esa-sceva/satcom-chunk-collection huggingface dataset:
# Rib Design for Improving the Local Stiffness of Gearbox Housing for Agricultural Electric Vehicles
###### Abstract  
In this work, a rib design process is proposed to improve the local stiffness of gearbox housing for agricultural electric vehicles. Unlike conventional engines, electric powertrain noise includes high frequency factors and pure tones. Considering these characteristics, local stiffness was evaluated in the frequency range of interest for the prediction of dynamic behavior. The local stiffness of the gearbox housing was evaluated using the finite element (FE) model. Experimental modal analysis and FE model update were conducted to ensure the reliability of the results. Using the results of local stiffness evaluations, the stiffness weak point was identified, and the rib design location was selected through a strain analysis. The shape of the ribs was parameterized by width, length, and height, and parameter studies were used to compare the local stiffness increase rate according to the rib design pattern. According to the results, the stiffness increase rate against the same mass was distributed differently according to the rib shape, and the most efficient rib shape was selected from a manufacturing perspective.  
Keywords:local stiffness evaluation; agricultural electric vehicle; rib design; FE model update; parameter study; noise and vibration +
Footnote †: journal: Applied Sciences
# Status of BIPV and BAPV System for Less Energy-Hungry Building in India--A Review
## 2 PV Technologies for BIPV/BAPV
DSSCs are considered for BIPV application due to its simpler and low-cost fabrication process, flexible, have potential to operate at diffuse solar radiation [40; 41]. Colored and semi-transparent windows are popular for BIPV application [42; 43]. However, factors inhibiting to its commercialization are long term- stability and durability. Table 1 listed the advantage and disadvantages of various PV technologies. Recently, Perovskite PV gained attention due to its efficiency improvement in 10 years. However, they are mostly operated and fabricated at inert atmospheric condition. Tunable transparency [44], and low temperature fabrication [45] makes it fascinating to researcher for BIPV application [46; 47]. \begin{table}
\begin{tabular}{|c|c|c|c|} \hline
**Solar cell** & \multicolumn{2}{c|}{**Advantages**} & \multicolumn{2}{c|}{**Disadvantages**} \\ \hline Monocrystalline silicon solar cells & \(\bullet\) & Matured PV technologies. & \(\bullet\) & There is a lot of waste \\
[17; 48; 49] & \(\bullet\) & Highly durable. & material when silicon is \\  & \(\bullet\) & Suitable for & removed during processing. \\  & BAPV application. & \(\bullet\) & At high temperature, \\  & & & performance degrades. \\  & & & \(\bullet\) & Opaque in nature, hence less \\  & & & suitable for artistic \\  & & & BIPV application. \\ \hline \end{tabular}
\end{table} 
Table 1: Advantages of disadvantages of various solar cells.
## 2 Related Work  
Deflectometry has high accuracy, speed, and is non-contact, making it suitable for a variety of industrial applications [9]. However, traditional deflectometry methods, such as phase-shifting techniques, require multiple images to be captured in order to obtain surface gradients in two perpendicular directions [9; 17]. This process can be time-consuming and inefficient, particularly for dynamic or real-time applications.  
Single-shot deflectometry was developed to overcome these limitations [9; 17; 18; 25; 26]. [PERSON] et al. developed a single-shot freeform surface profiler based on spatially phase-shifted lateral shearing interferometry [17]. This approach, while simplifying the measurement process, still struggles with complex surface geometries. Phase measuring profilometry has also seen advancements, with deep learning methods enhancing measurement capabilities [27; 28]. Working from a single image significantly reduces the time required for surface measurement; however, there are resulting challenges in interpreting the complex patterns reflected by specular surfaces, especially those with low reflectivity or intricate geometries.  
The application of deep learning in optical metrology, including deflectometry, has shown significant promise [12; 17; 26; 29]. Deep learning models can learn complex patterns and provide robust phase retrieval from noisy or low-quality data. DYNet++ [17] utilizes deep learning for single-shot deflectometry, capable of retrieving phase information from composite patterns even in challenging conditions with both closed- and open-loop fringe patterns.  
[PERSON] et al. proposed a deep learning-based deflectometric method for freeform surface measurement [26]. Their approach involves devising a deep neural network specifically for freeform surface reconstruction. This method offers a solution for the general measurement of freeform surfaces while minimizing measurement errors caused by noise and system geometry calibration.
## 3 Research Findings
### Case Study Result  
To validate the proposed model, a case study was conducted in a metropolitan area with significant telecommunication demands. The area was divided into three zones, each with distinct population densities and service requirements. Initial configurations of existing BTS hubs were evaluated, and potential improvements were identified. The application of the NSGAII and MOPSO algorithms demonstrated a 25% improvement in demand coverage and a 15% reduction in operational costs compared to the baseline configuration. These results highlight the model's effectiveness in addressing both economic and environmental objectives simultaneously.
## 4 Results of Probabilistic Stability and Performance Assessment
### The Grid SCR Impact on System Stability and Performance
#### 4.1.2 Nominal and Robust Stability and Performance Comparison  
The system's stability and performance under different grid conditions could also be compared in terms of \(NS\), \(RS\), \(NP\), and \(RP\). In Figure 6b, the lines show critical mode variations under grid inductance uncertainties, and white circles with colored multiplication signs show the critical mode location under nominal conditions. As an example, the following conclusion could be drawn for SCR = 10 (the purple curve): the critical mode under the nominal condition was in the LHP and met the performance requirements (the green line); hence, the system had nominal stability and performance.  
Under grid inductance variations, although the purple lines were in the LHP, they crossed the green line. Therefore, the system was robustly stable, but it did not meet the robust performance definition. In the same manner, conclusions could be drawn for other SCRs that are summarized in Table 4.
# A Tutorial for Feature Engineering in the Prognostics and Health Management of Gears and Bearings
## Appendix A
### Specific Gear Features Extraction  
function [feature, feature_name] = Gear_feat(tsa_sig,res_sig,diff_sig,gmf,sf,fs)  
% Extract specific gear features  
% Input  
% tsa_sig: TSA signal  
% res_sig: Residual signal  
% diff_sig: Difference signal  
% gmf: Gear mesh frequency  
% sf: Shaft speed  
% fs: Sampling frequency  
% Output  
% feature: Calculated feature value  
% feature_name: Corresponding feature name  
% Copyright 2020. SDOL  
% FFT  
N = length(tsa_sig); X = abs(fif(tsa_sig))/N*2; X = X(1:ceil(N/2));  
f = [0:N-1]/N*fs; f = {f1:ceil(N/2)};  
% Find GMF amplitude  
for nn=1:10 % harmonic number  
ind(nn) = find(f>gmf*nn-5 & f<gmf*nn+5);  
gmf_amp(nn) = X(ind(nn));  
for sn = 1:6 % sideband number  
ind_side(1,sn,nn) = find(f>gmf*nn-sf*sn-5 & f<gmf*nn-sf*sn+5);  
ind_side(2,sn,nn) = find(f>gmf*nn+sf*sn-5 & f<gmf*nn+sf*sn+5);  
side_amp(:,sn,nn) = X(ind_side(:,sn,nn));  
End  
% 1. FM0  
FM0 = (max(tsa_sig)-min(tsa_sig))/sum(gmf_amp);  
% 2. SER  
SER = sum(sum(side_amp(:,:,1)))/gmf_amp(1);  
% 3. NA4  
ress = res_sig - mean(res_sig);  
cur_res = res(:,end); % Current signal  
N = size(res,1); M = size(res,2); % N \(\times\) M: N samples M run ensemble  
NA4 = N*sum(cur_res.*4)/(sum((sum((s==5.2,1)),2))/M)^2;  
% 4. NB4  
[a,b] = butter(1,lgmf-sf gmf*sf*lf)/(fs/2),'bandpass');  
bp_sig = filter(a,b,tsa_sig);  
env_bp_sig = abs(hilbert(bp_sig)); % Envelope  
s = env_bp_sig - mean(env_bp_sig); % Current signal  
cur_s = s(:,end); % N = size(s,1); M = size(s,2); % N \(\times\) M: N samples M run ensemble  
NB4 = N*sum(cur_s_*4)/(sum((sum((s==5.2,1)),2))/M)^2;  
% 5. FM4  
diff = diff_sig - mean(diff_sig);  
FM4 = length(diff)*sum(diff:4)/(sum(diff:2).^2);  
M6A  
M6A = length(diff) 2*sum(diff:6)/(sum(diff:2).^3);  
% 7. MSA  
M8A = length(diff) 2*sum(diff:8)/(sum(diff:2).^4);  
% 8. ER  
ER = rms(diff,sig)/sum(gmf_amp+squeeze(sum(sum(side_amp))))');  
feature = [FM0, SER, NA4, NB4, FM4, M6A, MSA, ER];  
feature_name = {'FM0', 'SER', 'NA4', 'NB4', 'FM4', 'M6A', 'M8A', 'ER' };
# Review on Wearable Technology in Sports: Concepts, Challenges and Opportunities
## 3 Hardware and Software of WT in Sport
### Sensors Layer  
The first layer is the sensor layer, where information obtained from the human body through various means is transmitted as signals to the processor, the second layer of the WT, using analog-to-digital converters (ADC), digital inputs, Inter-Integrated Circuit (I2C), Universal Asynchronous Receiver/Transmitter (UART), Serial Peripheral Interface (SPI), and similar methods. In today's sports field, WT with sensors or sensor systems provides valuable data by measuring physiological, motion, position, and environmental conditions.
## 3 Proposed Methods
### Feature Alignment  
This study introduces a feature reconstruction module based on knowledge distillation, which facilitates the completion and alignment of occluded region features through a teacher-student model framework. The teacher model extracts features from full-face images, while the student model operates on occluded face images. After each residual block, the feature maps of the teacher model guide the reconstruction of the student model's feature maps.  
Let the input features of the full-face image and the occluded face image be represented as \(\textbf{F}_{\text{clean}}\in\mathbb{R}^{H\times W\times C}\) and \(\textbf{F}_{\text{occ}}\in\mathbb{R}^{H\times W\times C}\), respectively. Both the teacher and student models adopt the ResNet-34 architecture. The teacher model extracts global features from complete, unoccluded images, while the student model extracts partial features from occluded images. After each residual block, the feature map from the teacher model \(\textbf{F}_{\text{teacher}}^{l}\in\mathbb{R}^{H_{l}\times W_{l}\times C_{l}}\) is aligned with the corresponding feature map from the student model \(\textbf{F}_{\text{student}}^{l}\in\mathbb{R}^{H\times W_{l}\times C_{l}}\).  
At each residual block \(l\), a feature alignment module aligns the student model's feature map with the teacher model's feature map. The aligned features are then passed as inputs to the next network layer, allowing the student model to capture the complementary relationship between the occluded and unoccluded regions. The feature alignment process is computed as follows:  
\[\textbf{F}_{\text{aligned}}^{l}=\textbf{F}_{\text{student}}^{l}+\text{ Conv}(\text{Concat}(\textbf{F}_{\text{student}}^{l},\textbf{F}_{\text{ teacher}}^{l})),\]  
where Conv represents the convolution operation and Concat denotes the concatenation of feature maps along the channel dimension. By fusing feature information from both the teacher and student models, the aligned feature map \(\textbf{F}_{\text{aligned}}^{l}\) enables the student model to learn the implicit relationships between occluded and unoccluded regions, thereby facilitating the reconstruction of occluded features.  
Figure 2: The dataset is divided into 50% randomly occluded and 50% non-occluded images. These are fed into a binary classification convolutional neural network, which labels the images as _face_ and _occluded_face_. The images labeled as occluded faces are used as input for the feature reconstruction network to complete the missing features, while non-occluded images are directly sent to the original age estimation network.
# Evaluation of Diversification Techniques for Legal Information Retrieval +
## 2 Legal Document Ranking Using Diversification
### Diversification Heuristics
* Biased LexRank: Biased LexRank [6] provides for a LexRank extension that takes into account a prior document probability distribution, e.g., the relevance of documents to a given query. The Biased LexRank scoring formula is analogous to LexRank scoring formula Equation (10), with matrix \(A\), which represents the probability of jumping to a random node in the graph, proportional to the query document relevance. Algorithm 5 is also used to produce a diversity-oriented ranking of results with the Biased LexRank method. In Biased LexRank scoring formula Equation (10), we set matrix \(B\) as the connectivity matrix based on document similarity for all documents that are in the initial retrieval set \(N\) for a given query and matrix \(A\) elements proportional to the query document relevance. * DivRank: DivRank [7] balances popularity and diversity in ranking, based on a time-variant random walk. In contrast to PageRank [11], which is based on stationary probabilities, DivRank assumes that transition probabilities change over time; they are reinforced by the number of previous visits to the target vertex. If \(p_{T}(u,v)\) is the transition probability from any vertex \(u\) to vertex \(v\) at time \(T\), \(p^{*}(d_{j})\) is the prior distribution that determines the preference of visiting vertex \(d_{j}\) and \(p_{0}(u,v)\) is the transition probability from \(u\) to \(v\) prior to any reinforcement, then, \[p_{T}(d_{i},d_{j})=(1-\lambda).p^{*}(d_{j})+\lambda.\frac{p_{0}(d_{i},d_{j}). N_{T}(d_{j})}{D_{T}(d_{i})}\] (11) where \(N_{T}(d_{j})\) is the number of times the walk has visited \(d_{j}\) up to time \(T\) and, \[D_{T}(d_{i})=\sum_{d_{j}\in V}p_{0}(d_{i},d_{j})N_{T}(d_{j})\] (12) DivRank was originally proposed in a query independent context; thus, it is not directly applicable to the diversification of search results. We introduce a query dependent prior and thus utilize DivRank as a query-dependent ranking schema. In our setting, we use documents that are in the  
strategy: (i) qualitative analysis in terms of diversification and precision of each employed method with respect to the optimal result set; and (ii) scalability analysis of diversification methods when increasing the query parameters.
## 2 Data Analysis and Preprocessing
### Data Preprocessing
#### 2.4.1 Z-Score Standardization  
In this paper, both power data and NWP data are taken as the inputs of the model. Because the units of data are different, they are often on different orders of magnitude. To facilitate comparison and feature fusion, they must be unified on the same scale [33; 34]. In addition, to facilitate the gradient calculation and back propagation of the depth model itself, the input data often need to be standardized. This paper adopts the Z-Score standardization method. Its advantages are that the algorithm is simple, it is not affected by the order of magnitude, and the results are easy to compare. The formula is below:  
\[X_{\text{new}}=\frac{X-\mu_{X}}{\sigma_{X}} \tag{2}\]  
where \(X\) stands for the raw data and \(X_{\text{new}}\) represents the data after standardization. This method conducts standardized based on the mean and standard deviation of the original data. The processed data show normal distribution with a mean of 0 and a variance of 1.
## 3 Seizure Detection Performance and Power Consumption Evaluation  
### EEG Data  
The performance of the system proposed in this work was evaluated using the Children's Hospital Boston and the Massachusetts Institute of Technology (CHB-MIT) database described in [18] and is available at PhysioNet ([[http://www.physionet.org/physiobank/database/chbmit/](http://www.physionet.org/physiobank/database/chbmit/)]([http://www.physionet.org/physiobank/database/chbmit/](http://www.physionet.org/physiobank/database/chbmit/))). The database consists of 24 sets of EEG recordings collected from 23 pediatric subjects (age \(<\) 18) undergoing anti-seizure medication withdrawal for epilepsy surgery evaluation. One set of recordings (chb21) was recorded 1.5 years after the first recording (chb01) from the same subject. The EEG signals were recorded using 23 channels according to the International 10-20 system. A bipolar montage was adopted to reduce the effects of common artifacts and eliminate the influence of contaminated references. The signals were sampled at 256 Hz with 16-bit quantization and later down-sampled, offline, to 128 Hz to reduce the computational requirements. The EEG signals were visually inspected by medical experts to determine the start and end times of seizure events. A total of 182 seizure events were recorded during 971 h of EEG monitoring. The duration of a seizure event varied between six and 752 s.  
The EEG recording of each subject was segmented into many one-hour long records. The number of the resulting records from a single subject ranged from 17 to 156. Each record can contain zero, one or more seizure events. The records that do not contain any seizure events are referred to as _non-seizure records_, whereas the records containing one or more seizure events are referred to as _seizure records_.  
Note that in our analysis, although artifacts were present in the scalp EEG signals, no artifact removal or rejection was performed on the signals. The reasons are as follows. First, artifact rejection discardsthe epochs of EEG signals that are contaminated with artifacts. For online real-time seizure detection, however, this poses a huge drawback, as the system becomes inoperative during the time epochs when the signals are rejected and, hence, cannot be used to detect seizures. Second, while several blind source separation (BSS)-based artifact removal techniques have been shown to be successful in removing eye movement, blinks and muscle artifacts from EEG recordings [19], BSS-based technique require sufficient time samples in order to reliably estimate the weights of the unmixing matrix. For \(n\)-channel EEG data, it is recommended to have some multiple \(k\) of \(n^{2}\) time samples, where \(k\) is expected to be 10 or higher. In our case, the minimum number of samples required would be \(10\times 23^{2}=5290\), which leads to an epoch size of 41.3 s at a sampling rate of 128 Hz. Such a large epoch size is generally undesirable for online real-time applications.
## 7 Simulation Results and Discussion
### Simulation Settings  
To perform simulations, nodes are varied from 150-450 with 45 sonobuoys positioned at the water surface to gather data from underwater nodes. The network dimensions are 1500 m \(\times\) 1500 m \(\times\) 1500 m. Moreover, the transmission range is \(R_{c}=250\) m and DR = 50 kbps. Also, we consider a payload of 150 bytes in each data packet including 20 bytes of beacon message. The energy dissipation associated with transmission, reception, idle state and depth adjustment is \(P_{t}=2\) W, \(P_{r}=0.1\) W, \(P_{i}=10\) mW and \(E_{m}=1500\) mJ/m, respectively [5]. The average of 50 distinctive simulation runs is taken for getting near optimal results against each value plotted in the graphs.  
Figure 7: Feasible regions. (**a**) End to end delay: feasible region for GDGOR-IA; (**b**) end to end delay: feasible region for GRMC-SM.
# Improved Cryptanalysis of Some RSA Variants
## 5 Experimental Results
### Experiments with Large Examples  
We applied the procedure described in Theorem 3 to examine cases where the public key \((N,e)\) involves large values. By conducting a series of computational tests, we successfully resolved the modular equation  
\[x_{1}(x_{2}^{2}+ax_{2}+b)\equiv x_{3}\pmod{e},\]  
where  
\[a=2(\tilde{p}+\tilde{q}),\ b=(\tilde{p}+\tilde{q})^{2}-(N+1)^{2}.\]  
This method enables an efficient factorization of the modulus \(N=pq\) when an amount of the most significant bits of \(p\) is available. Moreover, the conditions \(d>N^{2-\sqrt{\tilde{a}}}\) and \(d>N^{2-\sqrt{2a\sigma}}\) are satisfied, which demonstrates that the optimal bounds in the literature [12; 13] fail to break these RSA variants.  
The outcomes of these experiments are presented in Table 2, where each row specifies the corresponding parameters:  
* \(\mathrm{bl}(n)\) represents the bit-length of the value \(n\).
* \(\delta\) is a parameter where \(|u|\leq N^{\delta}\) holds.
* \(\delta_{0}\) denotes a parameter such that \(d\approx N^{\delta_{0}}\).
* \(\alpha\) is defined through the relation \(e\approx N^{\alpha}\).
* \(\gamma\) corresponds to a parameter satisfying \(|z|\leq N^{\gamma}\).
* \(\sigma\) is a parameter satisfying \(|p-\tilde{p}|\leq N^{\sigma}\).
* \(\mathrm{Ns}(p)\) denotes the number of known most significant bits of \(p\).
* \(\mu\) and \(\zeta\) are parameters involved in the construction of the lattice \(\mathcal{L}\), which has dimension \(\omega\) as shown in Theorem 3.
* \(\mathcal{T}(s)\) refers to the computation time in seconds required for executing the LLL algorithm and the Grobner basis computation.
## 5 Test of Driving Condition and Power Matching  
Using a 35-ton EWL as an example, Figure 15 depicts the shovel loading test in the prototype mining area. The investigation described in this paper involved conducting comprehensive vehicle functional testing and validation, primarily focusing on the dynamic and operational performance of the EWL. The testing is performed under the condition of the straight-line movement of the EWL. During this movement, the power distribution among the four wheels of the EWL is observed. By analyzing the power curve of EWL, the adequacy of the overall power distribution for the loading requirements can be verified.  
(1) Straight-line Driving Conditions  
The speed variation curve for unloaded first-gear linear acceleration is shown in Figure 16a. From 10 to 42 s, the driver initially presses the throttle pedal to the maximum, accelerating the entire vehicle. At 42 s, electric braking is applied, and the vehicle decelerates to a speed of 0 within 58 s.  
The torque curves for all four wheels during no-load first-gear linear acceleration are depicted in Figure 16b. From Figure 16b, it can be observed that the variation trend of the motor torque for all four wheels is essentially consistent during no-load driving. The maximum motor torque is around 3500 Nm, with the sum of the torque from the front two motors being roughly equal to the sum of the torque from the rear two motors. Each set constitutes approximately 50% of the total motor torque, demonstrating the rationality of the motor-power-matching method. This validates the applicability of the proposed method.  
Figure 15: Prototype mining area shovel loading test.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Driving Conditions** & **Total Power/kW** & \
\begin{tabular}{c} **Power of Front Axle** \\ **Motor/kW** \\ \end{tabular} &
\begin{tabular}{c} **Power of Rear Axle** \\ **Motor/kW** \\ \end{tabular} \\ \hline No-load & & & \\ four-wheel-drive & 456 & 114 & 114 \\ acceleration & & & \\ \hline Full-load & & & \\ four-wheel-drive & 576 & 213 & 71 \\ acceleration & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Power Matching after optimization.  
Power-Matching Test  
After calculating the torque and speed, the power of the driving motor is obtained. The power curves of the two motors are shown in Figure 17. Under no-load conditions, the maximum power for front- and rear-wheel acceleration is 112.7 kW. Under full-load conditions, the maximum power for front-wheel acceleration is 211.4 kW, and for the rear wheel, the maximum power for front-wheel acceleration is 72.1 kW. The results indicate that the power data for the front and rear motors are close, with an overall deviation of less than 2%. Power matching and control meet the practical loading requirements.