esa-sceva/satcom-chunk-collection huggingface dataset:
# A Heuristic Solution Method for Multi-Depot Vehicle Routing-Based Waste Collection Problems
## 2 The Sector Combination Optimization Algorithm to Obtain Initial Solutions  
The above waste collection problem can be modeled as MDVRP. MDVRP can be defined as follows based on [20]: Let \(G=(V,E)\) denotes a graph where V represents a collection of points and E represents a collection of edges. V can be divided into two parts: \(V_{C}=\{v_{1},v_{2},\ldots,v_{N}\}\) represents a group of customers, N is the number of customers; \(V_{D}=[v_{N},v_{N+1},\ldots,v_{M}]\) represents a group of depots, M is the number of depots. Each customer has a non-negative demand \(d_{i}\); each arc belonging to E has an associated cost \(c_{ij}\), and the cost in this article is distance. K denotes the number of vehicles owned by each depot and \(P_{k}\) indicates the capacity of the vehicle. \(x_{ijk}\) is the decision variable as shown in Equation (1).  
\[x_{ijk}=\left\{\begin{array}{ll}1,&\text{i and j are visited by the vehicle k}\\ 0,&\text{else}\end{array}\right. \tag{1}\]  
Conditions:  
1. The number of depots and customers and the location of their coordinates are predetermined.
2. The customer's demand and vehicle capacity are known. Objective function: \[Min\sum_{i=1}^{N+MN+M}\sum_{j=1}^{K}\left(c_{ij}x_{ijk}\right)\] (2) Constraints: (1) Each route starts and ends at the same depot.
2. The total demand of the customers on each route does not exceed the vehicle capacity.
3. Each customer is served by one vehicle and is served only once.  
To understand the algorithm clearly, an MDVRP example shown in Figure 1 is proposed. Figure 1 is a graph with 24 points, where 22 points represent customers and two points represent depots. In the figure, the circle represents the customer and the square represents the depot. The coordinates of the depot and the customer are predetermined, the customer's demand is shown above the customer point, the first value in the brackets above the depot represents the number of vehicles and the second value represents the vehicle capacity.  
Figure 1: An MDVRP example.  
In the Equation (3), \(\sum\limits_{i}^{i\in D}d_{i}\) represents the total customer demand of the depot D, \(\sum\limits_{K}^{K\in D}K\times p_{k}\) represents the total supply of depot D. In the constraint that the total customer demand does not exceed the total supply in each depot, the customer will be assigned to each depot according to the nearest distance, and the vehicle routing problem (VRP) for each depot will be solved simultaneously.  
\[\sum\limits_{i}^{i\in D}d_{i}\leq\sum\limits_{k}^{k\in D}k\times p_{k} \tag{3}\]  
Taking the MDVRP problem into consideration, the assignment result is shown in Figure 1. The customer number assigned to the A depot is 1-2-3-4-5-6-7-8-9-10-11-12-13, and the customer number assigned to the B depot is 14-15-16-17-18-19-20-21-22. Next, to solve the VRP in each depot.
## 2 Technological Aspects of Smart Shoes for Digital Health
### Analysis Methods
#### 2.2.3 Estimation of Gait Patterns  
When a feature-based machine learning algorithm is employed to recognize important gait events (Section 2.2.4), the extraction of relevant gait parameters (referred to as gait patterns in this work) can significantly reduce the amount of data and help provide an application-specific interpretation of important gait events. Gait patterns can be represented via a finite number of spatiotemporal gait parameters. These parameters can be extracted from the time or frequency domain [42]. Commonly used time domain gait features include local extrema [43; 44], adaptive thresholds (or zero) crossings [23; 45; 46], and gait symmetry indexes [47]. Common frequency domain features include FFT (Fast Fourier Transformation) coefficients [42; 48] and wavelet transforms [49]. Other inter-domain features, such as principal component analysis-based approaches [50; 51], geometric template matching [52], or curve aligning [53] are also often used.  
The aforementioned parameters can be calculated based on a human gait model (e.g., [54]), which divides gait patterns into relevant application-based groups, or on an abstraction model, which directly estimates the gait parameters. Human gait models [27] usually do not involve subject-specific training, but can be problematic in the case of an impaired gait due to their generality. Abstraction models [55] use machine learning methods for a direct estimation of gait patterns. These models are more robust to inter-subject variability, but require substantial data for the training phase [56].
## 2 System Model
### LDPC Coding and Decoding Algorithms
#### 2.1.1 LDPC Codes  
Channel coding can be defined as converting bit sequence of length \(k\) in a codeword of length \(n\), following certain rules imposed by the channel codes. One important class of such codes is the block codes. Therefore, for a (\(n\), \(k\)) block code, the channel encoder accepts data in successive blocks of symbols of k length, adds (\(n-k\)) redundant symbols algebraically linked to the \(k\) message symbols resulting in an encoded block with \(n\) symbols called codeword. The block codes have to be linear, in order to reduce the complexity of the encoding process, and have to have a systematic structure, meaning that there is a message part of \(k\) information symbols that remains unchanged during the encoding process and a redundant checking part of (\(n-k\)) parity-check symbols.  
Figure 1: Receive and transmit operations.  
A useful matrix associated with linear block codes is the generator matrix, denoted by G, of (\(kxn\)) dimensions  
\[\mathbf{G}=\left[\begin{array}{c}g_{0}\\ g_{1}\\ \vdots\\ g_{k-1}\end{array}\right] \tag{1}\]  
In order to fulfill the encoding process, it has to be multiplied with the message vector \(m\)  
\[m=[m_{0},m_{1},\ldots,m_{k-1}] \tag{2}\]  
to obtain the codeword \(c=[c_{0},c_{1},\ldots,c_{n-1}]\)  
\[c=m\mathbf{G} \tag{3}\]  
Therefore, a codeword \(c\) is a linear combination of \(k\) basis vectors \(g_{i},i=0,1,\ldots,k-1\). A linear systematic (\(nxk\)) block code is described by \(\mathbf{G}\) in the following form  
\[\mathbf{G}=[\mathbf{I}_{k}|\mathbf{P}], \tag{4}\]  
where \(\mathbf{I}_{k}\) is the unity matrix of dimension (\(kxk\)) and \(\mathbf{P}\) is the matrix of dimension (\(kx(n-k)\)) where the columns represent the positions of parity checks. Therefore, the codeword of a systematic linear block code has the following form  
\[c=[m|b]=m[\mathbf{I}_{k}|\mathbf{P}], \tag{5}\]  
where \(b\) represents the parity-check bits.  
Another useful matrix is the parity-check matrix denoted by \(\mathbf{X}\) of dimensions (\((n-k)xn\)) defined as  
\[\mathbf{X}=\left[\mathbf{P}^{T}\mathbf{I}_{n-k}\right] \tag{6}\]  
and it can be easily seen that  
\[\mathbf{G}\mathbf{X}^{T}=0\to c\mathbf{X}^{T}=0 \tag{7}\]  
which means that the parity-check matrix for a (\(n\), \(k\)) linear systematic block code \(\mathbf{X}\) has a null-space vector of k dimension with basis \(\mathbf{G}\).  
The LDPC codes belong to the linear block codes family which can be defined by a very sparse parity-check matrix (very few 1 s in each row and column, the rest being all zero) [48] and respect the equations mentioned above. In this paper, we considered regular LDPC codes, with a constant number of 1 s on rows and columns. After the LDPC coding, the resulting codeword is mapped using a signaling constellation M, obtaining a vector s. In this paper, BPSK with \(M=\{-1,1\}\) and QPSK with \(M=\{1+i,1-i,-1+i,-1-i\}\) signaling are considered.
## 1 Introduction
Based on these assumptions, the guiding questions that underlie the present investigation are the following: (1) Do power poles and power lines cause discomfort due to low frequency noise? (2) Do power poles and power lines affect the quality of life of the population? The noise levels emitted by power poles and power lines can be a potential source of low frequency noise and discomfort in residential areas [1; 31]. Nevertheless, how the population perceives the noise emitted by power poles and power lines should be determined, corresponding to a more subjective approach and complementing a more objective approach. Thus, this article aims to analyze the impacts of low frequency sound pollution, emitted by power poles and power lines, in two predominantly urban areas of the municipality of Guimaraes (north-western Portugal), from the objective approaches (noise level and sound recording measurements) and the subjective assessment of noise discomfort (distributing surveys to the resident population and adapted audiometric test performance). It is hypothesized that people living in these spaces develop more health problems (e.g., insomnia, irritation, depression) than those living far away from these places. This research comprises a study of \"exposed\" and \"unexposed\" individuals carried out in the village of Serzedelo, which was classified as an \"exposed\" territory because of the high concentration of power poles and power lines. The second concerns the village of Abacao (Sao Tome) classified as \"unexposed\" due to the inexistence of this type of infrastructure. At an international level, existing production concerning the low frequency noise still remains very low. This paper can contribute to more complete methodologies to assess low-frequency noise discomfort. Additionally, it not only considers objective evaluation parameters (noise level measurement), but it also takes into account the perception of discomfort obtained from the surveys and the audiometric tests adapted to low frequencies. In addition, this type of study can contribute to the formulation of public policies in terms of installing power poles and power lines in residential areas. This article is structured in four sections. This section includes a literature review on noise pollution, as well as impacts on the well-being of the population and an introduction to the subject. Section 2 presents the methodology used in the objective and subjective dimensions of the investigation. Section 3 comprises the results and the discussion. In Section 4, we present some conclusions obtained from this investigation and suggestions for future investigations.
## 3 Case Study and Parametric Analysis
### Factors Influencing the Sealing Capacity of the EPDM Gasket  
#### 3.3.1 The Joint Gap of Lining Segments  
In the process of assembling the shield segments, the joint gap is common and has a great influence on the waterproof performance of the lining joints. The joint gap (shown in Figure 2) is inevitable between a pair of lining segments besides the designed value. Whether it is the error when assembling or the outcome from the deformation of the tunnel during operations, the joint gap will occur. This may alter the pressure on the opposite gasket, leading to less pressure on a pair of gaskets, thus, making their sealing capacity less efficient. For a designed shield tunnel, the waterproof ability of the joints  
Figure 9: EPDM sealing performance, curves with different symbols are test results, and the black curve is the calculation of the critical leak pressure in this paper. The dotted line shows that the contact stress and critical leakage pressure are equal.  
depends mainly on the contact stress provided by the assembly force. The smaller the clearance is, the closer the contact interface is and the stronger the sealing performance is.  
The joint gap can be converted into compressive stress (i.e., nominal contact stress \(p\)) through gasket deformation and reflected in the model. As shown in Figure 10, the trend of the calculation curve is consistent with that of the analysis; when there is the smallest gap in the joints, the contact stress is the largest, and the leakage is the smallest. Under several different groundwater pressure conditions, when the gap is less than 10 mm, the relationship between the logarithm of the leak rate and the joint gap is approximately linear, while the leakage begins to increase rapidly after the gap is more than 10 mm. This is because that, as the joint gap increases, the contact stress decreases significantly, and more of the non-contact region of the contact interface is penetrated. As a result, the amount and diameter of the percolation channels increase, which further leads to a rapid increase in leakage. Take that the daily average leakage does not exceed 0.1 L/m\({}^{2}\) as the standard, and the maximum allowable joint gap in groundwater pressure of 0.4, 0.8, 1.2, 1.6 and 2 MPa is 10.4 mm, 8.48 mm, 6.69 mm, 5.02 mm, and 3.69 mm, respectively.
# Energy Efficient Underlaid D2D Communication for 5G Applications
###### Abstract  
With the increasing importance of reduced-energy wireless communication in 5G networks, maximizing the energy efficiency of device-to-device (D2D) communication has gained a lot of research interest. But so far, the methods have only been able to address the single-cell scenario. This paper proposes an algorithm to improve the non-convex problem in underlaid D2D communication for multiple band scenarios. In 5G wireless communications, device to device is one of the methods used for achieving better energy efficiency. It also reduces the throughput latency. The optimization problem is formulated with a derivative algorithm and proposed modified derivative algorithm. Both the algorithms are compared, and this comparison shows that the modified derivative algorithm is more efficient than the derivative algorithm.  
5G; D2D communication; energy efficiency; optimization; derivative algorithm Article  
Energy Efficient Underlaid D2D Communication for 5G Applications  
1  
[PERSON]  
2022  
28 July 2022  
2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 4 Conclusions  
This paper proposes a potentiostat readout circuit with a low-noise and mismatch-tolerant current mirror using chopper stabilization and dynamic element matching (DEM) for electrochemical sensors. The potentiostat electrochemical current-mode sensors convert potential differences into current signals according to the concentration of the solution, and the readout circuit converts current signals into voltage signals. The proposed potentiostat readout circuit consists of a potentiostat biasing stage using CA, a gain-boosted cascode current mirror, TIA, I/V reference, SPI, oscillator, and a timing generator. In order to detect small changes in current, the proposed readout circuit with mismatch-tolerant and low-noise characteristics is essential. For this purpose, a chopper was adopted for the input and output stages of the CA, current mirror, and TIA. The DEM technique was applied to make it mismatch-tolerant. Thus, the clock generator for chopping, along with the DEM and an SPI for communication to the digital circuits, were integrated into a single chip. The proposed readout circuit was under fabricate by implementing a 0.18-\(\upmu\)m CMOS process with an active area of 1.073 mm\({}^{2}\). It consumed 287.9 \(\upmu\)W of power at a 1.8 V supply voltage. The input-referred current noise was reduced to 936 fA/\(\surd\)Hz at 1 Hz, and the input-referred noise from 0.1 Hz to 1 kHz was 21.7 pA\({}_{\text{RMS}}\). The input current range was in the range of 100 nA to 3.83 \(\upmu\)A (maximum nonlinearity \(<\) 1%). The standard deviation of the TIA output was reduced from 2.49 mV to 0.12 mV by the DEM operation. These results suggest that the readout circuit proposed in this paper is suitable as an electrochemical sensor using a potentiostat. Conceptualization, K.N. and H.K. ([PERSON]); methodology, H.K. ([PERSON]); software, K.N. ; validation, K.N., G.C. and H.K. ([PERSON]); formal analysis, G.C. ; investigation, M.Y. ; resources, H.K. ([PERSON]); data curation, K.N. ; writing--original draft preparation, K.N. ; writing--review and editing, H.K. ([PERSON]); visualization, K.N. ; supervision, H.K. ([PERSON]); project administration, H.K. ([PERSON]); funding acquisition, H.K. ([PERSON]). All authors have read and agreed to the published version of the manuscript. This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2021-2017-0-01635) supervised by the IITP (Institute for Information & Communications Technology Planning & Evaluation). Not applicable.
## 4 Research Challenges and Opportunities
### Advanced Buoy Design  
Considering the marine environment is aggressive and complex, it is very crucial to design an advanced flotation device (buoy) for a marine environment monitoring system. A buoy normally consists of a wireless sensor network node (CPU, sensors, radio, and batteries), an energy harvesting module, underwater sensors and a mooring system. For example, [PERSON] _et al._[78] proposed a special energy harvesting buoy which can effectively use sea wave energy conversion as a power source and has potential applications in marine environment monitoring. [PERSON] _et al._[59] designed a multisensory buoy system which can be effectively used for shallow marine environment monitoring.  
The design and deployment of an advanced buoy for marine wireless sensor networks should take into account the following requirements: low cost, waterproof, strong stability, energy harvesting, and mooring system.  
1. _Low cost:_ A marine environment monitoring system using wireless sensor networks is usually composed of a large number of sensor nodes. Therefore, each buoy device needs to be low cost.
2. _Watertightness:_ In order to protect the stability of marine environment monitoring system and prolong its lifetime, its electronic devices must be in a waterproof housing to avoid water damage.
3. _Strong stability:_ As the marine environment is aggressive and complex, the monitoring system should have a strong stability against adverse atmospheric conditions.  
* _Energy harvesting:_ Since it is not convenient to replace the batteries deployed on the marine surface and the sensor nodes, which are far away from the land and are power-hungry, it is necessary to consider the use of energy harvesting to reduce system maintenance requirements.
* _Mooring system:_ Due to tides, waves, marine currents, wind, _etc._, an anchor is required on the seabed in order to avoid the movement of the buoy devices.  
Besides the above mentioned requirements, the buoy mechanic design should meet a number of requirements including the buoy visibility with bright yellow color and a warning light for maritime traffic, the use of environmentally friend materials, the connection of several sensors, and the reasonable antenna height for the better communication propagation.
# A Fading Suppression Method for \(\Phi\)-OTDR Systems Based on Multi-Domain Multiplexing
## 4 Experimental Results and Analysis
### Spatial Weighting  
Based on the previous weighting method, with spatial weighting intervals being 0.25-2 of the gauge length (GL), the improvement of the SNR and the deterioration of the  
Figure 3: (**a**) Waveform diagram of the IF signal; (**b**) local waveform diagram of the IF signal; (**c**) STFT spectrum diagram of the IF signal; (**d**) power spectrum diagram of the IF signal.  
spatial resolution caused by the amount of weighting intervals were discussed. The GL and the SNR are defined as Equations (10) and (11), respectively:  
\[SNR=10 log_{10}(\frac{P_{s}}{P_{n}}) \tag{10}\]  
where \(P_{s}\) is the power of the signal and \(P_{n}\) is the power of the noise.  
\[GL=\frac{v_{g}\cdot\tau}{2} \tag{11}\]  
In Figure 4, as the number of spatial weighting intervals increases, the SNR of the disturbance signal is enhanced, the spatial distribution of the disturbance is obviously broadened, and the spatial resolution decreases. The increase in the weighting length has an impact on the improvement of the SNR, as shown in Figure 5.  
In Figure 5, when the number of spatial weighting intervals increases from 0 to GL, the SNR is improved by approximately 8 dB. However, when the spatial weighting intervals exceed GL, even if the amount of spatial weighting intervals is further increased, the improvement in the SNR is negligible. Therefore, for the selection of the number of spatial  
Figure 4: SNR and spatial distribution under different weighting intervals.  
Figure 5: SNR improvement and RMS under different weighting intervals.  
weighting intervals, it is better to select the corresponding spatial weighting length no larger than the GL.
## 4 Experiments
### Metrics  
The evaluation metrics are the peak signal-to-noise ratio (PSNR) and the structural similarity index (SSIM) [47], both computed on the Y channel in the converted YCbCr color space. The PSNR measures the ratio between the image signal and noise, reflecting the level of distortion introduced by image compression. The quality of image reconstruction is assessed by comparing the differences between original and compressed images. The higher PSNR value indicates better reconstruction quality.  
The SSIM evaluates the similarity between two images by comparing their luminance, contrast, and structure. Importantly, the SSIM emphasizes the structural information that includes the spatial relationships among pixels, rather than merely the differences in pixel values. SSIM values typically range from 0 to 1, with values closer to 1 indicating greater similarity between the images. Compared to the PSNR, the SSIM better aligns with the human visual perception of image quality.