esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Case Studies  
### Case Description  
To verify the performance of the proposed local ID mapping scheme, all objects in the modified database of DMS managing Korean distribution networks are considered. Since numerous power system resources exist in a distribution system, the DMS can be considered a good system to verify the performance of the proposed scheme. Figure 11 shows the layout of the distribution system considered for case studies, and it consists of 40 substations, 11,457 switches, and 231 distributed generations (DGs). The distribution system used for the case study is the one operating the actual Korean distribution system, and it is chosen to take advantage of the DMS for verifying the performance of the proposed scheme. Table 2 indicates a mapping table of power system resources and classes defined in IEC TCS7 CIM to create objects for the resources based on CIM. Power system resources in the DMS database such as lines, subtypes of switches, and DGs are transformed into objects based on CIM according to the mapping table shown in Table 2.  
A new DMS database is created by duplicating the original DMS database to generate two different IDs for the same objects and the corresponding local ID mapping table. The local IDs for power system resources in the duplicated database are randomly issued, which makes the local IDs in the duplicated database completely different from the local IDs in the original database.  
Figure 10: An example of a local ID mapping table.
## 3 Methodology
### Encoding Stage  
The ResNets contains multiple stacked residual units. Generally, each residual unit can be expressed with the following two formulations:  
\[y_{l}=H(x_{l})+F(x_{l},W_{l}), \tag{3}\]  
and  
\[x_{l+1}=f(y_{l}), \tag{4}\]  
Figure 1: An illustration of proposed network architecture for the 3D whole heart segmentation. Input is a volumetric CT or MRI image. Each red block is the FM-Pre-ResNet block. The VAE branch is added at encoders’ output and is used only during training. The decoder stage creates the final whole heart segmentation.  
where \(F\) is residual function, \(x_{l}\) and \(x_{l+1}\) denote the input and output of the \(l-th\) residual unit in the network, while the output of the \(l-th\) residual unit is denoted with \(y_{l}\). The parameters of the \(l-th\) residual unit are denoted as \(W_{l}\), while the function \(f\) refers to the rectified linear unit (ReLU).  
The identity mapping, by which ResNets learn residual function \(F\) in regard to \(H(x_{l})\), can be written as:  
\[H(x_{l})=x_{l} \tag{5}\]  
Therefore, the identity mapping of original residual block attaches an identity skip connection allowing information flow within a residual unit as shown in Figure 2a. As introduced in Pre-ResNets (Figure 2b), if \(H(x_{l})\) and \(f\) are both an identity mapping, the direct propagation of information through the entire network in forward and backward fashion can be written as:  
\[x_{l+1}=H(x_{l}+F(x_{l},W_{l})) \tag{6}\]  
Following the concept described in Equation (6), we propose a novel feature merge residual unit that can be written as follows:  
\[Z(x_{l})=F(z(x_{l}),W_{l})\circ z(x_{l})) \tag{7}\]  
and  
\[x_{l+1}=H(x_{l}+g^{\prime}(Z(x_{l}),W_{l}^{\prime}) \tag{8}\]  
where \(\circ\) presents the concatenation operation, \(Z(x_{l})\) denotes the concatenated result, while the functions \(z\) and \(g^{\prime}\) denote the convolution layers, added at the top and at the bottom of the residual unit, respectively. In this manner, the top convolution layers' output is concatenated with the residual signals' output, which allows the merge of features from preceding layers. After that, the concatenated result is passed through a bottom convolution layer to reduce channel dimension, as shown in Figure 2c.  
Figure 2: An illustration of different connectivity types of residual units. (**a**) original residual unit; (**b**) pre-ResNet unit; (**c**) proposed FM-Pre-ResNet unit
# Federated Learning for Maritime Environments: Use Cases, Experimental Results, and Open Issues
## 4 FL-Aided Fuel Consumption Prediction
### Preprocessing and Dimensionality Reduction  
Here, we note the preprocessing steps carried out to increase the signal-to-noise ratio of the raw data. Given the low sampling period (1 min), multiple successive samples of the time-series show zero or slow variations. This allowed us to simply replace the missing values by interpolation. Independently for each ship and variable, the missing values were identified and replaced by the average of the previous and next available samples. The missing values did not exceed the 8% of the total sample size for all variables. To maintain only on-board samples, the rows corresponding to stationary ship instances (SOG \(<\) 0.2 knots) were totally rejected. Subsequently, downsampling by a factor of 15 (1 sample per 15 min) was applied to the whole dataset for the purposes of reducing the data redundancy and facilitating the model execution time. Before downsampling, a moving-average filter was applied to smooth the curves and mitigate the distortion caused by aliasing. Then, a visual inspection of the parameter distributions was performed to identify additional outliers (samples exceeding the mean by three standard deviations). Finally, all features and target variables were normalized to take values in the range [0, 1], according to the following formula [30]:  
\[\begin{split} x^{\prime}_{i,j}&=\frac{x_{i,j}- \min\left\{X_{j}\right\}}{\max\left\{X_{j}\right\}-\min\left\{X_{j}\right\}}, \\ &\quad\forall i\in[1,2,\ldots,|X_{j}|],j\in[1,2,\ldots,10],\end{split} \tag{1}\]  
where \(x^{\prime}_{i,j}\) and \(x_{i,j}\) denote the scaled and non-scaled, respectively, sample \(i\) of variable \(j\), and \(X_{j}\) is the column vector of variable \(j\). The operator \(|\cdot|\) performs the vector element count.  
As an initial attempt to relax the regression model complexity and avoid feature redundancy, specific objective and multi-collinearity criteria were followed to retain or reduce the model dimensions (number of input neurons). First, the heading parameter was initially excluded as completely unrelated factor to the ship's MEP. DF and DA features were additionally avoided, as they are directly reflected in Trim. Finally, only the DWS was selected to represent the wind velocity amplitude. Note that, both SOG and STW were retained, since STW indirectly contains information about sea currents and flows and it is possible to be mismatched with the SOG (e.g., when the boat is drifting in a 5-knot current, then SOG = 5 kn and STW = 0). This was also confirmed by computing the correlation coefficient between SOG and STW ([PERSON]'s \(R_{coef}=0.23\)). Further elimination of the number of features to be included in the MEP models is presented in Section V.D.
## 2 The Overall Process of Charging Port Identification and Positioning  
First, to identify and locate the charging port through binocular vision, it is necessary to establish a binocular camera model. In order to solve the distortion problem in the perspective projection of the camera, camera calibration is conducted, through which the model parameters and distortion coefficients of the camera can be obtained for image correction. Second, based on the binocular vision system, the identification and localization algorithm of electric vehicle charging ports is studied, including filtering preprocessing of collected images, image segmentation, matching of pre-stored features of charging ports, and stereo matching of local graphic features. Finally, the three-dimensional spatial coordinates of the charging port are reconstructed; that is, the positioning of the charging port is completed [10]. The process of charging port image identification and positioning system is shown in Figure 1.  
Figure 1: Process flow of charging port image identification and positioning system.  
As the premise of identification and positioning of the charging port, the camera calibration process is based on the model of the existing camera. The parameters of the camera are calculated and transformed from the coordinates of the feature points. In the following, the three-dimensional reconstruction is carried out. Generally, \"[PERSON]'s calibration\" method can be used, in which the value of the coordinate transformation matrix can be solved through more than four sets of points. However, in order to reduce errors and acquire stronger robustness, generally, many images should be taken and a large number of angle points selected for calibration [10].
# A Dynamic-Routing Algorithm Based on a Virtual Quantum Key Distribution Network
## 2 Technical Foundation
### Blocking Probabilities and Thresholds
We can use the link-blocking probability to estimate the  
Figure 4: Link node structure diagram. likelihood that at least one link-blocking occurs per unit of time. Designated as \(P(LB)\), it can be calculated as:  
\[P(LB)=1-P(X=0)=1-e^{\wedge}(-\lambda) \tag{10}\]  
We can use the probability mass function \(P(X=m)\) to calculate the probability of having 0 link blocks in unit time, namely \(P(X=0)\). This probability can be expressed as the \(\hat{e}(-\lambda)\). Next, by calculating the complement of \(P(X=0)\), namely \(1-P(X=0)\), the probability of at least one link block per unit of time is denoted as \(P(LB)\). This probability can be expressed as \(1-\hat{e}(-\lambda)\). In conclusion, we can estimate the probability \(P(LB)\) of at least one link blocking per unit of time by combining the nature of the likelihood of node blocking, which can be used to evaluate the network performance and reliability and help in network planning, provisioning, and troubleshooting. Specifically, if \(P(LB)\) is high, it indicates that the link-blocking event is more likely, and the availability and performance of the network may be affected. Measures can be considered to improve network performance and reliability. In addition, by setting a reasonable \(P(LB)\) target value, it can be used as an indicator of network performance measurement and compared with the actual link-blocking situation to monitor and evaluate the quality of network services. Also, it is important to note that the upper limit of the link's blocking probability, i.e., the link-blocking probability threshold, needs to be calculated based on the link capacity and load situation. Assuming a link capacity of \(C\) (the maximum number of quantum bits a communication link can transmit, i.e., the bandwidth. ), the average arrival rate is \(\varepsilon\) and the average service time is \(\frac{1}{\mu}\). The following equation can calculate the threshold of link blocking probability \(P_{max}\):  
\[P_{max}=\frac{\varepsilon/c}{\frac{1}{\mu}+\lambda/c} \tag{11}\]  
For example, assuming a link capacity of 1 Gbit/s, an average arrival rate of 500 Mbit/s, and an average service time of 10 ms in a QKD network, the threshold for the link blocking probability can be calculated using the following equation:  
\(P_{max}=\) (500 Mbit/s/1 Gbit/s)/(1/10 ms + 500 Mbit/s/1 Gbit/s) = 0.00005. Therefore, the threshold value for the probability of link blocking is 0.005%. If the link-blocking probability exceeds this threshold, the measure must be taken to optimize network performance.
## 1 Introduction
The authors inspected the data for three weeks and observed that the network sensed 230 eruptions and other volcanic events. The authors also investigated the performance of relays in volcanic events. A three-level wireless sensor network for oil well health monitoring is proposed in [26]. Relays are used to transfer the data from one level to the other levels. An amplify and forward base linear WSN to increase the coverage is proposed in [25]. In [33], the authors present relay scheduling in a time-slotted source relay destination system, where a sensor (the source) has the option to have another sensor (the relay) help transmit its data to the destination. From an energy efficiency perspective, it is shown by the authors that the source may achieve the same bit error rate (BER) for a lower transmission power if it uses a relay, as compared to a direct transmission. In sensor networks, the transmission power dissipated by a sender node to transmit each bit of data to a receiver node is directly dependent on the distance between them. This use of multi-hop communication may reduce overall energy consumption; some nodes can be overloaded and drain out their energy more quickly (and die), as compared to some other nodes in the network. This may produce an undesirable effect on the functionality of the networks, even causing the network to become inoperable. The use of multiple relays that convey the same data with low power can reduce the chances of WSN failure. The major challenge is how to assign multiple relays efficiently that will increase the throughput of WSN and reduce the power consumption. One open research question of CRSN is the per-hop throughput optimization. Increasing the number of hops will increase the delay, complexity, deployment and transportation cost [34].
## 3 Results and Discussion  
### Composition of Volatile Compounds  
GC-MS was used to determine the volatile components in the samples collected from the dehydration stages, and typical 3D waterfall total ion chromatograms of volatile compositions of the samples are shown in Supplementary Figure S1. Peak alignment followed by mass spectra fragmentation patterns and retention index (RI) comparison, a total of 111 volatile compounds (Supplementary Table S1) were tentatively identified, including 19 aldehydes, 10 hydrocarbons, 10 esters, 13 alcohols, 17 ketones, 13 oxygen heterocyclic compounds, nine nitrogen heterocyclic compounds, five phenols, five acids, two sulfur compounds, two terpene compounds, and six other compounds.  
Prior to dehydration, 57 volatile compounds were identified in S-0 (S-t, where t represents the time when an aliquot was withdrawn from 0 to 240 min), and as dehydration proceeded the number of volatile compounds reduced to 22 at S-180 when the juice Brix was \(\sim\)65 \({}^{\circ}\)Bx (i.e., syrup) before rising to 87 with S-240, the final NCS product (Supplementary Table S1).  
To visualize the relationship among the samples collected at various intervals (nine in total), a Venn plot was drawn based on the 111 identified volatile compounds. This showed the compositional variation of the volatile compounds with processing time. Interestingly, as shown in Figure 1, the nine samples only shared eight volatile compounds, namely dimethyl sulfide, 3-methyl-butanol, 2-octanone, nonanal, 2-ethyl-1-hexanol, dimethyl sulfoxide, 2,4-di-tert-butylphenol, and 2-pentyl-furan, indicating these compounds persisted throughout the dehydration process. Sample S-240 possesses the most unique volatile compounds, 25, followed by S-0 which has 18 unique species. Only one, one, two, and one unique constituent were detected in S-30, S-60, S-90, and S-180, i.e., eugenol, 3-hydroxy-2,2,4-trimethylpentyl 2-methyl-propanoate, 2-pentadecanone and nonanoic acid, and benzyl alcohol, respectively, whereas S-120, S-150 and S-210 shared all the constituents with the other interval samples.
## 3 Experiment
### Acoustic Emission Data Acquisition  
The AE system was purchased from MISTRAS Group Inc. (Princeton Junction, NJ, USA) and featured a PAC Micro-30 AE sensor with an operational frequency range of 150-400 kHz. The amplitude threshold was configured at 32 dB, with a sampling rate of 5 MHz. A pre-trigger time of 256 us was established to ensure the acquisition system captured the complete signal initiation. The peak definition time (PDT), representing the duration from threshold crossing to peak amplitude, was set at 200 us. A signal duration of 2000 us was employed to identify the peak, while the hit definition time (HDT)--governing the termination of impact recording--was configured at 400 us. Signal recording commenced when the voltage exceeded the threshold value and ceased when the HDT parameter duration elapsed without additional threshold crossings. Notably, the HDT is typically twice the PDT. Lastly, the hit lockout time (HLT) was established at 400 us to prevent the inclusion of reflected hits and late-arriving signals. All the parameters above for the acquisition system were defined according to the criteria in [32].  
Figure 4: Dimensions of the elevator and the coordinates of impacts and sensor.
# IoT: A Long-Reach Fully Passive Low-Rate Upstream PHY for IoT over Fiber
###### Abstract  
Internet of things (IoT) devices heavily rely on wireless connectivity. There are intrinsic overlooked limitations such as reach, availability, security and safety vulnerabilities closely associated with wireless solutions. Wired connectivity is the alternative to tackle those issues, and optical fibers directly connecting IoT devices could provide them unique features such as huge bandwidth, long reach, signal integrity and high security grade for the transmitted information. Nevertheless, it can be prohibitive for IoT devices which are power hungry and have costly electrical-to-optical conversions. In this paper, first, a niche is identified for IoT over fiber (IoTof) based on fully passive optical solutions for long reach upstream of low data rate optical connectivity over dark fibers. Then, we proposed, implemented and characterized a prototype physical connectivity (PHY) based on fiber Bragg grating (FBG) low-cost acousto-optic modulation at IoT devices and respective optical edge-filtering as wavelength discriminator at the receiver. Finally, we performed an experimental demonstration of upstream data communication based on simple M-ary frequency-shift keying (FSK), with baud rate of 300 bps transmitted over 30 km range. In terms of data rate and reach for niche applications, IoTof can outperform traditional wireless technologies, such as Sigfox or LoRa. IoToF will enable monitoring urban areas with scarce and polluted spectrum, industrial areas requiring intrinsic safety, and upstreaming data from IoT devices in remote locations with unfavorable wireless propagation but with dark fibers available.  
internet of things; fiber Bragg gratings; acousto-optic modulator; frequency-shift keying 2019
# Transformer-Based Cascading Reconstruction Network for Video Snapshot Compressive Imaging
## 2 Related Works
### Video Compressive Snapshot Imaging  
We denote the dynamic scene with \(B\) frame images as \(\left\{X_{k}\right\}_{k=1}^{B}\in\mathbb{R}^{n_{x}\times n_{y}}\), where \(n_{x}\) and \(n_{y}\) are the spatial sizes. The CACTI system [7] uses the mask matrices \(\left\{C_{k}\right\}_{k=1}^{B}\in\mathbb{R}^{n_{x}\times n_{y}}\) to modulate the \(B\) frame sequence of the dynamic scene. Then, the modulated frames are accumulated on the sensor array to obtain the final single-frame measurement \(Y\in\mathbb{R}^{n_{x}\times n_{y}}\). The imaging process of the CACTI system can be mathematically expressed as  
\[Y=\sum_{k=1}^{B}X_{k}\odot C_{k}+N, \tag{1}\]  
where \(N\in\mathbb{R}^{n_{x}\times n_{y}}\) is the measurement noise and \(\odot\) denotes the Hadamard (element-wise) multiplication. According to Equation (1), each entry \(y_{i,j}\) in the snapshot measurement is calculated as  
\[y_{i,j}=\sum_{k=1}^{B}c_{i,j,k}x_{i,j,k}+n_{i,j}. \tag{2}\]  
The pixels at position \((i,j),i=1,\ldots,n_{x};\ j=1,\ldots,n_{y}\) of each modulated frame are collapsed to form one pixel in the snapshot measurement. We can also summarize the forward imaging process of the CACTI system as a concise matrix equation,  
\[y=\Phi x+n, \tag{3}\]  
In Equation (3), \(\Phi\in\mathbb{R}^{n_{x}\times n_{y}\times B}\) is the sensing matrix, and \(x=\left[x_{1}^{T},\ldots,x_{B}^{T}\right]\in\mathbb{R}^{n_{x}\times n_{y} \times B}\) is the vectorized image sequence, where \(x_{k}=vec(X_{K})\) and \(vec()\) vectorize the matrix by stacking columns. \(n\in\mathbb{R}^{n_{x}\times n_{y}}\) denotes the vectorized noise. The sensing matrix \(\Phi\) in (3) can be written as  
\[\Phi=[D_{1},\ldots,D_{B}], \tag{4}\]where \(\{D_{k}=diag(vec(C_{k}))\}_{k=1}^{B}\in\mathbb{R}^{n_{x}\times n_{y}}\) are diagonal matrices, and \(diag()\) reshapes the vector into a diagonal matrix by taking the vector as its main diagonal.  
In the imaging process of the CACTI system, the original video is compressed into one frame with a compression rate of \(1/B\). At present, coded modulation in the CACTI system is mainly realized by a digital micromirror device (DMD) [32; 33; 34] and code matrix shifting. For example, DMD is composed of many small aluminum mirrors. It can code and modulate the object by turning each mirror at a high speed. The literature [35] modulated the high-speed video frames by shifting the same coding matrix.
# Laser Technology in Photonic Applications for Space
## 3 Lasers in Space
### Communication  
Historically, one of the major drivers for the development of photonic technologies has been telecommunications and the growing demand for high-speed communications. Previously, the most common data transmission technology relied upon in space was radio communication, although it reached its limit in the rate of data transmitted per second. Today, satellites, rovers, and other space devices use new data acquisition technologies, such as high-resolution cameras, microphones, and spectrometers, among others, which require much faster data processing and communication, for instance, in the case of photo or video transmission. The data must be sent to paired devices or Earth control with the minimum possible delay to adjust to missions' requirements. A clear example of such a need is the current Martian rover missions, where the data acquisition and analysis help Earth-based scientists decide where to direct the rover next. Free space laser communication technologies have already demonstrated their potential for much faster data rates and, when combined with quantum technologies, much more secure communication channels. Nevertheless, these technologies still have to be space qualified [5] and approved for future missions. Optical fiber devices have already been qualified and used in a wide range of devices, resulting in less noise, lower power consumption, lower weight and volume, larger bandwith, etc., [63]. In general, the benefits of optical communications can be summarized as [85]:  
* almost limitless bandwidth thanks to the wavelength range available,
* lightweight and low volume,
* mechanical flexibility,
* galvanic isolation,
* propagation to longer distances thanks to the reduced divergence.  
The main communication frequency bands available for intersatellite laser links are around 830 nm, 980 nm, 1064 nm, and 1550 nm. The 830 nm band is the optimum in terms of quantum  
Figure 3: Diagram of the complete PHARAO (Projet d’Horloge Atomique Par Refroidissement d’Atomes en Orbite) laser system. Image source CNES [82].  
efficiency and sensitivity with silicon devices, whilst the 980 nm band is suitable for low data rate communication links. Semiconductor laser diodes are available for both those bands. The 1064 nm has proven suitable for high data rate and is facilitated by the use of Nd:YAG lasers, but the 1550 nm band can provide higher data rates up to approximately 10 Gbps and high sensitivity thanks to the use of erbium-doped fiber amplifiers (EDFA) [86].