esa-sceva/satcom-chunk-collection huggingface dataset:
# An Architecture for Reliable Transportation of Delicate Goods
## 5 Workflow
### Deliver of Merchandise  
The delivery of the merchandise is done in a similar way to the transmission between operators, but now the intervenients are the operator who has the merchandise and the final recipient. Effectively, this is the formal acceptance of the merchandise and the update of the records that may exist on the device since the last update. The data collected throughout the route and stored in the cloud is available to both the sender and the recipient, including the current update, list of non-compliances of transport conditions, a detailed report of the non-compliances (values, time during which the non-compliance occurred, absolute time), expected delivery date and other information.  
The same happens with the company that provided the transport service, but this also has access to who were the direct operators and contracted companies hired to perform the service, thus having the possibility of ascertaining the responsibilities for the non-compliances.
# Elliptic Curve Signcryption-Based Mutual Authentication Protocol for Smart Cards
## 6 Analysis of Security Functionalities of ECSSP-SC
### Analysis of Security Attributes of ECSSP-SC
#### 6.1.2 Mutual Authentication  
* _Authentication of the USR by the SVR_--When the \(SVR\) gets the signcrypted message \(\{c,T,w,T_{U}\}\) from the \(USR\), it first verifies the timestamp and regenerates the key \(K^{\prime}\) by using \(I_{S}\) and \(K_{RS}\). Then, it computes \(r^{\prime}\) and \(T^{\prime}\), and if \(T^{\prime}=T\), the \(USR\) is authenticated. The value of \(T\) generated by the \(USR\) depends on \(r\) and \(G\), since \(T=rG\). The value of \(r\) depends on the ciphertext \(c\) and the key \(K\). The key \(K\) generated by the \(USR\) is a function of secret \(v\) and password \(PWD\). If an _Attacker_\(A\) tries to pretend to be a genuine user, then they must produce the correct value of \(T\). However, as per Assumption 1, the secret \(v\) and password \(PWD\) cannot be obtained by _Attacker_\(A\). Thus, the signature of the user \(USR\) is unforgeable.
* _Authentication of the SVR by the USR_--On receiving the message \(\{A_{S}.T_{S}\}\) from the \(SVR\), the \(USR\) computes \(A_{s^{\prime}}=E_{K}(V)\), and if \(A_{S}{}^{\prime}=A_{S}\), it authenticates \(SVR\). Since \(A_{s}=E_{K^{\prime}}(I_{S}wT)\), i.e., encryption of the secret \(I_{S}wT\), it is available only with the \(SVR\) and due to the strength of the encryption algorithm mentioned in Assumption 3, an Attacker \(A\) can not reveal any secret form \(A_{s}\) and cannot forge it. Thus, ECSSP-SC successfully provides mutual authentication between \(USR\) and \(SVR\).
# A 5 Gb/s Optoelectronic Receiver IC in 180 nm CMOS for Short-Distance Optical Interconnects
## 2 Circuit Description
### Cross-Coupled Differential Transimpedance Amplifier (CCD-TIA)  
Figure 4 shows the schematic diagram of the cross-coupled differential transimpedance amplifier (CCD-TIA), in which cross-coupled active loads comprising PMOS loads (M3, M4) and NMOS source-followers (M5 with R1 and M6 with R2) are employed. Since the gates of the active loads are cross-connected, symmetric differential outputs can be generated at the output nodes of the CCD-TIA, not to mention that the bandwidth performance can be enhanced.  
Figure 3: Measured (**a**) current and (**b**) responsivity of on-chip P\({}^{+}\)/NW APD at reverse bias voltages [10].  
Figure 2: Cross-sectional view of the on-chip P\({}^{+}\)/N-well APD.  
When light pulses are absorbed by the on-chip APD, the optical signals are converted into electrical currents (i\({}_{pd}\)) that flow to the gate of M1. A variable feedback resistor (R\({}_{F}\)) is positioned between the input and output nodes, providing negative feedback operations. Using small-signal analysis, the transimpedance gain for this CCD-TIA can be given by  
\[\frac{v_{o}^{+}}{i_{pd}}\cong\frac{R_{F}g_{m1}}{g_{m1}+g_{m3}}\cong R_{F}\;if\;g _{m1}\gg g_{m3}\]  
\[\frac{v_{o}^{-}}{i_{pd}}\cong-\frac{R_{F}g_{m1}}{g_{m1}+g_{m3}}\cong-R_{F}\;if \;g_{m1}\gg g_{m3},\]  
where g\({}_{m(i=1,3,5)}\) denotes the transconductance of transistor M\({}_{[i=1,3,5)}\).  
Also, the equivalent noise current spectral density of the CCD-TIA is given by  
\[\overline{I_{eq}^{2}} \cong\frac{4 kT}{R_{F}}+\left(\frac{4 kTTg_{m1}+4 kTTg_{m3}}{S_{m1}^ {2}}\right)\left(\frac{1}{R_{F}^{2}}+\omega^{2}C_{tot}^{2}\right)\] \[+\left(\frac{4 kTTg_{m5}+\frac{4 kT}{R_{F}^{2}}}{S_{m5}^{2}R_{F}^{2 }}\right)\cong\frac{4 kT}{R_{F}}+\frac{4 kTT(g_{m1}+g_{m3})\omega^{2}C_{tot}^{2} }{S_{m1}^{2}},\]  
where C\({}_{tot}\) represents the total input capacitance including the photodiode capacitance (C\({}_{pd}\)) and the gate capacitance of M1. Therefore, this analysis reveals that enlarging g\({}_{m1}\) and g\({}_{m5}\) while minimizing g\({}_{m3}\) can be essential to obtain low noise performance.
# Frequency Comb-Based WDM Transmission Systems Enabling Joint Signal Processing
## 2 Comb-Based Superchannels
### Superchannel Transmitter Requirements
#### 2.1.1 OSNR Requirements
However, a 10-line superchannel would require a pumping power of 20 dBm, which is feasible with integrated laser technology [80]. While it can be challenging to support high line counts, it is also important to discuss these requirements from a broader perspective. Key considerations are then the maximum available comb seed power and the comb flatness. In the example above, we assume that all lines are modulated with the same format and the performance is simply dictated by the worst performing channel. By using adaptive-rate forward error correction (FEC), constellation shaping or similar techniques, the required flatness could likely be relaxed. Similarly, the challenge of maintaining high OSNR in transmission is likely to affect the number of lines included in each superchannel. While a single comb-source spanning the full C-band might be a clear target, spectral flatness combined with the simple line multiplication factor could make such implementations less attractive.
## 2 Materials and Methods
### Methods  
#### 2.3.1 Preprocessing  
**GF and Landsat preprocessing.** The GF data used in this study were the level-1 product, and the preprocessing included radiometric calibration, atmospheric correction, orthorectification correction, and mosaicking to obtain the surface reflectance. To unify the spectral features as much as possible, only selected Red, Green, Blue, and NIR bands from Landsat images were employed, as shown in Table 1, and OLI data were fused with PAN band to obtain a higher spatial resolution of 15 m. Relative radiometric normalization [25] was employed during preprocessing to ensure coherence among multi-source images and minimize spectral discrepancies.  
**DEM preprocessing.** The study required a 4-view DEM with full coverage of the study area, for which it was mosaicked, extracted by the shapefile of Huize County, checked for missing values, and filled in. To unify the spatial resolution with GF-1, GF-2, and Landsat for subsequent experiments, it was resampled to 16 m, 1 m, and 15 m resolutions, respectively.  
**Forest management inventory data preprocessing.** The forest management inventory data were in shapefile format, and the data were combined into two sets of segmentation datasets' labels according to the tree species information. One is forest and non-forest, and the other is needleleaf, broadleaf forest, and Others. The forest/non-forest and needleleaf/broadleaf forest areas were adjusted by visual interpretation according to the satellite images to guarantee accuracy. The shapefile was converted to r  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Description**} & \multicolumn{4}{c}{**GF-1 WFV**} & \multicolumn{4}{c}{**Landsat TM**} & \multicolumn{4}{c}{**Landsat OLI**} \\ \cline{2-10}  & **No.** & **WL (\(\mu\)m)** & **Resolution (m)** & **No.** & **WL (\(\mu\)m)** & **Resolution (m)** & **No.** & **WL (\(\mu\)m)** & **Resolution (m)** \\ \hline Blue & Band 1 & 0.45\(\sim\)0.52 & 16 & Band 1 & 0.45\(\sim\)0.52 & 30 & Band 2 & 0.45\(\sim\)0.52 & 30 \\ Green & Band 2 & 0.52\(\sim\)0.59 & 16 & Band 2 & 0.52\(\sim\)0.60 & 30 & Band 3 & 0.53\(\sim\)0.60 & 30 \\ Red & Band 3 & 0.63\(\sim\)0.69 & 16 & Band 3 & 0.63\(\sim\)0.69 & 30 & Band 4 & 0.63\(\sim\)0.68 & 30 \\ NIR & Band 4 & 0.77\(\sim\)0.89 & 16 & Band 4 & 0.76\(\sim\)0.90 & 30 & Band 5 & 0.85\(\sim\)0.89 & 30 \\ PAN & - & - & - & - & - & - & Band 8 & 0.50\(\sim\)0.68 & 15 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Image band information of GF-1, Landsat TM, and OLI. Note: no. = band number; WL = wavelength.  
information as code 0 for non-forest, code 100 for forest, code 0 for Other, code 100 for needleleaf forest, and code 200 for broadleaf forest.
## Materials and Methods
### NMR Relaxometry  
NMR Relaxometry measurements were performed on a 20 MHz spectrometer (Minispec PC 120, Bruker, Karlsruhe, Germany) equipped with a thermostated probe. The temperature was set at 20 \({}^{\circ}\)C. T\({}_{2}\) was measured using a Car-Purcell-Meiboom-Gill (CPMG) sequence with the 90\({}^{\circ}\)-180\({}^{\circ}\) pulse spacing \(\tau\) 0.1 ms. The signal of a single point at the echo maximum was acquired and data were averaged over 32 acquisitions. The number of successive echoes recorded was adjusted for each sample in order to reach the baseline of the relaxation curve. The recycle delay for each sample was adjusted to 5 X T\({}_{1}\) after measurement of the T\({}_{1}\) with a fast saturation recovery sequence. The total data acquisition time for T\({}_{2}\) (including spectrometer adjustments and T\({}_{1}\) measurement) was about 10 min per sample.  
Fitting was performed according to two methods using Scilab software (version 5.5.1, Scilab Entreprises SAS, Orsay, France): first T\({}_{2}\) relaxation curves were fitted using the maximum entropy method (MEM) [39], which provides the continuous distribution of relaxation components without any assumption concerning their number. In this representation, the peaks of the distribution are centered at the corresponding most probable T\({}_{2}\) values, while peak areas correspond to the intensity of the T\({}_{2}\) components. T\({}_{2}\) relaxation curves were also analyzed using the Levenberg-Marquardt algorithm, which allows a discrete solution for the fitting curve according to the equation: I(t) = I\({}_{1}\)  
\[\text{I(t)}=\text{I}_{1}\exp(-\text{t/T}_{2-1})+\text{I}_{2}\exp(-\text{t/I}_ {2-2})+\text{I}_{3}\exp(-\text{t/I}_{2-3})+\text{cte} \tag{1}\]  
where I\({}_{i}\) is the intensity of the \(i\)th exponential at the equilibrium state and T\({}_{2-i}\) the characteristic transverse relaxation time for the \(i\)th exponential. The initial fitting parameters used for the Levenberg-Marquardt method were chosen according to the results of the MEM. The NMR results of all the leaf samples agreed regardless of the fitting algorithm used.  
Specific leaf water weight per component (LWWi) was computed according to the equation:  
\[\text{L}\text{W}\text{Wi}=\frac{I_{\text{r}i}\times m_{\text{w}}}{\text{A}} \tag{2}\]  
where mw corresponds to the water mass (in g) of the NMR sample comprising six leaf discs, A to the area of the discs (in m\({}^{2}\)) and IR-i to the relative intensity of the \(i\)th NMR signal component (in %).
# Nonsmooth Optimization-Based Hyperparameter-Free Neural Networks for Large-Scale Regression
## 4 Nonsmooth Optimization Model of ReLU-NNR  
In this section, we formulate the nonsmooth optimization model for the RELU-NNR problem, but first, we give some notations. Let us denote the number of hidden nodes in NNs by \(H\) and let \(w\) be the weight connection vector and \(w_{s}\) be its element in the index place \(s\), where \(s\) is the index triplet \(s=abc\) (see Figure 1). Then \(w_{abc}\) states the weight that connects \(a\)-th layer's \(b\)-th node to \((a+1)\)-th layer's \(c\)-th node. In addition, denote by \(P\) an output index value for the weights connecting the hidden layer's nodes to the output node of the NN. In contrast to the hidden nodes, no activation function is applied to the output node. Finally, let \(B\) denote the middle index value for the weights connecting a layer's bias term to a node in the next layer. The bias terms with this index can be thought of as the last node in each layer. The weight connection vector \(w\) for \(m\) input features and \(H\) hidden nodes has \(H(m+2)+1\) components. For the sake of clarity, we organize \(w=w^{H}\) in an \"increasing order\" as  
\[w^{H}=(w_{011},w_{012},\ldots,w_{01H},w_{021},w_{022},\ldots,w_{02H },\ldots,\] \[w_{0m1},w_{0m2},\ldots,w_{0 mH},w_{01B},w_{02B},\ldots,w_{0 BH},\] \[w_{11P},w_{12P},\ldots,w_{1 HP},w_{1 BP})^{\top}.\]We define the following  
\[s_{ij} =w_{0 Bj}+\sum_{k=1}^{m}w_{0 kj}x_{k}^{i},\ \ \ \ i=1,\ldots,n,\ j=1, \ldots,H,\] \[t_{i} =w_{1 BP}+\sum_{j=1}^{H}w_{1 jp}\max\{0,s_{ij}\},\ \ \ \ i=1,\ldots,n,\]  
where \(x_{k}^{i}\) denotes the \(k\)-th coordinate of the \(i\)-th sample. The loss function using the \(L_{1}\)-norm (cf. regression error (1) with \(p=1\)) is  
\[F_{H}(w)=\sum_{i=1}^{n}|t_{i}-y_{i}|. \tag{2}\]  
To avoid overfitting in the learning process, we add an extra element to the loss function \(F_{H}\). In most cases, \(L_{1}\)-regularization is preferable as it reduces the weight values of less important input features. In addition, it is robust and insensitive to outliers. Thus, we rewrite (2) as  
\[f_{H}(w)=F_{H}(w)+\rho_{H}\|w\|_{1},\]  
where \(\rho_{H}>0\) is an iteratively self-updated regularization parameter (to be described later). Furthermore, the nonsmooth, nonconvex optimization formulation for ReLU-NNR can be expressed as  
\[\begin{cases}\text{minimize}&f_{H}(w)\\ \text{subject to}&w\in\mathbb{R}^{H(m+2)+1}.\end{cases} \tag{3}\]
## 4 Experimental Results of the Proposed Systems
### Results of ANN Classifier Based on Fusion of Features
#### 4.5.3 Error Histogram  
An error histogram is one of the tools used for evaluating the ANN on the two X-ray datasets for tuberculosis. Error values between the target and output values are recorded in all epochs during all stages of dataset splitting. The bin histogram is generated using an ANN classifier in different colors. Each color represents a phase; green represents the classifier's evaluation during validation, a blue color represents its performance during the training of the dataset, a red color represents its performance during testing new samples, and the best performance of the network is depicted in orange. Figure 11 shows the error histogram generated by an ANN classifier for classifying the two tuberculosis datasets. For the first dataset, it is noted that the ANN network with the features of ResNet-50, GLCM, DWT and LBP reached the best performance with 20 bins between the values of \(-0.9446\) and \(0.947\), while with the features of GoogLeNet, GLCM, DWT and LBP, it achieved the best performance for the network with 20 bins between the values of \(-0.9492\) and \(0.9496\). For the second dataset, note that ANN with ResNet-50, GLCM, DWT and LBP features achieved the best performance with 20 bins between \(-0.9483\) and \(0.9483\), while with GoogLeNet, GLCM, DWT and LBP features, it reached the best performance with 20 bins with values between \(-0.949\) and \(0.95\).  
Figure 11: Display error histogram of the ANN to classify the two datasets based on the hybrid features. (**a**) ResNet-50, GLCM, DWT first dataset and LBP. (**b**) GoogLeNet, GLCM, DWT and LBP first dataset. (**c**) ResNet-50, GLCM, DWT and LBP second dataset. (**d**) GoogLeNet, GLCM, DWT and LBP second dataset.
## 1 Introduction
of Beijing Jiao Tong University [34] made an asymmetric mode selection coupler in the 1550 nm band and built a doped annular cavity fiber laser, and obtained an LP\({}_{01}\) mode output with a central wavelength of 1550 nm and a mode purity of 94%. In 2019, [PERSON] et al. of Nanjing University of Posts and Telecommunications [35] made an asymmetric mode selection coupler to obtain an LP\({}_{11}\) mode output, and built a mode-locked laser to achieve an LP\({}_{11}\) mode output with a pulse width of 629 ps and a mode purity of 94.5%. Based on the coupling principle, we design and manufacture an all-fiber symmetrical two-mode coupler (STMC) in the 1 \(\upmu\)m band for the first time that can achieve high-purity conversion from an LP\({}_{01}\) mode to an LP\({}_{11}\) mode. Then, we provide a feasible solution for CVB all-fiber lasers.
###### Abstract  
In this study, the effects of walking speed on lower limb muscle activity and gait parameters during over-ground walking were investigated in individuals with chronic stroke. Twenty-four patients with chronic stroke participated in a cross-sectional repeated-measures study, walking 20 m at three different speeds: slow (80% of self-selected speed), self-selected, and maximal speed. Surface electromyography was used to measure muscle activity in five paretic-side muscles (rectus femoris, biceps femoris, tibialis anterior, gastrocnemius, and gluteus medius), while gait parameters, including stride length, stance and swing phases, single-limb support time, and the gait asymmetry index were assessed using a triaxial accelerometer. As walking speed increased, activity in the rectus femoris, biceps femoris, and gastrocnemius muscles significantly increased during the stance and swing phases (\(p<0.05\)), whereas the gluteus medius activity tended to decrease. Stride length on the paretic and non-paretic sides significantly increased with faster walking speed (\(p<0.05\)); however, no significant improvements were observed in other gait parameters or gait asymmetry. These findings suggest that although increasing walking speed enhances specific muscle activities, it does not necessarily improve overall gait quality or symmetry. Therefore, rehabilitation programs should incorporate multidimensional gait training that addresses speed and neuromuscular control factors such as balance and proprioception.  
xxxxxx; walking speed; surface electromyography; gait symmetry; muscle activity 2025
## 4 Dealcohized Grape Pomace Applications in Bio-Based Materials
### Incorporation of Dealcholized Grape Pomace in Biodegradable Plastics
Additionally, components such as lignin contribute to increased tensile strength, improved gas barrier properties, and overall durability of biopolymer films and foams [65; 67]. They also exhibit significant antioxidant activity, which can extend the shelf life of packaged foods and enhance their nutritional value [66]. Starch-based foams made with GS and PHA/PHB films containing grape seed lignin have shown promise for packaging foods--especially those with low moisture content--and for extending the shelf life of fruits such as grapes. While these positive results are promising, large-scale adoption remains limited due to production costs, variability in agro-waste availability, and the need for further optimization of mechanical and functional properties [68]. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Example/Material Type** & **DGP Component** & \begin{tabular}{c} **Biodegradable** \\ **Matrix/Polymer** \\ \end{tabular} & **Key Properties/Findings** & **References** \\ \hline \multirow{4}{*}{Starch-based foams with grape stalks} & \multirow{4}{*}{\
\begin{tabular}{c} Grape stalks (from \\ pomace) \\ \end{tabular} } & \multirow{4}{*}{
\begin{tabular}{c} Cassava starch \\ \end{tabular} } & Good morphology, complete biodegradation & \multirow{4}{*}{[65]} \\  & & & in 7 weeks, suitable for food storage with low moisture content & \\  & & & High strength, improved antioxidant activity, enhanced nutritional & \\  & & & profile & \\  & & & Improved gas barrier, antioxidant activity, enhanced & \\  & & & enhanced & \\  & & & biodegradation, non-toxic degradation products & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Additional examples of DGP-derived components incorporated in biodegradable plastics. Continued research is essential to refine formulations and processing techniques, thereby maximizing the benefits of incorporating grape pomace while ensuring economic feasibility and scalability.
# FOXS-GSC--Fast Offset Xpath Service with HexagonS Communication
## 3 Proposed Solution
### Overview  
FOXS-GSC is a congestion control service based FOXS. The major upgrade in FOXS-GSC is to the proposed communication protocol, allowing multi-hop communication.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Work** & **Geolocation** & **Story-Carry-Forward** & **Fault** & **Broadcast Suppression** & **Router Service** & **Scenario** \\ \hline DV-CAST [20] & X & V & X & delay vehicle direction & X & highway \\ \hline UV-CAST [22] & X & V & X & intersection vehicles & X & urban/highway \\ \hline DRIVE [24] & X & X & X & delay sweet spot & X & urban/highway \\ \hline ADDHV [26] & X & V & X & delay sweet spot & X & urban/highway \\ \hline CARRO [28] & X & V & X & delay sweet spot & X & urban/highway \\ \hline CC-DEGREE [27] & X & X & X & clustering coefficient & X & urban \\ \hline DDRX [25] & X & X & X & complex network metrics & X & urban \\ \hline DESTINy [16] & V & V & X & closeer destination vehicle & X & urban \\ \hline Singh-VDITN [29] & X & V & X & controlled packet replicas & X & urban \\ \hline DDP4V [30] & V & V & X & sections zones & X & urban/highway \\ \hline FOXS-GSC & V & X & backtracking & populated sectors & V & urban/highway \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of related solutions.  
Figure 1: Hexagonal multihop communication.  
FOXS-GSC uses the fog computing paradigm, which provides resources closer to devices; its advantages include low latency, wide geographical distribution, real-time interaction, mobility, scalability, and extensibility. Cloudlets are used to achieve this; these are fog computational entities with processing and communication capabilities. In the studied scenario, the cloudlets are integrated into a network infrastructure consisting of Roadside Units (RSUs). Cloudlets are capable of direct communication with other cloudlets using a direct link or via the internet.  
FOXS-GSC permits the use of fewer cloudlets; thus, full map coverage is not necessary, as multi-hop communication is available between both vehicles and cloudlets. Using the proposed communication protocol, it is possible to place cloudlets only at those strategic points that serve the largest number of vehicles with the least number of hops between vehicles and cloudlets (e.g., highly dense road or crossroads). In addition, the distribution of cloudlets can be based on the computing load for improved load balancing.  
The proposed protocol aims to send a message using multi-hop communication between two specific nodes. In the FOXS-GSC solution, the vehicle requests a route to cloudlet and responds to the vehicle requesting the new route. As a premise, all vehicles know the cloudlet's geographic location on the map, meaning that a request message from the routing service is sent to the nearest cloudlet.