esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Optimizing the UCSP Using POGA-DP
### Classroom Scheduling Based on Dynamic Programming  
In solving the classroom allocation problem, we employed an improved dynamic programming approach to minimize the seat resource wastage. We defined \(dp[i]\) as the minimum seat wastage for scheduling the first \(i\) teaching classes in the set of classrooms. Here, \(N_{j}\) represents the maximum capacity of classroom \(j\), \(P_{j}\) denotes the number of students in teaching class \(i\), \(W_{i}\) denotes the set of weeks for teaching class \(i\), and \(v_{j,w}\) indicates the usage of classroom \(j\) in week \(w\). The initial conditions are as follows:  
\[dp[0]=1 \tag{19}\] \[dp[i]=\infty,0<i\leq\ m\] (20) \[v_{j,w}=1,1<j\leq j\leq n,1\leq w\leq\ 20\] (21) \[F(x,y)=\left\{\begin{array}{c}x-y,x\geq\ y\\ \infty,x<y\end{array}\right. \tag{22}\]  
For \(dp[1]\), this can be updated using the following formula:  
\[\left\{\begin{aligned} dp[1]&=\min_{1\leq j\leq n} (dp[1],dp[0]+\sum_{w\in W_{i}}F(N_{j},P_{1}*v_{j,w}))\\ &\qquad\qquad\qquad v_{j,w}=0,\forall w\in W_{i}\end{aligned}\right. \tag{23}\]  
where \(v_{j,w}=0\) indicates that once classroom \(j\) has been used, it will not be available in week \(w\), aiming to avoid the reuse of an already occupied classroom. Therefore, the iterative equation is given by  
\[\left\{\begin{aligned} dp[j]&=\min_{1\leq j \leq n}(dp[i],dp[i-1]+\sum_{w\in W_{i}}F(N_{j},P_{i}*v_{j,w}))\\ &\qquad\qquad\qquad v_{j,w}=0,\forall w\in W_{i}\end{aligned}\right. \tag{24}\]  
Figure 3: Diagram of the repair mechanism.  
It is worth mentioning that iterating from the smallest to the largest class sizes will lead to a higher average utilization and flexibility in the scheduling results. Consider a UCSP consisting of four classes with the seating demands \(P=[30,50,20,20]\), weekly demands \(W=[(1,1),(2,2),(2,3),(1,1)]\), classroom capacities \(N=[40,30,60]\), and weekly availability \(\
u=[(1,1,0),(1,1,1),(1,1,1)]\). As shown in Figure 4, although the results for \(dp[4]\) are the same, a different ordering will produce two distinct results. The ordered scheduling improves the average utilization of the classrooms and accommodates changes in future class sizes better.
## 2 Numerical Simulation and Device Design
### Numerical Simulation
A maximum PPR frequency of 46.5 GHz was obtained when the SOA length was 220 \(\upmu\)m. Although shorter SOA lengths caused a higher PPR frequency (Figure 3b), the frequency response between the CPR and PPR peaks was lower than \(-3\) dB when the SOA length was shorter than 280 \(\upmu\)m. Hence, the SOA length should be carefully designed to modify frequency spacing between CPR and PPR peak for achieving a flat small-signal modulated response, as mentioned above. To balance the flat response and maximum bandwidth, the length of 360 \(\upmu\)m was chosen to fabricate our device.
# Traffic-Aware Secured Cooperative Framework for IoT-Based Smart Monitoring in Precision Agriculture
## 3 Material and Methods
### Machine Learning-Based Distributed Regressional Analysis  
Based on the undirected graph, the proposed framework identifies the initial and temporary routes \(R(r_{1},\ r_{2},\ldots,\ r_{n})\). The selected routes are based on the distance factor and each node maintains its neighbor table in a controlled manner. By exploring the neighbor table, each node formulates a route to the sink node for data transmission. To attain an efficient routing scheme with balances the load and bandwidth utilization, the proposed framework initiates the nodes management activity on the routes \(r_{i}\). Let us consider that \(N_{i}\) has a data \(D_{i}\) to transmit to the sink node. Then, the proposed framework first identified the overlapping nodes that lay in the same transmission range. If any such nodes exist, then set their flag's value in the routing table. The flag value indicates the forwarding status of the nodes. Let us suppose that \(D_{t}\) is the distance threshold of the node \(NDs\), and \(r\) is the predefined radius. To determine the overlapping nodes, the proposed framework exploits the search zone candidate nodes \(C_{N}\) based on the \(D_{t}\) and \(r\), as given below  
\[NDs\ (C_{N},\ D_{t})\leq r \tag{1}\]  
After determining the candidate nodes, the proposed framework updates the routing table of source node \(i\) with the latest statistical values of neighbors. Table 1 describes the format of the routing table. It comprised node identity, transmission power, flag value, computing score, and distance. The node identity which is one byte long is unique. The next field is one byte long and contains the value of preset transmission power. The flag field is just a Boolean parameter to indicate whether the node can be silent or not, and it is one bit long. Finally, the scoring factor is two bytes long and depends on the captured information  
Figure 2: Block diagram of the proposed methodology.  
about the nodes. In the end, the distance is 1 byte long and contains information about the space toward the fog system.  
The proposed framework only allows one node in the transmission radius for sensing and forwarding the agricultural data. To achieve this, it determines the node score \(sc_{i}\) with \(w_{i}\) weighted coefficient using distributed weighted regression function \(X(i)\), and based on the maximum score, the flag value of the nodes \(i\) is set to either \"_True_\" or \"_False_\", as given below:  
\[\left\{\begin{array}{c}\textit{if }sc_{i}==\textit{max}\\ \textit{f}_{i}\textit{ is True}\\ \textit{false, otherwise}\end{array}\right. \tag{2}\]  
\[X(i)\textit{= }w_{i}\cdot sc_{i} \tag{3}\]  
The \(sc_{i}\) value for node \(i\) is determined by exploring the residual energy \(e_{i}\), packets load \(PL_{i}\), distance \(Df_{i}\), as given below:  
\[sc_{i}\textit{= }e_{i}\textit{+}1\textit{/}PL_{i}\textit{+}\frac{1}{Df_{i}} \tag{4}\]  
where \(PL_{i}\) defines in terms of transmitted packets \(N\) at time interval \(T\), as defined below.  
\[PL_{i}\textit{= }\frac{N}{T} \tag{5}\]
# Research on Seismic Signal Analysis Based on Machine Learning
## 2 Application of Machine Learning Method in Seismology
### Machine Learning Methods  
Machine learning methods can be divided into supervised (full label) and unsupervised methods (no label) according to the use of prior information, that is, the amount of existing labels of the data. Different data may have different effects on data feature extraction and classification [31]. The advantage of machine learning technology lies in that it can directly extract the main features of different types of data and apply them to data classification, and it can obtain different types of data without prior knowledge of the statistical distribution features of the solution. Therefore, some human experience could be recognized as a priori information to distinguish the characteristics of seismic waveform signals [18; 29].
# Traffic Congestion Detection System through Connected Vehicles and Big Data
## 3 Proposal Architecture
### On-Board Unit
#### 3.3.1 Route Monitoring Algorithm  
The route monitoring algorithm is responsible for observing the path where the car should be free from traffic at one-minute intervals. If there is a traffic congestion alert near the car or if any alert is on the current vehicle route, the algorithm seeks and assigns a new path which contains no traffic congestion alerts. If the process of finding a new alert-free route does not succeed, or the search time expires, the same route is maintained. The route monitoring algorithm shown in Figure 8.
# Remote Sensing of Snow Cover Using Spaceborne SAR: A Review
## 5 Discussion
### Opportunities of Data Fusion for SAR-Based Snow Cover Detection
#### 5.6.4 Combination of SAR with Passive Microwave Imagery  
Theoretically, the temperature information sensed by passive microwave sensors would support analysis and detection of SCE, as the temperature of wet and dry snow and bare ground differs.  
However, the coarse spatial resolution of passive microwave sensors aggravates an integration of this data into SAR processing. Moreover, [PERSON] et al. [104] identified SAR-derived and passive microwave-derived SCE only match for 61%. They suggested this mismatch may be due to fact that the two sensors measure different temporal-physical snow parameters as the passive microwave sensor senses snow emissivity and brightness temperature, which can be analyzed for deriving snow depth and then linked to the actual SWE. In contrast, SAR records the backscattering representing long-term morphology processes prior to the sensing time. Consequently, combining the two data sources led to biased results, which represent different temporal-physical snow parameters. A similar magnitude of mismatch was also observed by [PERSON] and [PERSON] [96].
# Spatiotemporal Contextual 3D Semantic Segmentation for Intelligent Outdoor Mining
## 3 Experiments
### Datasets and Evaluation Metrics  
To evaluate the proposed method, we performed validation experiments on our self-constructed TZMD_NUC dataset (TZ Group Co., Ltd. Mining Dataset by North University of China) and conducted generalization experiments on the publicly available SemanticKITTI dataset.  
**TZMD_NUC Outdoor Mining Dataset:** This dataset was collected by our research team at several active mining sites, using a Hesai 64-line LiDAR sensor and a visible light camera with a resolution of 1920 \(\times\) 1200, as shown in Figure 4. The data was recorded during mining operations, with the instrument mounted on a machinery platform. A total of 9734 point cloud samples were used for model training, split into training, validation, and test sets in a 6:1:3 ratio. Based on the characteristics of the operational environment, the samples were categorized into three classes: mining trucks, roads, and material piles, tailored to the specific needs of semantic segmentation in mining scenarios.  
Figure 3: Spatiotemporal feature select state-space module.  
**SemanticKITTI dataset:** This large-scale, public outdoor point cloud dataset for autonomous driving tasks was collected using a 64-line LiDAR sensor. It contains 22 labeled sequences with over 43,000 frames of labeled scans. In this study, we followed the common experimental division settings for semantic segmentation tasks. Sequences 00 to 07 and 09 to 10 were used as the training set, sequence 08 as the validation set, and sequences 11 to 21 as the test set. Semantic labels were divided following MOS [10] for distance benchmarking.  
For performance evaluation, we used the mean Intersection over Union (mIoU) as the primary metric. This metric effectively measures the overall performance of the model in multi-class semantic segmentation tasks and is suitable for unified evaluation across different datasets. The mIoU is calculated by computing the Intersection over Union (IoU) score for each category, then averaging the IoUs of all categories to obtain the final metric value:  
\[\text{mIoU}=\frac{1}{C}\text{{sum}}_{i=1}^{C}\frac{TP_{i}}{TP_{i}+FP_{i}+FN_{i}} \tag{16}\]  
where \(TP_{i}\), \(FP_{i}\), and \(FN_{i}\) represent the true positives, false positives, and false negatives for category \(i\), respectively, and \(C\) is the total number of categories.
## 2 Materials and Methods
### Variable-Frequency Complex Demodulation Algorithm
#### 2.2.1 Complex Demodulation  
In this step, each signal was decomposed into numerous band-limited signals using a series of low-pass filters (LPFs). Then, the Hilbert transform was applied to these signals. The result was analytical signals that provide estimates of instantaneous characteristics, such as amplitude, frequency, and phase, within their respective frequency bands. Consider a signal \(s(t)\), which is characterized as a narrow-band oscillation. This signal exhibits a central angular frequency denoted as \(\omega_{1}\) and time-dependent attributes, including its instantaneous amplitude, represented as \(V(t)\), and the initial phase, denoted as \(\theta(t)\). Additionally, \(s(t)\) includes a DC component, referred to as \(V_{dc}\), which contributes a constant offset to the signal:  
\[s(t)=V_{dc}+V(t)\text{cos}[\omega_{1}t+\theta(t)] \tag{1}\]  
The time-dependent attributes, such as \(V(t)\), \(\theta(t)\) for a specific carrier, can be extracted by multiplying \(s(t)\) by \(e^{-i\omega_{2}t}\). This operation yields the unfiltered modulated signal \(m(t)\):  
\[m(t)=s(t)e^{-i\omega_{2}t}=V_{dc}e^{-i\omega_{2}t}+\frac{V(t)}{2}\Big{(}e^{j( \omega_{1}-\omega_{2})t+j\theta(t)}+e^{-j(\omega_{1}+\omega_{2})t-j\theta(t)} \Big{)} \tag{2}\]  
By assuming \(\omega_{1}\approx\omega_{2}\), the frequency spectrum of \(s(t)\) has been shifted left by an amount of \(\omega_{2}\). If we subject \(s(t)\) to an LPF (cut-off frequency = \(\omega_{c}<\omega_{2}\)), the signal \(m(t)\) is filtered such that only spectral components falling within the range \([-\omega_{c},\,\omega_{c}]\) will remain. In this filtered output, the only component within this range corresponds to the difference frequency term. The outcomes can be expressed as follows:  
\[m_{LP}(t)=\frac{V(t)}{2}\Big{(}e^{i\theta(t)}\Big{)} \tag{3}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Case** & & **Classes** & **Description** \\ \hline \multirow{3}{*}{I} & \(\bullet\) & Z vs. S & \\  & \(\bullet\) & O vs. S & Healthy vs. Seizure \\  & \(\bullet\) & (Z+O) vs. S & \\ \hline II & \(\bullet\) & (Z+O) vs. (N+F) vs. (S) & Healthy vs. Interictal vs. Ictal \\ \hline \multirow{3}{*}{III} & \(\bullet\) & (Z+O vs. N+F+S) & \\  & \(\bullet\) & (Z+O vs. N+F) & Healthy vs. Epileptic \\ \hline \hline \end{tabular}
\end{table}
Table 2: Description of cases considered for classification.  
\[V(t)=2|m_{LP}(t)| \tag{4}\]  
\[\theta(t)=\tan^{-1}\biggl{(}\frac{Im(m_{LP}(t))}{Re(m_{LP}(t))}\biggr{)} \tag{5}\]  
Here, \(m_{LP}(t)\) is denoted as the CDM of a signal. The time derivative of \(\theta(t)\) acts as a correction factor for the \(\omega_{2}\), which can be employed to estimate the \(\omega_{1}\) of the input signal using Expression (6):  
\[\omega_{1}^{\prime}(t)= \omega_{1}+\frac{d\theta(t)}{dt} \tag{6}\]
## 5 Conclusions  
The present work shows that neural networks, once they have been trained, can be used to rapidly retrieve building collapse ratios from optical and SAR remote sensed data. The implemented ANNs modelled the collapse ratio with quite high accuracy when applied to the post Amatrice earthquake independent dataset. High values of CR (more than 0.5) were retrieved over the historical center that are in agreement with a damage assessment observed by optical VHR imagery. The fusion of optical and SAR derived parameters seem to give a more reliable result using only optical data, even though it probably underestimates the occurrences of collapse ratios higher than 0.5, due mainly to a minor statistical characterization of values during the ANN training phase (only 3%).  
Considering that the technique is independent of different types of input data, both for radiometric characteristics and spatial resolution, our work has demonstrated that ANNs are powerful tools able to estimate damaged areas, and they are important in near real time monitoring activities, owing to their fast application.  
When looking at using an ANN approach in near real time monitoring, special care has to be taken during the training phase. This is because the neural network needs to be fed and trained continuously also during its operating phase in order to keep phenomena knowledge updated and retrieval performance accurate at the operating stage.  
Another aspect is related to the input data characteristics, such as the spatial resolution, which can limit the neural network retrieval accuracy. In this case, a possible improvement could be the use of VHR satellite data, which could provide information at a building scale.  
Future work will consider earthquake surveys in order get ground truth datasets to make a quantitative assessment of ANN performance. It will also consider extending the analysis to other areas affected by 2016 Central Italy seismic sequence. Furthermore, because the described approach needs both pre- and post-earthquake images, and in many cases a pre-earthquake image is not available or not up to date, a future goal will be the application of Neural Network approaches only using post-earthquake remote sensed images, available in near real time.  
The research leading to these results has received funding from the European Union's Seventh Framework Program (FP7/2007-2013) under grant agreement number 606738, APhoRISM project.  
All authors contributed in a substantial way to the writing of the manuscript: [PERSON] conceived the experiments. [PERSON], [PERSON] and [PERSON] performed the analysis and wrote the manuscript. [PERSON] supervised the research at all stages.  
The authors declare no conflict of interest.
# A Novel Integrated Gliding and Flapping Propulsion Biomimetic Mana-Ray Robot
## 2 The Design of the Manta Ray Robot
### The Structure Design of the Manta Ray Robot  
As shown in Figure 2a, the overall structure of the autonomous deformation bionic soft body submersible adopted a non-pressure truss structure. We installed the internal equipment and electronic devices in independent pressure-resistant and sealed cabins. The overall design is divided into five parts: the main body, the left and right solid wings, and the left and right flapping wings. The central body part includes the main frame, the buoyancy-adjustment system, the mass-adjustment system, the navigation control system, the security system, the satellite communication system, and the power system. The left and right solid wings include a wing frame, a magnesium seawater battery, and a flapping wing mechanism. The soft flapping fin comprises a biomimetic skeletal structure built by a linkage mechanism and an airfoil frame, and a flexible skin wraps the exterior. We show the actual prototype in Figure 2b.  
Figure 1: The shape design of the manta ray robot. (**a**) The schematic diagram of a real manta ray; (**b**) the scale of the robot; (**c**) the NACA0022 and optimized airfoils; (**d**) the NACA0016 and optimized airfoils; (**e**) the NACA0008 and optimized airfoils.
## 2 Results and Discussion
### Purification of Antioxidant Peptides from Hydrolysate of STB-G
#### 2.2.2 Anion-Exchange Chromatography  
Acidic and hydrophobic amino acid residues in peptide sequences can be absorbed by the anion-exchange resins on hydrogen bonds and/or van der Waals forces [44]. Therefore, anion exchange resins are usually applied to isolated bioactive peptides and proteins from solutions. As shown in Figure 3A, four fractions (GH-I-1 to GH-I-4) were separated from STB-GH-I using a DEAE-52 cellulose column. Amongst them, GH-I-1 was eluted using deionized water (DW), and GH-I-2, GH-I-3, and GH-I-4 were eluted using 0.1, 0.5, and 1.0 M NaCl, respectively. EC\({}_{50}\) values of STB-GH-I and its four fractions on the DPPH radical are shown in Table 2, and the data demonstrated that GH-I-3 with an EC\({}_{50}\) value of 1.32 mg protein/mL showed significantly stronger DPPH radical scavenging activity than those of STB-GH-I (EC\({}_{50}\) value of 1.84 mg protein/mL), GH-I-1 (EC\({}_{50}\) value of 8.73 mg protein/mL), GH-I-2 (EC\({}_{50}\) value of 3.47 mg protein/mL), and GH-I-4 (EC\({}_{50}\) value of 3.41 mg protein/mL) (\(p<0.05\)). Thus, GH-I-3 was selected for the following experiment.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \begin{tabular}{c} **Gelatin** \\ **Hydrolysate** \\ **and Fractions** \\ \end{tabular} & \begin{tabular}{c} **EC\({}_{50}\) Value** \\ **(mg protein/mL)** \\ \end{tabular} & \begin{tabular}{c} **Gelatin** \\ **Hydrolysate** \\ **and Fractions** \\ \end{tabular} & \begin{tabular}{c} **EC\({}_{50}\) Value** \\ **(mg protein/mL)** \\ \end{tabular} & \begin{tabular}{c} **EC\({}_{50}\) Value** \\ **and Fractions** \\ \end{tabular} & \
\begin{tabular}{c} **EC\({}_{50}\) Value** \\ **and Fractions** \\ \end{tabular} &
\begin{tabular}{c} **EC\({}_{50}\) Value** \\ **and Fractions** \\ \end{tabular} \\ \hline \hline STB-GH & 3.28 & GH-I-2 & 3.47 & GH-I-3C & \textgreater{}10 \\ STB-GH-I & 1.84 & GH-I-3 & 1.32 & GH-I-3B1 & 2.68 \\ STB-GH-II & 4.36 & GH-I-4 & 3.41 & GH-I-3B2 & 0.87 \\ STB-GH-III & \textgreater{}10 & GH-I-3A & 3.69 & GH-I-3B3 & 5.74 \\ GH-I-1 & 8.73 & GH-I-3B & 1.08 & & \\ \hline \end{tabular} All data are presented as the mean \(\pm\) SD of triplicate results.  
\end{table}
Table 2: EC\({}_{50}\) values of gelatin hydrolysate from the bones of skipjack tuna (STB-GH) and its fractions on 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical.
# Phase-Modulated Continuous-Wave Coherent Ranging Method and Anti-Interference Evaluation
## 2 PhMCW Mechanism  
The mechanism of PhMCW is briefly illustrated in Figure 1a. The laser phase \(\varphi_{T}(t)\) is modulated by a rectangular waveform, where \(\varphi_{0}\) is the initial optical phase, \(\Delta\varphi\) is modulation depth, \(n\) is an integer, and \(T\) is the period of phase modulation.  
\[\varphi_{T}(t)=\begin{cases}\varphi_{0},&\left(nT\leq t<\frac{T}{2}+nT\right) \\ \varphi_{0}+\Delta\varphi,&\left(\frac{T}{2}+nT\leq t<(n+1)T\right)\end{cases} \tag{1}\]The modulated laser is coupled as a local oscillator (LO) and transmitter signal (TX), which is transmitted into free space. The TX signal will be back-scattered by the target and collected by the receiver. Subsequently, the received signal (RX) and LO are mixed to obtain an intermediate frequency (IF) signal. For a stationary target, ignoring the DC term of the IF signal, it can be expressed as:  
\[\mathrm{I_{IF}(t)}=\begin{cases}\mathrm{A_{LO}A_{R}\cos(\omega_{0}\tau+\Delta \varphi),}&(0+n\mathrm{T}\leq t<\tau+n\mathrm{T})\\ \mathrm{A_{LO}A_{R}\cos(\omega_{0}\tau),}&\left(\tau+n\mathrm{T}\leq t<\frac{ \mathrm{T}}{2}+n\mathrm{T}\right)\\ \mathrm{A_{LO}A_{R}\cos(\omega_{0}\tau-\Delta\varphi),}&\left(\frac{\mathrm{T }}{2}+n\mathrm{T}\leq t<\frac{\mathrm{T}}{2}+\tau+n\mathrm{T}\right)\\ \mathrm{A_{LO}A_{R}\cos(\omega_{0}\tau),}&\left(\frac{\mathrm{T}}{2}+\tau+n \mathrm{T}\leq t<(n+1)\mathrm{T}\right)\end{cases} \tag{2}\]  
where \(\mathrm{A_{LO}}\) is the amplitude of the electric field of LO, \(\mathrm{A_{R}}\) is the amplitude of the electric field of RX, \(\omega_{0}\) is the optical frequency, and \(\tau\) is the time of flight. As shown in Figure 1b, the IF signal consists of a bunch of pulses whose pulse width is equal to \(\tau\). Therefore, by measuring the pulse width of the IF signal and then multiplying it by c/2, where c is the speed of light, we can obtain the distance information.
## 2 Methodology of Data Acquisition
### Description of Field Measurement Site  
The Tehran-Mashhad railway track is one of the important and high-traffic track railways of Iran's railway network. For a comprehensive study and investigation, a 15 km stretch of the Tehran-Mashhad railway line (from 230 to 245 km), which is located between the Semnan and Miandareh stations, was selected to conduct the field measurements. The specific location of the collected data is shown in Figure 1. The properties of the reference railway track, which were monitored and investigated in this study, are presented in Table 1.  
Figure 1: Data acquisition along Tehran–Mashhad railway line (between Semnan and Miandareh stations).