esa-sceva/satcom-chunk-collection huggingface dataset:
# VLSI-Friendly Filtering Algorithms for Deep Neural Networks
## 4 Minimal Filtering Algorithms
### Algorithm 3, \(M=7\)  
Let \(\textbf{X}_{13\times 1}=[x_{0},x_{1},\ldots,x_{12}]^{\text{T}}\) be a vector that represents the input data set, \(\textbf{W}_{7\times 1}=\left[w_{0}^{(7)},w_{1}^{(7)},\ \ldots,w_{6}^{(7)}\right]^{\text{T}}\) be a vector that contains the coefficients of the impulse response of a 7-tap FIR filter, and \(\textbf{Y}_{7\times 1}=\left[y_{0}^{(7)},y_{1}^{(7)},\ldots,y_{6}^{(7)} \right]^{\text{T}}\) be a vector describing the results of using a 7-tap FIR filter:  
\[\textbf{Y}_{7\times 1}=\left[\begin{array}{cccccccc}x_{0}&x_{1}&x_{2}&x_{3}&x_{4 }&x_{5}&x_{6}\\ x_{1}&x_{2}&x_{3}&x_{4}&x_{5}&x_{6}&x_{7}\\ x_{2}&x_{3}&x_{4}&x_{5}&x_{6}&x_{7}&x_{8}\\ x_{3}&x_{4}&x_{5}&x_{6}&x_{7}&x_{8}&x_{9}\\ x_{4}&x_{5}&x_{6}&x_{7}&x_{8}&x_{9}&x_{10}\\ x_{5}&x_{6}&x_{7}&x_{8}&x_{9}&x_{10}&x_{11}\\ x_{6}&x_{7}&x_{8}&x_{9}&x_{10}&x_{11}&x_{12}\end{array}\right]\left[\begin{array} []{c}w_{0}^{(7)}\\ w_{1}^{(7)}\\ w_{2}^{(7)}\\ w_{3}^{(7)}\\ w_{4}^{(7)}\\ w_{5}^{(7)}\\ w_{6}^{(7)}\end{array}\right] \tag{12}\]  
As can be seen, calculating the product (12) requires 49 multiplications and 42 additions.  
We can formulate a streamlined algorithm for computing \(\textbf{Y}_{7\times 1}\) by utilising the following matrix-vector calculation procedure:  
\[\textbf{Y}_{7\times 1}=\textbf{T}_{7\times 15}^{(7)}\textbf{T}_{15\times 25}^{(7 )}\textbf{D}_{25}^{(7)}\textbf{T}_{25\times 18}^{(7)}\textbf{T}_{18\times 13}^{(7 )}\textbf{X}_{13\times 1} \tag{13}\]  
where  
\[\textbf{T}_{7  
\[s_{5}^{(7)}=w_{2}^{(7)},\quad s_{6}^{(7)}=w_{3}^{(7)},\quad s_{7}^{(7)}=w_{3}^{(7) }+w_{4}^{(7)},\quad s_{8}^{(7)}=w_{4}^{(7)},\quad s_{9}^{(7)}=w_{3}^{(7)}+w_{5}^{ (7)},\]  
\[s_{10}^{(7)}=w_{4}^{(7)}+w_{5}^{(7)},\quad s_{11}^{(7)}=w_{5}^{(7)},\quad s_{12 }^{(7)}=w_{3}^{(7)}-w_{0}^{(7)},\quad s_{13}^{(7)}=w_{3}^{(7)}+w_{4}^{(7)}-w_{0 }^{(7)}-w_{1}^{(7)},\]  
\[s_{14}^{(7)}=w_{4}^{(7)}-w_{1}^{(7)},\quad s_{15}^{(7)}=w_{3}^{(7)}+w_{5}^{(7) }-w_{0}^{(7)}-w_{2}^{(7)},\quad s_{16}^{(7)}=w_{4}^{(7)}+w_{5}^{(7)}-w_{1}^{(7) }-w_{2}^{(7)},\]  
\[s_{17}^{(7)}=w_{5}^{(7)}-w_{2}^{(7)},\quad s_{18}^{(7)}=w_{0}^{(7)}+w_{6}^{(7) },\quad s_{19}^{(7)}=w_{1}^{(7)}+w_{6}^{(7)},\quad s_{20}^{(7)}=w_{2}^{(7)}+w_{ 6}^{(7)},\]  
\[s_{21}^{(7)}=w_{3}^{(7)}+w_{6}^{(7)},\quad s_{22}^{(7)}=w_{4}^{(7)}+w_{6}^{(7) },\quad s_{23}^{(7)}=w_{5}^{(7)}+w_{6}^{(7)},\quad s_{24}^{(7)}=w_{6}^{(7)}.\]  
Figure 4 illustrates a data flow graph of the proposed algorithm for implementing the basic filtering operation for a 7-tap FIR filter.  
Figure 4: Data flow graph of the algorithm for implementing the basic filtering operation for the case \(M=7\).
# Design of a SIMO Deep Learning-Based Chaos Shift Keying (Dlcsk) Communication System
## 3 SIMO DLCSK System Model
### Transmitter
To evaluate the system's generalization capability and robustness, we use different parameter settings to achieve \(\{\tilde{\mathbf{x}}^{(n)}\}_{n=1}^{N}\
eq\{\tilde{\mathbf{x}}^{(z)}\}_{z=1}^{Z}\) and \(\{\tilde{\mathbf{x}}^{(n)}\}_{n=1}^{N}\
eq\{\tilde{\mathbf{x}}^{(z)}\}_{z=1}^{Z}\). Hence, to generate Logistic maps, different bifurcation parameters are chosen as \(\mathbf{p}_{train}\) and \(\mathbf{p}_{test}\). In addition, to generate Chebyshev maps, different initial states are chosen for the training and the deployment phases, i.e., \(\tilde{x}^{\prime}_{1}\
eq\tilde{x}_{1}\). The transmission filter (or pulse shaping filter) is commonly used in communication systems. This filter can take different forms, such as the Gaussian filter or raised-cosine filter [72]. In this paper, we consider a rectangular pulse of unit amplitude on \([0,T_{c}]\), where \(T_{c}\) represents the chip time. The noise power can be restricted by a receiving filter at the receiver side. Note that, these filters are not our main focus.
# Thermal Interaction and Cooling of Electronic Device with Chiplet 2.5D Integration
## 3 Simulation Verification
### The Effect of Spacing  
The effect of chiplet spacing on thermal interaction between chiplets is examined. Chiplet1, with a heat flux of 200 W/cm\({}^{2}\), is the active chip, while chiplet2, with no heat flux, is the passive chip. Figure 5 shows the temperature distribution with a spacing of 0.1 mm; the maximum and minimum temperatures of the active and passive chips are recorded to study the effect of different parameters. The temperature rise is the temperature obtained from the simulation minus the ambient temperature. The active chip has a maximum temperature rise of 52.1 \({}^{\circ}\)C and a minimum temperature rise of 45.1 \({}^{\circ}\)C. The passive chip has a maximum temperature rise of 14.7 \({}^{\circ}\)C and a minimum temperature rise of 4.5 \({}^{\circ}\)C. Figure 6 illustrates the temperature rise of both active and passive chips when the chiplet spacing is changed while keeping the other parameters constant. As the spacing decreases, the temperature rise of the passive chip increases, and the temperature rise of the passive chip near the edge of the active chip is always higher than that of the other edge position, and the difference in temperature rise between the two increases. Additionally, the difference within the active chip increases.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Materials** & **Thermal Conductivity (W/mK)** \\ \hline Interposer & 148 \\ Bump & 5 \\ Chiplet & 148 \\ Heat sink & 390 \\ TIM & 5.2 \\ Substrate & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Thermal conductivity in the model.  
Figure 4: (**a**) Chiplet 2.5D integration simulation model. (**b**) Exploded view.
## 2 DQL Method
### Solution Acceptance Rule  
In the DQL method, each search step returns a set of candidate(s). Then, these candidate(s) are evaluated and compared to the current best solution \(x_{\text{best}}^{k}\). If the best candidate is accepted by the solution acceptance rule, then \(x_{\text{best}}^{k}\) is updated. There are two solution acceptance rules that are used in the DQL method. The first rule is used in the exploration stage and updates \(x_{\text{best}}^{k}\) whenever an improvement is found. The second rule is used in the convergence stage and updates \(x_{\text{best}}^{k}\) only when the candidate makes sufficient decrease. Specifically, in the convergence stage of the DQL method, a candidate \(x_{\text{current}}\in\text{Candidate\_Set}\) is accepted as \(x_{\text{best}}^{k}\) only if  
\[f(x_{\text{current}})<f(x_{\text{best}}^{k})-(\delta^{k})^{2}, \tag{11}\]  
where \(\delta^{k}\) is the current search step length. We show that this sufficient decrease rule is crucial for the convergence of the DQL method in the next section. The algorithm of the solution acceptance is denoted as  
Improvement_Check(Candidate_Set, \(x_{\text{best}}^{k}\), \(\delta^{k}\))  
and is shown in Algorithm 1.  
```
1:Evaluate Candidate_Set
2:\(x_{\text{current}}\leftarrow\text{arg}\min\{\text{function evaluations of Candidate\_Set}\}\)
3:if\(k\leq\text{max\_search}\)then
4:if\(f(x_{\text{current}})<f(x_{\text{best}}^{k})\)then
5:\(x_{\text{best}}^{k}\gets x_{\text{current}}\)
6:endif
7:else
8:if\(f(x_{\text{current}})<f(x_{\text{best}}^{k})-\left(\delta^{k}\right)^{2}\)then
9:\(x_{\text{best}}^{k}\gets x_{\text{current}}\)
10:endif
11:endif
```  
**Algorithm 1** Improvement_Check(Candidate_Set, \(x_{\text{best}}^{k}\), \(\delta^{k}\))
## 2 Object Detection Algorithm Based on Deep Learning
### yolov1 Algorithm  
The input of the yolov1 algorithm is the whole image, and the target detection is treated as a regression problem to be solved, and the position and category of the pre-selected box are obtained by using regression directly on the output layer [14]. The yolov1 algorithm does not use the RPN network structure, which makes its algorithm structure very simple, involving convolution, pooling, and two full connections. The activation function of the final output layer adopts a linear function, which not only obtains the probability of the object but also predicts the location of the bounding box [15]. The algorithm divides the original image into 7 \(\times\) 7 = 49 grids, in which each grid has to predict both the coordinates of two bounding boxes and a probability of the category of objects contained in the grid as well as the confidence of whether the objects are contained in the grid. The confidence formula is shown in Equation (1).  
\[\text{confidence}=\text{Pr}(\text{Object})\ \times\ IOU_{pred}^{truth} \tag{1}\]  
In the formula, Pr(Object) indicates whether there are artificial marks in the grid; if there is 1, then 0. \(IOU_{pred}^{truth}\) bounding box indicates the IOU between the bounding box and real box. The larger the value is, the closer the box is to the real position. By multiplying the class information predicted by each grid and the confidence information predicted, we can obtain the probability of predicting specific objects and the probability of overlapping positions PrIOU of each bounding box and sort the PrIOU obtained for each category. The PrIOU that is lower than the threshold value is eliminated, and finally, the non-maximum suppression operation is carried out [16]. Figure 2 shows the structure of the yolov1 algorithm.
## 4 IRS-Aided Wireless Network Performance and Deployment
### Physical Layer Security Optimization  
Physical layer security of a wireless communication channel in the presence of an eavesdropper can be evaluated using a basic wiretap model [98]. An IRS can enhance physical layer security in wireless communications by ensuring a lower wiretap transmission rate than the secrecy capacity of the channel [99,100]. MIMO beamforming techniques improve the security by suppressing the signal-to-noise-ratio (SNR) of the eavesdropper and enhancing the SNR of the legitimate receiver [101]. Similarly, deployment of an IRS, in  
Figure 5: Throughput of IRS against varying number of elements and minimum separation between source and destination.  
any wireless network having eavesdroppers, can improve the security of the network by jointly optimizing the beamformers at the BS and IRS reflection coefficients [100].
## The System Model
### Sensor Energy Model  
The radio model follows that in [23], as shown in Figure 1. Assuming that the wireless channel is completely symmetrical, the energy consumption of transmitting a message over the back and forth route between \(v_{i}\) and \(v_{j}\) is equal. If the communication distance \(d(v_{i},v_{j})\) from \(v_{i}\) to \(v_{j}\) is larger than the threshold distance \(d_{0}\), i.e., \(d(v_{i},v_{j})\geq d_{0}\), the multipath fading (\(d^{4}(v_{i},v_{j})\) consumption loss) channel model is used; otherwise, the free space (\(d^{2}(v_{i},v_{j})\) consumption loss) channel model is chosen. Then, for transmitting an \(l\)-bits message over \(d(v_{i},v_{j})\), the energy consumption is given by  
\[E_{T}\left(l,d(v_{i},v_{j})\right)=\left\{\begin{array}{ll}l*E_{\text{{el}} }+l*\varepsilon_{fs}*d^{2}(v_{i},v_{j}),&d(v_{i},v_{j})<d_{0}\\ l*E_{\text{{el}}}+l*\varepsilon_{mp}*d^{4}(v_{i},v_{j}),&d(v_{i},v_{j})\geq d_{0 }\end{array}\right. \tag{4}\]  
and the energy consumption for receiving the \(l\)-bits message over \(d(v_{i},v_{j})\) is given by  
\[E_{R}\left(l\right)=l*E_{\text{{el}}} \tag{5}\]  
Above, \(E_{\text{{el}}\text{{el}}}\) denotes the transmitting circuit loss, which depends on the digital coding, modulation, filtering, and spreading of the signal. The energy coefficients for power amplification in two different channel models are denoted by \(\varepsilon_{fs}*d^{2}(v_{i},v_{j})\) and \(\varepsilon_{mp}*d^{4}(v_{i},v_{j})\), respectively.
# Recommendation Systems: Algorithms, Challenges, Metrics, and Business Opportunities
## 5 Business Adoption and Applications
### Recommendation Systems in Agriculture  
In Agriculture, RSs have a significant impact on managing and using the resources efficiently, such as fertilizers, agrochemicals, irrigation. In [7], a fertilizer RS was developed to enrich the soil and increase its productivity. The authors used an ensemble classifier to suggest crops and evaluated their system using response time and accuracy measures. The issue of pests in crops was addressed and tackled in [74], where the researchers developed a RS that identifies the pests and recommends suitable treatments. In [75], the authors developed a web collaborative-based RS to answer the farmers' inquiries and update them with recent agriculture trends. They used a dataset from a call center that answers the farmers' queries by phone calls. In [76], the authors used the Apriori model and hybrid filtering model to build a web-based RS. They used Apriori in analyzing the data based on frequent items, and they recommended items to the users based on both the historical purchases and bestselling Agri-products. In [50], a collaborative based RS was proposed, where it suggests to the farmers the suitable crop according to the farmers' locations and the weather conditions. The authors used cosine similarity, and their dataset was based on four hundred farmers' information. In [77], the authors utilized an ensemble model with majority voting as they incorporated K-nearest and Naive Bayes to develop a crop RS. The authors in [78] took the weather conditions as an input in their RS model. They proposed a hybrid filtering based RS that recommends the best crop produced for specific weather conditions. The authors applied fuzzy c means, Support Vector Machines (SVM), and Artificial Neural Networks (ANN) for weather prediction and evaluated their model using accuracy measures. In [79], the authors aimed to improve crop productivity by proposing a RS based on ensemble technique where they incorporated different models such as (naive Bayes, random forest, and Linear SVM). Their proposed model recommends crop types based on the input soil dataset. They evaluated their model using the overall average accuracy measure.
# Power Autonomy Estimation of Low-Power Sensor for Long-Term ECG Monitoring
## 2 Background and Related Work
### Energy Consumption Modeling in Wireless Sensor Networks
For each of the approaches, they measured average current consumption using the Bluegiga BLE121 LR BLE platform. They derived analytical models by introducing BLE connectivity parameters and provided a detailed evaluation of proposed models considering the main BLE parameters. They derived the sensor node lifetime models for both cases, models for data collection efficiency in terms of energy consumed per collected data bit, and discussed the influence of bit error rate (BER) on all considered performance parameters. The authors in [10] conducted an in-depth analysis of BLE power consumption, particularly for estimation of discovery latency and energy consumption of both scanner and advertiser during a discovery process. The models were developed and validated for the BLE112 BLE chipset. If the user wants to use their models for other BLE chipsets, all platform-dependent parameters should be possible to adapt according to the selected BLE platform. They made the proposed model publicly available in the form of a software library. The authors in [20] presented their work with quantitative analysis for assessing the energy consumption of BLE advertisement procedures. The analysis includes a mathematical model for the device discovery dynamics and device performance evaluation under various BLE parameter settings. In [11; 21], the authors focused on developing an analytical model and simulations for discovery probability, influence of parameter selection on discovery latency, and energy metrics of the discovery process.
###### Abstract  
Airflow inside naturally ventilated dairy (NVD) buildings is highly variable and difficult to understand due to the lack of precious measuring techniques with the existing methods. Computational fluid dynamics (CFD) was applied to investigate the effect of different seasonal opening combinations of an NVD building on airflow patterns and airflow rate inside the NVD building as an alternative to full scale and scale model experiments. ANSYS 2019R2 was used for creating model geometry, meshing, and simulation. Eight ventilation opening combinations and 10 different reference air velocities were used for the series of simulation. The data measured in a large boundary layer wind tunnel using a 1:100 scale model of the NVD building was used for CFD model validation. The results show that CFD using standard \(k\)-\(\varepsilon\) turbulence model was capable of simulating airflow in and outside of the NVD building. Airflow patterns were different for different opening scenarios at the same external wind speed, which may affect cow comfort and gaseous emissions. Guiding inlet air by controlling openings may ensure animal comfort and minimize emissions. Non-isothermal and transient simulations of NVD buildings should be carried out for better understanding of airflow patterns.  
Airflow; ventilation openings; CFD; natural ventilation; cow barns +
Footnote †: journal: Applied Sciences
# Dynamic Response Analysis of Airport Pavement under Impact Loading
## 4 Sensitivity Analysis of Vibration Frequency
### Effect of Landing Pitch Angle  
Attitude estimation is used to characterize the orientation of the aircraft in three dimensions using the Earth as the reference system. The yaw angle, pitch angle, and roll angle of the aircraft, shown in Figure 5, are often mentioned in the attitude estimation of an aircraft. Among them, the pitch angle is an important parameter that pilots must master during takeoff and landing, which is the angle between the body axis and the horizontal plane.  
The aircraft landing process is a deceleration process; as the aircraft speed decreases, it is necessary to enlarge the landing pitch angle to increase the lift of the aircraft. Taking a Boeing 737-800 as an example, the upper limit of this aircraft type is theoretically \(11^{\circ}\)--if the landing pitch angle exceeds this value during landing, the aircraft tail may rub on the ground. With a lower limit of \(-0.7^{\circ}\), the three landing gears of the aircraft will be grounded at the same time. Therefore, in the general landing process, the aircraft landing pitch angle control is \(3^{\circ}\)-\(6^{\circ}\). In this interval, the landing attitude of the aircraft is more stable, which can effectively avoid the landing becoming out of control due to excessive deceleration and reduces the impact on the road surface to ensure the safety of the aircraft landing process. In this paper, under the premise of sinking speed (3 m/s) and tire pressure (2.0 MPa), we investigated the impact on the pavement during the landing process under different landing pitch angles (\(3^{\circ}\), \(4^{\circ}\), \(5^{\circ}\), and \(6^{\circ}\)), and analyzed the change law of the response of effective stress, effective strain, and vertical displacement on the airport pavement with the landing pitch angle, and the related simulation results are shown in Figure 6.  
Figure 4: Airport pavement dynamic response cloud charts at various tire pressures. (**a**) Conditions: tire pressure 1.0 mpa, landing pitch angle \(3^{\circ}\), sinking speed 3 m/s. (**b**) Conditions: tire pressure 1.5 mpa, landing pitch angle \(3^{\circ}\), sinking speed 3 m/s. (**c**) Conditions: tire pressure 2.0 mpa, landing pitch angle \(3^{\circ}\), sinking speed 3 m/s. (**d**) Conditions: tire pressure 2.0 mpa, landing pitch angle \(3^{\circ}\), sinking speed 3 m/s.  
Figure 5: Diagram of airplane positioning angle.
## 3 Results
### Session 3: User Group C in Assisted Living  
In session 3, we tested our system with user group C, who are residents of assisted living communities and have extensive previous experience with the social robot Pepper, as one lives in their community. Again, the KPI of the robot (1.33), the devices (1.42), and the app (1.92) were rated highly, albeit lower than in sessions 1 and 2. The app was rated best, with the devices rated only slightly higher than the robot. The overall system KPI is rated well at 1.45, while the overall system usability is rated at 67.5 of 100, indicating average usability. Regarding the health devices, the participants had difficulties on the first try of the measurements. However, when attempting a second measurement, all participants were able to conduct the measurements by themselves. Some of the participants also mentioned their interest in using the devices and liked their modern design. When using the mobile app, we observed that the participants were unsure which category to find the measured values in. However, after finding the first value we asked them to find (body temperature), they were quick to also find the other values.  
Session 3 was conducted with three participants so that a lower number of 103 turns was recorded. In relation to the number of 84 error-free turns, a success rate of 81.6% results. Based on transcripts and system logs, we identified the most common cause of error to be deviating from the ones of the previous sessions. In session 3, the system not recognizing speech input is the most prevalent issue, occurring 10 times and thus causing over 52% of the errors. The main consequence is that users have to repeat themselves. However, two turns were identified, in which the system did not register any user input in the logs, yet triggered a robot uttering. In session 3, again all assessments were completed in full, resulting in a completion rate of 100%. Additionally, we occasionally observed slow responses in the robot, which can be indicative of a slow internet connection, and also has an impact on how well or fast the robot's speech recognition functions.