esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Lignocellulose Feedstock Biorefineries
### Low-Input Crops: Sources and Availability  
#### 2.3.1 C\({}_{4}\)  
Grasses: Miscanthus  
According to the European Common Agricultural Policy regulations, there are three so-called \"greening measures\", including maintenance of permanent pastures, crop diversification, and ecological focus areas (EFA) [69]. Thus, 5% of the land has to be specified as EFA by European farmers. Very recently, _Miscanthus_ (an analogue to other perennial crops, such as short rotation coppice) was listed as an eligible EFA crop. _Miscanthus_ genotypes combine different advantages, such as biodiversity and a significant greenhouse gas emission reduction [70; 71; 72; 73; 74]. In 2019, [PERSON] et al. reported a detailed study of the breeding progress of various lignocellulosic-rich biomasses, including switchgrass, _Miscanthus_, willow, and poplar crops [75; 76].  
[PERSON] et al. studied both the crop composition and detailed chemical structure of the corresponding _Miscanthus_-derived lignins. In detail, harvest yields of six different _Miscanthus_ genotypes have been studied and compared for the years 2015 and 2016 [26; 27]. Here, _M. nagara_ showed the highest yields compared to various _M. x giganteus_ samples, with _M. robustus_ and _M. sinensis_ having lowest values of all different genotypes.  
_Miscanthus_ crops belong to the group of perennial C\({}_{4}\) plants. Unlike C\({}_{3}\) plants, which produce D-3-phosphoglycerate, C\({}_{4}\) plants generate oxaloacetate, which is correlated with a significant effect on carbon sequestration [77; 78; 79]. Due to a rather low level of required water and fertilizer they are called low-input crops [80; 81; 82]. Figure 4 shows fields with different _Miscanthus_ genotypes, cultivated at the Campus Klein-Altendorf in Rheinbach, Germany. _Miscanthus_ crops are rather tall (up to four meters), yielding up to 25 t/ha.  
The advantages of perennial plants in general are rather low production costs, due to less tillage [83; 84; 85; 86]. [PERSON] et al. recently reported the cascade utilization of _Miscanthus_, including exploitation of the stalks and fibers, as well as the leaves [87; 88]. Other research groups reported the utilization of _Miscanthus_ crops for the production of bioethanol [89], hydrogen [90], and other chemicals, including polymers and composites [91; 92; 93; 94; 95]. Although there is a huge number of published studies, very few systematic studies are available about _Miscanthus_-derived lignins [96; 97; 98; 99; 100; 101; 102; 103]. [PERSON] determined the cell wall composition of eight different M. _sinensis_ samples [104]. Various authors reported the enzymatic depolymerization of _Miscanthus_-derived lignins, such as Baker, Ion, and Sonnenberg [105; 106; 107]. However, all of these studies exclusively focused on crop composition analysis (lignin ratio and distribution), but no details were reported regarding the detailed lignin structure.
# Leather Defect Detection Based on Improved YOLOv8 Model
## 6 Conclusions and Future Work  
A leather defect dataset was constructed comprising five common types: holes, scratches, dirt, rotten surfaces and folds. Since leather defect detection is often applied in edge devices within industrial environments, this study focused on both the detection accuracy and model size when improving the YOLOv8 algorithm. The introduction of the AFPN structure and EMA attention mechanism enhances the model's feature sensitivity and multi-scale detection capabilities, thereby improving detection performance. The application of VoV-GSCSP and a lightweight detection head balances the increased model parameter size and computational complexity. The improved model has reduced model parameters and GFLOPs. On one hand, this reduces computational load and memory access, increases hardware cache utilization, and enhances opportunities for parallel computing, effectively improving the inference speed on typical industrial hardware. On the other hand, it has a multifaceted positive impact on model deployment optimization, including reducing hardware costs, increasing deployment flexibility, accelerating deployment speed, and optimizing energy consumption, thus better meeting various needs and enhancing overall efficiency in practical application scenarios. However, the improved YOLOv8-AGE  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **P** & **R** & **mAP50** & **Params (M)** & **GFLOPs** & **FPS** \\ \hline YOLOv5n & 0.938 & 0.920 & 0.950 & 2.50 & 7.1 & 76 \\ YOLOv6n & 0.944 & 0.923 & 0.947 & 4.63 & 11.34 & 62 \\ YOLOv7-tiny & 0.879 & 0.919 & 0.937 & 6.01 & 13.0 & 73 \\ YOLOv8n & 0.957 & 0.920 & 0.954 & 3.01 & 8.1 & 66 \\ YOLOv8s & 0.953 & 0.939 & 0.963 & 11.13 & 28.4 & 47 \\ SSD & 0.881 & 0.587 & 0.79 & 3.98 & 5.61 & 81 \\ Our Model & 0.958 & 0.939 & 0.967 & 2.73 & 7.5 & 68 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Parameters of each model after training.  
model still has some deficiencies in detecting scratch and fold defects; the model's ability to distinguish between scratch, fold, and the natural texture of leather is not strong enough, with detection accuracy for scratch and fold defects being lower than that for hole and dirt defects. In the future, we will continue to research how to enhance the model's resistance to interference from the natural texture of leather.  
Conceptualization, Z.P. and C.Z.; methodology, W.W. and C.Z.; software, Z.P.; validation, Z.P.; formal analysis, Z.P.; investigation, Z.P.; resources, C.Z.; data curation, Z.P.; writing--original draft preparation, Z.P.; writing--review and editing, Z.P.; visualization, Z.P.; supervision, C.Z. and W.W.; project administration, C.Z. and W.W.; funding acquisition, C.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (grant number 61702384).  
Not applicable.  
The data used in this study can be obtained from the first author at [EMAIL_ADDRESS] with a reasonable request.  
The authors declare no conflicts of interest.
# Random Vibration Fatigue Analysis Using a Nonlinear Cumulative Damage Model
## 4 Fatigue Damage Accumulation Procedure
### Fatigue Damage Accumulation  
Here, to determine the vibration fatigue damage caused by the PSD loading applied to the mechanical component, Equation (14) is applied. That equation determines the fatigue damage accumulation as a nonlinear cumulative process by using the bending vibration stress (\(\sigma i_{vb}\)), the vibration cycles (\(n_{i}\)), and the vibration total cycles (\(N_{i}\)). The results are shown in Table 7.  
From the results given in Table 7, we noticed that the damage \(D=0.70\) is reached at block 22. Some industries act when their products reach this quantity of damage, and the damage \(D=1.0\) (fatigue failure) is reached at block 28 see Figure 8.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Frequency (Hz)**} & **Bending Stress** & **Total Cycle** \\  & \((\sigma_{vb})\) & \((N_{i})\) \\  & **Equation (24)** & **Equation (29)**  
The curve in Figure 8 represents the damage line relationship of the support submitted to random vibration loading.  
From Table 7, it is seen that the frequency that most affects the mechanical component and induces more damage is the frequency of 50 Hz. The reason is explained as follows:  
The natural frequency \(W_{n}\) of the component is determined by  
\[W_{n}=\sqrt{\frac{3\varepsilon I}{ml^{3}}} \tag{30}\]  
where \(E\) is the modules of elasticity in \(\frac{\text{lb}}{\text{in}^{2}}\), \(I\) is the moment of inertia in in\({}^{4}\) given by Equation (23), \(m\) is the load mass in \(\frac{\text{lb}-\text{s}^{2}}{\text{in}}\) determined by Equation (19) and \(l\) is the component's length in inches. With these data, \(W_{n}\) is  
\[W_{n}=\sqrt{\frac{3\big{(}29007548\frac{\text{lb}}{\text{in}^{2}}\big{)}\big{(} 0.00114\text{ in}^{4}\big{)}}{\big{(}0.11\frac{\text{lb}-\text{s}^{2}}{\text{ in}}\big{)}(2\text{ in})^{3}}}=311.87\ \frac{\text{rad}}{\text{s}}\]  
Consequently, from Equation (4), the natural frequency \(f_{n}\) is  
\[f_{n}=\frac{311.87}{2\pi}\approx 50\text{ Hz}\]  
Thus, since the material's natural frequency is \(f_{n}=50\) Hz, then, because the frequencies of the applied PSD range from 10 to 55 Hz, at the moment, they coincide with the resonant frequency presented. This can be seen in Figure 8 where, due to the resonant frequency, the maximum acceleration that induces damage is reached at 50 Hz.  
As a general conclusion, in the proposed nonlinear random vibration method, the damage accumulation model can be applied by using the vibration response acceleration PSD to obtain the bending stress induced and, therefore, determine the fatigue damage accumulation. However, it is highlighted that the efficiency of the proposed method depends on the accuracy at which the response acceleration PSD is obtained. In this case, we used vibration shaker equipment and its accelerometer, but for the design and prototype phase, it can be performed by using a software simulation. In general, the proposed method can be applied to any mechanical component analysis where the response acceleration \(A_{res}\) is known.
###### Abstract  
In this paper, a non-equilibrium thermodynamic approach to cancer is developed. The thermo-electric effects in the cell membrane are analysed, in relation to the Seebeck-like and the Peltier-like effects. The role of the cell membrane electric potential is studied from a thermodynamic viewpoint, pointing out the relation between the proliferation rate and the membrane potential, the existence of a thermodynamic threshold for the mitotic activity, the relation between metastases and membrane potential and the comprehension of the role of ions fluxes in the cell behaviour.  
membrane electric potential; heat flux; ions fluxes; cancer; non-equilibrium thermodynamics 2020 10 7166 2020 10 7166 202  
double layer [14; 15; 16; 17; 18; 19]. The analytical and numerical models for the analysis and description of the structure and energy of the electric double layer have represented a fundamental development in the comprehension of their properties [14; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31] also in relation to their application in medicine and biophysics. Concepts like charge neutrality, Debye length and double layer have been proven to be very useful to explain the electrical properties of a cellular membrane [32].  
In the last years, the role of the membrane electric potential has been shown also in the control of the fundamental cell functions, such as proliferation, migration and differentiation [33; 34; 35]. In this context, many experimental evidences pointed out significant depolarisation during malignant transformation of normal cells [36; 37], due to and increase of Na\({}^{+}\) intracellular concentration in tumour, in relation to the normal cells. The intracellular concentration of K\({}^{+}\) seems to maintain approximately the same values [38].  
As a consequence of the previous results, some questions arose:  
* The relation between the proliferation rate and the membrane potential.
* The possible existence of a threshold for the mitotic activity.
* The possible relation between metastases and membrane potential.
* The comprehension of the role of ions fluxes in the cell behaviour.  
The aim of this paper was to develop a non-equilibrium thermodynamic analysis of the membrane electric potential, recently published in Ref. [39], in order to suggest a new approach to respond to these questions, by considering the link between ions and heat fluxes.
## 6 Simulation Experiment
### Simulation Experiment on Networking and Systematic Performance Calculation of UWSNs
This is because the network coverage is only associated with the detection radius of the nodes, but not with the number and communication radius of the nodes when it reaches 1-coverage. Figure 10b shows that the connectivity of network increases as the communication radius increases. This is because when the communication radius of a node increases, that of the nodes connected with it will increase accordingly. Meanwhile, the connectivity diminishes as the detection radius increases due to the influence of the number of nodes. Under the condition of 1-coverage, the bigger the detection radius of the nodes is, the fewer the number of nodes needed. If the communication radius remains unchanged, reducing the number of nodes can lead to a decrease of the number of neighboring nodes, so the connectivity of network will be weakened. Figure 10c shows that the durability of the network diminishes as the communication radius and detection radius of nodes increase. This is because the bigger the communication radius and detection radius of nodes are, the greater the node energy consumption rate will become, which leads to a reduction of the network's lifetime. Figure 10d shows that the rapid-reactivity of a network increases as the communication radius increases and it diminishes as the detection radius of the nodes increases. The phenomenon is related to connectivity. When the communication radius of a node increases, the connectivity of the network also increases. This can improve rapid-reactivity. When the number of nodes diminishes, the connectivity of the network diminishes too, and the number of nodes that can be used as redundant nodes also decreases, which leads to a weakening of rapid-reactivity. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \(r_{b}\) = 3.46 & \(r_{b}\) = 4 & \(r_{b}\) = 5.66 & \(r_{b}\) = 6.93 \\ \hline NBCL & 1011.46 h \(\approx\) 42 days & 872.74 h \(\approx\) 36 days & 755.66 h \(\approx\) 31 days & 688.73 h \(\approx\) 29 days \\ CBCL & 1585.44 h \(\approx\) 66 days & 1129.4 h \(\approx\) 51 days & 1076.16 h \(\approx\) 45 days & 946.85 h \(\approx\) 39.5 days \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Durability comparison between NBCL and CBCL of UWSN. In conclusion, our approach proves that CBCL and CDNM are better than NBCL, and at the same time, compared to some previous works ([19] (2011), [20] (2013), [21] (2014), [22] (2015), [26] (2016) and [27] (2017)), the SQPCM can be employed to evaluate the systematic performance of a network on coverage, connectivity durability and rapid-reactivity, as shown in Table 4.
## 2 Proposed Method
### Multi-Scale Feature Fusion Module (MFFM)  
Successive pyramid super-resolution networks [41; 42] have already proven the importance of multi-scale feature information in the image reconstructing process; however, the pyramid methods mentioned above progressively reconstruct high-resolution images from neighboring layers, ignoring the others. Aiming to further utilize the multi-scale feature information and to provide the network with better feature representation capabilities, the Multi-scale Feature Fusion Module (MFFM), the details of which shown on the bottom side of Figure 1, is introduced here. First, the multi-scale feature map sizes are unified to the same size as the target feature map layer. Considering that the magnification scale between different layers is always \(2^{n}(n\in\mathbb{Z})\), a \(3\times 3\) convolution layer with a stride of 2 and a \(6\times 6\) transposed convolution layer with a stride and padding of 2 are introduced for /2 downscaling and \(\times 2\) upscaling processes, respectively. Moreover, for a larger magnification scale such as /4 or \(\times 4\), double \(3\times 3\) convolution or \(6\times 6\) transposed convolution layers, etc., would be applied. After unifying all required feature map sizes, the multi-scale feature maps are concatenated to pass through a full connection layer, followed by the channel attention network, the details of which can be viewed in Figure 2(a). Finally, the well-handled multi-scale feature information is added to the target feature map layer in the encoding stage.
## 3 Observer and Control Law Design  
### Observer Design  
In order to apply a generalized augmented transform to the system. First, we must define the following variables and augmented matrices.  
\[\bar{x}(t)=\left[\begin{array}{cc}x(t)\\ 0_{p}\end{array}\right]\bar{A}_{i}=\left[\begin{array}{cc}A_{i}&0_{  
where \(\bar{H}_{i1}\in\mathbb{R}^{(n+p)\times n}\), \(\bar{H}_{i2}\in\mathbb{R}^{(n+p)\times p}\) we can further derived as \(\bar{H}_{i1}\bar{M}_{i}+\bar{H}_{i2}\mathbb{C}_{i}^{(1)}=I_{n+p}\), thus we have:  
\[\bar{H}_{i1}\bar{M}_{i}=I_{n+p}-\bar{H}_{i2}\mathbb{C}_{i}^{(1)}. \tag{18}\]  
By decomposing \(\bar{H}_{i2}=\left[\begin{array}{cc}\bar{H}_{i21}\\ \bar{H}_{i22}\end{array}\right]\), Equation (17) can be rewritten as:  
\[\bar{H}_{i1}\bar{M}_{i}=\left[\begin{array}{cc}I_{n}&-\bar{H}_{i21}\\ 0&I_{p}-\bar{H}_{i22}\end{array}\right]. \tag{19}\]  
Then we derive:  
\[E_{i}^{(2)}=\bar{H}_{i}^{-1}E_{i}^{(1)}=\bar{H}_{i}=[\bar{H}_{i1} \quad\bar{H}_{i2}]\left[\begin{array}{cc}\bar{M}_{i}&0_{p\times(n+p)}\end{array} \right]^{\rm T}=\left[\begin{array}{cc}I_{n}&-\bar{H}_{i21}\\ 0&I_{p}-\bar{H}_{i22}\end{array}\right]. \tag{20}\]  
Similarly, use the decomposing that \(\bar{A}_{i}^{(2)}=\bar{H}_{i}\bar{A}_{i}^{(1)}=\left[\begin{array}{cc}\bar{ A}_{i11}^{(2)}&\bar{A}_{i12}^{(2)}\\ \bar{A}_{i21}^{(2)}&\bar{A}_{i22}^{(2)}\end{array}\right]\), \(\bar{B}_{i}^{(2)}=\bar{H}_{i}\bar{B}_{i}=\left[\begin{array}{cc}\bar{B}_{i1 }^{(2)}\\ \bar{B}_{i2}^{(2)}\end{array}\right]\) obtain the following equation:  
\[\hat{x}_{1}^{(1)}(t)-\bar{H}_{i21}\hat{x}_{2}^{(1)}(t)=\bar{A}_{i11}^{(2)} \hat{x}_{1}^{(1)}(t)+\bar{A}_{i12}^{(2)}\hat{x}_{2}^{(1)}(t)+\bar{B}_{i1}^{(2) }u(t). \tag{21}\]  
The reduced linear observer is designed as follows, based on the above:  
\[\left\{\begin{aligned} \hat{x}_{1}^{(1)}(t)&=z(t)+\bar{H}_{i21}y (t),\\ \dot{\hat{z}}(t)&=\bar{A}_{i11}^{(2)}z(t)+\bar{B}_{i1}^{(2) }u(t)+\Big{(}\bar{A}_{i11}^{(2)}\bar{H}_{i21}+\bar{A}_{i12}^{(2)}\Big{)}y(t), \\ \hat{x}(t)&=\bar{T}_{i}^{-1}\Bigg{[}\begin{array}{c}\hat{ x}_{1}^{(1)}(t)\\ y(t)\end{array}\Bigg{]},\\ \hat{x}(t)&=\bar{R}_{i1}\hat{x}_{1}^{(1)}(t)+\bar{R}_{i2}y(t).\end{aligned}\right. \tag{22}\]  
In the above equation, \(\hat{x}(t)\in\mathbb{R}^{(n+p)}\) and \(\hat{x}_{1}^{(1)}(t)\in\mathbb{R}^{n}\) are the estimations of \(\bar{x}(t)\) and \(\bar{x}^{(1)}(t)\). z(t) is the intermediate variable.  
**Lemma 1**.: _By the process above, we have the following equations:_  
_(1)\(A_{i}\bar{R}_{i1}=\bar{R}_{i1}\bar{A}_{i11}^{(2)}+\bar{R}_{i2}\bar{A}_{i21}^{(2)},\) (2)\(\bar{A}_{i21}^{(2)}=-\bar{R}_{i3},\) (3)\(\bar{R}_{i1}\bar{A}_{i12}^{(2)}-\bar{R}_{i2}\bar{R}_{i4}-A_{i}\bar{R}_{i2}=0,\) (4)\(\bar{R}_{i2}+\bar{R}_{i1}\bar{H}_{i21}=0,\) (5)\(\bar{R}_{i1}\bar{B}_{i1}^{(2)}=B_{i},\) (6)\(\bar{R}_{i3}\hat{x}_{1}^{(1)}(t)+\bar{R}_{i4}y(t)=0.\)_  
The proof of Lemma 1 is shown in Appendix A.
# Supervised Blockchain Anonymous Transaction Model Based on Certificateless Signcryption
## 7 Experimental Analysis  
### Experimental Simulation  
The feasibility verification of the CLSC_SAT model was conducted on a server running Ubuntu 20.04, equipped with an Intel i5-10210U 1.60 GHz CPU and 8 GB RAM. The model's nine algorithms (Setup, CreateAddr, CreateRkey, SetPartialKey, SetPub/PrivKey, CreateTx, VerifyTx, RenewalTx, TraceTx) were implemented using the PBC library and C programming language. Each algorithm was executed 1000 times, and the average execution times are presented in Table 3.  
To further validate the feasibility of the model, the total transmission time, signature time, and verification time were tested for transaction volumes of 20, 40, 60, 80 and 100 in the account model, as shown in Figure 3. The experimental results indicate that as the number of transactions increases, the total transmission time grows linearly, aligning with the expected data transmission performance of the CLSC_SAT model under large-scale transactions. Additionally, the signature time is higher than the verification time, which is attributed to the greater complexity of the signature algorithm.  
This paper also analyzes the performance of CLSC_SAT model in terms of computing overhead and communication overhead.
# A Review of Neurotransmitters Sensing Methods for Neuro-Engineering Research
## 2 Neurotransmitter Detection Methods
### Electrochemistry
#### 2.3.2.  
Amperometry applies a fixed potential to a working electrode and measures the resulting oxido-reduction reaction current, which is proportional to the analyte concentration [198]. Like voltammetry, the current through the working electrode is measured with respect to a reference electrode, but, unlike FSCV, the temporal resolution of amperometry is not limited to the duration of the cycle. Thus, amperometry is more suitable to analyze the kinetics of neurotransmitter release.  
The catecholamines and indolamines in a cell are efficiently detected with amperometry. A carbon-fiber microelectrode maintains a constant voltage above the oxidizing potential of the neurotransmitter (Figure 5), which is 650 mV and 800 mV for serotonin and catecholamine, respectively. The electrons released by the neurotransmitters are then transferred to the electrode [179; 199]. The temporal resolution is limited by two factors: the diffusion of the neurotransmitter to the electrode and the kinetics of the electron transfer [180]. The current measured by the electrode is dependent on the concentration of neurotransmitter released by the cell [200], but the released neurotransmitter cannot be identified [179]. In this case, a complementary method such as high performance liquid chromatography is needed [200].  
Figure 4: Dopamine (DA) and serotonin (5-HT) voltammograms. The voltammogram of dopamine 1 \(\upmu\)M shows one oxidation and one reduction peak. The voltammogram of serotonin 1 \(\upmu\)M shows one oxidation and two reduction peaks [177].
## 5 Performance Evaluation
### Comparison of BSMH and Other Benchmarks  
In the following sections, we will compare the performance of our protocol with those of three existing protocols, namely GPSR [23] and LVGR [34] and EDGR [45]. GPSR uses greedy forwarding as the default mode. When a packet encounters the local minimum phenomenon, it switches from greedy to perimeter mode whereby the packet is forwarded along the hole boundary. In LVGR, the packet is forwarded along a hole-bypassing Euclidean path from the source to the destination. This path is determined by using a visibility graph whose vertices are the vertices of the convex hulls of the holes. In EDGR, the source nodes randomly choose an anchor lists from two candidates. The packet then is forwarded towards the chosen anchor lists, gradually. Moreover, the next hop is heuristically chosen based on not only location information but also the residual energy.  
The stretch factor (i.e., \(\epsilon\)) of BSMH is set to 0.3, 0.5 and 1.2, the number of vertices of the core polygons is set to 8.