esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Conventional Detection Schemes
### Conventional Scheme B  
Here, another blind detection method is presented employing a data-based objective function between two consecutive QPSK symbols [15]:  
\[\Omega_{b}(m)=\sigma_{Z}^{2}\sum_{k\in\mathcal{D}}\log\left(2\text{cosh}\left( \frac{\mathcal{R}\left\{\hat{R}_{l}(k+m)e^{-2\pi m\rho}\right\}}{\sigma_{Z}^{ 2}}\right)+2\text{cosh}\left(\frac{\mathcal{I}\left\{\hat{R}_{l}(k+m)e^{-2\pi m \rho}\right\}}{\sigma_{Z}^{2}}\right)\right), \tag{8}\]  
Figure 1: Configuration of ISDB-T transmission.  
where \(\cosh(x)=(e^{x}+e^{-x})/2\), \(\hat{R}_{l}(k)=R_{l}^{*}(k)R_{l+1}(k)\) denotes the conjugate product from two consecutive observations, and \(\mathcal{R}\{\cdot\}\) and \(\mathcal{I}\{\cdot\}\) are the real and imaginary components of the enclosed complex number, respectively. Considering that the QPSK modulation is used for date subcarriers and the signal-to-noise ratio (SNR) is high, the data-based objective metric is approximated as  
\[\Omega_{b}(m)\approx\sum_{k\in\mathcal{D}}\max\left\{\left|\mathcal{R}\left\{ \hat{R}_{l}(k+m)e^{-j2\pi m\rho}\right\}\right|_{\tau}\left|\mathcal{I}\left\{ \hat{R}_{l}(k+m)e^{-j2\pi m\rho}\right\}\right|\right\}. \tag{9}\]  
The IFO can be estimated identically to (7) with replaced \(\Omega_{a}(m)\) by \(\Omega_{b}(m)\). Despite achieving good performance, this scheme can only be applied to QPSK modulation, and its computational complexity is high, proportional to the number of used subcarriers.
# A Multi-Dimensional Matrix Product--A Natural Tool for Parameterized Graph Algorithms
## 1 Introduction
### Clique Detection
At the beginning, we form a list of subgraphs isomorphic to \(K_{2}\) (i.e., edges), and then for each subgraph on the list, we count the number of vertices outside it that are adjacent to both vertices of the subgraph; in other words, we count the number of extensions of the subgraph to a clique on three vertices. The latter task can be completed efficiently by squaring the adjacency matrix of the host graph. We can generalize the algorithm to include detection/counting \(K_{r}\) copies, \(r\geq 3\), by replacing \(K_{2}\) with \(K_{r-1}\) and using the \((r-1)\)-dimensional matrix product of \(r-1\) copies of the adjacency matrix instead of squaring the matrix. Listing the subgraphs of the host graph isomorphic to \(K_{r-1}\) can be completed by enumerating \((r-1)\) vertex subsets and checking if they induce \(K_{r-1}\). For \(r=O(1)\), it takes \(O(n^{r-1})\) time, so the overall time required by this simple alternative method is \(O(n^{r-1}+n^{\omega_{r-1}+o(1)})\), where \(\omega_{k}\) is the smallest real number such that the \(k\)-dimensional matrix product of \(k\)\(n\times n\) matrices can be computed using \(O(n^{\omega_{k}+\epsilon})\) operations over the field of reals, for all \(\epsilon>0\) (see Definition 1 in Section 3). Recall that we show in particular \(\omega_{k}\leq\omega(\lceil k/2\rceil,1,\lfloor k/2\rfloor)\). Hence, our alternative method in particular computes the number of \(K_{4}\) copies in an \(n\)-vertex graph in \(O(n^{\omega(2,1,1)+o(1)})\) time and the number of \(K_{5}\) copies in \(O(n^{\omega(2,1,2)+o(1)})\) time. In addition, if the input graph contains a copy of \(K_{4}\) or \(K_{5}\), respectively, then a copy of \(K_{4}\) can be found in the graph in \(O(n^{\omega(2,1,1)+o(1)})\) time while that of \(K_{5}\) can be found in the graph in \(O(n^{\omega(2,1,2)+o(1)})\) time by a slightly modified alternative method. Thus, our upper time bounds for \(K_{4}\) and \(K_{5}\) at least match those for \(K_{4}\) and \(K_{5}\) yielded by the generalized triangle method [20]. If \(\omega_{k}<\omega(\lceil k/2\rceil,1,\lfloor k/2\rfloor)\) for \(k\) equal to \(3\) or \(4\), then we would achieve a breakthrough in the detection/counting of \(K_{4}\) or \(K_{5}\), respectively. For \(K_{r}\), where \(r\geq 6\), the generalized triangle method asymptotically subsumes our alternative method and for \(K_{3}\), the methods coincide.
## 5 Applications of BSNs
### State-of-the-Art Research on BSN Applications
#### 5.1.2 Social Welfare  
Along with the ceaseless improvement of social welfare, highly intelligent electronic systems are needed to help long-term monitoring of patients, the disabled, the elderly, and children. Some BSN applications utilize behavioral recognition and context awareness technology to better the quality of care for vulnerable groups.  
In a study of electronic monitoring system for daily activities, Reference [140] proposes a behavioral recognition method which mainly focuses on the classification results of human body posture conversion, achieving daily activity monitoring of patients and the elderly. As fall-detection is also very important, in Reference [141], researchers utilize wearable inertial sensors to predict fall actions. A threshold detection method is introduced based on the magnitude of inertial frame vertical velocity as the main variable to separate fall activities from non-fall ones. The algorithm is able to detect all fall events at least 70 ms before impact, thus proving the feasibility of the application. As a more in-depth research, Reference [142] develops a wearable airbag incorporating a fall-detection system that uses both acceleration and angular velocity signals to trigger inflation of airbag, which prevents serious injury caused by falls. In Reference [143], its authors study fall-detection from another perspective. They design a kind of sensor called ear-worn activity recognition (e-AR) with the ability of predicting the risk of falls by gait and balanced detection, which is more wearable than others mentioned before. In addition to taking care of people with disordered movements, electronic monitoring systems are widely used in nursing care of people with cognitive impairments. In Reference [9], researchers design an action reminder system for dementia patients, which utilizes a sensor node arrangement in the environment and on the body to recognize patients' actions and trigger different levels of alarm. Similarly, Reference [144] designs a kind of adaptive recognition system. The system can recognize behaviors as well as prompt patients with visual and auditory messages. In Reference [10], researchers achieve real-time detection of dirty diapers by context awareness technology. It can remind nursing staff to replace diapers and solve the problem of nursing inconvenience caused by urinary incontinence, and effectively improve the quality of nursing.
## 2 Hybrid Edge-Cloud Approach
### Edge-Cloud Computing  
Before the emergence of edge computing, the traditional cloud computing approach was to transfer the entirety of the raw data from metering devices to the cloud-based computing center and deal with computing and storage problems in a centralized manner. In recent years, the massive increase of the Internet of Things (IoT) devices caused the communication network to operate close to the bandwidth limit despite all the improvements in network technologies. This resulted in slower transfer rates and response time, which is a critical requirement for many protection and demand response applications. Consequently, edge computing technologies started to emerge [13], pushing the cloud's computing and storage capabilities from the data centers to the proximity of data-generation devices at the edge of the network [14], thus reducing the burden on the communication network.  
Given that the PMU data transmission over LTE might not be reliable, the edge-cloud approach presents an appealing alternative to the centralized approach used in TNs. The main advantage is that, since the entirety of the PMU data does not need to be sent to the data center anymore, but can rather be stored in a local manner, we completely avoid any potential issues with missing data, i.e., data are stored at the edge and can be retransmitted if necessary. As such, the edge-cloud architecture represents a perfect groundwork for the requirements of the heavily data-reliant fault localization algorithm (FLA).  
Figure 3: One-way delay for the transmission control protocol (TCP) protocol.  
Furthermore, edge computing also offers the capability to process the data locally and react accordingly. Consequently, the responsiveness of local controllability is improved since the data do not need to be uploaded and processed in the cloud anymore. This is important, since our goal is not to establish a framework capable of executing solely the FLA, but we want to provide a generic framework that will also allow the easy integration of other time-critical applications at the edge in the future when it may also leverage the shared mobile edge computing (MEC) capabilities of 5G networks as an additional intermediary computational level between edge devices and the cloud.
## 2 Materials and Methods
### Characterization of MNPs and LHRH-MNPs  
Both MNPs and LHRH-MNPs were characterized through a variety of techniques, following protocols established by our group [25; 26]. Transmission electron microscopy (TEM) (Philips, Amsterdam, Netherlands) was used to assess nanoparticle size and morphology. Dynamic light scattering (DLS), polydispersity index (PDI) and zeta potential (ZP) measurements were performed to assess the particle size, monodispersity and surface charges (Malvern Instruments, Malvern, UK). Fourier transform infrared spectroscopy (FTIR) (Shimadzu, Kyoto, Japan) was carried out to assess chemical composition of nanoparticles before and after conjugation. A vibrating-sample magnetometer (VSM) (Lake Shore Cryotronics, Inc., Westerville, OH, USA) was used to measure the magnetic properties of MNPs and LHRH-MNPs.
## 4 Image Analyses
### Image Analyses without Solar Shading by Tree Leaves above the Pond Surface
#### 4.1.4 Determination of Sensitivity Factor of the Digital Camera  
The sensitivity factor of the digital camera, by which images of Ao-ike Pond were taken, was determined. The gray standard paper, purchased from Shenzhen Neewer Technology Co., Ltd. (Shenzhen, China), with the albedo of \(\mathrm{R_{S}}=0.18\) was used.  
The illuminance from the halogen bulb (Panasonic JD110V65W-NP/EW) was measured using the illuminance meter and the reflected light from the gray standard paper was photographed with the digital camera.  
The illuminance at a certain position from the halogen bulb was \(\mathrm{E_{E}}=747\) \(\mathrm{lx}\). The digital camera output as sRGB values on a picture of the gray standard paper at this position was converted to linear RGB values (R, G, B) = (167, 149, 115) giving \(\mathrm{O_{E}}=0.591\) by Equations (14) and (15). The digital camera conditions at this time were \(\mathrm{X_{E}}=2.8\), \(\mathrm{I_{E}}=100\), and \(\mathrm{T_{E}}=1/30\) s. The standard conditions are \(\mathrm{X_{S}}=2.8\), \(\mathrm{I_{S}}=100\)\(\mathrm{T_{S}}=1/100\) s, and \(\mathrm{E_{S}}=2000\)\(\mathrm{lx}\). By substituting these into Equation (18), the digital camera sensitivity coefficient \(\beta^{\prime}{}_{1}=11.7\) was obtained.  
Likewise, from Equation (19), \(\mathrm{F^{\prime}_{S}}=3.53\times 10^{-4}\)\(\mathrm{m^{2}/cd}\) was determined.
# Analysis and Evaluation of Performance Gains and Tradeoffs for Massive MIMO Systems
## 2 System Description
### Downlink Communication Phase  
Let \(s_{jk}\) be the symbol to be transmitted to the \(k\)th user with E \(\{\,|\,s_{jk}\,|^{2}\}=1\). The BS uses the channel estimate to linearly precode the symbols, and then moves the precoded signal vector to all users. The \(K\times 1\) received signal vectors (\(Q_{\ell}\)) at the \(K\) terminals in \(\ell\)th cell from base stations in other cells is given by  
\[Q_{\ell}=\sqrt{p_{d}}\sum_{j=1}^{L}G_{j\ell}^{T}O_{j}s_{j}+n_{d\ell}, \tag{8}\]  
where \(O_{j}\) is the precoding matrix with \(M\times K\) complex values of the \(j\)th BS, consequently \(O_{j}\) is selected to be as \(p_{d}=E\left\{\,|\,1\,\sqrt{p_{d}}O_{j}s_{j}\,|\,1^{2}\right\}\), where E\(\{\,|\,1\,\cdot\,|\,2\}\)is the expected value of the Euclidean norm value and the transmit precoded signal is given by \(\sqrt{p_{d}}O_{j}s_{j}\). \"\(T\)\" is the transpose operator and \(n_{d\ell}\) is the AWGN vector with variance \(\sigma^{2}\). Using the linear precoding method MRT, the precoding matrix is computed as in [24]  
\[O_{j}=f_{MRT}\hat{G}_{jj}^{*}, \tag{9}\]  
where \(f_{MRT}\) is the normalization constant chosen to satisfy the transmit power constraint at the BS and \"\({}^{**}\)\" is the complex conjugate.  
Figure 2 explains the data transmissions at downlink broadcast channel from base station to their terminals where terminal with the same pilot sequence will interfere with users in other cells.
## 4 Experimental Results  
The measured impedance signatures in the frequency range from 20 to 200 kHz for three PZTs for the unloaded and loaded conditions are shown in Figure 7. The unloaded condition means that the PZTs are separate and not bonded onto the duct. The loaded conditions mean that the PZTs are connected in series and mounted onto the duct with empty grouting compactness. As shown in Figure 7a, the peak frequencies of three PZT wafers are different from each other, which is consistent with the analysis in Section 2.3. The resistance signal of serially connected PZTs in Figure 7b indicates that the three frequency peaks can be obtained when three PZT wafers of different diameters are connected in series and mounted onto the duct. Compared with unloaded conditions, the peak of these three PZTs for the loaded conditions are shifted to the left, and the impedance values are reduced.  
The electrical impedance signatures of the serially connected PZTs mounted onto tendon duct at different grouting compactness levels are shown in Figure 8. The frequency peaks labeled P1, P2, and P3 correspond to PZT1, PZT2, and PZT3, respectively. For the first frequency peak P1, when the grouting compactness increases from empty to 25%, the impedance signal has a clear upward trend, which reflects that the stiffness of the structure near PZT1 increases as the grouting compactness increases. When the grouting compactness increases from 25% to 100%, the impedance signal changes slightly. For the second frequency peak P2, the impedance signature changes obviously during the grouting compactness from 25% to 50%. Relatively speaking, the impedance signature does not change significantly from empty to 25% and from 50% to 100% of grouting compactness, which means that there are relatively sensitive areas when the EMI method is used to monitor structural health. For the third frequency peak P3, the resistance values change slowly from empty to 75% grouting compactness and vary considerably from 75% to 100% grouting compactness, which could be used to judge whether the grout is compact or not. The above analyses show that by comparing the relative changes of the impedance signatures at each frequency peak, the grouting compactness can be determined qualitatively.  
Figure 6: Five grouting levels. (**a**) Case G1; (**b**) Case G2; (**c**) Case G3; (**d**) Case G4; and (**e**) Case G5.  
Figure 7: The measured impedance signature of PZTs for unloaded conditions and loaded conditions: (**a**) unloaded condition; (**b**) loaded conditions.  
Based on Equation (3), 3-dB MAPDs for different grouting compactness levels are calculated and shown in Figure 9. It can be seen that the 3-dB MAPD increases with the increases of grouting compactness, which indicates that the 3-dB MAPD could be used to determine the grouting compactness quantitatively. Additionally, it is clearly shown that there is a sudden rise in the 3-dB MAPD for each frequency peak, which can be used to determine the approximate location of the grouting level.
## Security and Privacy Challenges in e-Health Cloud
A patient needs to be aware of the process of protecting and disseminating his medical records which would be very hard for a significant percentage of users. In addition, the overhead imposed on the patient's processes is high and may cause problems. Furthermore, the issue of emergency access does not have a clear and feasible solution. What happens if the patient is not available to grant access? What if he/she is in a serious condition and cannot respond? These questions require more work to be done to enhance this model. On the other hand, delegating the responsibilities to the service provider may relieve the patient of this burden, yet it will increase the issues of trust and control as the patient in this case cannot control the privacy of his/her data. In addition, handling multiple patients' records imposes more overhead on the provider and complicates policy handling. Later on, we could move the responsibility to the cloud owners; however, we risk further exposure and cannot have strong guarantees to the patient about the privacy of his/her data. In addition having to handle multiple service providers each with many patients will increase the overhead and complexity of deploying specific privacy policies for each one of them. As a result, more work is needed in this area to come up with more flexible, less complex solutions to ensure the security and privacy of medical records. One possibility is to distribute the responsibilities among all stake holders and provide methods to localize parts of the process at each level. Patients, service providers and cloud owners must collaborate to provide a solution that is acceptable to all parties. Table 1 provides a summary of the security approaches we discussed above and how they offer some of the security features. Such solutions are hampered by a number of challenges that emanate mainly from various complicated clinical, administrative and financial workflows in the healthcare process, and also to the type of \"life critical\" medical data. For example, what if an authorized person is unable to access the system due to any reason? The system must ensure that a correct way is devised to access medical data in case of an emergency where the patient is not available to access the data to avoid endangering the patient's life. Another issue that may arise is what if the owners of the patient records decide to delete a record? How will the patient be assured that no data is left in the Cloud? What about the patient's safety in this case? This is a crucial issue because security is still one of the key deterrents that prevent many healthcare organizations from adopting e-Health Cloud solutions.
# A Knowledge Graph Framework to Support Life Cycle Assessment for Sustainable Decision-Making
## 3 Methodology  
The proposed methodology, illustrated in Figure 2, presents a systematic framework for integrating, enriching, and analyzing heterogeneous data sources within a KG environment. As stated in Section 2.2, the KG, in the center of Figure 2, should be an LPG, since LPGs are renowned for their efficient storage, rapid traversal capabilities, and flexibility in modeling a wide array of real-world domains. They are characterized by their structure, where edges are used to connect nodes that contain embedded properties [24]. This approach takes advantage of domain expertise, real-world data and synthetically generated data to construct a comprehensive KG that supports advanced analytical capabilities essential for informed decision-making in complex domains.  
To optimize workflow efficiency, the methodology distinguishes between cascade and parallel capabilities. The cascade capabilities represent the sequential processes of gathering domain expertise, schema definition, and data integration, with each stage building on the previous one to ensure a structured and methodical construction of the KG. Once the KG is established, analytical applications such as Cypher queries, Graph Data Science techniques, causal inference, and graph retrieval-augmented generation (Graph RAG) operate independently and concurrently.  
The proposed methodology integrates AI to improve the efficiency and comprehensiveness of LCAs. AI is primarily utilized for estimating missing environmental data, particularly during the early stages of the product lifecycle during which data availability is often limited. Advanced language models, such as GPT-4o, are leveraged to generate synthetic data by inferring plausible values based on contextual understanding and patterns learned from extensive corpora of domain-specific knowledge. This enables rapid iteration and decision-making during the product design phase without the delays associated with manual data collection.
## 3 Methods and Data
### Data Augmentation
#### 3.1.2 Data Augmentation
This dropout layer serves to reduce the model's reliance on specific input features, enhance its generalization ability, and mitigate overfitting. The dropout parameter is set to 0.5, indicating that 50% of the neurons are randomly deactivated during each training iteration. The training process for the modified DCGAN network has demonstrated success, as evidenced by the stabilization of loss functions for both the generator G and the discriminator D after 500 rounds. This suggests that the model has effectively learned the underlying data features and can generate images that closely resemble those in the original dataset. Following this, the generator G is employed to generate 1300 images for each category, including ocean internal waves, clouds, land, and ocean. A selection of these generated images is presented in Figure 5. To further enhance the dataset, these generated images were combined with the original dataset, resulting in the creation of a new data augmentation training set referred to as Training Set 2. The generated images, whether depicting clouds, oceans, land, or internal waves, convincingly simulate various real-world scenarios. These images effectively capture the texture of cloud layers, the topography of the land, the undulations of the ocean's surface, and the oscillations of internal waves. Consequently, our approach provides an effective means to expand existing remote sensing image datasets, with potential applications extending to various other applications within the realm of remote sensing. The dataset consisting of original data, traditional data augmentation, and the modified DCGAN-generated data are shown in Table 2.  
In Part 4, we will compare the effects of two data enhancement methods in model training. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Original Data** \\ **Volume** \\ \end{tabular} } & \multirow{2}{*}{**Batch Size**} & \multirow{2}{*}{**Learning Rate**} & \multirow{2}{*}{**Training Epochs**} & \multirow{2}{*}{**Optimizer**} & \multirow{2}{*}{\
\begin{tabular}{c} **Exponential Decay** \\ **Rate for the** \\ **Moment Is** \\ **Estimated in the** \\ **Optimizer** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Exponential Decay** \\ **Rate for the** \\ **Second-Moment** \\ **Estimates in the** \\ **Optimizer** \\ \end{tabular} } \\ \hline
700 & 16 & 0.0005 & 1000 & Adam & 0.5 & 0.999 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Experimental parameters for the modified DCGAN network. Figure 5: Some of the images generated by the modified DCGAN network.