate product counting is impossible due to resolution limitations during the calculation process. For example, if the resolution of the load cell is rounded to 500 g, overcounting or undercounting may occur if the actual weight deviates from the standard value. Additionally, in a manufacturing environment, if a worker temporarily stands on or near a load cell while loading product, the system may mistakenly recognize the worker's weight, leading to incorrect calculations. These inaccuracies have serious implications for inventory management and reduce the overall efficiency of the manufacturing process. To address these issues, research on smart load cells has shown that smart load cells achieve measurement errors of less than 100 g in industrial applications weighing up to 400 kg, providing more-accurate readings than traditional systems [37]. However, despite these improvements, these systems are still limited in terms of maximum weight capacity and resolution, making them inadequate for use in a wide range of industrial applications.  
In this study, the goal is to develop a system that improves the accuracy of product counting and inventory management processes by setting the product counting value as the unit weight. This is achieved using a technology that recognizes when a worker approaches the load cell and prevents counting from being affected, along with the difference image technique. We believe that this system will contribute to reducing product counting errors and increasing the reliability of inventory management by superseding the limitations of existing techniques.
## 6 Example
For ATE, many methods were implemented, including the traditional regression with all covariates (SAS LOGISTIC), g-computation (SAS CAUSALTRT), PS stratification (SAS PSMATCH), PS weighting (SAS PSMATCH), and doubly robust estimators (SAS CAUSALTRT, the AIPW and PSWeight packages in R). For the remaining ATT, ATM, and ATO estimands, almost all estimators were employed as well. In Table 2, the results from using SAS are presented whenever there is a suitable procedure available in SAS, with the exception that the PSWeight package in R is used for the doubly robust estimator for ATT and overlapping weighting. The crude estimate, without accounting for any covariates, and the CATE with a traditional regression, accounting for all covariates, give relatively larger risk differences (0.074 and 0.072, respectively) compared to most other methods (0.050-0.059). Most other methods, such as g-computation, PS weighting, etc., produce similar results in terms of having comparable point estimates and confidence intervals. The risk difference between the RHC and No-RHC control groups is all positive, indicating that patients with RHC had an increased rate of 30-day mortality after adjusting for treatment selection bias. This result confirms with the original analysis by [PERSON] et al. that RHC patients had decreased survival rates compared to No-RHC patients [70]. Figure 2: A love plot comparing the absolute standardized difference of selected covariates included in the model with different methods: (1) blue for original data in the traditional regression, (2) purple for ATT weighting, (3) red for ATE weighting with IPTW, (4) black for matching, and (5) green for overlap weighting. PS stratification can be unstable when the number of strata is large or the data size is small. In this example, stratification with five strata showed some signs of instability, with the first stratum and the last stratum having a relatively large standard error; additionally, it would be even worse in terms of having estimates that are very different from stratum to stratum when the number of strata increases to 10. The ATM risk difference obtained from the matched observations is the smallest (0.049), proving to be a little different than most ATE and ATT estimates. This is because ATM represent the matched observation pool, a subset of the whole population that most ATE and ATT is derived from. In general, it seems that the ATO with overlap weighting and its doubly robust version have the smallest standard errors and, therefore, the smaller confidence intervals, as claimed in [PERSON] et al. [17]. However, the performance of any method must be considered in terms of the estimand and that estimand's relevance to the motivating scientific question.
## 3 Results and Discussion
### Responses and Optimization
#### 3.1.2 Device Performance
Along with the simple and low-cost optoelectronic interrogation at normal incident conditions compared with the  
Figure 5: **(a)** Spectral responsivity of the optimized device for different values of the refractive index of the analyte medium, \(n_{a}\). **(b)** Resonance wavelength in terms of the refractive index change. It shows a linear behavior through the whole scanned refractive index range. optical-angular interrogation, the device high sensitivity and high FOM values are comparable with many recent reported optical sensors with high sensitivity [30; 31; 32; 33; 34].
## 3 Experimental Results and Analysis
### The Influence of Deep Spatial-Temporal Features on the Accuracy of QoE Model  
In addition, to verify the validity of the deep spatial-temporal features extracted in this paper, we compared it with SI and TI (Model 6). The obtained QoE model using SI and TI is labeled as Model 6. Table 5 shows that compared with SI and TI, the deep spatial-temporal features proposed in this paper can increase PLCC and SROCC by 7.51% and 7.66% on Waterloo Streaming QoE Database-III, and 6.03% and 6.26% on LIVE-NFLX-II dataset, which indicates that deep spatial-temporal features can more effectively characterize the spatial and temporal characteristic of a video. It also should be noted that the dimension of deep spatial-temporal features is much higher than SI and TI.  
In summary, among the four aspects of influencing factors, namely, video content characteristic, video quality, fluency and volatility, video quality has the greatest impact on QoE and video fluency and volatility have an exceedingly important impact on QoE.
## 2 Concentration-Position Shift Keying (CPSK)  
Before delving into the specifics of our proposed TH-CPSK system model, it is beneficial to establish a foundational understanding of the conventional CPSK utilized in CEMC [24]. CPSK is a modulation method that encodes information using the concentration position of molecules, drawing parallels to \(M\)-PPM prevalent in EM communication, where \(M\) is the modulation order. Just as \(M\)-PPM encodes data in the position of pulses to minimize ISI, CPSK aims to use the spatial position of molecules for information encoding, where a concentration pulse can be placed in one of the \(M\) possible time slots, thereby seeking to mitigate ISI effects. This method underscores the innovative adaptation of concepts from EM communication to the molecular domain, laying the groundwork for advancements such as TH-CPSK.  
Figure 1: A multiple access architecture where eight transmitter nanomachines (TNs) are trying to communicate with a receiver nanomachine (RN) at the same time.  
Figure 2 illustrates a block diagram of a CEMC system employing 4-CPSK, which comprises two nanomachines: a transmitter nanomachine (TN) and a receiver nanomachine (RN). The system utilizes the molecule concentration, denoted as \(Q_{s}\), to encode information. Specifically, the TN sends data bits \"01\" and \"10\", and these are encoded into positions 2 and 3, respectively, each with concentration \(Q_{s}\). Consequently, the transmitted signal, \(Q(t)\), maintains this constant concentration, \(Q_{s}\), across all symbol intervals. The signal emitted by the TN traverses a free-diffusion channel, characterized by the unimpeded movement of molecules through the medium, often air. This movement is influenced by thermal energy from the environment, leading to random molecular motion and diffusion as a result of collisions with solvent molecules. Upon reaching the RN, the transmitted molecules are subject to a ligand-receptor binding process (LRBP). This process involves the interaction between the ligand molecules, which carry the information, and the RN's receptors. The collision of ligand molecules with these receptors produces a chemical pulse, facilitating the transmission of information [28; 29; 30]. Through the LRBP, the received concentrations of molecules, \(U(r,t)\), are measured by the RN's chemical receptors located on the cell boundary. It is important to note that not all molecules may bind to the receptors during the LRBP, leaving some within the channel and potentially influencing subsequent signal transmissions. The RN employs a probabilistic approach to interpret the concentration data, converting it into a probability value, \(N_{j}\), which helps identify the position with the highest probability. This process enables the RN to determine the correct symbol position received, demodulate the data, and decode the output bits.