esa-sceva/satcom-chunk-collection huggingface dataset:
# Review and Evaluation of MAC Protocols for Satellite IoT Systems Using Nanoatellites
## 5 MAC Protocols for Satellite IoT and M2M
### Reservation and Adaptive Protocols
#### 5.2.3 Fixed Competitive TDMA (FC-TDMA)  
Similar to R-Aloha, the fixed competitive TDMA (FC-TDMA) protocol defines a set of \(m\) slots grouped in frames. In each frame, the nodes select a slot for transmission in a pseudo-random manner [62]. To calculate the allocated slot for packet transmission, \(n_{slot}\), a node employs its ID--a previously assigned integer number--and follows the calculation shown in (5). The receiver has to estimate the number of stations on the ground based on the colliding slots and those with successful transmissions; the frame is further divided according to that estimation. An example of the operation of FC-TDMA is illustrated in Figure 10. The authors in [62] have suggested this MAC protocol for LEO satellite systems.  
\[ID\%m=n_{slot}. \tag{5}\]  
The theoretical maximum normalized throughput of the protocol is \(S=1\) with a load of \(C=1\). This performance corresponds to a scenario with a number of slots in the frame equal to the number of devices on the ground, and all the devices having an assigned ID such that there are no collisions. The complexity of this protocol is related to the TDMA functionality and the variable slot lengths. In addition, the algorithm for estimating the number of terminals on the ground may impact both the energy performance and the complexity. However, the details of this algorithm are not provided in the literature.  
Figure 10: Fixed competitive TDMA (FC-TDMA) operation. In the first frame, packets collide in every slot. For the following frame, the number of slots is increased, resulting in five successful transmissions out of ten slots. The number in each packet represents the node’s ID.  
Figure 9: Exchange of packets for a successful channel reservation with request-to-send/clear-to-send (RTS/CTS) signaling in carrier sense multiple access with collision avoidance (CSMA/CA).  
Similar to R-Aloha, the maximum normalized throughput of FC-TDMA is achieved under very specific conditions: (a) when the number of devices is constant; and (b) when there are no conflicting IDs among all users. However, FC-TDMA could potentially match the traffic characteristics of an IoT application as long as the estimation of the number of nodes under its coverage is accurate and fast.
# Laser Welding on Cerclage Wire Joining in Femur Surgery
## 5 Discussion  
In this study, the cerclage wires with laser welded joints are proposed to replace the traditional cerclage wire. The objective is to resolve the untwisting problem of traditional cerclage wire as well as to increase the strength of wire joints. Laser welding technology was chosen and applied to the stainless steel wire joints because it is suitable for small pieces of specimen that require high precision.  
The experiments of flare weld condition were carried out in the study to determine the range of adequate parameters during laser welding. The experimental results show that laser welding technology can be applied to weld on stainless steel wire 316L. With adequate laser parameters, the weld appearance has a nice ripple and free oxidation. The adequate area of the laser parameters obtained from the experiments includes the charge voltage range of 80-110 V and multiple frequency ranges of 6-9 Hz while the pulse width was kept at 3 ms. The welded lengths of 5, 10, and 15 mm were also tested on the mechanical test to validate the joint strength. The results also show that the welded cerclage wire joints have the potential of larger joint strength and less loosening failure than that of traditional joint devices. Meanwhile, the ultimate load at failure is within acceptable range and higher than that of previous study [25].
## 3 The First-Order Resonant Periodic Families
### The CRTBP Model: \(\mu>0\)  
With the increasing of the mass parameter \(\mu\), research [31; 32; 34] indicates that the continuation of circular periodic families of the small body in the CRTBP model is possible in all cases except at the first-order interior resonance \((p+1)\):\(p\) and exterior resonance \(p\):\((p+1)\), \(p=1,2,3\ldots\). In the CRTBP model, the two branches of each first-order resonant periodic family split in the \(T-C\) plane. Example orbits in \"Branch-I\" (_Type-I_) and \"Branch-II\" (_Type-II_) of the interior 3:2, 4:3, and 5:4 resonances and the exterior 2:3, 3:4, and 4:5 resonances in the Sun-Earth system are presented in Figure 2. Recently, [PERSON] and [PERSON] [4] presented the genealogy of the first-order interior as well as exterior resonant periodic families in the \(T-C\) plane for the Sun-Earth, Sun-Jupiter, and Earth-Moon systems. Studies indicate that the two branches of each first-order resonant periodic family split and combine with the near-circular branch to form a new family in the \(T-C\) plane (see Figure 4a,b). This phenomenon is also verified in [11] using the Hamiltonian approach. More specifically, for the interior first-order resonances, the studies [4; 11] indicate that starting from \(p=1\), \"Branch-II\" of the \((p+1)\):\(p\) resonant periodic family and \"Branch-I\" of the \((p+2)\):\((p+1)\) resonant periodic family combine with part of the near-circular family to form a new periodic family. In the following, we call these new interior periodic families \(IB((p+1)-(p+2))\), indicating that they are connecting the \((p+1)\):\(p\) resonant periodic family with the \((p+2)\):\((p+1)\) resonant periodic family. Similarly, starting from \(p=2\), \"Branch-I\" of the exterior \((p-1)\):\(p\) resonant periodic family is recombined with \"Branch-II\" of the exterior \(p\):\((p+1)\) resonant periodic family and part of the near-circular family to form a new periodic family. We denote this new family as family \(OB(p-(p+1))\) in this study. Details of the family \(IB(3-4)\) and \(OB(3-4)\) in the Sun-Earth system are presented in Figure 5a,b, respectively.  
Figure 3: Characteristic curves of the circular families and the first-order resonant periodic families in the unperturbed model. The solid lines indicate the circular families and the dashed lines indicate the resonant periodic families. (**a**): Interior circular family and the interior first-order resonant periodic families (32, 4:3, and 5:4 resonances). (**b**): Exterior circular family and the exterior first-order resonant periodic families (2:3, 3:4, and 4:5 resonances). The abscissa is half the orbital period (\(T/2\)) in the synodic frame and the ordinate is the Jacobi constant \(C\).
# Advantages and Limitations of Current Microgravity Platforms for Space Biology Research
## 2 Ground Microgravity Simulators
### Clinostat 1D/2D/Rwv
Moreover, in plants, the distinction between gravitropism and gravisensitivity must be considered when choosing the type of clinostat. When studying gravisensitivity, two axes rotation is to be preferred, even if 2D clinostats can also be used. For the study of gravitropism and architecture in plants, 1D clinostats should be used, asropic responsiveness has radial symmetry around the shoot-root axis. Despite their limitations, clinostats remain an invaluable tool to study Space condition on Earth for their versatility and cost efficiency.
# A Blockchain-Based Cooperative Authentication Mechanism for Smart Grid
## 1 Introduction
A federated chain is a type of blockchain that is formed by multiple institutions or organizations and is under common control. Individuals or organizations wanting to access a particular federated chain must be authorized to do so. The use of federated chains not only meets the scenario of a multi-domain smart grid but also the high efficiency of its consensus algorithm can meet the timeliness of cross-domain authentication. Our main contributions are as follows. * We propose a blockchain-based cross-domain authentication mechanism for multi-domain smart grids. In this mechanism we store the hash of the device's certificate in the blockchain, which not only saves storage space but also improves security. With this mechanism, devices from different domains can be securely and efficiently authenticated across domains before they can access each other. * We have a domain manager and a blockchain proxy node in each domain. In particular, the domain manager is mainly responsible for managing the smart grid devices in its domain, while the blockchain proxy node is mainly responsible for participating in the consensus process of the blockchain. In this way, we have improved the efficiency of cross-domain authentication. * We performed a comprehensive safety analysis and conducted simulation experiments to verify the feasibility and efficiency of our proposed solution. The remainder of the paper is organized as follows. Section 2 describes the related work. Section 3 is preliminaries, which mainly describe some theoretical knowledge and the overall structure of our proposed mechanism. Section 4 describes our proposed mechanism in detail. Section 5 performs a detailed security analysis to illustrate the security of our proposed mechanism. Section 6 conducts some simulation experiments to evaluate the efficiency of our proposed mechanism. Section 7 concludes this work.
## 2 Materials and Methods
### Analysis Methods
#### 2.3.2 COD Mass Balance Calculation  
The COD mass balance of the AWFR system was conducted during spring, summer, autumn and winter. Seasonal periods were characterized by the following operation HRTs: 2 days (spring period covering Day 31-90), 1 day (summer period covering Day 91-180), 2 days (autumn period covering Day 181-270), and 3 days (winter period covering Day 271-364).  
The COD mass balance was calculated using the Equation (1):  
\[\mathrm{COD_{in}}=\mathrm{COD_{VFAs}}+\mathrm{COD_{CH4(g)}}+\mathrm{COD_{CH4 (s)}}+\mathrm{COD_{others}}, \tag{1}\]  
where: \(\mathrm{COD_{in}}\) (g/day) represents the average COD concentration of real decentralized domestic wastewater; \(\mathrm{COD_{VFAs}}\) (g/day) represents the average COD concentration of acetate and propionate in the effluent; \(\mathrm{COD_{CH4(g)}}\) (g/day) represents the average COD concentration of methane produced in biogas; \(\mathrm{COD_{CH4(s)}}\) (g/day ) represents the average COD concentration of methane dissolved in the effluent; \(\mathrm{COD_{others}}\) (g/day) represents the organic matter that has been utilized for biomass formation, the complex organic matter that is not biodegradable, COD consumed by sulphate reducing bacteria (SRB), and COD removed and converted to \(\mathrm{CO_{2}}\).  
Dissolved methane was calculated using Equations (2) and (3) suggested by a previous study [35]:  
\[\mathrm{CH_{4}(s)}=4\times\mathrm{K_{H}}\times\mathrm{P_{gas}}\times\mathrm{ flow(Q)}, \tag{2}\]  
\[\mathrm{K_{H}}=0.384\times t+36.44, \tag{3}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Chemical Oxygen Demand (COD)** & **Total Phosphorus (TP)** & **Total Nitrogen (TN)** & pH \\ \hline Value 1 & 284 \(\pm\) 69.2 & 2.8 \(\pm\) 0.8 & 32.2 \(\pm\) 7.7 & 7.1 \(\pm\) 0.2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The chemical characteristics of decentralized domestic wastewater (unit: mg/L, except for pH).  
where K\({}_{\rm H}\) (mg/L/atm) is [PERSON]'s constant, P\({}_{\rm gas}\) (atm) is the partial pressure of the gas above the liquid, flow(Q) (L/day) is feed flow and \(t\) (\({}^{\circ}\)C) is the temperature.
# Retinal OCT Images: Graph-Based Layer Segmentation and Clinical Validation +
## 4 Discussion
### Algorithm Performance  
The algorithm employs a shortest-path graph-search technique to accurately delineate seven intra-retinal boundaries, effectively segmenting six retinal layers. This includes the retinal nerve fiber layer and ganglion cell layer (RNFL + GCL), inner plexiform layer (IPL), inner nuclear layer (INL), outer plexiform layer (OPL), outer nuclear layer and inner segment (ONL + IS), and the outer segment and retinal pigmented epithelium (OS + RPE). The algorithm's effectiveness on both foveal and non-foveal slices of the retina makes it more useful in clinical settings, allowing for thorough examination of macular SD-OCT scans.  
One of the key features of the algorithm is its region of interest (ROI) selection tool, which focuses processing power on relevant image areas, thus saving time and computational resources. This feature, combined with the algorithm's robustness in noisy conditions facilitated by simple Gaussian filters, underscores its efficiency and practicality in clinical settings.  
The layer thickness values computed by the algorithm provide critical markers for retinal health, assisting clinicians in diagnosing pathologies. Accurate segmentation of retinal layers can reveal abnormalities indicative of diseases such as macular degeneration and glaucoma, which typically alter the thickness of specific retinal layers.
# Remote Sensing for International Peace and Security: Its Role and Implications
## 5 Future Perspectives
### Technology Development Perspective
#### 5.1.4 Visual Inertial System  
The role of the machine vision in inertial navigation systems for robotic vehicles dates back to the 1960s where cameras mounted on robotic vehicles were used for industrial inspection. The conventional cameras were replaced by 3D cameras, which were capable of capturing depth information along with intensity variation in the scene. The depth information is beneficial for the localization of objects in a scene and also overcomes the limitations of variation, in contrast to ambient light and parallax. However, in such systems, inertial drift is an issue as it makes the registration of consecutive scans erroneous. Leica Geosystems came up with a solution to overcome the problem of inertial drift with Visual Inertial System (VIS) technology. The technology uses five cameras along with an inertial measurement unit (IMU) to introduce a delta pose between two consecutive scans in real-time. It helps in the fusion of huge amounts of visual information very accurately, which can be used by various remote sensing applications for international peace and security [123].
# Comparison of Methods to Identify and Monitor Mold Damages in Buildings
## 2 Materials and Methods
### Fungal DNA Metabarcoding  
The fungal communities present in mold and dust samples were characterized following the DNA metabarcoding workflow described by [PERSON] et al. [22]. In brief, this protocol included six key steps: (i) DNA extraction from the swabs using chloroform and the EZNA Soil DNA Kit (Omega Bio-tek); (ii) PCR amplification of the ITS2 region using the primers gITS7 [30] and ITS4 [31], both primers including sample specific tags at the 5\({}^{\prime}\)-end; (iii) DNA purification and normalization using a SegualPrep Normalization Plate Kit (Applied Biosystems); (iv) pooling of the 96 samples, including: 48 mold samples, 42 dust samples--3 of them duplicated as technical replicates, an extraction blank, a PCR negative, and a mock community; (v) additional purification using Agencourt AMPure XP magnetic beads (Beckman Coulter); and (vi) library preparation and 250 bp paired-end MiSeq v3 sequencing (Illumina) carried out at Fasteris SA, Plan-les-Ouates (Switzerland).  
The resulting raw sequencing data contained 12,234,449 paired reads and are available at the European Nucleotide Archive (ENA), EMBL-EBI, under accession no. PRJEB50946 ([[https://www.ebi.ac.uk/ena/browser/view/PRJEB50946](https://www.ebi.ac.uk/ena/browser/view/PRJEB50946)]([https://www.ebi.ac.uk/ena/browser/view/PRJEB50946](https://www.ebi.ac.uk/ena/browser/view/PRJEB50946)), accessed on 14 September 2022).  
Figure 1: Sampling of mold and dust in this study. (**a**) Mold-damaged gypsum board where M62-M64 samples were collected; (**b**) mold growing on wood, sample M65; (**c**) tape lifts taken directly on discoloration of building materials; (**d**) dust sample collected by swabbing the upper doorframe, picture extracted from [PERSON] et al. [22].
# Fault Diagnosis System of Power Grid Based on Multi-Data Sources
## 4 Fault Tracking Quasi-Real-Time Processing System and Fault Diagnosis Batch Processing System
### Implementation of Fault Tracking Quasi-Real-Time Processing System
#### 4.1.1 Relation Table between Fault Reasons and Fault Features of SF6 Circuit Breaker  
Circuit breakers are various, including oil circuit breakers, vacuum circuit breakers, SF6 circuit breakers and so on. Due to limited space, this article only studies the most widely used SF6 circuit breaker.  
According to the relevant content in references [19; 20], this article gives the table of partial fault features and partial fault reasons (including the prior probability of each reason), as shown in Table 4. Combined with the engineering practice experience, the probabilities of each fault reason leading to corresponding fault features are calculated. The relation table between fault reasons and fault features of the SF6 circuit breaker is constructed according to calculation results.  
The construction process of the relation table of fault reasons and features of the relay protection device is consistent with that of the circuit breaker. Due to limited space, the detailed construction process and data, which can be found in reference [19] are not given here.
# Human Activity Recognition Using Cell Phone-Based Accelerometer and Convolutional Neural Network
## 4 Experiment Study
### The 2D CNN Model  
After the prerequisite steps were completed, the next step was the development of a two-dimensional convolutional neural network. The data were, therefore, made ready to be processed by Keras. Moving on, the model was developed using five layers. The first layer is the sequential layer, which enabled us to add further layers to the neural network. This layer also performs reshaping of the data by bringing them into the \"old\" format. The filter size for this layer is set to 16. Following the previous layer, the next three layers are the hidden layers. Six classes were used in the model (going downstairs, jogging, sitting, standing, going upstairs, walking). The aforementioned layers use the Rectified Linear Unit (ReLU) as a universal activation function and have a filter size of 32. The kernel size for the first four layers is (2,2), which implies that the height and width of the convolution window are 2 and 2, respectively. Lastly, the fifth layer comprises the dense layer which identifies the six classes and runs a Softmax activation function for multi-class probability calculation. To reduce overfitting, a dropout of 0.1 is used for the sequential layer and a dropout of 0.2 is used for the next three hidden layers. Next, for the dense layers, a dropout of 0.5 is used. The model is finally built. The input, convolution, pooling, fully connected and output layer is depicted in Figure 6. The next step is the compilation of our model. This is carried out by executing ADAM optimizer with a learning rate of 0.001, the loss is taken as sparse categorical cross-entropy and the metrics are based on accuracy. Furthermore, the training data are fitted into the model with 10 epochs and the training begins. The first epoch had achieved an accuracy of only 24% with training, whereas the validation was 42.06% accurate. Coming to the tenth epoch, the training was highly accurate with 93.88% accuracy whereas the validation was 89.72% accurate. Section 4.6.1 describes how well the model has learnt, and the final confusion matrix for activity prediction is shown in Section 4.6.2.
# Toward a Practical Digital Twin Platform Tailored to the Requirements of Industrial Energy Systems
## 2 State of the Art
### Characteristics of Industrial Energy Systems  
As a typical use case for a DT platform in this domain, we consider an exemplary industrial energy system, as illustrated in Figure 1. It is composed of a conventional combined heat and power unit, electrically powered heating units and internal renewables based on photovoltaics for electric and thermal energy supply. A number of production processes typically account for electric and thermal energy demand. Heat exchanger networks and heat pumps are used for waste heat recovery. Electrical and thermal energy storage components are employed to provide additional flexibility and improve the energy efficiency enhancement of the IES. Furthermore, the IES is not isolated but instead coupled with both the electricity grid and a district heating/cooling network.  
Such an IES is a complex integrated energy system that has a number of characteristics that set it apart from typical manufacturing systems:  
* Many sources of uncertainty such as weather influences, prices on the energy market and stochastic processes within the system itself make the optimal operation of an IES challenging.
* Energy system units feature complex thermophysical behaviors, hence leading to strong nonlinearities in mathematical model descriptions.  
Figure 1: Sketch of the target use case: a typical industrial energy system.  
* Energy systems are often vast and distributed systems with specialized equipment.
* Power plants as well as industrial and urban energy systems have very long lifetimes or are even continuously refurbished instead of being rebuilt. Thus, \"off the shelf\" solutions have a limited field of applications.
* Energy systems are very dynamic systems with a broad range of operational time scales.
* Some process quantities cannot be measured directly, e.g., due to high temperatures or abrasive conditions.
* System properties change over time, e.g., the efficiency of a unit can change due to wear, while the mechanical resistance of a system can degrade due to fatigue.
* Energy systems consist of continuous processes as opposed to processes with discrete units in manufacturing; hence, scalar and vector fields instead of single values are used to describe the system.
* Given their critical nature within the power grid infrastructure, cyber attacks targeting IES have potential for disastrous consequences.  
All of these aspects more or less differentiate from manufacturing systems and can be considered specific features of IES, thus contributing to the fact that DTs are underdeveloped in the energy sector. We deduce that these characteristics explain why DT frameworks that have been applied successfully in the manufacturing domain have not been extensively transferred to the energy domain yet. One way or another, if DTs are to be widely deployed in the energy sector, these specific properties must be properly considered.
## 5 Results and Discussion
### Hyperparameter Experiments
#### 5.4.5 Effect of Number of Spatial-Temporal Layers  
Figure 16 shows the impact of the number of spatial-temporal layers. With more layers, the model can learn increasingly complex dependencies, as each layer extracts higher-level features from the previous layer. Meanwhile, as the number of spatial-temporal layers grows, each layer introduces more parameters and processing demands and raises the risk of gradient vanishing or explosion. Therefore, the best prediction result is achieved when the number of spatial-temporal layers is set to five.