esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey: Network Feature Measurement Based on Machine Learning
## 2 Machine Learning Method in Network Measurement
### Unsupervised Learning in Network Measurement
The CPU, memory, and bandwidth were only a few of the various parameters that were used to validate the decision system. The K-Means approach may typically be used on continuous datasets with tiny dimensions and values since it is trained by including its features in the input. It can be applied in a variety of situations, including document classifiers, item transmission optimization, and customer classification. The K-Means algorithm can perform a preliminary examination of the data and even identify the hidden value after suitable pre-processing. However, it might be challenging to understand how to choose the K-value for the K-Means technique, and it struggles to converge for non-convex datasets. Using an iterative approach, only a locally optimal solution could be found.
## 6 Experiment and Result Analysis
### Limitations and Future Research Work
#### 6.10.3 Future Research Work  
Based on the limitations identified in this study, future research will focus on the following aspects: (1) Occlusion-Robust Architectures: Develop attention-guided feature completion networks to dynamically reconstruct occluded regions; (2) Efficiency Optimization: Explore more lightweight feature compression and model distillation techniques to maintain accuracy while reducing complexity; (3) Cross-Cultural Generalization: Build more diverse deepfake datasets that include a wide range of ethnic features and cultural attributes; (4) Edge-Deployable Frameworks: Utilize Neural Processing Units (NPUs) to achieve hardware-aware acceleration.  
These limitations highlight critical research opportunities for developing deployable deepfake defense systems. Future work will prioritize balancing robustness and efficiency without compromising detection integrity.  
Figure 23: The predicted results on four datasets are depicted in the graph above.
###### Abstract  
The optical system of an imaging spectrometer working on a geostationary earth orbit (GEO) covering a full optical spectrum of 0.3-12.5 \(\upmu\)m is analyzed and designed. It enables a ground coverage of 400 \(\times\) 400 km by internal scanning and achieves a high spatial resolution of 25 m. The full spectrum is divided into five sub-bands, and each band adopts four spectrometers to splice in the field of view to achieve the ultra-long slit required by the wide swath. The total length of the slit is up to 241.3 mm. This paper focuses on compact spectrometers with long slits that can meet the splicing requirements and points out that low spectral distortions, low stray light, high signal-to-noise ratio, and uniform spectral response are necessary for high-fidelity performance. The Offner and Wynne-Offner high-fidelity spectrometers based on convex blazed gratings are designed, and prototypes of each band are developed as well. The properties of long slits and convex blazed gratings are presented. The maximum length of a single slit is 61.44 mm. The groove density of gratings for five bands ranges from 8.8 lp/mm to 312.1 lp/mm, and the peak efficiency is up to 86.4%. The alignment and test of the spectrometers are introduced. Results show that the developed spectrometers have high fidelity and fulfill all requirements.  
hyperspectral imaging; optical design; geostationary orbit; spectrometer; remote sensing 202315396[[https://doi.org/10.3390/rs15020396](https://doi.org/10.3390/rs15020396)]([https://doi.org/10.3390/rs15020396](https://doi.org/10.3390/rs15020396))
## 5 Analysis of Parameters Influence
### Waist Beam Size  
The waist beam size in the original design scheme is 1.0 m \(\times\) 1.4 m. This study set the waist beam size as 1.2 m \(\times\) 1.6 m, 1.4 m \(\times\) 1.8 m, 1.6 m \(\times\) 2.0 m and 1.8 m \(\times\) 2.2 m when other parameters remained unchanged and compared the maximum surface settlement and the maximum deformation of the retaining structure to analyze the influence of waist beam size on support effect.  
From Figure 15a,b, the change of the waist beam size has limited influence on the maximum surface settlement and the maximum lateral deformation of the retaining structure. When waist beam size increased from 1.0 m \(\times\) 1.4 m to 1.2 m \(\times\) 1.6 m, the maximum surface settlement and the maximum lateral deformation of the retaining structure reduced by 13% and 8%, respectively. When waist beam size increased from 1.2 m \(\times\) 1.6 m to 1.4 m \(\times\) 1.8 m, the maximum surface settlement and the maximum lateral deformation of the retaining structure reduced by 3% and 6%, respectively. Compared with the influence of embedded depth and ribbed beam size, the change of waist beam size has limited influence on the support effect of the retaining structure. Therefore, if it is necessary to improve the support effect of the retaining structure, it is suggested to increase the pile embedded depth and the ribbed beam size.  
Figure 14: Different ribbed beam size. (**a**) Maximum surface settlement. (**b**) Maximum lateral deformation.  
Figure 13: Different embedded depth. (**a**) Maximum surface settlement. (**b**) Maximum lateral deformation.
## 5 BN-MAC Protocol Design
### Intra-Semi-Synchronized Transmission Schedule
The average short preamble reception time could be reduced because the receiving node wakes up based on the stored schedule of the neighbor nodes. The average energy consumed by the parent and child nodes can be obtained as follows:  
\[\alpha=\sum_{k=0}^{n}p_{k}\frac{(\
abla\sigma.\mu*\delta\
u^{2}\ )*(\gamma*\ 2 \omega*\ \Delta_{t}^{2})}{\Delta_{t}} \tag{3}\]  
\[\alpha=\sum_{k=0}^{n}p_{k}\frac{(\
abla\sigma.\mu*\delta\
u^{2}\ )*(\gamma*\ 2 \omega*\ \Delta_{t}^{2})+(\gamma*\ \omega*\ \Delta_{t})}{\Delta_{t}}\] (4 BN-MAC can clearly identify the consumed energy of the short preamble prior to sending the data. BN-MAC has an advantage over low-duty-cycle long-preamble-enabled MAC protocols and X-MAC. The reduced energy consumption and time requirements of BN-MAC compared to the other protocols is shown in Figure 5.0 Figure 8 presents the superiority of BN-MAC compared with other low-duty-cycle MAC protocols in terms of time consumed in sending the short preamble to confirm the synchronization process for forwarding the data. All of the nodes in BN-MAC maintain the same time frame during synchronization and maintain a time slot of 0. Each node maintains its own local frame, which matches the frame size of the neighborhood to avoid potential conflicts while contending with neighbors. The nodes compete for CSMA equally during the contention phase because the random exponential back-off (an algorithm that uses response to multiplicatively reduce the node's frequent access to the  
Figure 5.1 Channel accessing and data delivery time for BN-MAC and other low-duty-cycle MAC protocols. Figure 5.2 Energy consumption for BN-MAC and low-duty-cycle MAC protocols. channel dynamically in order to find the acceptable node to access the channel, as the part of network congestion avoidance) preserves the right of each node to compete fairly for scheduled slots. Intra-semi-synchronous communication is performed inside the region because BN-MAC is designed purely for the region-based network, as many WSN application areas require a region-based network. The intra-semi-synchronized transmission schedule is compatible with all types of radios, such as CC2420 and CC2500.
## 3 Resource Allocation Considering NS and D2D
### Channel Model  
In this work, the bandwidth allocated to the D2D pair is the same as the UEs connected to the D2D pair. The spectrum between the UEs and the connected D2D pairs is reused; hence, the total throughput of the network slice is increased. When packet transmission occurs in the downlink direction, UEs are exposed to interference when any D2D peer transmits using the same allocated bandwidth. Our focus in this work is on a single cell scenario. Therefore, we do not consider intercell interference. When two devices communicate using the same frequency band, there is the possibility of interference. This interference can reduce the quality of communication between these devices or even lead to a total loss of communication. One of the main challenges of D2D communication is managing this interference [10].  
The interference from the D2D communication to the UEs in the cellular network must be restricted to maintain a minimum level of network performance. If the distance between the D2D devices is long, the D2D will require more power. In this case, the cellular network loses performance because of interference [17]. The interference level will not only depend on the transmission power of the D2D pair but also channel gain between the D2D pair and the UEs. The signal to interference plus noise ratio (SINR) of UE \(j\) is given as follows:  
\[\Gamma_{j}=\frac{p_{j}\times G_{Bj}}{\sigma^{2}+\sum_{d=1}^{D}p_{d}\times G_{ jd}}, \tag{1}\]  
where \(G_{jd}\) denotes the channel gain between the UE \(j\) and the D2D pair \(d\), \(G_{Bj}\) denotes the channel gain between the BS and the UE \(j\), \(p_{j}\) and \(p_{d}\) are transmission signal power from UE \(j\) and the D2D pair \(d\), respectively. \(\sigma_{z}^{2}\) is the noise power. The SINR of the D2D pair \(d\) is given as  
\[\Gamma_{d}=\frac{p_{d}\times G_{dd}}{\sigma^{2}+\sum_{j=1}^{D}p_{j}\times G_{ Bd}}, \tag{2}\]  
where \(G_{dd}\) denotes the channel gain between the D2D pair \(d\) devices, and \(G_{Bd}\) denotes the channel gain between the BS and the D2D pair \(d\). According to [17], the channel gain between two devices is represented as  
\[G_{uv}=K_{uv}\times d_{uv}^{-\delta}, \tag{3}\]  
where \(d_{uv}\) is the distance between the transmitter \(u\) and the receiver \(v\); \(\delta\) is the constant path loss exponent; and \(K_{uv}\) is a normalization constant that depends on the propagation properties of the environment. In this work, we assumed that \(\delta=2\) and \(K=0.001\). The linear gain between the BS and the UE is given as follows:  
\[G_{Bj}=10^{-PL/10}, \tag{4}\]  
where \(PL\) is the path loss received by the small cell. The Okumura-Hata model was used to predict path loss [2]:  
\[PL=140.7+36.7\times log(dist[\mathrm{km}])\quad\mathrm{dBm}, \tag{5}\]  
where _dist_ is the distance between the UE and the BS. The achievable data rate of D2D pair \(d\) can be obtained by Shannon capacity:  
\[C=w_{d}\times log_{2}(1+\Gamma), \tag{6}\]  
where \(w_{d}\) is the bandwidth of the D2D pair shared with the UE.
# Upper Body Posture Recognition Using Inertial Sensors and Recurrent Neural Networks
###### Abstract
[17] used three tri-axial accelerometers to monitor sitting postural changes and compared the results with those obtained from an optoelectronic motion analysis system. For postural measurements, the RMS error was approximately 2\({}^{\circ}\) in quasi-static conditions, while the error was relatively large in dynamic conditions. Therefore, a gyroscope was added to the system in order to reduce errors in collecting dynamic information. In addition, [PERSON] et al. [14] used two inertial measurement units (IMUs) to perform binary classifications of normal and abnormal postures. Their results showed that the mean square error was approximately 0.15 for the pitch angle, and the heading angle was within \(\pm\)10\({}^{\circ}\) range of motion, indicating that collected IMU signals can be used to calculate back curvature. However, this system is only suitable for monitoring sitting posture under static conditions, since it has only been confirmed for small-angle motions. Even though the systems mentioned above demonstrated high accuracy for posture recognition in static conditions, recognition of postures under dynamic conditions still needs to be improved. Human activity recognition (HAR) aims to identify specific movements based on different sensing signals. This technique has been developed for multiple applications, such as fall detection systems, disease prevention, the Internet of Medical Things (IoMT), etc. [18]. In addition, smart wearable devices present an application of HAR for collecting kinematic parameters of the human body by using sensors. Sensor data are used to identify human activity with the aid of deep learning and other machine learning algorithms. According to [PERSON] et al. [19], although many researchers prefer classic machine learning (CML) models because of their small datasets and lower dimensionality of input data, deep learning (DL) models exhibit greater accuracy in large activity datasets because DL networks are capable of feature learning or automatic feature extraction. In addition, DL networks have shown the ability to recognize human activities, such as walking, climbing ladders, and falling [20]. Therefore, HAR research that uses DL models, such as the Convolutional Neural Network (CNN) [21; 22; 23], the Long Short-Term Memory Network(LSTM) [24; 25; 26], and the Recurrent Neural Network (RNN) [27; 28], has recently increased in popularity. In this study, we aim to recognize human unsupported sitting postures by using a deep learning algorithm. IMUs were uniformly distributed along subjects' spines in order to collect kinematic parameters of subjects' backs in static and dynamic conditions. For the deep learning algorithm, an LSTM-based RNN (LSTM-RNN) was used to classify seven common unsupported sitting postures by using raw data from IMUs as inputs. With the aim of preventing chronic spinal problems, this system may be useful for long-term tracking of sedentary people as well as for providing alerts regarding poor posture.
## 4 Numerical Applications
### Case Study  
NA Island is located at GD province, China. The geographical position of this island is shown in Figure 14, and the single line diagram of the NA island power system is shown in Figure 15. There are approximately 70,000 inhabitants living on the NA Island. The island has abundant wind resources, and the local consumers' energy demand is supplied by the NA Island grid. The grid structure of NA Island is a simple unidirectional radial network. The yearly peak load of system is 1500 kVA. The installation capacity of the conventional generator is 600 kW. NA Island is located on the southwest side of Taiwan Strait flared between Fujian and Guangdong, near the sea. Because of the throat effect of the channel and the dynamic uplift of the protrusion causing by the upwind terrain, NA Island has  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Location**} & \multicolumn{4}{c}{**SAIDI**} \\ \cline{2-5}  & \(P_{dclimax}\) **= 200 kW** & \(P_{dclimax}\) **= 300 kW** & \(P_{dclimax}\) **= 400 kW** & \(P_{dclimax}\) **= 500 kW** \\ \hline DG & 32.4320 & 26.9814 & 18.0828 & 16.1843 \\ LP30 & 25.3161 & 20.3838 & 15.1260 & 10.3821 \\ LP40 & 24.1648 & 18.3297 & 14.7320 & 9.0440 \\ LP45 & 31.1175 & 24.4641 & 18.4252 & 14.1344 \\ DG 50\%, LP40 50\% & 31.1809 & 25.1327 & 24.7912 & 21.5757 \\ \hline \hline \end{tabular}  
* SAIDI: System average interruption duration index; DG: Distributed generation; LP30: Load Point 30; LP40: Load Point 40; and LP45: Line Point 45.  
\end{table}
Table 1: Comparison of the installed \(P_{dclimax}\) of energy storage when the energy storage devices are considered centralized and decentralized. 1  
Figure 13: Changing in \(P_{dclimax}\) of energy storage against the rated power of wind turbines.  
a rich wind resources. The total installed capacity of wind power generator at NA Island is 3100 kW. To maintain the supply-demand balance of the power system, it is considered to allocate the energy storage system to peak load shifting in this distribution network. The energy storage will charge the surplus power when the wind speed is high, and discharge power to supply the load demand when the wind speed is low.  
The method is applied to the allocation of energy storage on the island, and the energy storage system is planned to be located at the Qingao (QA) wind farm, Niutouling (NTL) wind farm, Yunao (YA) wind farm or Guoshan (GS) line, respectively. The reliability assessment results of the energy storage allocation are listed in Table 1.  
From the analysis of Table 2, it follows that the increased system reliability is related to the increase in the installed \(P_{ddmax}\) of energy storage at any of the locations listed in the table. Moreover, the degree of improvement of each allocation scheme is almost the same. Notably, with the same installed \(P_{ddmax}\) of energy storage, the system reliability differs when the energy storage is located at different nodes, and when located at YA, the energy storage will better improve the system reliability.
# Assessment of the Quality of Video Sequences Performed by Viewers at Home and in the Laboratory
## 3 Results and Discussion
### Subjective Quality Assessment of Video Encoded Using H.264 Standard
The \(t\)-test values obtained using the Statistica tool for each resolution are as follows:  
* 640 \(\times\) 360; t = 17.6 \(>\) t \(\alpha=2.1\), at \(\alpha=0.05\);
* 858 \(\times\) 480; t = 14.9 \(>\) t \(\alpha=2.1\), at \(\alpha=0.05\);
* 1280 \(\times\) 720; t = 10.7 \(>\) t \(\alpha=2.1\), at \(\alpha=0.05\);
* 1920 \(\times\) 1080; t = 2.9 \(>\) t \(\alpha=2.1\), with \(\alpha=0.05\). It can be concluded that the differences between the MOS values obtained under the laboratory and home conditions are significant. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Bit Rate** & \multicolumn{2}{c}{**\(\Delta\text{MOS}\)**} & \\ \hline
**(kbps)** & **640 \(\times\) 360** & **858 \(\times\) 480** & **1280 \(\times\) 720** & **1920 \(\times\) 1080** \\ \hline
300 & 0.33 & 0.21 & 0.16 & 0.00 \\  
(-\)0.04 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Value of the difference in \(\Delta\text{MOS}\) (for H.264 codec) between the results obtained under laboratory and home conditions. Figure 7: Difference between the results of the subjective evaluation of the H.264-encoded video (\(\Delta\text{MOS}\)), conducted in the laboratory and at home, as a function of bit rate for different resolutions.
# The Design of a Long-Distance Signal Transmission System in a Bistatic RCS Testing System
## 2 System Principle and Simulation
### Derivation of the System Formulas  
Let us assume the modulating signal \(S_{r}(t)\) in Figure 2 is a standard cosine signal:  
\[S_{r}(t)=A_{m}\cos(\omega_{m}t+\varphi_{m}) \tag{4}\]  
\(A_{m}\) is the amplitude of the modulating signal. \(\omega_{m}\) and \(\varphi_{m}\) represent the angular frequency (the following will be referred to as \"frequency\") and initial phase of the modulating signal, respectively. Since the MZM operates in QTP mode, \(V_{DC}=0.5V_{\pi}\). Therefore, the modulated optical signal \(S_{r}(t)\) obtained after the modulating signal \(S_{m}(t)\) passes through the electro-optic converter can be represented as follows:  
\[\begin{array}{ll}S_{m}(t)&=\frac{\alpha_{m}}{2}E_{in}(t)\left[\begin{array}[] {c}\exp\left(j\pi\frac{A_{m}\cos(\omega_{m}t+\varphi_{m})+0.5V_{\pi}}{2V_{\pi }}\right)\\ +\exp  
The signal undergoes transmission through a single-mode fiber (SMF) with a length of \(l\), wherein the transfer function of the SMF is given by the following:  
\[H(\omega,l)=e^{-\gamma l}e^{-j\beta(\omega)} \tag{10}\]  
\(l,\ \beta(\omega),\ \gamma\) represent the length, propagation constant, and amplitude attenuation coefficient of the SMF, respectively. By performing a Taylor expansion on \(\beta(\omega)\) and using \(\beta(\omega_{0}),\ \beta^{\prime}(\omega_{0}),\ \beta^{\prime\prime}(\omega_{0})\) to denote the propagation constant at point \(\omega_{0}\) and the values of the first and second derivatives, we can obtain the following:  
\[\beta(\omega)=\beta(\omega_{0})+(\omega-\omega_{0})\beta^{\prime}(\omega_{0})+ \frac{1}{2}(\omega-\omega_{0})^{2}\beta^{\prime\prime}(\omega_{0})+\cdots \tag{11}\]  
From Equations (9)-(11), we can obtain the expression for the modulated optical signal obtained after it propagates through the SMF with a length of \(l\) as follows:  
\[S_{c}(l,t)=\frac{\sqrt{2}}{2}\alpha_{m}E_{0}\left[\begin{array}{c}J_{0}(m) \exp(j(\omega_{c}t-\beta(\omega_{c})l)+j\varphi_{c})\\ -J_{1}(m)\exp((j(\omega_{c}t+\omega_{m}t+\varphi_{m})-\beta(\omega_{c}+\omega _{m})l)+j\varphi_{c})\\ -J_{1}(m)\exp((j(\omega_{c}t-\omega_{m}t-\varphi_{m})-\beta(\omega_{c}-\omega _{m})l+j\varphi_{c}))\end{array}\right]e^{-\gamma l} \tag{12}\]  
Finally, the photocurrent detected by the photodetector is given by the following:  
\[\begin{array}{ll}S_{r}^{\prime}(l,t)&=\mu|S_{c}(l,t)|^{2}\\ &=\frac{1}{2}\alpha_{m}^{2}E_{0}^{2}\mu{I_{0}}^{2}(m)e^{-al}+\alpha_{m}^{2}E_{ 0}^{2}\mu{I_{1}}^{2}(m)e^{-al}\\ &-\alpha_
## 4 Result Experiment
### Case Study: Estimation of Remaining Lifetime Using ACI
#### 4.4.2 Step 2: Calculation of ACI for Cable \"A\"  
* The tensile strength-based ACI is defined by Equation (5). This value indicates the current aging level of the insulation in terms of tensile strength performance. Assuming the measured tensile strength of cable \"A\" is \(z=18.0\) MPa, the ACI can be calculated as:  
\[ACI_{\text{\emph{Tensile strength}}}(T,\,t)=\frac{18.0-12.5}{20.94-12.5}= \frac{5.5}{8.44}\approx 0.652 \tag{7}\]
## 3 Evaluation
### Model Evaluation
#### 3.3.3 Mask Detection  
The mask detection component requires localized information about masks. Thus, the UT-interaction dataset was re-annotated. However, this dataset only consists of unmasked faces, and as such, the annotated UOP dataset was used together with the UT-interaction dataset to train and evaluate the mask detection component. The 17 videos from the UT-interaction dataset and the five videos from the UOP dataset were used for training. The dataset was annotated with the two class information: namely, masked and unmasked faces in frames, where the faces were visible and the presence of masks can be interpreted by a human.  
The mask detection model was evaluated using both the AP and mAP measures. First, the model's ability to localize the faces was determined by measuring the AP of the localization component of the models disregarding the class labels.  
Next, the performance of the model in terms of both the localization and the accuracy was determined by the mean average precision (mAP) value. Note that since both the classes correspond to the same object (i.e., faces), this two-metric evaluation process helps us identify the specific shortcomings of the model considered. For instance, a high AP and a low mAP show poor mask detection (classification), whereas a high accuracy and low mAP denote poor face localization.