esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Summary of HOE Results  
Results of the T-H-O-Risk analysis compare reasonably with fire risk reported in the literature where available [45; 46; 47; 48]. The influence of HOEs is significant for Case #4 and Case #7 at 32% and 33% respectively with active systems of sprinklers, BOWS, and smoke detectors as these require regular maintenance. Case #4 is a building that has been converted from office to residential use while Case #7 is an old residential building that has been remodelled--both have inherent inflexibilities and legacy compliance issues which likely heightened their risk levels. As the number of active systems increases, the influence of HOEs becomes more significant since human interventions are required for maintenance and operations of these systems. Results indicate that overall, Cases #1, #2, #5, and #6 have comparably lower risk levels compared to Cases #3, #4, and #7. Case #1 is a double-loaded straight corridor block with full-height window openings at either end, which results in longer tenability conditions. Case #2 is a four-unit high-rise point-block with a single naturally ventilated stairwell and short cross-ventilated corridors. It is to be noted that this configuration can only be built in a tropical climate. Case #5 has naturally ventilated corridors and stairwells while Case #6 has low occupant load, short corridors, and travel distance to stair. Fire modelling results indicate that tenability conditions remain infinite at these corridors for Cases #2, #5, and #6 on account of the natural ventilation. The absence of a second exit stair in both Case #2 and Case #6 does not seem to compromise their risk levels likely due to the low occupant loads and corridor tenability. Case #7 has the highest overall risk on account of the sole stairwell and low tenability due the corridors filling up with smoke rapidly. Further, there is a strong positive correlation between the ratio HOE/ERL with the number of active systems-for each case, the correlation among its variables is high.
## 8 Current Challenges and Future Prospects
### Energy Consumption, Renewable Energy, Microgrid, and Smart Grid  
Despite technological advances in agriculture, advanced farming still faces many issues that hinder the extended use of the IoT. One of the notable issues regarding energy/power consumption is that advanced agriculture needs extensive energy. The major problems for a large amount of energy consumption include the long-term deployment of sensors, repeated transmission of sensing information by general packet radio service, and GPS use. Traditionally, growers in rural areas have randomly bought and used renewable energy at high prices, which has largely restricted their use for advanced agriculture. However, to resolve the energy problem, in the long run, an in-depth analysis of the power source (such as remote information communication) can address the problem to some extent. In addition, smart grids and microgrids enable the perfect integration of distributed energy sources (DER), so they have attracted farmers' adoption. The advent of smart meters has made farmers more confident in investing in DER, particularly because farmers can select full excess electricity to the grid. If a continuous energy source is not available for users in remote areas, alternative rechargeable devices or thermal/electrical/mechanical energy storage systems should be available for them to use. Current advances in energy storage apparatus, heating system, and electricity will further make DER smarter to growers. They will be able to store energy and use the heat created via heating and cooling when required [190; 191]. Moreover, requirements of healthy investment and public awareness are two other obstacles to the success of these resolutions.
## 2 Materials and Methods
### Determination of Microbial Diversity  
To assess microbial diversity in the fermented juice, the samples were pretreated, and total microbial DNA was extracted. Samples (0.5 mL) were quickly thawed, placed in 2 mL centrifuge tubes with extraction lysis solution, and homogenized using a TissueLyser II (Qiagen, Hilden, Germany) at 60 Hz. Total DNA was extracted using the OMEGA Soil DNA Kit (M5635-02) (Omega Bio-Tek, Norcross, GA, USA), following the manufacturer's protocol. The quality of the extracted DNA was assessed using a NanoDrop ND-2000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA) and 0.8% agarose gel electrophoresis.  
The V3-V4 region of the bacterial 16S rRNA gene was amplified using primers 338F and 806R. For fungi, the internal transcribed spacer (ITS) region was amplified using primers ITS5 and ITS2. Each 25 \(\upmu\)L PCR reaction contained 5 \(\upmu\)L of 5\(\times\) buffer, 0.25 \(\upmu\)L of Fast Pfu DNA polymerase (5 U/\(\upmu\)L), 2 \(\upmu\)L of 2.5 mM dNTPs, 1 \(\upmu\)L of each primer (10 \(\upmu\)M), 1 \(\upmu\)L of DNA template, and 14.75 \(\upmu\)L of double-distilled H\({}_{2}\)O. Thermal cycling conditions for bacterial amplification were as follows: initial denaturation at 98 \({}^{\circ}\)C for 5 min; 25 cycles of denaturation at 98 \({}^{\circ}\)C for 30 s, annealing at 53 \({}^{\circ}\)C for 30 s, and extension at 72 \({}^{\circ}\)C for 45 s; with a final extension at 72 \({}^{\circ}\)C for 5 min. For fungal amplification, the annealing temperature was 55 \({}^{\circ}\)C, and the number of cycles was increased to 30 [45].  
PCR products were purified using Vazyme VAHTS DNA Clean Beads (Vazyme, Nanjing, China) and quantified with a Quant-iT PicoGreen dsDNA assay kit (Invitrogen, Carlsbad, CA, USA). Equimolar amounts of purified amplicons were pooled, and paired-end 2 \(\times\) 250 bp sequencing was performed on the Illumina NovaSeq platform at Shanghai Bioprofile Technology Co., Ltd., Shanghai, China.
# Enhancing Smart City Safety and Utilizing AI Expert Systems for Violence Detection
## 3 Results
### Performance Metrics of Model
Classification loss remained at 0.001 during training and dropped to 0.0028 during validation. On the other hand, the individual precision, recall, and mean average precision (mAP) at thresholds of 0.5 and 0.95 were evaluated with respect to the increasing number of epochs. These graphs depict the performance at each epoch. The graph depicting precision and recall illustrates an increasing accuracy concerning the number of epochs. The average precision graph at a threshold of 0.5 initially showed instability, but after 50 epochs, precision accuracy steadily increased, maintaining stable performance. Similarly, at the threshold of 0.595, a similar pattern emerged but stabilized around 30 epochs. These graphs collectively depict the model's accurate performance at individual points. Figure 8 displays the performance metrics of the action classification using the LSTM model based on pose estimation. This performance evaluation reflects the model's ability to classify actions trained using MediaPipe pose estimation outputs. The three actions of the validation results are shown in the graph as follows: baseball bat action, knife action, gun action, and no action. Each separate class and average of action performance is evaluated. The accuracy values of these three action classes are 96%, 82%, and 87% for the baseball bat, knife, and gun violent actions. These performances are quite impressive for this type of complex model as depicted in the figure below. Figure 6: (**a**) The confusion matrix; (**b**) the precision recall curve for attack types or divergent classes. Moreover, we compare the detection performance of the proposed YOLO v7 model with other violent attack detection approaches using different data and within different environments as presented in Table 3. In this comparison, we consider previous studies that focused on object detection. As shown in the table, our proposed model's accuracy is higher than those of others. Therefore, the results prove that the proposed model has the capacity to accurately detect violent attacks and violent actions. Figure 8: Performance of violence action classification in MediaPipe using LSTM. Figure 7: The YOLO v7 main metrics of the training and validation loss and accuracy of the model.
## 5 5G FWA Field Trials
### 5G FWA Trial Case Study  
The 5G network aims at offering near-ubiquitous connectivity with an enhanced capacity. To realize this, Orange considers FWA as a feasible 5G use case in markets where fiber, as well as other fixed broadband standards, are limited. Therefore, in the test, Orange aims at delivering broadband services to Romanian households where there is a prospect of approximately 10% of the total broadband customer to be supported by the FWA connection [329, 347, 348].  
The 5G FWA trial setup conducted in Floresti comprises two Samsung FWA points that run at 500 MHz of experimental 26 GHz spectrum. Furthermore, 128 element MIMO antennas are employed in the access points (APs). One of the APs was deployed on a tower that was placed on top of a hill, while the other one was deployed on a comparatively lower tower, which is 20 m. Furthermore, there was a virtual baseband unit (vBBU) capacity which was based on Samsung COTS server hardware. It was deployed approximately 30 km away in the Orange data center. The employed FWA core was from Cisco and it was a dedicated Virtual Core. Note that FWA deployments may not be based on vRAN; the implementation was just to investigate the possible advantages regarding efficiency and flexibility that could be exploited from the architecture. In addition, a high layer split was employed between the AP and vBBU [348]. To validate the connectivity, 15 residential customers who were living in various types of housing were chosen to participate in the test. They were to have a first-time experience with the novel system from the comfort of their respective houses. A number of the customers had an external unit for receiving the FWA signal which is subsequently relayed to an indoor Wi-Fi Cisco router. The rest of the customers had an indoor Samsung terminal, that was daisy-chained to the Wi-Fi Cisco router, for receiving the FWA signal directly. Generally, the outdoor terminals demand professional fitting, as well as positioning. On the other hand, the indoor units are just plug-and-play devices [329, 347, 348].
## 3 System Model  
As illustrated in Figure 1, there are four entities in the V2X network: local and cloud-based trusted authority (TA), 5G base station (5G-BS), roadside units (RSU), and vehicle (V). First, we describe the functions of these entities.  
Trusted authority (TA): In the proposed V2X network architecture, we defined three types of TAs. Local TA is overall responsible for the V2X network and the authentication of RSUs. Each vehicle belongs to a particular TA called Home TA, which holds all information  
Figure 1: System model of V2X network.  
about the vehicle and can validate and authenticate the vehicle's identity. In real life, Home TA could be a country, state, or council vehicle registration authority. When the network is initialized, the Local TA will connect with all available Home TAs to form the Cloud TA network. Third-party TA is in a vehicle prospective is all Cloud TA's, excluding Home and Local ones. TAs have high computational and storage capacity and can be connected via low-latency secure networks. In an actual situation, these could be achieved using any public cloud environment such as AWS or Azure. All the TAs initialize themself with secret and commitment coefficients, while global cryptographic parameters, described in Table 1, will be the same for all TAs. RSUs and vehicles must receive partial share and commitment coefficients from all TAs to be securely enrolled on the V2X network.  
Fifth generation base station (5G-BS): 5G-BS provides a communication link to connect RSU, and 5G enables vehicles to TAs and V2X applications. This is also responsible for providing authentication services to 5G-enabled vehicles to use side links, i.e., V2V communication.  
Roadside units (RSU): RSU provide connectivity to vehicles which do not have 5G. Vehicles and RSU can communicate with ad hoc Wi-Fi using IEEE 801.11 P. In addition, RU and Vehicle provide multi-hop communication, which enables V2V multi-hop communication. Additionally, RSU can help vehicles without 5G to securely enroll to the V2X network and applications.  
Vehicle (V): This is the main entry that consumes the V2X application and generates a vast amount of data that must be securely transferred to other vehicles, cloud-based data analytic platforms, and AI and ML algorithms. The vehicles can have only Wi-Fi, only 5G, or both, and they can participate in V2V and V2I communication.
# Turning Video Resource Management into Cloud Computing
## 3 The Video Resource Management System in HDFS-Based Cloud Computing
### HDFS-Based Architecture of Video Resource Management
#### 3.2.1 The Clients  
The clients provide functions of video information searching, video playing control, etc. Video detailed attributes are stored in Web Clusters, and maintained (such as update, upload, and delete manipulations) by administrators. Users can get continuous streaming media information that is encoded, compressed, and cached through sending a request to the HDFS server for access to videos.
# 3D U-Net for Skull Stripping in Brain MRI
## 2 Materials and Methods
### Network Architecture  
The complete network architecture is illustrated in Figure 1. There are two main parts of the network. The first part is the contracting encoder (left side in Figure 1), which extracts global features. It consists of convolution block and max pooling. A convolution block consists of convolution, batch normalization [48], and a rectified linear unit (ReLu). This was repeated two times. Batch normalization (BN) is used for faster convergence. BN is the normalization of the activation function value or the output value of the convolution. When BN is used, it is not influenced by a parameter scale during weight propagation. Thus, the learning rate that controls how much we adjust the weights can be increased, enabling rapid learning. In our training, the decay value of the BN was set to 0.9. The convolution filter size was 3 \(\times\) 3 \(\times\) 3 voxels. The max pooling employed for downsampling, has the size of 3 \(\times\) 3 \(\times\) 3 voxels with a stride of 2 in each dimension. Although max pooling has the advantage of reducing computational volume and adding robustness to noise, it has the disadvantage of losing important information. After many experiments, we found that three max pooling operations (in each dimension) are the most appropriate.  
The number of feature channels is doubled after each downsampling. The dropout layer follows the fourth convolutional block. Dropout is a regularization technique to avoid overfitting [49]. Overfitting refers to the case where the model fits very well with the training dataset but does not perform well with test dataset. The reason for this is that the model is excessively optimized and fit for the training dataset. Therefore, it only works for the training dataset. The dropout technique intentionally drops out some units in the network in the training process to reduce the overfitting. Dropout improves performance by virtually creating many models and executing predictions. Learning by one model may inevitably lead to overfitting. However, if multiple models are trained and predictions with each model are made, then the risk of overfitting can be reduced. Dropout is good for training, but it generally takes more training time. Therefore, we applied dropout once at the fourth layer because the most concentrated features appear after the fourth layer.  
The second part is the decoder (right in Figure 1) called expanding path in which each step consists of up convolution, concatenation with the respective cropped feature map from the encoder part, and the two convolution blocks. In CNNs, the convolution layer reduces the size of the feature map through convolution. Up convolution, however, increases the size of the feature map. It works in a way that it makes zero-padding around each pixel followed by convolution on the padded image. The final convolution filter size is 1 \(\times\) 1 \(\times\) 1. It is designed to get the desired class number from 64 features.
## 3 Simulation and Analysis  
The parameters of SINS inertial devices and GNSS error parameters in the simulation experiment are set as shown in Table 2. We use MATLAB to perform the simulation studies, the real train trajectory and speed are simulated first, and then, the GNSS observations are simulated by adding Gaussian white noise to the train trajectory and speed information.  
The generation of gyro and accelerometer increments in the IMU data is implemented based on the inertial navigation inversion algorithm, and the device noise is generated based on the first-order Markov process.
## 3 Methods
### Class-Specific Dictionary Learning
It is known that \(\lambda_{1}\cdot\left\|X_{i}\right\|_{1}+\lambda_{2}\cdot f(X_{i})\) is a constant when \(X_{i}\) is fixed, so Equation (7) can be simplified as  
\[\underset{D_{i}}{\text{min}}\ \Big{\{}\left\|Y_{i}-D_{i}X_{i}\right\|_{F}^{2} \Big{\}}\ s.t.\ \left\|d_{i}^{k}\right\|_{2}=1 \tag{9}\]  
In this case, the objective function is the same as that in conventional dictionary learning, and dictionary \(D_{i}\) can be updated according to Algorithm 1. ```
Input:\(D_{i}=\left\{d_{i}^{1},d_{i}^{2},\ldots,d_{i}^{n_{i}}\right\}//\text{ Initialized class-specific dictionary}\) Input:\(Y_{i}=\left\{y_{i}^{1},y_{i}^{2},\ldots,y_{i}^{N-1},y_{i}^{N}\right\}//\text{Training data of }i-th\) class Input:\(X_{i}=\left\{x_{i}^{1},x_{i}^{2},\ldots,x_{i}^{N}\right\}//\text{Sparse coefficient matrix}\)
1:For\(k=1,2,3,\ldots,n_{i}\)do
2: It's known that \(D_{i}X_{i}=d_{i}^{k}x_{i}^{k}+\sum_{j\
eq k}d_{i}^{k}x_{i}^{k}\) (\(j=1,2,\ldots,n_{i}\)). Let \(E_{i}^{k}=Y_{i}-\sum_{j\
eq k}d_{i}^{j}x_{i}^{j}\), then Equation (9) can be represented as \(\underset{d_{i}^{k}}{\text{min}}\left\{\left\|E_{i}^{k}-d_{i}^{k}x_{i}^{k} \right\|_{F}^{2}\right\}\ s.t.\ \left\|d_{i}^{k}\right\|_{2}=1\). 3: Solve \(d_{i}^{k}\) by least square method. 4: Normalize \(d_{i}^{k}\), and \(d_{i}^{k}=d_{i}^{k}/\left\|d_{i}^{k}\right\|\). 5:endfor Output:\(D_{i}=\left\{d_{i}^{1},d_{i}^{2},\ldots,d_{i}^{n_{i}}\right\}//\text{The updated class-specific dictionary}\)
```  
**Algorithm 1**Fix \(X_{i}\) and optimize \(D_{i}\)  
SAR target recognition includes two steps: feature extraction and classification. Incremental SAR ATR methods require incremental feature extraction, and it is a subject worthy of study. In this paper, we directly convert SAR images into vectors and then construct the training set and test set. The method does not have the process of feature extraction.
# Study on Bending Performance of Tightly Spliced Truss-Reinforced Plate-Honeycomb Flat Beam
###### Abstract  
The tightly spliced truss-reinforced plate-honeycomb flat beam is a novel type of composite beam, with the advantages of high rigidity, high bearing capacity, and ease of construction. In this study, on the basis of the performance in bending tests, the numerical analysis method is used to study the influence of the honeycomb hole-height ratio, and section height on the bearing capacity of the honeycomb composite flat beam. On this basis, the simplified method to calculate the ultimate bending capacity of the new honeycomb composite flat beam was proposed. The research results show the failure modes of the specimens are mainly divided into two states, including the deflection exceeding the limit and the concrete flange plate separating from the steel beam. The tightly spliced truss-reinforced plate-honeycomb flat beams have good ductility, of which the average value reaches 8.2. The simplified method proposed in this article for calculating this type of honeycomb composite beam has an error of less than 10% in terms of bending bearing capacity, which has advantages over the double T-shaped steel method. The calculation method and design suggestions proposed in this study provide a basis for the research and application of this type of composite flat beam.  
Keywords:plate-honeycomb flat beam; bending performance; failure mechanism; design method +
Footnote †: journal: Applied sciences
## 2 Experimental Setup and Data Collection
### Experimental Data Collection  
The output signal of the FMCW fuze detector in this experiment has two channels, one is the fuze beat output signal and the other is the fuze start signal. The FMCW fuze signal processing circuitry processes the beat signal and outputs a start signal if it is judged to be a target, which in turn allows the combat section to effectively destroy the target.  
The collected signal data are shown in Figure 2 below. Each group of graphs is divided into 3 rows of data; the first row is the fuze output beat signal, and the second row in red is the fuze start signal, it can be seen that the fuze start signal is a downward negative pulse signal. The third-row data is the signal obtained by taking 50,000 points forward in the  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Type of Parameter** & **Parameter Values** \\ \hline FMCW fuze centre frequency (\(f_{c}\)) & 3 GHz \\ FMCW fuze modulation bandwidth (\(\Delta F\)) & 15 MHz \\ Jammer distance (\(L_{j}\)) & 1.75 m \\ Target distance (\(L_{t}\)) & 10 m \\ Jamming signal frequency sweeping bandwidth (\(B_{j}\)) & 30 MHz (\(2\Delta\)F) * \\ \hline \hline \end{tabular}  
* To cover the bandwidth of the FMCW fuze, the frequency sweeping range is set to 2 times the modulation bandwidth of the fuze.  
\end{table}
Table 1: Experimental parameter settings.  
Figure 1: Experimental layout diagram.  
beat signal at the moment of the start signal output. Since the sampling frequency of the oscilloscope is 1 MHz, 50,000 points correspond to a time of 50 ms.  
In this experiment, 180 sets of fuze output signals under target action, 60 sets of fuze output signals under noise amplitude frequency sweeping, sine amplitude frequency sweeping, and square amplitude frequency sweeping jamming signals were collected separately. Overall, 360 groups of data were collected. The data collected were used to validate the method proposed in this paper and are explained in detail in the subsequent sections.
## 3 Control Algorithm Design
### Attitude Controller Design  
The attitude controller of the proposed sun-oriented spin-stabilized attitude control method in this paper consists of four control phases: initial damping phase, sun-aligned phase, spin-up phase, and spin-stabilized phase.