esa-sceva/satcom-chunk-collection huggingface dataset:
# QoS-Aware Scheduling Algorithm Enabling Video Services in LTE Networks
## 3 LTE Schedulers
### Exponential Rule (EXPRILE) Scheduler  
The algorithm is designed to increase user channel allocations to the maximum possible in varied channel conditions by analysing the network state in general. The EXP rule stipulates that a single user/queue is to receive service in every scheduling instance. Channel information is utilised that is queued without previous information on traffic access or statistics [22]. Order of priority in the EXP rule schedule therefore may be expressed in the following:  
\[M_{i,k}^{EXP,blue}=b_{i}\exp\left(\frac{a_{i}D_{HOL,i}}{c+\sqrt{\left(\frac{ 1}{N_{rt}}\right)\sum_{j}D_{HOL,j}}}\right)\cdot\Gamma_{k}^{i}, \tag{5}\]  
where \(a_{i},b_{i},\ \text{and}\ c\) denote optimal parameters as required by the model. \(N_{rt}\) denotes the number of downlink flows at RT, \(D_{HOL,i}\) denotes the peak of line latency for user \(i\), and \(\Gamma_{k}^{i}\) represents the efficiency of spectrum for the \(i\)-th user on the \(k\) sub-channel.  
\(a_{i}\) and \(b_{i}\) are expressed as follows when \(c\) equals 1:  
\[a_{i}\ \ \in\left[\frac{5}{0.99\tau_{i}},\frac{10}{0.99\tau_{i}}\right], \tag{6}\]  
\[b_{i}=\frac{1}{E\left[\Gamma^{i}\right]}, \tag{7}\]
## 3 Results
### Reflectance Spectral Characteristic
#### 3.1.3 Short-Wavelength Infrared (SWIR) HSI  
Figure 6c illustrates the SWIR image (1000-1700 nm) used for the classification of multiple flours along with its mean plot. In the 1500-1700 nm range, strong absorptions were observed, along with peaks at approximately 1520 and 1640 nm, particularly for the rice flours. Starch is a major carbohydrate reserve and a homopolysaccharide made up of glucose, and mainly consists of amylose (20-30%) and amylopectin (70-80%) [26]. The spectral data between 1400 and 1600 nm, particularly at 1414 nm, is related to the first overtone of water and the following hydroxyl groups: O-H stretching vibrations; O-H and H-O-H combinations of the starch; C-H stretching bond (1338 nm); and C-H stretching, the second overtone of which is related to anthocyanin content (1292 nm) [23; 27]. The content of amylose in starch is within the range of 1700 and 1800 nm, and the absorption peaks provide chemical information about the third overtone of O-H stretching vibration (964 nm), first overtone of O-H stretching band (1414 nm), and O-H stretching and deformation with respect to water content [28].
# Leveraging the Internet of Things and Blockchain Technology in Supply Chain Management
## 3 The Internet of Things and Its Application in Supply Chains
Active RFID tags or devices have a power source and send out an active beaconing signal to a network. When RFID devices are connected to an IoT network, the WSN is described as a cluster of nodes (i.e., connection points for data transmission within a network) that cooperatively sense and actuate physical or environmental conditions (e.g., temperature, humidity, light intensity, velocity), allowing interaction between persons or computers and the surrounding environment [46]. WSNs benefit supply chains with more effective real-time monitoring of logistics activities through smart sensing capabilities. This capability leads to a better allocation of critical resources due to the higher level of data granularity in decision making and resource allocation. The middleware serves as a hub for collecting and storing the data generated by the connected devices and structures the data in a usable format for IoT applications [47]. This layer enhances the availability of the data, offers more granular insights and can make data more accessible to operational staff and management for analysis and decision making. Cloud computing facilitates the ubiquitous and on-demand access to a shared pool of configurable computing resources such as computers, networks, servers, storage, applications, services, and software applications [43]. Cloud computing offers some benefits in orchestrating parts of the supply chain data and information flows between multiple fragmented entities, facilitating cooperation among all value chain exchange partners [48]. Typically, an IoT application resides in the cloud and is accessed either by mobile application-based (apps) running on smartphones, tablets or desktop computers [49]. These applications advance machine-to-machine and human-to-machine interactions, resulting in a seamless processing of data and timely transfer of information.
## 3 System Model and Problem Formulation
### Problem Formulation  
Under the above settings, our goal is maximizing the sum of the residual energy of \(N\) time blocks and \(K\) devices. The residual energy of user \(i\) in the \(t\)-th time block is obtained by the following function:  
\[\begin{array}{ll}H_{l}(t)(q_{i}(t),T_{i,h}(t),\beta_{i,o}(t))&=(\eta_{i}T_{i,h }(t)P_{d}(t)g_{i,d}(t)-\frac{\zeta_{i}C_{i}^{3}(Z_{i}(t)-q_{i}(t))^{3}}{T^{2}} \\ &-(p_{i,o}(t)+p_{c,i})(\frac{q_{i}(t)}{\beta_{i,o}(t)\log_{2}\left(1+\frac{p_{ i,o}(t)h_{i,o}(t)}{\sigma^{2}}\right)}))s_{i}(t)\end{array} \tag{17}\]  
Therefore, the residual energy maximization problem can be expressed as:  
\[\begin{array}{ll}\text{P1}:\max\limits_{q,T,\beta}\sum\limits_{t=1}^{N}\sum \limits_{i=1}^{K}H_{i}(t)(q_{i}(t),T_{i,h}(t),\beta_{i,o}(t))\\ s.t.\text{C1}:Z_{i}(t)-\frac{T_{i,i}^{max}}{\zeta_{i}}\leq q_{i}(t)\leq Z_{i}(t), \forall i\in K,\forall t\in\mathcal{T}\\ \text{C2}:\frac{\zeta_{i}C_{i}^{3}(Z_{i}(t)-q_{i}(t))^{3}}{T^{2}}+(p_{i,o}(t) +p_{c,i})(\frac{q_{i}(t)}{\beta_{i,o}(t)\log_{2}\left(1+\frac{p_{i,o}(t)h_{i,o }(t)}{\sigma^{2}}\right)})\leq\eta_{i}T_{i,h}(t)P_{d}(t)g_{i,d}(t)\\ \text{,}\forall i\in K,\forall t\in\mathcal{T}\\ \text{C3}:T_{i,h}(t)+\frac{q_{i}(t)}{\beta_{i,o}(t)\log_{2}\left(1+\frac{p_{i,o }(t)h_{i,o}(t)}{\sigma^{2}}\right)}\leq T,\forall i\in K,\forall t\in\mathcal{ T}\\ \text{C4}:\frac{K}{i=1}\ q_{i}(t)C_{i}\leq Q_{max},\forall t\in\mathcal{T}\\ \text{C5}:\sum\limits_{i=1}^{K}\beta_{i,o}(t)\leq B,\forall t\in\mathcal{T}\\ \text{C6}:0\leq T_{i,h}(t)\leq T,\forall i\in K,\forall t\in\mathcal{T}\\ \text{C7}:0\leq q_{i}(t)\leq Z_{i}(t),\forall i\in K,\forall t\in\mathcal{T}\\ \text{where }q(t)\triangleq[q_{1}(t),q_{2}(t),\ldots,q_{2}(t)]\text{, }T(t) \triangleq[T_{1,h}(t),T_{2,h}(t),\ldots,T_{K,h}(t)]\text{ and }\beta(t) \triangleq[\beta_{1,o}(t),\beta_{2,o}(t),\ldots,\beta_{K,o}(t)]\text{ denote the number of offloaded bits, energy harvesting time and offloading bandwidth, respectively.}\)
# Intelligent Detection and Segmentation of Space-Borne SAR Radio Frequency Interference
## 4 Methodology
### SISNet Structure
To solve this problem, it is preferable to carry out channel weighting through channel attention, increase the output weight of channels with salient features, and achieve more accurate prediction masks after fusion. The proposed channel weighting module is shown in Figure 13.  
The proposed channel weighting module uses 3D permutations to preserve information in three dimensions. For the input feature map, dimension transformation is conducted first, and the dimensionally transformed feature map is then input to a multi-layer perceptron. The two-layer MLP embedded in the module amplifies the cross-dimensional channel-spatial co-dependence. Finally, we convert the tensor to its original dimension and perform sigmoid activation to output. Since U2 Net is a nested structure of UNet, the network depth is relatively large, which is prone to over-fitting. The deeper the network is, the less smooth the solution space is, and the more necessary it becomes to increase the number of layer-skip connections. Adding shortcut connections can make the parameter space smoother and makes convergence easier. Since there already exist shortcut connections between the input and output of each RSU module of U2 Net, we add some shortcuts to each convolutional layer to achieve dense shortcut connections so that the parameters of the shallow layer can be easily passed to the deep layer of the network smoothly; the modified convolutional layer is shown in Figure 14 below. Combining all the above improvement measures, the SISNet is formed. The overall network is shown in Figure 15 below. The SISNet is trained on an NVIDIA GeForce RTX 3060 GPU with 12 GB memory using PyTorch [53]. SISNet utilizes the Adam optimizer. Batch size is 2 considering the  
\[\text{MaxPool}_{k,s}=\text{Subsample}_{s}\circ\text{Max}_{k}\,, \tag{7}\]0 Modified convolutional layer with shortcut layer between input and output. \[\text{MaxPool}_{k,s}=\text{Subsample}_{s}\circ\text{Max}_{k}\,, \tag{7}\]1 The framework of the channel weighting module. limited computation sources, the momentum is 0.9 with a weight decay of \(1\times 10^{-4}\), and the starting learning rate is 0.05 and uses a polynomial descent strategy.
## 6 Sensitivity Analysis of Kinematic Reliability
### Kinematic Reliability of the Mechanism in the Combined Direction  
Assume that \(Y\) in Equation (1) is the output position error of the improved Delta parallel mechanism in the combined direction, and \(U\) is the maximum allowable limit of the error, so the limit state function is  
\[g(x)=U-Y=U-\left(C_{0}+\sum_{i=1}^{N_{R}}C_{i}X_{i}+\sum_{i=1}^{N_{R}}\sum_{j=1 }^{N_{R}}C_{ij}X_{i}X_{j}\right) \tag{26}\]  
The limit state function represents two states of the output position error of the mechanism in the combined direction:  
\[\left\{\begin{array}{ll}g(x)<0\;\;\text{failure state};\\ g(x)>0\;\;\text{safe state};\end{array}\right.\]  
In Equation (26), the random parameters are independent of each other, and the mean matrix and variance matrix are \(\mu=\)\((\mu_{1},\;\mu_{2},\cdots,\;\mu_{NR})\) and \(D=(D_{1},\;D_{2},\cdots,\;D_{NR})\), respectively,  
so:  
\[E\left(X_{i}^{2}\right)=E^{2}(X_{i})+D(X_{i})=\mu_{i}^{2}+D_{i} \tag{27}\]  
\[E(X_{i}X_{j})=E(X_{i})E(X_{j})=\mu_{i}\mu_{j} \tag{28}\]  
\[D(X_{i}^{2})=4\mu_{i}^{2}D_{i}+2D_{i} \tag{29}\]  
\[D(X_{i}X_{j})=\mu_{i}^{2}D_{j}+\mu_{j}^{2}D_{i}+D_{i}D_{j} \tag{30}\]  
Therefore,  
\[E(g(x))=\mu_{g}(\mu_{1},\mu_{2},\cdots,\mu_{NR},D_{1},D_{2},\cdots,D_{NR}) \tag{31}\]  
\[D(g(x))=D_{g}(\mu_{1},\mu_{2},\cdots,\mu_{NR},D_{1},D_{2},\cdots,D_{NR}) \tag{32}\]  
The reliability index is defined as  
\[\beta=\frac{\mu_{g}}{\sqrt{D_{g}}} \tag{33}\]  
If the \(g(x)\) obeys the normal distribution, the reliability can be expressed as  
\[R=\Phi(\beta) \tag{34}\]  
Note that the Monte Carlo method can be used to calculate reliability if \(g(x)\) has an arbitrary distribution [37].
# Instantaneous Beam Prediction Scheme against Link Blockage in mmWave Communications
## 2 Related Work
### Beam Recovery Schemes Using Out-of-Band Information  
Another set of recovery schemes leverage out-of-band links to recover defected mmWave links. Foremost, the work in [17] extracts spatial channel characteristics at the sub-6 GHz band and then use them to reduce mmWave beam training overhead, here mapping functions are predicted directly from the sub-6 GHz channel. Specifically, the model leverages transfer learning to reduce the learning time overhead. However, the estimation of mapping functions is often complicated and requires a large neural network to achieve accuracy. In addition, the work relies on sub-6 GHz bands to realize beam access at the mmWave. Namely, dual-band (microwave and mmWave transceivers) systems are  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Algorithm** & **Advantages** & **Limitations** \\ \hline
[25] & Ergodic capacity in beam blockage in urban mmWave networks. & Consideration for AP, blockage density, and degree of multi-connectivity. & Blockage due to pedestrians only (low mobility), dual connectivity adds redundancy and power demands. \\ \hline
[26] & Optimization method for mobility and blockage problems in mmWave and THz downlink transmission. & Considerations for mobility, high sum rate, mitigated Doppler and delay dispersion effects. & Limited to downlink transmission, THz bands features different propagation characteristics and transceivers. \\ \hline \hline \end{tabular}
\end{table}
Table 2: \(Cont\).  
needed at the BS and MS; here the power consumption analysis and access time need to be further investigated. In [18], a deep learning-based scheduler at the BS predicts future blockage statuses and optimal beamforming vectors of the MS, based on sub-6 GHz channel knowledge, i.e., out-of-band information. The designed deep neural network (DNN) extracts temporal correlations from low-frequency channel knowledge to be leveraged for beam prediction. The goal here is to improve the prediction accuracy of beamformers at a reduced complexity, user mobility, and high achievable rates.
## 2 Materials and Methods
### Quasi-Static Compression Experiments
Based on the ISO 13314-2011 standard [39], when the total compressive strain is 50%, the compression experiment is carried out at a strain rate of 2 mm/min, and the corresponding compressive force-displacement data are recorded. The tests were stopped when the effective crush stroke was reached. At the same time, three specimens were then repeatedly tested to ensure the accuracy of results, and a high-resolution video camera recorded the whole experiment process to observe the deformation behavior of the sample in the experiment. Subsequently, in order to accurately describe the mechanical properties and deformation behavior of the porous TPMS structure in the compression process, a joint simulation analysis was carried out using Hypermesh(r) v2022 and Abaqus(r) v2022 software. Firstly, the porous structure was divided into a tetrahedral (C3D10M) mesh model in the Hypermesh(r) v2022 software, with an average mesh cell size of 0.4 mm. Then, the bottom plate was fixed during the simulated compression, and the top plate was imposed with a smooth analytical step value curve. Friction was modeled using the Coulomb friction model with a friction coefficient of 0.3 to define the friction characteristics between the contacts (Figure 6). The AlSi10 Mg (Farsoon Technologies Co., Ltd., Hunan, China) was used as the material of the porous TPMS structure simulation models. Figure 5: Test setup for determining the mechanical and energy-absorbing properties. Figure 6: The quasi-static compression finite element analysis model of the porous structure.
# Firmware Updates over the Air via LoRa: Unicast and Broadcast Combination for Boosting Update Speed
## 5 Simulation Tests and Results
### Effect of the Number of Broadcast Rounds  
In Section 4, when analyzing the two broadcast methods, we discussed the possibility of having more than one initial broadcast round (\(B>1\)) and concluded that, in some cases, it could save update time, in particular if the number of nodes is high, and also if the loss probability is significant. In this subsection, we present the results of a set of simulations run in a big scenario (2 km) with different numbers of initial broadcast rounds (\(B\) ranging from 1 to 15) and \(N=10\) or 20 nodes.  
As far as the _broadcast + unicast_ method is concerned (Figure 19a), the case \(B=0\) is equivalent to the _only unicast_ method, as there is no initial broadcast round. The results in this case show that be best option is \(B=1\): making more rounds at the beginning does not improve the update time, which fits with the analytical results reported in Figure 4a.  
In the case of the _only broadcast_ method (Figure 19b), the behavior with \(B=0\) is exactly the same as that with \(B=1\), as explained in Section 4. It can be observed that this method is faster than the _broadcast + unicast_ method. The optimal number of initial broadcast rounds is 1 in this case. When compared to Figure 5, it can be observed that the assumptions made in the analysis result in a slight underestimation of the update time: on the one hand, some  
Figure 19: Effect of the number of initial broadcast rounds (\(B\)) on the update time, with 10 and 20 nodes, and a maximum radius of 2 km: (**a**) _Broadcast + unicast_ method and (**b**) _Only broadcast_ method. Note that the \(Y\) axis is in logarithmic scale.  
simplifications were made; on the other hand, each simulated node has a different distance to the gateway, so the loss probability is not the same for every node.
# Plate Size Effects in Gravelly Soil Based on In Situ Plate Load Tests and Finite Element Analysis
## 2 Geotechnical Profile and Soil Parameter
### Soil Parameter  
To identify the load-deformation behavior of the geotechnical mass at the Sichuan-side bridge location of the Kahlauo Jinsha River Bridge, a horizontal tunnel was excavated within the slope, and physical and mechanical tests were conducted on the gravelly soil inside. The horizontal tunnel was approximately 25 m long, 1.8 to 2.0 m wide, and 2 m high, as shown in Figure 2, which illustrates the tunnel and the internal rock-soil mass. In accordance with Chinese code DL/T 5356-2006, large-scale direct shear tests and horizontal plate load tests were performed on the undisturbed soil within the tunnel to determine the shear strength and deformation parameters of the geotechnical mass. The elastic modulus of the gravelly soil was obtained from a cyclic plate load test with five cycles. The large-scale direct shear test specimens measured 900 mm in side length and 500 mm in height, while the configuration of the plate load test is shown in Figure 3. The physical and mechanical properties of the gravelly soil are provided in Table 1, where \(\gamma\) denotes the unit weight, \(w\) represents the moisture content, \(E_{s}\) refers to the elastic modulus, \(\varphi\) indicates the soil's friction angle, and \(c\) represents the soil's cohesion. The soil was analyzed according to Chinese code DL/T 5356-2006. Particles greater than 20 mm were measured in the field using a tape measure, and those smaller than 20 mm were tested for particle size distribution in the laboratory. The results indicated that the soil was well-graded gravel.  
Figure 2: Horizontal tunnel and the internal rock–soil mass.
# Identification of Volatile Sulfur Compounds Produced by _Schizophyllum commune_
## 3 Results  
### Volatile Sulfur Compounds Are the Major VOCs of S. commune  
Volatiles from _S. commune_ strains cultured in slants with air-permeable silicone rubber plugs at 35 \({}^{\circ}\)C smelled like sulfur compounds. By GC/MS analysis using 49 _S. commune_ strains cultured in securely sealed vials for 29 days, we detected five volatile sulfur compounds (VSCs) as major VOCs (Figure 1 and Table S2). Methyl ethyl disulfide (MEDS) was detected from every strain. DMDS was detected in all strains except for IFM 54722 strain. Diethyl disulfide (DEDS) and dimethyl trisulfide (DMTriS) were detected, respectively, from 34 and 40 strains. Dimethyl tetrasulfide (DMTetraS) was detected from 32 strains. DMTetraS-producing strains, except for IFM 58318, also produced DMDS and DMTriS. Strains not producing DEDS, except for IFM 58052 and IFM 55614, were confirmed as producing four other polysulfides. We analyzed the correlation of ion counts among detected sulfides. As shown in Figure 2, the ion counts of DMDS, DMTriS, and DMTetraS were correlated significantly with each other. The ion count of DEDS showed a weaker correlation with DMDS, DMTriS, or DMTetraS. We also examined GC/MS analysis for _Aspergillus_ spp. presented in Table S1. The VSCs described above were not detected by GC/MS analysis (example data from the _A. fumigatus_ IFM 51748 strain are shown in Figure 3). These data indicate that _S. commune_, but not _Aspergillus_ spp., produces VSCs as major VOCs.  
Figure 1: Major polysulfides produced by _S. commune_ IFM 59347, a representative strain in which five polysulfides were detected: dimethyl disulfide (DMDS), methyl ethyl disulfide (MEDS), diethyl disulfide (DEDS), dimethyl trisulfide (DMTriS), and dimethyl tetrasulfide (DMTetraS). They are shown in the total ion chromatogram (**a**) and each extracted-ion chromatogram (**b**).  
Figure 2: Plots of peak areas of each polysulfide and their respective coefficients of determination. The \(x\)-axis and \(y\)-axis in each plot are shown respectively in the top row and the right column.
# Energy-Efficient On-Off Power Control of Femto-Cell Base Stations for Cooperative Cellular Networks
## 1 Introduction
### Our Contributions  
We herein propose a novel on-off power control scheme considering the cooperative transmission, i.e., CoMP. The main contributions are as follows:  
1. Our proposed on-off power control scheme efficiently minimizes the power consumption of MCS. Unlike previous approaches, we take proper account of cooperative transmission, i.e., CoMP, of FBSs such that the data rate of MCS is not degraded excessively. To this end, we propose to use a new metric, which is level of contribution (LOC), such that the optimal set of turned off FBSs can be found with low computational complexity. Although the clustering of FBSs considering CoMP has been studied in previous literature [5], the switching-off scheme of FBSs considering CoMP has not been taken into account previously.
2. We evaluate the performance of our proposed scheme through simulations based on realistic system parameters. The results verify that the power consumption of MCS can be reduced without violating the constraint on data rate degradation. It is also shown that our proposed scheme achieves near-optimal performance while the number of computations can be greatly reduced compared with the optimal scheme.  
The remainder of the paper is organized as follows. In Section 2, we show the limitation of the conventional on-off power control scheme that relies on the traffic load using toy example. We describe our system model, formulate the optimization problem to find the optimal set of shutdown FBSs, and explain the proposed scheme in Section 3. Our simulation results are shown in Section 4, and in Section 5, we provide our conclusions.
# Hazard Prediction of Water Inrush in Water-Rich Tunnels Based on Random Forest Algorithm
## 2 Construction of Evaluating Indicator System and Grading Standards
### Preprocessing of Data
#### 3.3.4 Data Standardization  
In machine learning, standardization is a common data preprocessing technique that refers to a scaling of the features in a dataset on order that they have the same scale  
Figure 4: Box plot detection results. (**a**) Stratigraphic lithology. (**b**) Inadequate geology. (**c**) Rock dip angle. (**d**) Negative terrain area ratio. (**e**) Surrounding rock grade. (**f**) Hydrodynamic zonation.  
and distribution range. Due to the inconsistent interval range of the selected evaluating indicators in this article, there may be differences in their expression in machine learning. In the combination of disaster causing factors, there is often a problem whereby the expression of large data is stronger than that of small data, resulting in the inability of the small data to express its features. Therefore, a standardized approach is adopted to eliminate the inequality caused by data features. Standardization can improve the training efficiency of machine learning algorithms and make the weights between different features more equal and reasonable.  
The standardized formula used in this article [30] is shown below.  
\[X_{norm}=\frac{x_{i}-\overline{x}}{s} \tag{4}\]  
where \(x_{i}\) represents the original data in the data set, \(\overline{x}=\frac{1}{n}\sum_{i=1}^{n}x_{i}\) is the average of the original data under the same variable, \(s=\sqrt{\frac{1}{n-1}\sum_{i=1}^{n}(x_{i}-\overline{x})}\) is the standard deviation of the original data in the same variable, and \(X_{norm}\) is the standardized value of the original data, with a value between 0 and 1 and no dimension.  
Based on the water inrush accidents selected in this article, the data were preprocessed using the above method, and a dataset suitable for the RF model was obtained. Taking two representative water inrush accidents as examples, the six evaluating indicators for the hazard of water inrush in water-rich tunnels were processed according to Table 1, and their numerical and standardized results were obtained. At the same time, according to Table 2, the hazard level of water inrush was classified, and the treatment results are displayed in Table 3.