esa-sceva/satcom-chunk-collection huggingface dataset:
# A Design for an Internet Router with a Digital Optical Data Plane
## 5 Optical Checksum  
Routers processing IPv4 packets are required to validate the IPv4 checksum field and to update its value to accommodate header field changes (e.g., hop count decrement). Although the shift to IPv6 (which has no header checksum) is already underway, it is unclear when (if ever) the shift will affect most Internet traffic. Furthermore, the Internet checksum used as an IPv4 header checksum is also used in other protocols such as TCP and UDP even when layered with IPv6, and these too need to be updated if the packet payload or certain header fields are changed, such as for network address translation. Even if optical routers never process IPv4 packets and never update TCP or UDP checksums, some type of optical checksum could be useful for link error checking, so exploring the IP checksum provides an opportunity to evaluate the potential to convert a simple checksum calculation to optical processing.  
The IP checksum is a one's complement 16-bit sum of the 16-bit words of the IP header [12]. It is commonly implemented using commonly available two's complement adders, in which the high-order carry-out is wrapped around as the low-order carry-in. When implemented in hardware, this carry wraparound results in a symmetric device in which each bit of an output word depends on all bits of both input words vs. the asymmetry of a two's complement system in which the carries propagate from low to high bit only. This is why the one's complement is a more effective error checksum--its pattern represents a more thorough mixing of the input patterns.  
Over two decades ago, our team developed a 1.26 Gb/s IP checksum using a simple programmable logic device (PLD) [21]. The device was composed of four 4-bit full adders, whose carries and data are cascaded in a cycle (Figure 11, left). For an optical version, we propose using a single full adder with 16 bits of recirculation delay for both the data and carry (Figure 11, right). This variant may also require data regeneration ('R' boxes in the right figure), depending on the number of 16 bit words being summed. For most IPv4 packets, the header is 20 bytes long, requiring the summation of 10 16-bit words, in which the partial sums and carries cycle 10 times. When the TCP or UDP checksum needs to be updated, as when a router supports network address translation, as many as 750 cycles of 16-bit words may be required (e.g., for a 1,500 byte packet). Our team has developed a proprietary variant of this design that requires only four cycles for the IP checksum (or 10 for the TCP or UDP checksum) to reduce the signal degradation that occurs during each cycle.  
Implementation of this design requires the development of an optical one-bit full adder. Other teams have developed such a device for binary RZ encoded data [22], and we are currently planning on implementing the IP checksum based on existing binary adders.  
Figure 10: Optical hop count decrement implementation, using an electronic S/RFF (using an electronic data or “D” FF).
# Optical Flow Odometry with Panoramic Image Based on Spherical Congruence Projection
## 3 Materials and Methods
### Spherical Mapping and Pixelation  
Consider a pixel in the panoramic image \(\mathcal{S}\) with the coordinate denoted as \(P_{c}\), where its corresponding point in world coordinate is represented as \(P_{w}\), as shown in Figure 3. Due to the unique imaging characteristics of panoramic cameras, the traditional pinhole camera model can not accurately represent the translation between \(P_{c}\) and \(P_{w}\). In this paper, the omnidirectional camera model proposed by [PERSON] [32] is adopted to describe the relationship between the point \(P_{c}\) and the vector \(\mathbf{p}_{w}\) emanating from the viewpoint. By normalizing the incident light vectors for all pixels, the panoramic image \(\mathcal{S}\) in the planar structure can be transformed into a spherical image \(\mathcal{S}^{{}^{\prime}}\). In this spherical structure, the spatial coordinate of the corresponding pixel point within the viewpoint coordinate system is \(P_{c}^{{}^{\prime}}=\mathbf{p}_{w}/\|\mathbf{p}_{w}\|=\{x,y,z\}\).  
Figure 2: The outline of the proposed SCP-based optical flow visual odometry (SCP-VO).  
Conducting optical flow on the spherical-structure image \(\mathcal{S}^{{}^{\prime}}\) directly presents significant challenges due to its varying pixel density and the complexity of motion patterns on the spherical surface. To overcome the above issue, the Quasi-Uniform Voronoi Sphere (QUVS) strategy [33] is employed to resample the pixels of \(\mathcal{S}^{{}^{\prime}}\) uniformly. Specifically, the QUVS is an icosahedron-based spherical homogenization method, which conducts iterative refinement using the Voronoi algorithm. The vertices are treated as electrons in QUVS, and the uniform pixel distribution is achieved through electrostatic repulsion balance. Then, the pixel grids are generated according to those vertices. Owing to the intrinsic properties of the icosahedron, the pixel grids contain two types: hexagonal pixels and pentagonal pixels. Denote the QUVS image as \(\tilde{\mathcal{S}}_{L}\), whose refinement level is \(L\). The corresponding number of pixels in \(\tilde{\mathcal{S}}_{L}\) is \(N_{L}\) of which 12 are pentagonal pixels, and the remaining \(N_{L}-12\) pixels are hexagonal pixels. The QUVS image \(\tilde{\mathcal{S}}_{L}\) maintains geometric consistency across the entire view, eliminating the feature distortion of pixels in the associated 2D planar image.
## 3 Results
### Impact of Environmental Factors on SIF, Tr, and the SIF-Tr Relationship
The SIF and Tr of poplar and arborvitae exhibited similar responses to VPD, with no decline observed in the SIF-Tr relationship at high VPD (Figure 8g-i). As both Ta and VPD increased, Tr/SIF in Chinese cork oak showed an increasing trend, while for poplar and arborvitae, Tr/SIF first increased and then decreased. SM showed a higher the SIF-Tr relationship at intermediate levels, whereas excessively low or high SM levels attenuated the relationship. As SM increased, the Tr/SIF in three plantations first rose and then fell, with peak values occurring at different moisture levels (Figure 8j-l). Figure 7: The impact of key environmental factors on SIF (PAR, (**a**); Ta, (**b**); VPD, (**c**); SM, (**d**)) and Tr (PAR, (**e**); Ta, (**f**); VPD, (**g**); SM, (**h**)) in Chinese cork oak (red), poplar (blue), and arborvitae (gray) plantations. The dashed and solid lines represent piecewise linear regression fits for the data dots.
# User Representation Learning for Social Networks:
## 4 Methodology
### Using Non-Textual Features in Doc2 Vec (Enrichment)  
Doc2 vec is basically an extension of the word2 vec word representation method. Document (paragraph) vectors are learned along with word embeddings. In this approach, user representations are extracted from a doc2 vec model trained not only on user tweets but also with non-textual data. The tweet text is embodied with various information about the user (name, location, etc.) and the interactions of the tweet (retweet, like, comment). The default features of the doc2 vec model are a tag and a list of tokens. The tokens of a tweet are captured as all words except stop words and words with non-alphanumeric characters in the tweet text. In this approach, the tag and token list pairs (\"tweet_id\", \"tkn\") of the documents are determined as the tweet ID and the tokens of the tweet. Non-textual features (\"usr_name\", \"loc\", \"desc\"; \"Lust\", \"Rust\", \"Cusr\") are added together with the standard features to enrich the learning capability of the model.  
D and E datasets were created for the implementation of this approach. Table 6 illustrates an example enrichment operation on a tweet from Dataset A with Dataset E. The concatenation process of the features of a single tweet with an id of \"tweet_id_1\", composed by the user with the handle \"usr_name_1\", is explained in this table. The location information for this user is \"loc_1\", and profile description text is \"dec_1\". Tokens of the tweet are \"tkn_1\",... \"tkn_5\". Handles of the users who liked this tweet are \"Lust_1\",..., \"Lust_6\". Users who retweet this tweet are \"Rust_7\",..., \"Rust_10\" and who commented are \"Cusr_11\" and \"Cusr_12\".  
The primary information about a tweet is the tweet text. Therefore, tokens of the tweet text are placed in the beginning of the Enriched_Data_1. Tokens are combined together for each tweet and they are treated as documents. A dummy token such as \"nametag\" is added between the tokens in the concatenation. The intuition here is that the model learns the probability of these tokens coexisting in documents. The tokens are not directly put together side by side. Dummy tokens (\"loctag\", \"liketag\", etc) are put between for each value of non-textual features in order to avoid order effects among them. They are added five times before and five times after the values because the window size parameter of the doc2 vec algorithm is chosen as five. A special dummy token (\"entrag\") is also added between the end of tweet tokens and the starting of the non-textual features. A  
Figure 2: Overview of the doc2 vec approach.  
similar approach for padding \"username\" and \"location\" features with dummy tokens is introduced by [33]. Experiments of this approach are listed in Table 7.
## 3 Delay Spread, Doppler Shift, and Doppler Spread
### Doppler Shift  
For the proposed system in this paper, the Doppler shift is defined as the difference between the transmitted carrier and the received actual frequencies [42]. Firstly, at time \(t\), the Doppler shift for the direct link can be obtained as  
\[DS_{0}(t)=-f_{c}\frac{d}{dt}\bigg{(}\tau_{0}(t)\bigg{)}, \tag{20}\]  
and the Doppler shift for the cascaded link can be obtained as  
\[DS_{i}(t)=-\max_{i_{m},i_{n}}\bigg{\{}\bigg{|}DS_{i_{m},i_{n}}(t)\bigg{|} \bigg{\}},\;\;i=1,2,\ldots,K, \tag{21}\]  
where \(DS_{i_{m},i_{n}}(t)\) is the Doppler shift of the \((m,n)\)-th element of the \(i\)-th RIS, which can be defined as  
\[DS_{i_{m},n}(t)=-f_{c}\frac{d}{dt}\bigg{(}\tau_{i_{m},i_{n}}(t)+\frac{\phi_{ i_{m},i_{n}}(t)+\gamma_{i_{m},i_{n}}}{2\pi f_{c}}\bigg{)}. \tag{22}\]  
Therefore, the Doppler shift for the \(K\) RISs is  
\[DS(t)=-\max_{i}\bigg{\{}\bigg{|}DS_{0}(t)\bigg{|},\bigg{|}DS_{i}(t)\bigg{|} \bigg{\}},\;\;i=1,2,\ldots,K. \tag{23}\]  
**Remark 2**.: _Note that \(\mathbb{E}\big{\{}\frac{d}{dt}\big{(}\frac{\gamma_{i_{m},i_{n}}}{2\pi f_{c}} \big{)}\big{\}}=\frac{d}{dt}\big{(}\mathbb{E}\big{\{}\frac{\gamma_{i_{m},i_{n }}}{2\pi f_{c}}\big{\}}\big{)}=0\). Then, (22) becomes_  
\[DS_{i_{m},n}(t)=-f_{c}\frac{d}{dt}\bigg{(}\tau_{i_{m},i_{n}}(t)+\frac{\phi_{i_ {m},i_{n}}(t)}{2\pi f_{c}}\bigg{)}. \tag{24}\]  
_Consider (21), (23), and (24), it can be seen that the RIS HWI \(\gamma_{i_{m},i_{n}}\) has no impact on Doppler shift. Furthermore, similar to delay spread, the transceiver HWI would not affect the Doppler shift either._
## 4 Traditional MPPT Techniques  
Traditional MPPT techniques are used to optimize power output in PV systems. These techniques are characterized by their simple implementation and relatively low computational requirements.
## 3 Proposed Methodology
### Threshold and Feature Subset Selector  
The purpose of the threshold value is to select a subset of features from the given feature list after incorporating the cost value. Ensemble-1 produces a feature list based on average scores. In this regard, we can find a point of intersection between feature weightage and the corresponding accumulated cost values. The point of intersection between 'FW' and accumulated cost score 'Cscore' can be found where \(FW<C_{score}\). A sample graph based on the feature-cost intersection curve is depicted in Figure 1b. A threshold value is automatically selected based on the point of intersection, e.g., the point of intersection is at feature number 6 in Figure 1b. Hence, all the features starting from feature number 3 leading up to feature number 6 would be retained while the rest of the features would be discarded. The assumption taken in this regard is that the features over the interaction point are reasonably useful and cost-effective, whereas, the accumulated cost of features below the intersection point out weigh their importance.  
In the case of ensemble-2, we consolidate individual feature subsets by accounting for the occurrence of a feature in multiple subsets and taking a majority vote among the partial solutions. For example, we have features such as \(\alpha\), \(\beta\), and \(\gamma\), placed at arbitrary positions in three separate subsets produced by three scoring functions, \(DT_{s1}\), \(RF_{s2}\), and \(GBT_{s3}\). Then we compute the frequency of these features e.g., \(<\)\(\alpha\):3\(>\), \(<\)\(\beta\):2\(>\), and \(<\)\(\gamma\):1\(>\). According to the aforementioned selection strategy, we select both \(\alpha\) and \(\beta\) features based on our majority voting heuristic, i.e., 2/3 rd of feature frequency, while discard \(\gamma\) and all other features which are having a frequency as that of \(\gamma\) or lower. The intuition between the second approach is that, if a feature appears more frequently in multiple subsets, then it is less likely due to any spurious patterns or any particular bias of the scoring function. Table 3 shows a sample scenario for the ensemble-2 approach. In this case, we have three different subsets. We generate an integrated subset by taking into account the frequency of a particular feature regardless of its position in the subset. The highest score of a feature is determined by the number of scoring functions in the ensemble. As we have three classifiers, therefore, the highest score a feature may get is 3. In Table 3, the selected features are denoted with boldface letters while the remaining features are discarded.  
It is important to note that the subsets generated by scoring functions may not be of equal size. Since for each scoring function e.g., CART, features' weightage is obtained and then subsequently based on the intersection of the FW and the accumulated cost i.e., Cscore, similar to the ensemble-1 approach, a subset of features is selected for each function. More details regarding this step are presented in Section 4.
# Symbol-Level Selective Channel Estimation in Packet-Based OFDM Systems
## 2 System Model
### Channel Model  
In realistic vehicular environments, the performance of the communication system relies on the condition of each communication link. That is, reliable vehicular communication is dependent on the channel conditions [14]. The challenging properties of wireless channels have a significant impact on the performance that can be achieved in communication systems; thus, analyzing the characteristics of a wireless channel is key to improving the system performance. For this reason, many studies and measurements of vehicular wireless channels have been performed [13; 14; 15; 22; 23; 24; 25; 26; 27].  
In this study, the vehicular channel models presented in [23; 24] are implemented in a simulation. The channel models for [23; 24] are designed for standardization as channel models for V2V and V2I communications in high-speed vehicles. These channel models are classified into six scenarios according to V2V and V2I environments. These six channel models are schematized as a tapped delay line (TDL), including the number of taps, tap power, and delay value. The TDL model is commonly employed to model multipath channels, and it is easy to implement in a simulator. Therefore, TDL models are widely used for evaluating system performance.  
Of the six models, V2V Expressway Oncoming and V2I Urban Canyon in Table 2 are considered because of their importance in real situations. The two models represent a typical vehicular environment. A specific summary of channel models such as TDL and the Doppler frequency for each channel tap is provided in [24].  
Figure 1: Pilot arrangement of IEEE 802.11p.
## 1 Introduction
We also introduce a statistical model, the complex Watson Mixture Model (cWMM), to describe the observations and formulate the localization task as weighted Maximum Likelihood (ML) estimation, rather than investigating the classical SRP-PHAT or MUSIC algorithms. The cWMM was recently proposed as an alternative to the complex Gaussian Mixture Model (cGMM) for beamforming and blind source separation [25; 26]. It is a mixture of Watson distributions. It has also been applied for general source localization [27] and source counting [28] with different spatial directions as states of the distribution. In this paper, we follow the cWMM-based localization technique and apply it to TSL in combination with our time-frequency selection scheme. To extract the target speaker, a Bidirectional Long Short-Term Memory (BLSTM) neural network is leveraged to estimate binary target masks. A short speech segment containing only the target speaker is additionally used as input to an auxiliary network for tuning the hidden layer parameters of the mask selection neural network. In this way, the network obtains speaker-awareness, but remains speaker independent. The output masks indicate the target time-frequency regions that are later integrated with the weight parameter estimation of the cWMM. Simulative experiments are conducted using real recorded Room Impulse Responses (RIRs), and the performance of the proposed method in various environments, especially when a competing speaker exists, is reported. This paper is organized as follows. Section 2 introduces the signal model and the cWMM employed to describe the directional statistics. Section 3 details the proposed method including the time-frequency bin selection neural network and the weighting parameter estimation of the cWMM. Section 4 presents the experimental setup, the localization results with competing speakers and noises, and discussions regarding the results. Section 5 concludes the paper.
## 1 Introduction
These considerations are divided into four perspectives: infrastructure, development, apps, and usage. In this study, we focus on the development and usage perspectives, particularly regarding preparation time, speed of development, and user management. With respect to preparation time, a cross-platform application must consider the learning curve, i.e., the developer time needed to explore \"getting started\" guides, such as tutorials, programming code examples, and community support backup. By focusing on this aspect, the initial learning efforts can be minimized, in line with the consideration of development speed, as it relates to the time it takes to develop applications. Moreover, in terms of user management, a cross-platform application must support various types of user access (web-based, phone-based, and desktop-based). These are the main aspects that underlie the development of an affordable cross-platform monitoring application based on website creation tools. To shorten the development time and increase the ease of development, the proposed application allows the developer to create a cross-platform monitoring system that can be developed and accessed from various platforms through Google Sites. To reduce the development cost of the data acquisition device, the proposed monitoring system uses an Arduino microcontroller board in combination with commonly available hardware components to wirelessly collect data from the desired implementation. Furthermore, the proposed system was applied to various types of applications, such as data collection systems and parameter monitoring in CNC lathe machines. In addition, the system was applied to monitor two different CNC lathe machines and revisualize the results in the CPMA. This method is similar to that proposed by [PERSON] et al., who developed condition monitoring based on IoT for predictive maintenance of four CNC machines [32]. Therefore, the monitoring of multiple machines can be performed using only one device in the future.
## 2 Study Area and Data
### Data and Processing
#### 2.2.2 Meteorological Data  
The daily meteorological data at 15 weather stations within and around the study area during 2000-2013 were used in this study. Meteorological data, including minimum temperature, relative  
Figure 2: The Landsat TM, ETM+, and OLI images used as the inputs to the STARFM simulations; images used for validations were marked with rectangles; images used for classifications were underlined.  
humidity, and solar radiation, were obtained from the Chinese National Meteorological Information Center ([[http://data.cma.gov.cn](http://data.cma.gov.cn)]([http://data.cma.gov.cn](http://data.cma.gov.cn))). Vapor pressure deficit (VPD) was calculated from the relative humidity as:  
\[\text{VPD}=\text{VP}\times 100/\text{RH}-\text{VP} \tag{1}\]  
where VP is the daily mean vapor pressure, and RH is the daily mean relative humidity [34].  
The inverse distance weighting method was used to interpolate meteorological variables at 30-m spatial resolution. Daily meteorological data were then averaged for every eight days to estimate GPP in the study area.
# Frequency-Based Haze and Rain Removal Network (FHRR-Net) with Deep Convolutional Encoder-Decoder
## 2 Proposed Frequency-Based Haze and Rain Removal Network (FHRR-Net) Framework
### Image Decomposition Using Guided Filter  
One of the most effective edge preserving smoothing algorithms is the guided filter [23; 24]. We apply a guided filter to the degraded input image by haze and rain and then obtain the low-frequency layer of the degraded image. The difference between the obtained low-frequency layer and the input image is used to obtain a high-frequency layer of the degraded image. As shown in Figure 4, the high-frequency layer contains most of the rains streaks and edge components. The low-frequency layer looks very blurry, but some elements present in the low-frequency layer constitute a degraded input image excluding rain streaks and edge components present in the high-frequency layer. Therefore, it is possible to deduce that the components of the haze exist in the low-frequency layer. Figure 4 shows an example of the applying guide filters to degraded input images. Figure 4 shows that there are many raindrops and edge components. We use decomposed layers to restore rain and haze images.  
Image decomposition using a guided filter has been proved to be easier to remove rain streaks and achieve excellent results [9; 10]. Proposed FHHR-Net estimates transmission maps using only low-frequency layers, which are more effective than using whole single image. Since the block operation is used for convolution, the estimated transmission maps have block artifacts. Because block artifacts adversely affect the dehaze process, various methods have been proposed to eliminate block artifacts [25]. However, when estimating the transmission maps using a low-frequency image, block artifacts are rarely generated because the edges are already smoothed.  
In this work, to take advantage of end-to-end learning, we use trainable guided filter [26]. The image decomposition process using guided filter is as follows:  
\[O(x)=O_{low}(x)+O_{high}(x), \tag{6}\]  
where \(O\) indicates hazy and rainy image, which is separated in low-frequency \(O_{low}\) and high-frequency \(O_{high}\). FHRR-Net uses \(O_{high}\) to remove non-stripes and \(O_{low}\) to estimate the transmission maps effectively.
# Wideband Mixed Signal Separation Based on Photonic Signal Processing
## 2 Interference Management
### Future Perspectives  
With the mutual benefits of the photonic and digital system, two types of well-known tough problems in BSS can be solved. (1) The interference changes so fast (1 GHz and beyond) that the duration of each sample is a very short (\(10^{-10}\) to \(10^{-12}\) s level), which is difficult to be achieved by ADC with a low sampling rate (MHz range); (2) the signals are mixed with a strong interference with a weak SOI, and to separate the signals requires 60-100 dB cancellation ratio, or at least 20-33 bits ADC.