esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Network Costs
### Unforeseen Costs
#### 5.4.1 Node ID Change Phenomenon  
Node identifiers (IDs) are the unique numbers configured for each mote in the network. Nodes were assigned four digit numbers that were associated with the node's location (_i.e._, site number), orientation (PVC mounted, tree mounted, or wire hung from a tree branch) and sensor configuration. Therefore, node identifiers serve a vital role in network monitoring, such that data can be correctly analyzed.  
Throughout the deployment time of the network, there have been isolated incidents when a node ID is permanently changed to a different value at runtime (e.g., node restart). The initial impression was that the issue might have been related to the Sluggo gateway, where incidences of data corruption were common. However, the occurrence of node ID changes continued after the adoption of the Linux gateway. It was then suspected that the issue was with XMesh (e.g., unsuspecting bugs in the proprietary software). This hypothesis was also dismissed when node ID changes were found after the switch to the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Category** & **Total Cost** & **Per Visit** & **\%** \\ \hline Hardware and Enclosures & \$3700 & \$24 & 74 \\ Power & \$1300 & \$8 & 26 \\
**Total** & \$5000 & \$32 & **100** \\ \hline \hline \end{tabular}
\end{table}
Table 9: Summary of cumulative maintenance cost and average cost per visit.  
CTP-based application. At least 18 occurrences of node ID changes have been encountered during the first four years of the network deployment. The cause of node ID changes remains an open problem. Still, in most cases, it is possible to determine the original node ID by analyzing features of the data such as more type (MicaZ/IRIS), type of application (relay/regular node), number of environmental sensors, and neighboring node IDs (e.g., parent and child nodes).  
Due to the unpredictable nature of node ID changes, their occurrence is not always noticed until the data are analyzed. Their presence adds maintenance costs to the network in the form of identifying a switched node's original ID and mapping the old and new node IDs for network and environmental data analysis. Currently, node ID changes are logged in a spreadsheet; however, to facilitate data analysis, node ID maps may be incorporated directly into the network database.
# High-Speed GaN-Based Superluminescent Diode for 4.57 Gbps Visible Light Communication
## Materials and Methods
### Experimental Setup  
The light output current-voltage (L-I-V) measurement was carried out in a device tester with a thermal-electric cooled SLD mount, a Keithley 2400 source measure unit (SMU), and an integrating sphere with a calibrated Si photodetector (Labsphere LPMS-020-SF-SDTP, North Sutton, NH, USA). The optical spectra were collected using a spectrometer (Ocean Optics QE Pro, Orlando, FL, USA). All measurements were performed at room temperature.  
The schematic and experimental setup of the SLD-based VLC system using the bit-loading DMT modulation scheme is depicted in Figure 2. For proof-of-concept demonstration, the signal modulation and demodulation processes were performed offline using MATLAB. Firstly, a pseudo-random binary sequence was generated. We used the adaptive bit-loading DMT modulation scheme containing 256 subcarriers, the QAM order of which depended on SNR estimation. The up-sampling factor was 2. The signal was loaded into an arbitrary waveform generator (AWG, Keysight M8190A, Santa Clara, CA, USA) with a maximal sample rate of 12 GSa/s. Afterwards, the signal was amplified by an electronic amplifier (iXblue Photonics DR-AN-10-MO, Lannion, France), and attenuated by an attenuator (Key-Press Attenuator kT2.5-30/1S-25, Shenzhen, China). The 290 mA DC bias generated by a source measure unit (Keithley 2400, Santa Clara, CA, USA) was coupled with the modulated signal by the bias-tee (Mini-Circuits ZFBT+4R2 GW-FT+, Brooklyn, NY, USA). A thermoelectric cooler (TEC) was attached to the SLD so as to keep the device at a constant temperature. The transmission distance was set as \(\sim\)1 m as a test bed for practical deployment of the VLC system. An iris was placed in front of the Rx to reduce the spontaneous emission component of the beam. Finally, the light was received by a customized Si avalanche photodetector (APD) with a bandwidth exceeding 1 GHz. Then the optical signal was converted into an electrical signal and was resampled by the oscilloscope (OSC, Keysight DSO9404A, Santa Rosa, CA, USA) for offline signal processing. The maximal sample rate of the OSC was 20 GSa/s. The frequency response of the SLD-based VLC system was tested by a network analyzer (Agilent Technologies PNA-L N5230C, Santa Clara, CA, USA).  
Figure 2: A diagram of blue SLD-based VLC system utilizing adaptive bit-loading DMT modulation scheme.
## Results and Discussion
### Response Time and Reversibility  
The oxygen sensor's response time was measured by the oxygen/nitrogen saturation method (Figure 7), where t\({}_{0.95}\) is the time required to generate 95% of the total phosphorescence intensity change. The oxygen sensor recovers from the oxygen saturation state to the deoxygenation state for a relatively slow time. As shown in Figure 7, according to the calculation, the response time t\({}_{0.95}\) of **M1-M3** in the aqueous solution from the deoxidation state to the oxygen saturation state is between 20 and 30 s, and the response time from the oxygen saturation state to the deoxygenated state is between 170 and 220 s (t\({}_{0.95-r}\)). This response time is faster than that of PfTFPP physically doped in poly(2-hydroxyethyl methacrylate) (PHEMA) matrix and polystyrene (PS) matrix, where t\({}_{0.95}\) from the deoxygenation state to the oxygen saturation state is 50 s [43]. It is shown that the encapsulation of PfTFPP in micelles formed from amphiphilic multi-arm block polymers is a kind of reasonable oxygen sensing material. Besides, the repeatability of these oxygen sensors from oxygen saturation state to deoxygenation state is excellent, indicating that these micellar oxygen sensors can be reused for continuous dissolved oxygen detection.  
Figure 6: Phosphorescent decays (\(\lambda_{\text{ex}}\) = 405 nm) of micelles **M1 (A)**, **M2 (B)**, and **M3 (C)** respectively under air and nitrogen conditions.
# Failure Mechanism of Multilayer Ceramic Capacitors under Transient High Impact
## 3 Modeling of MLCC Parameter Fluctuation Failure
### Modeling of Leakage Current Fluctuation Based on the Piezoresistive Effect
#### 3.2.1 Piezoresistive Model of Ceramics  
As shown in Figure 10, a static load is applied to the ceramic dielectric and the constant voltage (10V) of the electrochemical workstation (CHI600E Series) is used to monitor the real-time current change in order to obtain the resistance values of the ceramics under different pressures. The static pressure test showed that, with the increase in pressure, the insulation impedance of the ceramic dielectric decreased. In Figure 11 we see that the resistance of the ceramic dielectric began to decrease when the threshold pressure was 2.7 MPa, and the resistance dropped gradually when the pressure reached 18.1 MPa. In Figure 12, the relationship between ceramic conductivity and pressure is shown.  
Figure 10: System diagram of the ceramic static pressure test: (**a**) hydrostatic machine; (**b**) ceramic static pressure diagram; (**c**) ceramic static pressure experiment.  
Figure 9: Deformation of the MLCC at different stages: (**a**) the first stage; (**b**) the second stage.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Capacitor Model**} & \multicolumn{4}{c}{**Capacitance Variation/\(\mu\)F**} \\ \cline{2-5}  & **5026 g** & **5608 g** & **6305 g** & **8297 g** \\ \hline \({}^{*}\)J”CT41G-1210-X5R-16V-22\(\mu\)F & 0.011 & 0.028 & 0.082 & 0.129 \\ \({}^{*}\)J”CT41G-1210-X5R-25V-22\(\mu\)F & 0.017 & 0.033 & 0.091 & 0.  
According to [PERSON]'s second law, the relationship between acceleration and pressure is as follows:  
\[p(t)=ma(t)/A \tag{11}\]  
where \(m\)--mass; \(A\)--cross-sectional area.  
According to the experimental results, the relationship between pressure \(P\) and resistance \(R\) (P-R equation) is as follows:  
\[R(p)=\left\{\begin{array}{c}256,p\leq 2.34;\\ R_{0}+A_{1}\cdot(1-e^{(-(p-T_{1})/M_{1})})+A_{2}\cdot(1-e^{(-(p-T_{2})/M_{2})} ),p>2.34;\end{array}\right. \tag{12}\]  
The parameters in the P-R equation are shown in Table 4.
## 2 Problem Description  
Consider a periodically non-uniformly sampled-data Hammerstein system as depicted in Figure 1, in which \(H_{\tau}\) is a periodic non-uniform zero-order hold, converting a discrete-time input sequence \(\{u(kT+t_{i})\}\) to a continuous-time input \(u(t)\), i.e.,  
\[u(t)=\left\{\begin{aligned} & u(kT+t_{0}),\quad\,\,kT+t_{0}\leq t<kT+t_{1} \,(t_{0}=0),\\ & u(kT+t_{1}),\quad\,\,kT+t_{1}\leq t<kT+t_{2},\\ &\vdots\\ & u(kT+t_{q-1}),\,\,kT+t_{q-1}\leq t<kT+t_{q}\,(t_{q}=T),\end{aligned}\right.\]where \(k=0,\ 1,\ \cdots;\ T\) is the frame period spaced by \(q\) non-uniform sampling instants \(t_{i}\ (i=0,\ 1,\ \cdots,\ q-1)\).  
By passing through a nonlinear static block \(f(\cdot)\), \(u(t)\) is transformed into an unmeasurable inner input \(\overline{u}(t)\) to a linear dynamic process \(P\) with order \(n\), which can be expressed as:  
\[\overline{u}(t)=\sum_{m=1}^{n_{c}}c_{m}f_{m}[u(t)], \tag{1}\]  
where \(f_{m}[u(t)]\) are known nonlinear basis functions and \(c_{m}\) are unknown coefficients to be estimated.  
The noise-free output \(w(t)\) of the process \(P\) is corrupted by a white noise \(v(t)\), generating a measurable output \(y(t)\). The non-uniform sampler \(S_{\tau}\) has a synchronous sampling pattern with \(H_{\tau}\); thus, the discrete-time output sequence \(\{y(kT+t_{i})\}\) is obtained at sampling instants \(t=kT+t_{i}\ (i=0,\ 1,\ \cdots,\ q-1)\).  
For the notational simplicity, in the following the data \(s(kT+t_{i})\), at non-uniform sampling time \(t=kT+t_{i}\), is denoted by \(s_{i}(k)\). By using a time-varying backward shift operator \(\delta^{-1}\left[\delta^{-1}s_{i}(k)=s_{i-1}(k)\right]\) proposed in [35], the mapping relationship between the inner input \(\overline{u}_{i}(k)\) and the noise-free output \(w_{i}(k)\) of the periodically non-uniformly sampled-data Hammerstein system can be represented as:  
\[w_{i}(k)=\frac{B_{i}(\delta)}{A_{i}(\delta)}\overline{u}_{i}(k),\ i=0,\ 1,\ 2,\ \cdots,\ q-1, \tag{2}\]  
where:  
\[A_{i}(\delta) :=1+a_{i1}\delta^{-1}+a_{i2}\delta^{-2}+\cdots+a_{in}\delta^{-n},\] \[B_{i}(\delta) :=b_{i0}+b_{i1}\delta^{-1}+b_{i2}\delta^{-2}+\cdots+b_{in}\delta^ {-n}.\]  
From the system schematic diagram in Figure 1, we have:  
\[y_{i}(k)=w_{i}(k)+v_{i}(k). \tag{3}\]  
Given the non-uniformly-sampled input-output data \(\{u_{i}(k),\ y_{i}(k),\ k=0,\ 1,\ 2,\cdots,\ i=\ 0,\ 1,\ 2,\cdots,\ q-1\}\), the objective of this paper is to estimate the parameters of the nonlinear block in (1):  
\[\mathbf{c}:=[c_{1},\ c_{2},\ \cdots,\ c_{n_{c}}]^{\mathrm{T}}\in\mathbb{R}^{n_{c}},\]  
and the parameters of the linear block in (2):  
\[\mathbf{a}_{i} :=[a_{i1},\ a_{i2},\ \cdots,\ a_{in}]^{\mathrm{T}}\in\mathbb{R}^{n},\] \[\mathbf{b}_{i} :=[b_{i0},\ b_{i1},\ b_{i2},\ \cdots,\ b_{in}]^{\mathrm{T}}\in \mathbb{R}^{n+1}.\]  
Figure 1: Periodically non-uniformly sampled-data Hammerstein system.
## 7 Experimental Results and Analysis  
### Encryption Effect Test  
The encryption effect of the algorithm can be judged based on the comparison of the resultant lengths of encryption of plaintexts of different lengths. Therefore, the effect will be illustrated using the traditional DES encryption algorithm and the present chunked and disordered encryption algorithm. First, the traditional DES encryption and the  
Figure 11: Blocked and disordered resource platform architecture.  
chunked jumbled encryption were utilized for plaintexts of different lengths. It can be seen that the traditional DES encryption algorithm shows a linear growth in the length of the ciphertext as the length of the plaintext increases. The length of the plaintext in this chunked disorder algorithm is independent of the length of the encrypted ciphertext but fluctuates within a certain range. This is because this algorithm uses techniques such as dynamic grouping and disordered mapping, which makes the plaintext generate a random sequence after several iterations, and then realizes disordered arrangement, which improves the encryption efficiency and has a better encryption effect. The ciphertext becomes completely unrecognizable compared with the plaintext and has good security.As shown in the Figure 12.  
In addition, to avoid experimental chance, the same plaintext \"planText123\" was encrypted 1 million times using this chunking disorder algorithm, and the ciphertext length distribution had a certain randomness.As shown in the Figure 13.
## 3 Results
### Accuracy and F1-Score of BCI Competition's Dataset  
To evaluate the classification capacity of conventional and proposed frameworks on a reliable published dataset, both parameters for evaluation are composed in Table 1, for pair 1, pair 2, and pair 3, accordingly. The variation between F1-score and accuracy in Table 1 is not considerable, mostly under 4%, except for the pair 1 discrimination by DFBCSP--Fisher in subject 4, which is nearly 7%. Furthermore, the mean values of the two metrics are not different from each other, indicating the stability of the method as well as the data. A noteworthy point here is that DFBCSP frameworks achieve higher performance than the FBCSP framework in nine subjects. The mean values show that DFBCSP is 7-15% better than FBCSP in terms of accuracy and F1-score. This result is consistent with previous studies [40,65]. Subjects \"01\", \"02\", \"03\", \"07\", and \"08\" have good performance based on DFBCSP frameworks in general. Moreover, the proposed DFBCSP outweighs other methods in subjects \"03\" and \"06 for pair 1, subjects \"03\", \"05\", \"06\", \"07\", and \"08\" for pair 2, and subject \"04\", \"07\", \"08\", and \"09\" for pair 3 (bolded values). It can be concluded that DFBCSP--FmRmR is comparable to other methods.  
Another key point is that the classification of pair 2 and pair 3 is better than that of pair 1 in most subjects, and the mean values also reassure that. In the FBCSP approach, approximately 60% from pair 1 compared to 65% from pair 2 and 64% from pair 3. In DFBCSP (3 approaches) around 68% from pair 1 compared to 78% from both pair 2 and pair 3. Only subject \"08\" shows an opposite trend. It can be concluded that MI patterns between hands and feet are easier to be recognized than those between the left and right hand.  
However, the variations in performance between subjects are considerable as they elicit high standard deviation, which indicates the methods are not suitable for every participant. Further discussion will be given to the matter in the next section.  
Figure 7: Averaged ROC curve and AUC of the self-acquired dataset. Four feature extraction algorithms are in different colors.
## 2 Background
### HVAC Modeling Techniques
#### 2.1.1 Physics-Based Models  
Physics-based models are constructed on the foundational principles of thermodynamics, such as Fourier's law of thermal conduction and the Navier-Stokes equations, among other energy and mass balance equations. While these models can yield high accuracy by solving the equations analytically, this approach is often impractical due to the complexity and time-consuming nature of numerically solving the coupled partial differential equations. Additionally, an in-depth understanding of the system is essential; otherwise, numerous assumptions must be made, which can lead to discrepancies between the computed results and real-world observations. Numerous studies have focused on various components of HVAC systems. [PERSON] and [PERSON] [20] employed a thermal capacitance approach to predict chiller performance, achieving results within \(\pm 10\%\). [PERSON] and [PERSON] [21] utilized mass and energy balance techniques to analyze the transient response of refrigeration systems. In the context of thermal zones, [PERSON] and [PERSON] [22] applied heat equations to dynamically simulate heating loads, while [PERSON] et al. [23] used an RC network model to predict zone temperatures, accounting for heat losses through walls. These studies highlight the importance of selecting appropriate methodologies to accurately predict and optimize HVAC system performance.
## 2 Related Work
### Network Coding Schemes for Underwater Networks  
[PERSON] _et al._[20] proposed the Practical Coding-based Multi-hop Reliable Date Transfer (PCMRDT) protocol to avoid sender-receiver and receiver-receiver collisions and to decrease overall average end-to-end delay by combining random linear coding and selective repeat. PCMRDT can significantly reduce the network delay while achieving a high energy efficiency. [PERSON] _et al._[21] studied the problem of transmitting data efficiently in underwater sensor network. They compared the solutions based on Automatic Repeat Request (ARQ), network coding and erasure coding and found that the network coding based solution achieved a higher throughput than other solutions did.  
[PERSON] _et al._[13] presented the Time Slot based Routing (TSR) algorithm, where network coding was used to further reduce the probability of node conflicts, decrease node energy consumption and extend network lifetime. [PERSON] _et al._[22] contributed a reliable underwater sensor routing algorithm VBF_NC based on network coding. They found that combining network coding and multi-path routing can achieve higher robustness in UWSNs. They compared their approach with single-path forwarding, multi-path forwarding, end-to-end Forward Error Correction (FEC) and even hop-by-hop FEC and proved that their approach was more efficient in terms of both error recovery and energy consumption.  
These algorithms only use the error correction property of full network coding to improve the reliability of data transmission. However, nodes in a network have to wait for the data packets from all other nodes to arrive before starting decoding, which inevitably increases the network delay. To reduce the latency in data transmission, [PERSON] _et al._[23] proposed an opportunistic routing protocol based on partial network coding. In this work, we combine partial network coding and geographic routing to improve the performance of UWSNs. To the best of our knowledge, GPNC is the first geographic routing protocol based on partial network coding that is able to reduce the size of transferred data, the energy consumptions at nodes, and the network delays in data transmissions while improving the bandwidth utilization and the network lifetime.
# Enhanced Flow Control for Low Latency in QUIC
###### Abstract  
Low-latency communication is becoming more popular as applications that demand real-time interaction, such as autonomous mobile vehicles and tactile Internet, have recently gained prominence. In this paper, we propose a fast autotuning algorithm to support low-latency communication in the Quick UDP Internet Connection (QUIC) protocol. The transmission rate is adjusted by the fast autotuning based on the quantity of unused buffers. If the buffer has large free space, the receive window is quickly enlarged to increase the transmission rate and reduce the transmission delay. The fast autotuning is evaluated in this paper through extensive simulations, and the results show that the fast autotuning effectively reduces the transmission latency and increases throughput.  
low latency; QUIC; flow control Article
# Automatic Detection of Diabetic Hypertensive Retinopathy in Fundus Images Using Transfer Learning
## 3 Proposed Method
### Image Preprocessing
#### 3.1.2 Adaptive CLAHE  
The deviation of intensity levels in an image is referred to as the image's histogram. The histogram method can cause the medical image to be noisy. In order to restrict the noise, CLAHE can be used. We found that choosing the clip limit for optimal improvement using CLAHE is very critical. CLAHE is a method for improving adaptive contrast. CLAHE is based on AHE, which calculates the histogram for a pixel context area. In the display range, the intensity of the pixel is transformed into a value proportional to the pixel intensity range in the histogram of the local intensity. CLAHE has two parameters: block size (N) and clip limit. These parameters are used mainly to monitor image quality but are determined by users heuristically [19].  
The bin size in the local histogram has a significant impact on the clip level selection. The clip limit (CL) value is calculated automatically from the inputs in the proposed technique. The maximum bin height is taken in the local sub-image histogram and the pixel is distributed to each gray level in equal measure. The algorithm for adaptive CLAHE is shown below (Algorithm 2):  
```
Input: Original image I Output CLAHE enhanced image \(I_{g}\) 1. \(\forall\) Iresize A * B, I = {i} tiles \(\
i\) i = A * B/i * I
2 H i = Histogram (i) 3. \(\text{CL}_{i}\)(i) = \(\text{MC}_{\text{CL}}\)\(\land\)\(\text{M}_{\text{avg}}\)\(\text{M}_{\text{avg}}\)\(=\)\(\sum\text{M}_{\text{Clip}}/\text{M}_{\text{gray}}\)\(\
i\)\(\text{M}_{\text{gray}}\) = {i}g\(\mid\)\(\text{M}_{\text{CL}}\) = normalized I (0,1) \(\sum\text{M}_{\text{Clip}}\)\(=\)\(\sum\) pixels \(\in\) x,y tile dimension 4. \(\text{Clip}\) If (\(\text{H}_{i}\) > \(\text{CL}_{i}\)) then  Clip Histogram H i Else {H i = H i + \(\text{CL}_{i}\)} ] endif
5. \(\sum\text{M}_{\text{CL}}\) = \(\text{M}_{\text{cp}}\)
6. CLAHE (i)
7. Evaluating Ig using bilinear interpolation
```  
**Output:** CLAHE Enhanced \(I_{g}\)  
**Algorithm 2**Algorithm of Adaptive CLAHE  
The enhanced image's PSNR value after applying adaptive CLAHE is about 35, which is significantly improved in comparison to prior methods. In addition, the DNCNN network is applied for enhancing the retinal images and reducing the noise. DNCNN is the valuable pre-trained model for image denoising. The PSNR value of the denoised images is significantly improved. After applying DNCNN, a Wiener filter is applied, and the PSNR value obtained is around 60, which is significantly higher than most existing approaches.
# Enhancing Recommendation Accuracy of Item-Based Collaborative Filtering via Item-Variance Weighting
## 1 Introduction
* Unlike the traditional IBCF that utilizes the information of a target item's similar items to predict rating scores [21; 22], the proposed approach further selects neighborhood for each similar item of the target item and presents a new covering degree function to increase the weights of items that are closer to the target user's preference. By this way, the classification accuracy of recommendations can be improved effectively and experimental results in Section 4.2 confirm this. * Both predictive and classification accuracy of recommendations are improved. Unlike most related work that can enhance either predictive accuracy or classification accuracy but not in both [18; 19; 20; 21; 22], the proposed approach can provide recommendations with satisfactory predictive and classification accuracy simultaneously and experimental results in Section 4 confirm this. The remainder of this paper is organized as follows. In Section 2, we introduce the traditional IBCF approach and the problem setting. In Section 3, we present the proposed time-aware similarity computation and covering-based rating prediction and provide detailed information about the proposed approach. We describe experiments and compare the results to the traditional IBCF approach and other work in Section 4. Conclusions and suggestions for future work are presented in Section 5.