esa-sceva/satcom-chunk-collection huggingface dataset:
# Flexible, Brain-Inspired Communication in Massive Wireless Networks
## 3 Simulation Results
This is due to the domination of the power consumed by the transmitter and receiver components and the baseband signal processing power over the transmission (signal emission) power. So far we have considered the source of the \(x_{\mathrm{m}}(t)\) signal to be time-continuous. Let us also consider the case, when the source generates digital data (discrete in time and in the set of values). For this case, in Figure 9, the power consumption of the digital transmission with QAM and ASK modulation as a function of the distance between the transmitter and the receiver is compared. In the top figure, the results have been determined for the optimal bandwidth extension (in the context of the minimization of power consumption) of the digital transmission with QAM modulation \(B_{\mathrm{SigD}}^{\star}\), while in the bottom figure, for the optimal bandwidth extension (in the context of the minimization of power consumption) of the digital transmission with ASK modulation \(B_{\mathrm{SigD}}^{\mathrm{ASK}^{\star}}\). There, one can observe that for short distances, the digital communication system with ASK modulation is more energy efficient than the one with QAM modulation due to the less complex transmitter and the receiver. Figure 8: The consumed power as a function of the distance between transmitter and receiver for the bandwidth extension which minimize the power consumption of the digital scheme (top figure) and the analog scheme (bottom figure).
## 3 System Model and Problem Formulation
### System Model
_Propagation Delay_: The orbital height of LEO satellites is about 300 km to 2000 km, and the long transmission distance can cause the larger propagation delay. Note that the influence of atmosphere on propagation delay mainly includes ionospheric delay and tropospheric delay [26]. When the signal passes through the ionosphere, due to the refraction effect of the electromagnetic wave, the propagation path and speed of the signal will change. Meanwhile, the ionospheric delay is irregular, which is difficult to describe with a physical model. When the signal passes through the troposphere, the propagation speed, direction and path of the signal will change, which can result in propagation delay. The tropospheric delay is related to air pressure, air humidity and satellite elevation. The commonly used tropospheric delay correction model is given in the literature [27; 28]. The round-trip delay of the LEO satellite with an orbit altitude of 1200 km is about 20 ms. The long propagation delay will lead to the expiration of CSI, which can result in CSI errors, phase disturbance and other problems. To handle this problem, the delay compensation of size \(\tau^{cps}=\beta\tau_{\min}+(1-\beta)\tau_{\max},\ (0\leq\beta\leq 1)\) is usually implemented at the receivers, and \(\tau_{k,l}^{\min}=\min\Big{\{}\tau_{k,l,p}\Big{\}}_{1}^{P_{k,l}}\) and \(\tau_{k,l}^{\max}=\max\Big{\{}\tau_{k,l,p}\Big{\}}_{1}^{P_{k,l}}\) represent the minimum propagation delay and the maximum propagation delay of the \(k\)th user in the \(l\)th multicast group, respectively. However, due to that the atmospheric propagation delay is irregular, the transmission delay cannot be fully compensated. Therefore, the incomplete delay compensation, expired CSI and distortion of high-frequency devices would cause the channel phase disturbance [29]. Let the phase disturbance be \(\phi_{\tau}\), which follows a real-valued Gaussian distribution with mean zero and variance \(\sigma^{2}_{\phi_{\tau}}\), i.e., \(\phi_{\tau}\sim N\Big{(}0,\sigma^{2}_{\phi_{\tau}}\Big{)}\). The channel error vector caused by \(\phi_{\tau}\) can be expressed as \(\mathbf{v}_{\phi_{\tau}}=\left[e^{j\phi_{\tau,1}},e^{j\phi_{\tau,2}},\dots,e^{j \phi_{T,N}}\right]^{T}\). In conclusion, considering the influence of the residual Doppler shift and the phase disturbance, the relationship between the real channel vector \(\mathbf{h}_{k,l}\) and the estimated channel vector \(\hat{\mathbf{h}}_{k,l}\) can be expressed as:  
Figure 1:7  
where \(\odot\) represents the Hadamard product. Let the channel phase of the \(k\)th user in the \(l\)th multicast group be \(\theta_{k,l}=[\theta_{k,l,1},\theta_{k,l,2},\dots,\theta_{k,l,N}]^{T}\), which satisfies the uniform distribution between \(0\sim 2\pi\). Then, the real channel phase with phase errors at instant \(t_{1}\) is as follows:  
Figure 1:8
## 2 LTE  
### LTE Network Structure  
The LTE network consists of LTE entities dealing with wireless access network technology and EPC entities dealing with core network technology. Its structure is shown in Figure 1.  
Of the LTE entities, UE accesses the evolved Node B (eNB) through the LTE-Uu Uu wireless interface. eNB, serving as the base station, provides the user with a wireless interface and provides wireless remote resource management (RRM) features, such as radio bearer control, wireless admission control, dynamic wireless resource allocation, load balancing and inter cell interference control (ICIC) [10].  
EPC entities consist of the mobility management entity (MME), S-GW, P-G and home subscriber server (HSS). MME is an E-UTRAN control plane entity, communicating with HSS for userauthentication and user profile download, and through NAS signaling, it provides the user terminal with EPS rambling management (EMM) and EPS session management (ESM) features. S-GW is the termination point between E-UTRAN and EPC and the anchoring point in the handover with eNB and the handover with the 3 GPP system. P-GW connects UE to an external PDN network and provides packet filtering. In addition, P-GW allocates an IP address to the user terminal and serves as the mobile anchoring point in the handover between 3 GPP and non-3 GPP. Lastly, HSS manages the users' personal profiles [8, 10, 11, 12, 13, 14].  
The IMS/Internet domain is the domain commonly calling external Internet services.
## Materials and Methods
### Antimicrobial Activity Assessment
#### 4.3.2 Qualitative Screening of Antimicrobial Activity  
The qualitative screening tests were performed using the agar diffusion method following the CLSI (Clinical and Laboratory Standards Institute) guidelines. The inoculums were prepared from 18-24 h microbial cultures obtained on tryptone soy broth (TSA) for bacteria and on yeast peptone glucose agar (YPGA) for yeast by direct colony suspension in sterile phosphate-buffered saline (PBS). The microbial suspensions turbidity was adjusted to 0.5 McFarland scale and used for the inoculation of the agar plates (Mueller-Hinton agar). Then, 5 \(\upmu\)L of the solution of the tested compound at 450 \(\upmu\)g/mL concentration, prepared in DMSO, was placed on the agar surface. The negative (DMSO) and positive (standardized antibiotic discs of ciprofloxacin 5 \(\upmu\)g and fluconazole 25 \(\upmu\)g) controls were prepared. The Petri dishes were incubated at 37 \({}^{\circ}\)C, and the diameters of inhibition growth zones were then measured.
# A Decentralized Receiver in Gaussian Interference
## 3 Bounds on Communications Rates
### Achievable Rate by Decoding and Forwarding  
Treating each helper node as a user seeking to receive its own message, the link from the broadcaster to helpers is a scalar Gaussian broadcast channel. The capacity region of this channel was characterized in Reference [18], and in particular its sum-rate was given in Reference [19]. In the scalar case, this sum rate reduces to:  
\[\max_{n}\log_{2}\left[\frac{\mathbf{\Sigma}_{n,n}+|h_{n}|^{2}}{\mathbf{\Sigma}_{n,n}} \right], \tag{17}\]  
where only the point-to-point channel between transmitter and the best receiver is used.  
**Remark 2**.: _The following rate is achievable_  
\[R_{BC}(L)=\max\left\{\log_{2}\left[\frac{\mathbf{\Sigma}_{0,0}+|h_{0}|^{2}}{\mathbf{ \Sigma}_{0,0}}\right],\min\left\{L,\max_{n}\log_{2}\left[\frac{\mathbf{\Sigma}_{n,n }+|h_{n}|^{2}}{\mathbf{\Sigma}_{n,n}}\right]\right\}\right\}. \tag{18}\]  
_The rate is achievable with the following facilities:_  
* _Helpers are informed of which helper is the best (best is derived from_ \(\mathbf{h},\mathbf{\Sigma},L\)_)._
* _The transmitter and best helper coordinate a codebook of appropriate rate and distribution._Proof.: By having the broadcaster send a message to the \(n\)th receiver, a rate  
\[\log_{2}\left[\frac{\mathbf{\Sigma}_{n,n}+|h_{n}|^{2}}{\mathbf{\Sigma}_{n,n}}\right] \tag{19}\]  
is achievable from transmitter-to-receiver. If \(n=0\) (i.e., the transmitter broadcasts to the base), then Equation (19) is achievable from transmitter-to-base. Otherwise, Equation (19) is achievable from transmitter-to-base if it is less than \(L\) by forwarding the receiver's decoding over the LAN.  
**Remark 3**.: _When the base does not have its own reception (i.e., \(I(X;Y_{0})=0\)) and_  
\[L\leq\max_{n}\log_{2}\left[\frac{\mathbf{\Sigma}_{n,n}+|h_{n}|^{2}}{\mathbf{\Sigma}_{n,n}}\right] \tag{20}\]  
_then the system's capacity is \(L\)._  
Proof.: By assumption, the upper bound in Equation (16) equals \(L\). This is achievable by Remark 2.  
The strategy to achieve this rate requires a strong amount of cooperation from the broadcaster, since the decoding receivers must all share codebooks with the transmitter.
# A Systematic Deep Learning Based Overhead Tracking and Counting System Using RGB-D Remote Cameras
## 6 Conclusions  
We have proposed an efficient method for head tracking and counting which works well despite variations in occlusion, illuminaton and background complexity. The proposed HASTAC model is sub-divided into six domains. First, using Kernel Density Function (KDE), preprocessing is done to remove complex backgrounds under varying illumination conditions. Second, head detection is achieved using Hough Circular Gradient Transform and shoulders detection is achieved using the HOG based symmetry method. Third, robust features, like Fused joint HOG-LBP, Energy based point clouds and Fused intra-inter trajectories are extracted which further help the tracking of the heads and shoulders of each individual detected in the video frames. These features are then projected to the Apriori-Association technique which can find frequent itemsets and remove them to get ideal results for head tracking. Fifth, these features are then passed to our proposed 1D CNN model in order to distinguish between heads and non-heads in the video frames. In the last step, the cross line judgement technique is used to count the number of heads frame by frame. We evaluated the proposed model on three publicly available datasets and found that the results are very promising with respect to accuracy and computational time.  
Figure 18: Accumulative head counting accuracies achieved on the three benchmark datasets.
# Feature-Attended Federated LSTM for Anomaly Detection in the Financial Internet of Things +
## 2 Related Work
### Machine Learning-Based Anomaly Detection  
Detection algorithms based on machine learning are widely studied. [PERSON] et al. [15] performed PCA on the correlation matrix of the normal group to realize a novel scheme using a robust principal component classifier in intrusion detection problems, based on an assumption that the number of normal instances has to be much larger than the number of anomalies. [PERSON] et al. [16] proposed a new mixed model of information entropy and support vector machine \"Ent-SVM\", which classifies abnormal network behaviors based on the normalized eigenvalues of six characteristics of the traffic. But the above methods are not applicable to dynamic and time-varying anomaly detection for the reason that they are based on static, time-invariant models.  
To solve that problem of anomaly detection with dynamic and time-varying characteristics, the LSTM [17; 18] network was adopted for anomaly detection in a dynamic environment, somehow combined with support vector machine (SVM) [19]. Based on the predictive error, LSTM was combined with the Gaussian Bayes model in [20] for anomaly detection. The literature [21] proposed a novel LSTM model, combining attention mechanism and convolutional neural network (CNN) to detect anomalies. Similarly, a new hybrid deep learning (DL) approach based on CNN to classify the flow traffic into normal or attack classes is mentioned in [22]. [PERSON] et al. [23] proposed a proposal to detect collective anomalies based on the LSTM network and improved detection accuracy by predicting and modeling the correlation between stationary and non-stationary time so as to realize effective detection of time abnormal structures. These methods take full advantage of the temporal relevance of IoT to LSTM. However, in these methods, the time correlations inner each feature (or a feature group including several features) are ignored. This leads to the result that such an important character in IoT traffic, which may greatly improve anomaly detection performance, is not utilized.
# Acoustic Detection of Vaccine Reactions in Hens for Assessing Anti-Inflammatory Product Efficacy
## 3 Experiments and Results
### Visualisation of Centroid and VocalNum Features  
Information encoded in animals' calls can be used to detect stress. In poultry, the most important features related to stress are the number of vocalizations per unit of time (vocalNum) [24; 41; 42] and the centroid, indicating the center of mass of the call [16].  
Figure 4 shows the evolution over time, starting from three days before vaccination, and the continuous monitoring of the animals up to eight days after vaccination. The centroid plot shows a periodic pattern between days, showing differences between daylight (higher values) and night-time (lower values). From day 0 to day 5 after vaccination, there is a difference between the curve of the control group and that of the treatment group, where CR obtained maximum values during the day and night, compared to TX. The vocalNum graph shows peak values above 1000 for days 0, 1, and 2 of vaccination for both groups. Similar values of vocalization counts can be found before and after vaccination.  
Looking at the periodic pattern in Figure 5, which was dependent on the light cycle, a deeper analysis was performed by filtering the data between the light range (all day, light, and night) and the block (no reaction, reaction, and post-reaction), to find the interval in which the vaccine reaction was most evident.  
Figure 4: Time series: (**top**) centroid, (**bottom**) vocalNum, for the control and treatment groups.  
Figure 5: Acoustic feature centroids: (**top**) time series evolution of CR and TX; (**bottom**) boxplots per block interval of CR and TX.  
Table 1 shows the means and standard deviations of intervals and blocks. A reduced standard deviation and mean data value were observed for all acoustic features during the night-time period, regardless of the group to which the samples belonged. The values obtained after filtering the data for all daytime intervals were more similar than for night-time. Furthermore, higher centroid values were found in the reaction block, as shown graphically in Figure 4. This observed increase in centroid values in the reaction block was greater in the CR group than in the TX. A statistical analysis of mean differences by feature and blocks was performed, to determine whether vocal differences during vaccine reaction were significantly different due to the treatment offered.
## 3 Experiments and Analysis
### Experimental Settings
#### 3.1.2 Evaluation Metrics  
In this experiment, five commonly used evaluation metrics in remote sensing segmentation are employed to verify the accuracy and inference speed of road extraction for each model. These metrics are as follows: mean Intersection over Union (mIoU), mean Pixel Accuracy (mPA), Recall, Accuracy, the number of parameters (PN) and Frames Per Second (FPS).  
mIoU: Intersection over Union (IoU) measures the overlap between the predicted road region and the ground truth road region. The mean Intersection over Union (mIoU) is the average IoU of the road and background classes. The formula is as follows:  
\[\text{IoU}=\frac{\text{TP}}{\text{TP}+\text{FP}+\text{FN}}\quad\text{and} \quad\text{mIoU}=\frac{\sum\text{IoU}}{\text{C}} \tag{7}\]  
mPA: Pixel Accuracy (PA) represents the proportion of correctly predicted pixels in the road or background category out of the total pixels for that category. The mean PixelAccuracy (mPA) is the average PA of the road and background classes. The formula is as follows:  
\[\text{PA}=\frac{\text{TP}}{\text{TP}+\text{FN}}\quad\text{and}\quad\text{mPA}= \frac{\text{$\sum$PA}}{\text{C}} \tag{8}\]  
Recall: It refers to the proportion of pixels that are correctly predicted by the model among the pixels that are actually road. The formula is as follows:  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{9}\]  
Accuracy: It refers to the proportion of pixels in the image that are correctly predicted by the model as road or background. The formula is as follows:  
\[\text{Accuracy}=\frac{\text{TP}}{\text{TP}+\text{FP}+\text{FN}} \tag{10}\]  
TP (True Positive) refers to the number of pixels correctly predicted as roads, FP (False Positive) refers to the number of pixels incorrectly predicted as roads, FN (False Negative) refers to the number of pixels that are actually roads but predicted as non-roads, TN (True Negative) refers to the number of pixels correctly predicted as non-roads, and C represents the number of classes, which is 2 for road extraction tasks.  
Number of Parameters (PN): It represents the sum of all trainable parameters in the model to measure the complexity and computational resource requirements. A lower parameter count enables the model to be deployed on embedded and mobile devices, reducing the energy consumption associated with computation and storage. Additionally, a lower parameter count can help reduce the risk of model overfitting to some extent.  
Frames Per Second (FPS): It is used to indicate the number of remote sensing image frames that a model can process or display per second. In real-time application scenarios such as autonomous driving and real-time road extraction, a high FPS means the system can process images or video streams faster, providing more timely road information.
## 2 The Proposed Method
### Preparation  
To be able to extract just the embedded message and nothing more, the message length needs to be known. This can be done in many ways, but assuming that most messages will be less than 256 bytes of length one approach is to add an eight-bit message header to indicate the message length. This approach allows for longer messages if that would be required by stacking several blocks after one another. Assuming most messages are less than two blocks in length this approach will have the same or less overhead than an approach where 16 bits would be used to indicate the message length. To prepare the data for encryption the clear text message \(M\) of length \(w\) bytes where \(w=|M|\), \(M\) is divided into \(n\) blocks \(B_{1},B_{2},\ldots,B_{n}\), where \(n=\left\lceil\frac{w}{256}\right\rceil\). An eight-bit header \(H_{i}\) is introduced for each block \(B_{i}\), where \(H_{i}=|B_{i}|\) and where \(|B_{i}|\) is the block size in bytes. As can be seen in the Figure 1, the prepared message \(P\) is equal to the assembly \(P=(S,H_{1},B_{1},H_{2},B_{2},\ldots,H_{n},B_{n})\).
## 3 Performance Evaluation and Analysis
### Clustering Evaluation
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **WFCS** & **ESSC** & **WSRFCM** & **WFCM** & **FCS** \\ \hline \multicolumn{6}{c}{Australian} \\ \hline mean & **0.0400** & 0.7194 & 1.4636 & 1.2056 & 0.1995 \\ std & 0.0007 & 0.3455 & 0.8407 & 1.0488 & 0.0267 \\ \hline \multicolumn{6}{c}{Breast Cancer} \\ \hline mean & 0.3216 & 0.2961 & 0.4288 & 0.3270 & **0.1094** \\ std & 0.0021 & 0.0344 & 0.0903 & 0.0003 & 0.0000 \\ \hline \multicolumn{6}{c}{Balance-scale} \\ \hline mean & **0.4435** & 0.7051 & 0.6970 & 0.7392 & 2.8475 \\ std & 0.0000 & 0.0248 & 0.0287 & 0.0725 & 0.0979 \\ \hline \multicolumn{6}{c}{Heart} \\ \hline mean & **0.1593** & 0.4033 & 0.7942 & 0.6348 & 0.2267 \\ std & 0.0081 & 0.0982 & 0.7522 & 0.5030 & 0.0000 \\ \hline \multicolumn{6}{c}{Iris} \\ \hline mean & **0.0844** & 0.0861 & 0.2700 & 0.1964 & 0.2922 \\ std & 0.0019 & 0.0059 & 0.0226 & 0.0000 & 0.0000 \\ \hline \multicolumn{6}{c}{Pima} \\ \hline mean & **0.1443** & 0.4942 & 0.7610 & 0.5955 & 0.4759 \\ std & 0.0002 & 0.0235 & 0.1977 & 0.0406 & 0.0000 \\ \hline \multicolumn{6}{c}{Vehicle} \\ \hline mean & **0.2532** & 0.2601 & 0.8538 & 0.5480 & 3.2949 \\ std & 0.0000 & 0.0917 & 0.0372 & 0.0047 & 0.0097 \\ \hline \multicolumn{6}{c}{Wine} \\ \hline mean & **0.2577** & 0.3970 & 0.6775 & 0.4987 & 0.4061 \\ std & 0.0034 & 0.0009 & 0.0640 & 0.0000 & 0.0000 \\ \hline \multicolumn{6}{c}{ENGINE} \\ \hline mean & 0.1699 & 0.1836 & 0.3755 & 0.2130 & **0.1267** \\ std & 0.0030 & 0.0685 & 0.0295 & 0.0217 & 0.0000 \\ \hline \multicolumn{6}{c}{LTT} \\ \hline mean & **0.1019** & 0.1075 & 0.3299 & 0.2131 & 0.2105 \\ std & 0.0014 & 0.0983 & 0.0029 & 0.0000 & 0.0000 \\ \hline \end{tabular}
\end{table} 
Table 5: Xie-Beni (XB) index of algorithms.
# Sensor-Level Mosaic of Multistrip KOMPSAT-3 Level 1R Products
## 3 Experimental Results
### Sensor Modeling of Each Image Strip  
The long strip images were delivered with an ephemeris and attitude data for the physical sensor modeling. However, RPCs are much compatible and easier to use than the physical sensor model, whereas the accuracy is similar. Therefore, we first converted the physical sensor model of each strip into RPCs. The conversion into RPCs is conducted by interpolating satellite attitude information such as roll, pitch, and yaw angles with the first-order equation.  
Figure 6 depicts the interpolation residuals for the roll angles of Strip #1, demonstrating that the original roll angle varies locally along the image line. The conversion residuals from the physical model into RPCs are presented in Table 2 for two cases using the original ephemeris and the interpolated ephemeris. Using the interpolated ephemeris shows residuals that are a little better than the other case, which is affected by the local variation in the ephemeris. In Strip #1, the residual in the sample direction improved by more than one pixel.