esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Problem Formulation and Dataset Generation
### Problem Description
These constraints force the model to consider which parts of the input data need to be copied first, so it can often learn the useful characteristics of the data. Generally, there are two kinds of constraints [25]: one is to make the hidden dimension smaller than the input dimension, which is called undercomplete; the other is to make the dimension of the hidden layer larger than the dimension of the input data, which is called overcomplete. In a complex network traffic environment, the undercomplete method is generally used to reduce the computational complexity. During the training process, a target loss function is used to measure the similarity between input and output data, which is expressed as follows:  
\[Loss1:f_{recon}(\mathbf{x})=\|\mathbf{x}-f_{Decoder}(f_{Encoder}(\mathbf{x}))\|^{2}, \tag{2}\]  
where \(f_{recon}(\cdot)\) is the loss function to measure the difference of input data \(\mathbf{x}\) and output data \(\mathbf{x}^{\prime}\). Unsupervised clustering divides datasets into different classes or clusters according to specific criteria, such as a distance. It makes the data samples in the same cluster as similar as possible, and the database samples in different clusters as different as possible. That is, the intraclass distance is the smallest, and the interclass distance is the largest. The specific implementation process of an unsupervised clustering algorithm can be roughly divided into the following four steps:  
* Randomly set \(\{C_{1},C_{2},\cdots,C_{k}\}\) initial cluster centers, and \(k\) represents the number of clusters. Figure 1: Flowchart of unsupervised-learning-aided NTC method. For each sample data point \(\{x_{1},x_{2},\cdots,x_{n}\}\), where \(n\) is greater than \(k\), each object has attributes of \(m\) dimensions. Calculate its distance from the center of each cluster and divide each sample into the nearest cluster. * Recalculate the average value of each cluster as the new cluster center, and update the original cluster center. * Repeat the above two or three steps to iterate continuously until the center of each cluster does not change. Generally speaking, in clustering algorithms, the attributes of samples are mainly represented by their relative distances in the feature space. This makes the concept of distance very important for clustering. The Euclidean distance from each object to each cluster center is calculated as follows:  
\[Loss\ 2:\ dis(x_{i},C_{j})=\sqrt{\sum_{t=1}^{m}(x_{it}-C_{jt})^{2}} \tag{3}\]  
where \(t\) represents the \(t\)th attribute of each object. Hence, the focus of this paper is to optimize and adjust the model from the two directions of complex data feature dimension reduction and unsupervised clustering.
## 4 Enterprise Network Functionality  
The primary goal of enterprise networks for many years has always been to interconnect everyone and everything. All connections are routed to the self-hosted, on-premise centralized data centers, where programs are run and data are stored [60]. Users and devices can then be connected to the LAN in different branch offices to enable this access. The LAN in individual branch offices is connected to broader enterprise WAN using a dedicated multiprotocol label switching (MLSP) links [29; 30].  
Every layer facilitates an organized method of controlling network access and traffic, which eliminates bottlenecks, supports efficient data processing, and promotes effective management of security policies. All these processes are facilitated by different techniques in order to guarantee seamless interconnectivity.
## 3 Results
### Serum Biochemistry Parameters
#### 3.5.4 Lipid Profile  
Figure 9 represents the serum lipid profile in WKY and SHRs receiving TW, NAB, TW/NAB, AB and TW/AB. The lipid parameters analyzed include total cholesterol, triglyceride, HDL-cholesterol and LDL-cholesterol.  
The total cholesterol levels (Figure 9A) show significant differences between treatment groups but only in WKY animals, with the AB group showing a statistically significant decrease (\(p<0.01\)) when compared with the NAB group and TW/AB group (\(p<0.05\)).  
Regarding triacylglycerglycerol levels (Figure 9B), no significant differences were found across all treatment groups.  
The HDL-cholesterol levels, shown in Figure 9C, show a significant increase in the NAB group (\(p<0.05\)) in WKY animals. No significant differences appear in SHRs.  
Finally, in Figure 9D, the LDL-cholesterol levels show significant differences between groups only in WKY animals, with higher values in the NAB group and the lowest values in the AB group. As in the whole lipid profile, SHRs showed lower concentrations than WKY animals for all the parameters.  
Figure 8: Serum levels of (**A**) glucose, (**B**) creatinine, (**C**) urea, and (**D**) uric acid in Wistar–Kyoto rats (WKY) and spontaneously hypertensive rats (SHR). Experimental groups included animals provided with tap water (TW), alcohol-free beer (NAB), tap water or alcohol-free beer at their discretion (TW/NAB), alcoholic beer (AB), or tap water and alcoholic beer at their discretion (TW/AB). Results are expressed in milligrams per deciliter (mg/dL) for glucose, creatinine, urea, and uric acid (mean \(\pm\) SEM; n = 8; **\(p<0.01\)).
## 4 Simulation and Results  
### Simulation Vehicle  
The proposed controller was verified using a simulation platform, which was conducted via MATLAB/Simulink and CarSim, and it included a validated high-fidelity full-vehicle dynamics model. The parameters of the vehicle and path are listed in Table 1.
# Research and Analysis of MEMS Switches in Different Frequency Bands
###### Abstract  
Due to their high isolation, low insertion loss, high linearity, and low power consumption, microelectromechanical systems (MEMS) switches have drawn much attention from researchers in recent years. In this paper, we introduce the research status of MEMS switches in different bands and several reliability issues, such as dielectric charging, contact failure, and temperature instability. In this paper, some of the following methods to improve the performance of MEMS switches in high frequency are summarized: (1) utilizing combinations of several switches in series; (2) covering a float metal layer on the dielectric layer; (3) using dielectric layer materials with high dielectric constants and conductor materials with low resistance; (4) developing MEMS switches using T-match and \(\pi\)-match; (5) designing MEMS switches based on bipolar complementary metal-oxide-semiconductor (BiCMOS) technology and reconfigurable MEMS' surfaces; (6) employing thermal compensation structures, circularly symmetric structures, thermal buckle-beam actuators, molybdenum membrane, and thin-film packaging; (7) selecting Ultra-Nano Crystalline diamond or aluminum nitride dielectric materials and applying a bipolar driving voltage, stoppers, and a double-dielectric-layer structure; and (8) adopting gold alloying with carbon nanotubes (CNTs), hermetic and reliable packaging, and mN-level contact.  
microelectromechanical systems (MEMS) switch; isolation; insertion loss; dielectric charging; contact failure; temperature-stable +
Footnote †: journal:
## 2 Carrier Phase and Pseudorange Measurement Model
Therefore, the DD \(I\) and \(T\) terms in Equations (8) and (9) are negligible and they can be rewritten as:  
\[\rho_{j,\mathrm{sb}}=\lambda^{-1}r_{j,\mathrm{sb}}+\varepsilon_{j,\mathrm{sb}} \tag{10}\]  
\[\rho_{j,\mathrm{b}}=\lambda^{-1}\left(r_{j,\mathrm{b}}+I_{j,\mathrm{b}}+T_{j, \mathrm{b}}\right)+f\left(\delta t_{\mathrm{b}}-\delta t_{j}\right)+ \varepsilon_{j,\mathrm{b}} \tag{2}\]0  
Equations (10) and (11) give the carrier phase and pseudorange measurement model under the short baseline condition. The baseline vector we need is buried in \(r_{j,\mathrm{sb}}\) terms. We expand \(r_{j,\mathrm{sb}}\) and reserve the first-order terms:  
\[\rho_{j,\mathrm{b}}=\lambda^{-1}\left(r_{j,\mathrm{b}}+I_{j,\mathrm{b}}+T_{j, \mathrm{b}}\right)+f\left(\delta t_{\mathrm{b}}-\delta t_{j}\right)+ \varepsilon_{j,\mathrm{b}} \tag{2}\]1  
where \(\mathbf{a}_{j,x}\) is the normalized line-of-sight (LOS) vector pointing from the rover to the \(j\)th satellite, and we assumed that the LOS vector of the rover equals that of the base under short baseline conditions, _i.e._, \(\mathbf{a}_{j,x}=\mathbf{a}_{j,\mathrm{b}}\). \(\mathbf{b}_{\mathrm{rb}}\) is the baseline vector pointing from the base to the rover. With measurements from single or multiple epochs, the baseline vector \(\mathbf{b}_{\mathrm{rb}}\), the float DD ambiguity \(N_{\mathrm{j},\mathrm{rb}}\) and the ambiguity variance-covariance (VC) matrix can be obtained through a weighted least square method that is commonly found in literature, or a Kalman filter that is to be discussed in the following part. An integer searching method such as LAMBDA [12] can be employed to obtain the fixed integer ambiguities that are then used to correct \(\mathbf{b}_{\mathrm{rb}}\) to generate a high precision baseline solution.
# Supervised Segmentation of NO2 Plumes from Individual Ships Using Troppomi Satellite Data
## 3 Materials and Methods
### Experiment Design
#### 3.3.3 Benchmarks  
To quantify the performance improvement gained by the usage of multivariate supervised models, we performed ship plume segmentation by applying a thresholding method on a single selected feature. First, we applied a thresholding method on the tropospheric vertical column of the NO\({}_{2}\)TROPOMI product regridded in accordance with the description in Section 3.1.1. No image enhancement technique was applied. This simplest way of plume-background separation was used, among others, in [17] for the quantification of NO\({}_{2}\) emission from the international shipping sector. In [11], the separation of pixels related to NO\({}_{2}\) plumes from individual ships was also performed based on solely TROPOMI NO\({}_{2}\) data. In this paper, we refer to this benchmarking method _NO\({}_{2}\) threshold_. Visualization of the input data for this thresholding technique can be found in Figure 9a.  
As a second benchmarking method, following the suggestion made in [12], we performed a ship plume segmentation based on images enhanced with [PERSON]'s 1 statistic. The satellite image enhancement allows effective separation of a greater amount of NO\({}_{2}\) plumes. However, as mentioned in Section 3.2.3, the application of [PERSON]'s 1 statistic may result in the enhancement of low-value clusters that are not a part of a plume. Visualization of the input data for this benchmarking technique is presented in Figure 9b. In the rest of the article, we call this method _Moran's 1 threshold_.  
Figure 9: Input data example for univariate threshold-based benchmarks. (**a**) Input data for a benchmark method _NO\({}_{2}\) threshold_. (**b**) Input data for a benchmark method _Moran’s 1 threshold_. At the top of the _ship sector_, the reader can find an example when a cluster of low-value NO\({}_{2}\) was mistakenly enhanced by Moran’s 1. (**c**) Input data for a benchmark method, _Moran’s 1 on high NO\({}_{2}\)_. For all presented images, the size of the pixel is equal to \(4.2\times 5\,\mathrm{km}^{2}\).  
To overcome the problem of the enhancement of low-value clusters by [PERSON]'s \(I\), we propose to assign zero value to all pixels of the image with intensity lower than the median of the given _ship sector_ picture and afterward apply the [PERSON]'s \(I\) enhancement. This is the third benchmarking method used in this study. We call it _[PERSON]'s \(I\) on high NO\({}_{2}\)_. Visualization of the results of the application of [PERSON]'s \(I\) only on high NO\({}_{2}\) values can be found in Figure 9c. As presented in Figure 9, for all three benchmarking methods, only pixels that lie within the _ship sector_ area were taken into account for segmentation.
# MT-CMVAD: A Multi-Modal Transformer Framework for Cross-Modal Video Anomaly Detection
## 3 Supposed Method
### Video Encoder Layer
#### 3.3.2. Hierarchical Transformer Encoder Architecture  
The encoder consists of \(L\) stacked layers, each of which combines Multi-Head Self-Attention (MHSA), Feed-Forward Network (FFN) and residual connections, as mentioned in Classic Transformer Encoder, but with slight modifications. For the \(l\)th layer (\(1\leq l\leq L\)):  
Figure 4: The detailed structure of the Video Encoder Layer.  
**Windowed Self-Attention with Shifted Windows**: To reduce computational complexity, input features are divided into non-overlapping local windows of size \(M\times M\) (e.g., \(8\times 8\)). Self-attention is computed within each window, while shifted window partitioning in alternating layers enables cross-window interactions:  
\[\text{Attention}(\mathbf{Q},\mathbf{K},\mathbf{V})=\text{softmax}\Bigg{(} \frac{\mathbf{Q}\mathbf{K}^{\top}}{\sqrt{d_{k}}}+\mathbf{M}\Bigg{)}\mathbf{V}, \tag{8}\]  
where \(\mathbf{M}\) is a masking matrix that restricts attention to valid spatial-temporal regions. Global dependencies are captured through iterative window shifting [34].  
**Pre-Layer Normalization (Pre-LN) Design**: Layer normalization is applied before MHSA and FFN operations to stabilize training dynamics:  
\[\mathbf{z}_{l}^{\text{norm}}=\text{LayerNorm}(\mathbf{z}_{l}), \tag{9}\]  
\[\mathbf{z}_{l}^{\prime}=\mathbf{z}_{l}+\text{MHSA}(\mathbf{z}_{l}^{\text{norm }}), \tag{10}\]  
\[\mathbf{z}_{l}^{\prime\prime}=\text{LayerNorm}(\mathbf{z}_{l}^{\prime}), \tag{11}\]  
\[\mathbf{z}_{l+1}=\mathbf{z}_{l}^{\prime}+\text{FFN}(\mathbf{z}_{l}^{\prime \prime}). \tag{12}\]  
Here, the FFN employs a Gaussian Error Linear Unit (GeLU) activation and expands the hidden dimension to \(4D\):  
\[\text{FFN}(\mathbf{x})=\text{GeLU}(\mathbf{x}\mathbf{W}_{1}+\mathbf{b}_{1}) \mathbf{W}_{2}+\mathbf{b}_{2}, \tag{13}\]  
where \(\mathbf{W}_{1}\in\mathbb{R}^{D\times 4D}\) and \(\mathbf{W}_{2}\in\mathbb{R}^{4D\times D}\) are learnable weights [38; 39].
# Passive Harmonic RFID System for Buried Assets Localization
###### Abstract  
A passive harmonic tag for buried assets localization is presented for utility localization. The tag design is based on a dual-polarized patch antenna at Ultra High Frequency (UHF) band. One of its feeders is connected to a frequency doubler based on a Schottky diode that generates the second harmonic, which is transmitted using a linear-polarized patch tuned at this frequency. The power received at the other feeder of the dual-polarized antenna is harvested by an RF to DC converter based on a five-stage voltage multiplier whose energy is used to bias a low-power quartz oscillator that modulates the output of the doubler. The different parts of the system are presented, and the theoretical read range is estimated as a function of the soil composition and the water content. A low-cost reader based on a software defined radio is also presented. Finally, experiments with a prototype of the tag are performed for different soil conditions.  
harmonic radar; RFID; underground communications; passive tag +
Footnote †: journal: Sensors
## 2 Priority-based Coverage-aware Congestion Control (PCC)
### Evaluation and Comparison  
In this section, we evaluate _PCC_ and compare its performance with other existing solutions. Compared with the dynamic queue scheduling in _PCC_, most queue schedulers, such as _FIFO_ or _RED_[13] use tail-dropping. Since it is not our focus to compare different existing queue schedulers, we only show comparison results with _FIFO_. Note that the conclusions in this section still apply to other queue schedulers.  
In the following simulations, we use standard IEEE 802.11 protocol for the MAC and physical layer. Ad hoc On-Demand Distance Vector (AODV) [14] is used as routing protocol in network layer and User Datagram Protocol (UDP) and Constant Bit Rate (CBR) are used in the transport and application layers, respectively. For the results presented in the following subsections, the X-axis represents the rate at which the _Non-Event_ packets are generated. Sensors with events generate _Event_ packets at a higher rate, which is \(1.5\) times the basic rate. Note that in our simulations, all the sensor nodes that detect the _Event_ generate at the same rate. However, this is not a requirement of our scheme. We compare the performance based on different metrics, such as throughput, packet delay and fairness. We consider random topologies with \(24\) sensors and one sink, where 12 sensors are _Event_ nodes and the others are _Non-Event_ nodes. The following results are the average of \(25\) simulation runs.
# Flooding Message Mitigation of Wireless Content Centric Networking for Last-Mile Smart-Grid +
## 4 Performance Evaluation
### Content Availability Comparision in Lossy Network Environment  
Content availability represents the accuracy of the face information, which is deeply depending on the network connectivity changing rate. When a node receives an interest packet from one of its neighbors, the incoming face information is added to reply the corresponding content packet to sender. However, in harsh network environments, incoming face information is not stable due to the link disconnections. Figure 13 shows the average content availability depending on the network instability. In this simulation we use the node mobility that can increase Face information instability. In this figure, TOP-CCN shows the highest content availability through CA based topology information update. If a node does not receive any CA packet from the connected neighbor during 6 s, the corresponding face information of a neighbor is removed to prevent packet loss and transmission overhead. The performance of TOP-CCN with flooding range control algorithm is slightly decreased through the limited interest and content packet flooding. Figure 14 shows the network wide overhead of the request message. In this figure, CCN over MANET shows total 8 Mb/s network overhead due to the uncontrolled message flooding. E-CHANET shows the highest performance until five consumers due to the overhearing and interest aggregation algorithm. However, its performance decreases in high congestion environment due to inaccuracy of the content availability. Thus, interest retransmission is dramatically increased in E-CHANET. Even if TOP-CCN uses the proactive CA approach, it shows the lowest overhead as 2.4 Mb/s in 15 pair meter and DCU environments due to the increased content availability. The average content downloading time is shown in Figure 15. The performance is measured by a summation of 10 packet's delay because a metering content item is fragmented into 10 chunks. The delay performance of the E-CHANET has the smallest delay until 5 users smart meter firmware download (5 consumers) scenario. However, 10 and 16 firmware download environments, TOP-CCN with flooding range control shows the lowest delay due to the increased content availability and reduced network overhead.  
Figure 12: Average content downloading time of the BestEffort applications.
## 6 Conclusions and Future Work  
Visualization, band ordering and compression of hyperspectral data are topics of interest in the remote sensing field.  
For visualization we consider the visualization of single bands of the hyperspectral data or a combined visualization of three bands, where each band is assigned to a separate RGB component.  
We have shown how to perform this visualization and have implemented a visualization tool for portable devices.  
We then studied the lossless compression of hyperspectral images and examined the Spectral oriented Least SQuares (SLSQ) algorithm.  
SLSQ is an efficient and low complexity algorithm for the lossless compression of hyperspectral images, it is suitable for on-board implementation. We have proposed our Java-based implementation of SLSQ.  
Finally we have considered band ordering for hyperspectral image compression.  
We have proposed an efficient band ordering approach that leads to improvements to SLSQ performance. The proposed approach is based on two metrics: Pearson's Correlation and Bhattacharyya distance.  
The experimental results achieved by our Java-based implementation of the SLSQ algorithm respectively without band ordering, with band ordering based on Pearson's Correlation and with band ordering based on Bhattacharyya distance have been examined.  
The band ordering based on Pearson's Correlation achieved the best experimental performance.  
The results achieved by the band ordering based on Bhattacharyya distance instead are not good enough when compared to the results achieved without band ordering.  
In the future we will also test the proposed approach with other distance measures.  
Future work will also include improvements for the tool we have developed for the visualization of hyperspectral imagery.
# SASLedger: A Secured, Accelerated Scalable Storage Solution for Distributed Ledger Systems
## 2 Related Work
### Off-Chain Solutions  
Most of the off-chain solutions focus on reducing the data size before the data are uploaded to blockchains [14; 15; 16; 17]. In these solutions, blockchains work as proof repositories to ensure the integrity of off-chain data, but the problems are: (1) off-chain data cannot be retrieved through a blockchain node; (2) each node still need to store the full copy of the blocks.  
Block archiver [18] is a solution used to reduce the data size of the data that has been uploaded to the blockchain and stored in the blockchain nodes by transmitting the data to an off-chain block archiver repository. It addresses the storage issue for HLF. In an organization (formed by serveral blockchain nodes) of HLF, a block archiver repository is deployed off-chain to store archived block files, a \"block archiver\" is deployed on the anchor/leader peer (a kind of blockchain node), and \"block archiver clients\" are deployed on other peers. The \"block archiver\" is responsible for transmitting block files from the peer's local file system to the block archiver repository, deleting local block files and notifying the \"block archiver clients\" to delete relevant block files from their local file systems. Fast fabric [36] also mentioned an approximate idea that storing the blocks in a distributed storage cluster. However, these solutions have not considered the method to keep the integrity of the block data after they are stored off-chain.  
Our solution is proposed to address the aforementioned problems in current solutions. A remote DB server is used to store the data that has been stored in the blockchain nodes, while the off-chain stored data are still retrievable through blockchain nodes. Concise commitments are stored in each node to ensure the data integrity for the blocks, therefore the size of data that stored on blockchain nodes are extremely reduced. The concept of CU is also adopted to avoid centralization and reduce network overhead.
## 3 Results and Discussion  
### Principle of E. coli Detection by Electrochemical Biosensor  
Figure 1 depicts the biosensing process of _E. coli_ detection. Firstly, the thiol-modified capture probes (SH-Capture) are immobilized on the surface of the polished gold electrode by Au-SH covalent bond, and then the unreacted active sites on the electrode surface are blocked with MCH [38]. Secondly, excess biotin-modified aptamer probes (Bio-Aptamer) that are complementary to the capture probes are introduced onto gold electrode by DNA hybridization. Due to the stronger interaction between the biotinylated aptamer probes and _E. coli_, a part of the biotinylated aptamer probes can dissociate from the capture probes in the presence of _E. coli_. After that, quantitative ST-APs can be immobilized on the surface of gold electrode by linking to biotin on the residual aptamer probes. Under strong alkaline conditions, ST-APs can dissociate from the biotinylated aptamer probes and catalyze the hydrolysis of \(\alpha\)-NP substrate generating the electrochemical signals [39]. As a result, the electrochemical response to _E. coli_ can be measured by using DPV in the presence of \(\alpha\)-NP.