esa-sceva/satcom-chunk-collection huggingface dataset:
# Internet of Things in Marine Environment Monitoring: A Review
## 2 Overview of IoT in Marine Environment Monitoring
### Wireless Communication Technologies  
A sensor node requires a radio module for wireless communication. The access network, with a communication range from a few hundred meters to several kilometers, includes all the devices between the backbone network and user terminals.  
For the IoT-based marine environment monitoring and protection systems, wireless communication networks have different requirements than other applications, because of the following reasons:  
1. Reliability: Radio antenna oscillations and bad ocean weather conditions can cause instability of radio signals.
2. Energy efficiency: Low power consumption is the key to supporting long-flow and reduced maintenance costs in stand-alone battery-powered equipment. This is particularly critical for devices deployed in remote offshore areas that are difficult and costly to replace.  
According to the current requirements for IoT applications, the development of wireless communication technologies has already made significant progresses. Various wireless communication standards and technologies have been proposed and developed, including WiFi, ZigBee, Bluetooth, GPRS, GSM, and WiMAX. A summary and brief comparison of these communication standards and technologies can be found be in our previous survey paper [18]. Typically, multiple wireless communication technologies are used in an IoT-based marine environment monitoring and protection system. In some specific applications, underwater acoustic communication technologies are used for data collection and communication among underwater marine environment sensors [30; 31; 32; 33]. Generally, a longer-range communication consumes more energy. Selection of the most appropriate wireless communication technology for an application depends on the transmitted data volume, transmission frequency, transmission distance, and available power supply.
## 5 Numerical Tests
### Performance of the DLMU Scheme
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**ne**} & \multicolumn{3}{c}{**SIMP**} & \multicolumn{3}{c}{**DLMU**} & \multirow{2}{*}{**Acceleration (\%)**} & \multirow{2}{*}{**Reduction (\%)**} \\ \cline{2-2} \cline{6-9}  & **Iterations** & **Objective** & **Time (s)** & **Iterations** & **Objective** & **Time (s)** & **Acceleration (\%)** & **Reduction (\%)** \\ \hline
75,000 & 401 & 159.86 & 505.88 & 79 & 158.83 & 292.43 & 42.19 & 0.64 \\
100,000 & 444 & 158.44 & 774.02 & 58 & 157.83 & 291.76 & 62.31 & 0.38 \\
150,000 & 514 & 156.35 & 1438.26 & 98 & 155.73 & 461.50 & 67.91 & 0.40 \\
200,000 & 367 & 156.01 & 1382.55 & 107 & 155.10 & 595.76 & 56.91 & 0.58 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Test example B—Performance of the DLMU scheme. Figure 6: Optimized domain for test example B for the case of 200,000 FEs discretization: (**a**) SIMP output and (**b**) DL-SCALE output. Figure 7: Optimized domain for test example C for the case of 200,000 FEs discretization: (**a**) SIMP output and (**b**) DL-SCALE output. All recorded data for test example E are given in Table 5. Similar to the previous cases, DLMU achieved a maximum reduction of computational time equal to 67.48% for the case of 200,000 FEs in \(D_{(0)}^{f}\) with respect to SIMP, while the objective function value slightly increased by 0.77%. The final optimized topologies generated by SIMP and DLMU for the finer discretization with 200,000 FEs are shown in Figure 9a,b, respectively.
# _Sci_
## 2 Materials and Methods
### History of Laser Employment as Link Source
#### 2.1.1 Prior to the Twenty-First Century  
Shortly after WWII, [PERSON] wrote about employing light beams to transmit information [6]. His foresight was not realized until the 1960s with the development of the laser. Furthermore, it was not demonstrated as intended until the 1995 bidirectional ground-to-orbit laser communication demonstrations. The program successfully used lasers to uplink and downlink to the _engineering test satellite-VI_ in elliptical geosynchronous transfer orbit, sending and receiving at a 1 mega-bits per second rate [7]. This unhurried pace (50 years between idea and demonstration: 30 years between technology development and adaptation) exemplifies the field of laser communications.  
Before considering lasers for uplinks and downlinks [8], lasers were employed for geodesy first [9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22] and also orbital determination [23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33]. The 1990s jump-started scientific examination of space and satellite laser communications [34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53]. Over roughly the next decade, the U.S. national air and space administration, and the European and Japanese space agencies, completed successful trials of space-based laser communications [34]. In 2001, the European space agency's semiconductor intersatellite link experiment and advanced relay and technology mission satellite received fifty mega-bits per second from the French observation satellite SPOT-4 [34], demonstrating bi-directional links between a geosynchronous orbiting satellite and both low-earth orbiting satellite and ground receiving stations [35], which is depicted in Figure 1. That same year, the geosynchronous lightweight technology experiment also successfully demonstrated bi-directional laser communications, and this time added an aircraft to the reception station list [35].
## 5 Toxicity of MPs
### Impacts of MPs on Soil Organisms  
MPs can interfere with soil organisms (animals, plants and microorganisms) indirectly by changing the physicochemical and biogeochemical properties and the function of the soil or directly by affecting their condition and the function of the organism [41; 53; 69]. MPs accumulated in the soil reduce plant development and growth, reduce seed germination and slow down the root development during germination [36; 37]. Until a few years ago, MP particles were thought to be too large to pass through the physical barriers of undamaged plant tissue. Research on fluorescently labeled polystyrene microspheres entering rice plants by [PERSON] et al. (2022) [70] provided solid evidence that micro-sized MPs can be absorbed by roots and subsequently translocated to aerial parts of plants, thereby possibly transferring to higher trophic levels through food chains. However, MPs are rarely observed in above-ground plant tissue [70]. Plants that have accumulated MPs from the soil express weaker growth and lower yield [36; 37; 48] due to reduced nutrient intake and the accumulation of MPs in roots, stems and leaves [69]. Enhanced activity of oxidative stress enzymes was observed in plants exposed to MPs and bound pollutants such as heavy metals and additives [69].  
The main model organisms for soil research are earthworms (Oligochaetes). Together with others soil invertebrates, they assure loose, fertile and good-quality soil. They directly ingest MPs and thereby contribute to the production of secondary MPs and participate in the transport of plastic through the soil [14]. When consuming a large amount of MPs, _Oligochetes_ die due to a lack of nutrients and energy caused by damages of the digestive system wall [41]. MPs increase the mortality of earthworms up to 25% [71]. Other commonly studied soil organisms are nematodes, isopods, collemboles, gastropods, mites, birds and mammals. Studies on the impact of MPs on animal organisms in soil indicate physical injuries due to ingestion and disorders of growth, development, reproduction, immune system and intestinal microbiota [14; 36; 41; 72].
## 6 Performance Evaluation
### Evaluation Results
#### 6.3.1 Interest Satisfaction Rate (ISR)  
To analyze the Interest satisfaction rate (ISR), CIDF-WSN vary the number of Interest packets between 5 to 30 Interest packets/sec. Figure 7 depicts the ISR as a function of Interest frequency in the network. It is evident from the results that the CIDF-WSN has a better ISR compared with both benchmark schemes. The graph shows that the CIDF-WSN achieves around 80% satisfaction rate in extreme conditions (e.g., 30 Interests/second) compared to the GIF; however, the GIF satisfied below 50% of generated Interests at the same rate. The rationale is that in the CIDF-WSN when the designated forwarder fails to make further transmissions--Interest or Data packet--the neighbors of the designated forwarder pointing towards the consumer (by consulting their ICT and TCT) collaborate and resume the transmissions to avoid communication failure. Moreover, the updated NFIB-based next forwarder selection and TCT-assisted Data packet forwarding play a key role in minimizin the Interest and Data packet losses and retransmissions. This significantly contributes to enhancing ISR.  
Figure 7: Interest satisfaction rate as a function of Interest.  
In contrast, the GIF may involve several retransmissions in vulnerable conditions when the designated node fails to transmit the Interest packet. In addition, if the potential node fails to find the next-hop forwarder, it reinitiates the neighbor table development to assign the potential forwarder to the received Interest for further transmissions. All these factors induce congestion and collisions and increase the overall traffic in the network. In comparison with the vanilla NDN, the CIDF-WSN shows much better results. The minimized Interest and Data transmissions avoid the congestion in the network and increase the performance in terms of ISR. Whereas, in NDN the blind Interest and Data packets transmissions turn into the broadcast storm and collisions which, as a result, lower the ISR.
## 2 Background and Related Work  
### Model Checking  
#### 2.1.1 Discrete Time Markov Chains  
Discrete-Time Markov Chains (DTMCs) are widely used mathematical models to represent systems that transition between different states at discrete time steps [12]. DTMC is defined by a set of states, a transition probability matrix, and an initial state distribution, allowing for the probabilistic modeling of sequential behaviors. Formally, DTMC is defined as a 4-tuple:  
\[\mathcal{M}=(S,P,L,AP)\]where  
* \(S\) is a finite set of states.
* \(P\) is a transition probability matrix, where each element \(P_{ij}\) represents the probability of transitioning from state \(i\) to state \(j\).
* \(L\) is a labeling function that maps each state to a set of atomic propositions in \(AP\).
* \(AP\) is a set of atomic propositions that describe the properties or conditions of the system's states.  
The transition probabilities in the matrix \(P\) must satisfy the following property:  
\[\sum_{j\in S}P_{ij}=1\quad\forall i\in S\]  
This ensures that for each state \(i\), the total probability of transitioning to any other state (including itself) is \(1\).
## 2 Samples and Experimental Details  
Five samples were fabricated from 22 NiMoCr37 nuclear reactor pressure vessel steel material. Specimens have the standard Charpy sample dimensions (\(10\times 10\times 55\) mm\({}^{3}\)). Different surface roughnesses were produced by applying various manufacturing parameters such as the primary (spindle RPM) and secondary (feed rate) machine cutting forces. The obtained surface roughness was characterized where specimens had an L-T orientation. Longitudinal direction is parallel to the length of the sample. The investigated samples are shown in Figure 1. Manufacturing parameters and surface roughness parameters of the measured samples are shown in Table 1. A reference sample (stamped 23, on the left side of Figure 1) represents the surface manufacturing condition for normal inspection parameters of RPV (in the field conditions) steels. For the samples labelled 24-27, the surface roughness is different. The surface condition can be seen in better detail in Figure 2 (plan view).  
Surface roughness was characterized by an Accretech Handysurf Tokyo Seimistsu E-35B device. Evaluation length was 4 mm, cut-off value was 0.8 mm, and an automatic measuring range was used. The surface roughness parameter \(R_{a}\) is the arithmetical mean deviation for sampling length \(L\):  
\[R_{a}=\frac{1}{L}\int\limits_{0}^{L}\big{|}Z(x)\big{|}dx \tag{1}\]  
Grooves were manufactured (with the identical settings for the given sample) on both top and bottom sides of samples, and magnetic measurements were also performed on both sides. The results of magnetic measurements were identical for these two sides for all samples. Due to this, we will deal only with the top side measurements. For checking the possible influence of mechanical treatment, measurements were performed also on the two opposite, not mechanically treated sides, as well.
# A New Concept of Reconfigurable Antenna Structure Based on an Array of RF-MEMS Switches
## 4 Numerical and Experimental Assessment  
In this section, the numerical and experimental assessment of the obtained prototype is carried out in order to demonstrate the effectiveness of the proposed structure.
# Single-Layer Pressure Textile Sensors with Woven Conductive Yarm Circuit
###### Abstract  
Today, \(e\)-textiles have become a fundamental trend in wearable devices. Fabric pressure sensors, as a part of \(e\)-textiles, have also received much interest from many researchers all over the world. However, most of the pressure sensors are made of electronic fibers and composed of many layers, including an intermediate layer for sensing the pressure. This paper proposes the model of a single layer pressure sensor with electrodes and conductive fibers intertwined. The plan dimensions of the fabricated sensors are \(14\times 14\) mm, and the thickness is 0.4 mm. The whole area of the sensor is the pressure-sensitive point. As expected, results demonstrate an electrical resistance change from 283 \(\Omega\) at the unload pressure to 158 \(\Omega\) at the load pressure. Besides, sensors have a fast response time (50 ms) and small hysteresis (5.5%). The hysteresis will increase according to the pressure and loading distance, but the change of sensor loading distance is very small. Moreover, the single-layer pressure sensors also show high durability under many working cycles (20,000 cycles) or washing times (50 times). The single-layer pressure sensor is very thin and more flexible than the multi-layer pressure sensor. The structure of this sensor is also expected to bring great benefits to wearable technology in the future.  
single-layer-textile pressure sensor; carbon nanotubes (CNTs); hysteresis; conductive wool yarns +
Footnote †: journal: Applied Physics Letters
## 1 Introduction
### Dementia and Machine Learning  
Automated dementia diagnosis in clinical translations in machine learning-based areas is promising. However, generalizable machine learning models with improved robustness are also needed. While research is ongoing to this end, there are many examples of inconsistency in validation, whereas the use of common datasets improves interpretability and stability. Therefore, there is a need for an increase in clinical expertise and focus [7]. Another study presents possible options for the automated prediction of dementia diagnosis through machine learning. A review of the models of dementia diagnosis from 2011 to 2022 includes data modality considerations. The results point to excellent performance with many limitations [8]. In studies focusing on dementia, there is a rapid growth of applications of machine learning that enable automation of prediction, diagnosis, and treatment of patients.  
Comparison is made between machine learning models and deep learning models to demonstrate the ability of the deep learning models to yield more accurate results compared to traditional machine learning models, as well as commenting on the need for additional computational power and resources. Scarcity of data and applications is a challenge in the further development of such models, even though they can be utilized in dementia care [9].  
A meta-analysis describes the complexity of Alzheimer's Disease (AD) diagnosis due to limitations of current methods in cognitive assessments and image analysis. The study explores the viability of machine learning models combined with novel biomarkers and points toward a novel way to enhance the accuracy of AD diagnosis [10]. Similarly, machine learning was also utilized for the prediction of dementia at an early stage, where scores from cognitive function tests were used as features to make the predictions. The ensemble model of AdaBoost achieved an exceptionally high accuracy of 83%, which was higher than other models and has potential use for Artificial Intelligence (AI) in diagnosis [11] in another study on Alzheimer's Disease (AD), Frontotemporal Dementia (FTD), and the difficulties these cause for caregivers and patients. A new approach was created within this research utilizing deep learning through Electroencephalography (EEG) classification of AD and FTD with better preprocessing and classification. The results exhibited a potential solution for such an application of deep learning in screening dementia [12].  
Finally, another study investigated Alzheimer's Disease, Amyotrophic Lateral Sclerosis (ALS), and Frontotemporal Dementia (FTD) and their shared molecular pathophysiology with the help of unsupervised learning. The result identified a high correlation between key molecules [13].
# A Dynamic Restructuring Algorithm Based on Flexible PON Slices
###### Abstract  
In recent years, with the introduction of the concept of the Internet of Things, a large number of terminals connected to the network, the pressure of network bandwidth is increasing. The bandwidth resources wasted by the traditional fixed access network architecture have attracted more and more attention of researchers. In order to meet the different needs of different users for service quality and improve the flexibility of network, network slicing technology arises at the right moment. Based on the slicing idea of the flexible time- and wavelength-division multiplexing passive optical network (TWDM-PON), a dynamic PON slice restructuring algorithm (DRA) is proposed in this paper. The proposed algorithm avoids the influence of previous slicing on subsequent slicing in the step-by-step slicing process, slices at the global level, and is less affected by the randomness of initialization. The simulation results show that the performance of DRA is about 10\(\sim\)30% higher than that of the dynamic optical network units (ONUs) grouping algorithm (DGA) and the dynamic ONU slicing algorithm (DONUSA) when there are 8 OLTs, and is about 30% higher than that of DGA and 10% higher than that of DONUSA when there are 16 OLTs. Therefore, the proposed DRA has more positive significance to relieve the traffic pressure in the increasingly tight bandwidth resources.  
passive optical network; network slicing; software defined network; traffic dredging 202310614. [[https://doi.org/10.3390/photonics1006014](https://doi.org/10.3390/photonics1006014)]([https://doi.org/10.3390/photonics1006014](https://doi.org/10.3390/photonics1006014))
# Low-Cost Data-Driven Robot Collision Localization Using a Sparse Modular Point Matrix
## 2 Materials and Methods
### Collision Localization Model
#### 2.3.2 ESN  
An echo state network (ESN) is a type of recurrent neural network proposed by [PERSON] et al. [19], consisting of three main components: an input layer, a reservoir, and an output layer. The reservoir is essentially a randomly connected recurrent network of a certain size, where neurons form a dense structure through random connections. These connections are predetermined and remain unaltered during training. A basic ESN model is illustrated in Figure 3.  
Let \(N_{in}\), \(N_{res}\), and \(N_{out}\) represent the numbers of neurons in the input layer, reservoir, and output layer, respectively. The matrices \(\mathbf{W}_{in}\), \(\mathbf{W}_{res}\), and \(\mathbf{W}_{out}\) denote the weight matrices from the input layer to the reservoir, within the reservoir, and from the reservoir to the output layer, respectively. \(\mathbf{W}_{in}\) and \(\mathbf{W}_{res}\) are randomly initialized and remain fixed throughout the training process. Only \(\mathbf{W}_{out}\) undergoes adjustments during the learning process. The specific ESN model can be expressed as follows:  
\[\mathbf{h}(t)=\epsilon tanh(\mathbf{W}_{in}\mathbf{x}(t+1)+\mathbf{W}_{res}\mathbf{h}(t)+\mathbf{W}_{ out}\mathbf{y}(t)) \tag{1}\]  
where \(\tanh(\cdot)\) represents the non-linear activation function of the reservoir and \(\epsilon\in(0,\ 1]\) is the leakage rate. \(\mathbf{x}(t)\), \(\mathbf{h}(t)\), and \(\mathbf{y}(t)\) denote the input vector, the state vector of the reservoir, and the output vector, respectively. Compared to conventional RNNs, the training process of the ESN is simpler, only involving parameter adjustments in the output layer. The entire network does not require the complex process of backpropagation. Furthermore, due to the randomness and dense connections in the reservoir, this structure facilitates enhanced generalization capabilities, enabling the network to capture the non-linear dynamics of input signals effectively. This property contributes to the ESN's strong performance in handling time-series tasks.  
In terms of hyperparameter settings, since \(\mathbf{W}_{in}\) and \(\mathbf{W}_{res}\) are generated through random initialization, it is essential to predefine the range for their random initialization before training. The appropriate values for these two weight matrices were adopted from Ref. [20].  
Figure 3: The structure of the ESN.  
Additionally, following our previous work [4], the leakage rate \(\varepsilon\), spectral radius \(\rho\), and \(N_{res}\) were set. Specifically, the hyperparameters of the ESN used in this paper are presented in Table 1.
## 4 Measurements and Testing of the Measuring System
### System Current Consumption Testing  
The energy efficiency tests of the system with the implemented adaptive algorithm were carried out on the basis of current consumption. The supply voltage of the system was 4 V. The current consumption was determined by measuring the voltage across the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Case Number**} & \multicolumn{4}{c}{**Number of Sensors for Which Data Are Transmitted with a Fixed Frequency**} \\ \cline{2-5}  & **5 Hz** & **15 Hz** & **30 Hz** & **60 Hz** \\ \hline P1 & 16 & 0 & 0 & 0 \\ P2 & 14 & 2 & 0 & 0 \\ P3 & 14 & 0 & 2 & 0 \\ P4 & 14 & 0 & 0 & 2 \\ P5 & 0 & 14 & 2 & 0 \\ P6 & 0 & 0 & 14 & 2 \\ P7 & 2 & 0 & 0 & 14 \\ P8 & 13 & 3 & 0 & 0 \\ P9 & 13 & 0 & 3 & 0 \\ P10 & 13 & 0 & 0 & 3 \\ P11 & 0 & 13 & 3 & 0 \\ P12 & 0 & 0 & 13 & 3 \\ P13 & 3 & 0 & 0 & 13 \\ P14 & 8 & 8 & 0 & 0 \\ P15 & 8 & 0 & 8 & 0 \\ P16 & 8 & 0 & 0 & 8 \\ P17 & 0 & 8 & 8 & 0 \\ P18 & 0 & 0 & 8 & 8 \\ P19 & 15 & 1 & 0 & 0 \\ P20 & 15 & 0 & 1 & 0 \\ P21 & 15 & 0 & 0 & 1 \\ P22 & 14 & 1 & 1 & 0 \\ P23 & 13 & 1 & 1 & 1 \\ P24 & 0 & 16 & 0 & 0 \\ P25 & 0 & 0 & 16 & 0 \\ P26 & 0 & 0 & 0 & 16 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Configurations of defined measurement cases.  
shunt resistor. Due to slight differences between the measured current consumption values (below 0.5%), the measurement cases were grouped in accordance with Table 3.  
All measurement cases where the maximum frequency \(f_{T}\) was 15 Hz (P2, P8, P14, P19, and P24) are characterized by similar current consumption (about 242 mA). A similar value of the consumed current (about 251 mA) was recorded in the tested measurement cases, where the maximum frequency \(f_{T}\) was 30 Hz or 60 Hz.  
The tests, conducted in accordance with the measurement protocol, show that the greatest savings in the current consumed by the system are obtained when data from sensors are sent with the frequency \(f_{T}\) equal to 5 Hz. A reduction in the system current consumption in this case can even reach 19.9% in relation to the system in which data from sensors are transmitted at frequencies of 60 Hz or 30 Hz.