esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Related Work  
Studies from the literature can generally be divided into two groups: (1) Strategies based on channel borrowing from cells that are lightly loaded, like load balancing with selective borrowing [8], and QoS priority-based dynamic fractional frequency reuse tapping the resources from lightly loaded cells [9], etc.; and (2) strategies based on traffic transmission to loaded cells, like cell breathing techniques [10], mobility-aware admission control [11], and bias-based offloading in HetNets [12].  
Figure 1: Mobile personal cell (mPC) deployments based on public safety situations.  
We adopt the traffic transmission strategy that is described under the second group. Many contributions exist to address the issue of user association in HetNets with static small cells. These results have restrictions, because they optimize the efficiency of the system or network based on user associations. Therefore, the effect of offloading has not been analyzed or well-studied, because it is more difficult because of the different signal and interference issues [13]. The existing systems do not take into account the different moving patterns and QoS requirements of users and the base station (BS). Nonetheless, effective user associations should be able to decide on public safety scenarios depending on critical requirements. Thus, allocating resources to users has to be considered based on users' priorities and locations in the specific network situations. This kind of user information is referred to as _context information_ in the proposed scheme [13].
## 4 Discussion
Moreover, intra-rater reliability was similar to test-retest, which may indicate that this device provided comparable results not only during one session but even on separate days. [PERSON] et al. [43] used the deadlift exercise and loads ranging between 20 and 100% of 1 RM for assessing velocity and power. They reported that reliability starts to decrease at loads below 30% of 1 RM. In the study by [PERSON] et al. [42] a decrease in ICC was observed at lower barbell loads. The authors reported good reliability at loads of 60, 80,and 90% of 1 RM. Reliability of velocity and power tended to be lower at 20% of 1 RM [42]. Contrarily, in our study, such a decrease in reliability was observed when the barbell load increased. This discrepancy was probably present because they examined professional athletes, but in our work, the participants comprised students. Therefore, the subjects in our work were probably weaker and less fit, so their performance was better at lower loads. Moreover, [PERSON] et al. [42] used squat and bench press exercises, which may require different movement skills than the landmine punch throw. Bench press and squat are simple, one-plane exercises commonly used in strength training by both athletes and recreational amateurs. Therefore, this movement could finally provide higher reliability, even in the case of heavy loads. LPT, on the other hand, is a complex multi-plane movement requiring more motor skills and greater control for precise execution with a heavy load. In our study, series were performed progressively increasing the load to refusal, so the participants were subjected to loads from 30-40% of 1 RM (20 kg) to 90-100% of 1 RM (35 kg). Therefore, we used a similar spectrum of loads and also observed good to excellent inter-rater and test-retest reliability for force, power, and velocity. In numerous studies, the validity or reliability of velocity parameters provided by various VBT mobile devices have been assessed. Generally accepted are parameters of mean and peak concentric velocity, which are usually obtained by linear position transducers [17; 29; 44]. Our study is the first in which more variables than power and velocity have been reported, which provides a fuller and broader picture of barbell movement during the LPT test. Such information can be used by coaches as well as athletes in VBT. This study has some limitations which should be addressed. We evaluated young male students who were not professional athletes. Therefore, this study should be extended to professional athletes, especially from combat sports. Additionally, the reliability of the LPT test may be further evaluated by other velocity-based devices for comparison. Additionally, only young men were included in the present study; therefore, research should also include the participation of women and people above the age of 30. Moreover, it is worth assessing the reliability of the LPT test, taking the circadian cycle into account, by comparing measurements taken in the morning with those obtained in the afternoon.
# \"Network Sentiment\" Framework to Improve Security and Privacy for Smart Home
## 5 Smart Home Testbed  
In order to evaluate the SHIELD framework, we built a prototype of the SH and the SLU. In particular, the SH prototype is equipped with Ethernet, Wi-Fi and IEEE 802.15.4 interfaces. Moreover, different attack types have been tested to evaluate the system feasibility.  
The SHIELD testbed is shown in Figure 4. The SH (shown to the left) is an UDOO board and an OpenMote CC2538 module as Border Router for 6 LoWPAN (6 LBR). The UDOO board is a single board with an ARM Cortex-A9 CPU, RAM DDR3 (1 GB), GPIOs, microUSB ports, Gigabit Ethernet and WiFi module while OpenMote-CC2538 is based on Texas Instruments CC2538 System on chip with an IEEE802.15.4 transceiver. The SH has three local interfaces (Wi-Fi, Ethernet, and IEEE 802.15.4) and an Ethernet link to the (emulated) ISP network. The SLU and part of the SCN have been emulated with a virtual machine ( PC at top-right of Figure 4). The used Smart Home devices are connected trthough the Ethernet, Wi-Fi and IEEE 802.15.4 interfaces of the SH. The attacks have been performed with a normal PC (center right in Figure 4).  
The SH monitors all data traffic of the private network on all involved interfaces. Whenever SH detects an attack to some devices connected to the network, it generates an alert which is sent to the SLU.  
Without loss of generality, we used the Bro Network Security Monitor [33] to monitor the traffic, with custom rules to detect the possible attacks used during the tests. Moreover, we have prepared a python3 script in order to parse the IDS file logs searching for warning/alarms. The script parses both Ethernet and Wi-Fi logs and, when a warning/alarm is found, contacts the SLU. In particular, the Bro alerts have been converted to an appropriate interexchange format (IDMEF) and sent to the SLU for further processing. In response to an alert, the SLU will send a command to the SH which will take appropriate actions according to the type of threat detected. A simplified and interactive visualization of the alert is also provided to the user through a graphical interface.  
As mentioned earlier, the threat report could also not trigger any action of the SH in case the _Network Sentiment_ for that particular user, according to that particular attack, is not changed (i.e., the attack is not relevant for the user). Nonetheless, the attack could be relevant for other users, and it is important to report its presence. For this reason, the SLU inserts the threat in the Shield database. In our experiment, the database is a MySQL db with customized tables.  
The chosen hardware reflects as much as possible a normal Linux-based (CPE). As a consequence, we expect that implementing the SH functionalities on a commercial CPE will be very easy. In this case, the CPE becomes the central element of the Smart Home network, guaranteeing not only the device connectivity, but also the whole network security. However, it is also possible to use an external SH unit, provided that it can monitor the home network links (wired and wireless).
# A Novel Dynamic Transmission Power of Cluster Heads Based Clustering Scheme
## 4 Proposed Approach
### Analysis of Cluster Head Transmission Power  
The motion states of UAVs include the position, speed, direction, and so on. To adapt the clustering algorithm to the highly dynamic characteristics of nodes, the motion characteristics of UAVs was analyzed. Assume that UAVs can obtain high-precision positioning information in real time through the Global Positioning System(GPS). In the system, each UAV can obtain the specific location information according to its positioning equipment, and transmit the location information through the HELLO message broadcast by the system. The node within the receiving range receives the information to obtain the geographical location information of the neighboring node, so that the neighbor list can be maintained. After the UAVs are clustered, the cluster head can find the distance between the cluster members and itself according to its neighbor list.  
According to the distance between the cluster members, the cluster head can adjust the transmitting power by reducing the transmitting power when transmitting information to the node within a short distance and increasing the transmitting power when sending data packets to the node within the maximum communication range. The ideal communication loss (free space) [24] between UAVs can be expressed as  
\[P_{\text{LOS}}(\text{dBm})=32.5+20\lg(d)+20\lg(f), \tag{20}\]  
where \(d\) is the propagation distance and \(f\) is the working frequency. In practical applications, because wireless communication is affected by various external factors, such as atmosphere, barriers, multipath, etc., the \(P_{\text{LOS}}\) is high. By plugging the reference value into (20), the total transmission loss can be calculated [25].  
The transmitting power of the cluster head should not be less than the sum of the wireless transmission loss and the reception sensitivity of the UAV. Considering that the working frequency and reception sensitivity of UAVs are constant, the relationship between the transmitting power and propagation distance can be obtained, as given by  
\[P_{\text{LOS}}(\text{dBm})=32.5+20\lg(d)+20\lg(f)+P_{\text{rs}}+P_{\text{LOS}^{ \prime}}, \tag{21}\]  
where \(P_{\text{rs}}\) is the transmitting loss caused by the reception sensitivity, and \(P_{\text{LOS}^{\prime}}\) is the transmitting loss due to the multipath fading, atmospheric loss, and so on.
## 2 Collected Data and Research Method
### Sensitivity Analysis  
Once the ANN had been trained, a sensibility analysis was performed using a stochastic one-at-a-time (OAT) approach. That is, to assess the impact of an input latent variable on the output latent variables, we slightly increased the value of the input latent variable and computed the difference between the predicted values for the different output latent variables. Since this difference depends on the value of the given input variable and the other input variables, this process was performed for the different input variables. The sensitivity of an output variable \(z_{out}\) in terms of the input variable \(x_{i}\) can then be considered as the partial derivative of \(z_{out}\), with respect to \(x_{i}\) at \(\mathbf{x}\). Therefore, it can be computed as:  
\[\delta_{z,x_{i}}=\frac{\partial z_{out}}{\partial x_{i}}\approx\frac{H_{ \mathbf{w}_{2},\mathbf{W}_{1},\mathbf{x}_{01},\mathbf{x}_{02}}(\delta_{i} \mathbf{x})-H_{\mathbf{w}_{2},\mathbf{W}_{1},\mathbf{x}_{01},\mathbf{x}_{02}}( \mathbf{x})}{\Delta}, \tag{10}\]  
where \(\delta_{i}\mathbf{x}\) has the same elements of \(\mathbf{x}\), except for the \(i\)-th component that has the value of \(x_{i}+\Delta\) instead of \(x_{i}\), with \(\Delta\) being a small, constant value.  
In Figure 2, we summarize the methodological flow adopted in the present work. The first step is the data collection, conducted in order to obtain a set of original variables. These variables are then reduced via a dimensionality reduction process. Afterward, these processed data are used to train an ANN that models the system. Finally, this model can be used to analyze the sensitivity of the output variables to the input variables. These steps are described in detail in the following subsections.
## 3 Results and Discussion
### Enzymatic Hydrolysis of PWPs  
Based on weighing method, the yield of the enzyme hydrolysate from _P. major_ water-extractable polysaccharide (indicated as PLM) was estimated at 22.35% \(\pm\) 1.07. HPAEC analysis of PLM showed that the main monosaccharide was galacturonic acid (62.07%), followed by arabinose (11.96%), galactose (10.83%), rhamn (5.25%), glucose (3.13%) and xylose (1.38%). The conducted analysis confirmed that PLM was a pectic fraction composed of oligo-rhammogalacturonan backbone (molar ratio of 11.82 GalA/Rha) with side chains of arabinocalactans (molar ratio of 1.09 Ara/Gal). The average molecular weight of PLM was estimated at 1.69 kDa by using SEC analysis performed in two columns TSK G5000 PWXL and TSK G3000 PWXL coupled in series. The obtained result proved the efficient production of low molecular weight fractions of PWPs using our enzymatic process with a mixture of hemicellulases and endo-1,4-\(\beta\)-xylanase. Finally, to estimate the distribution of oligosaccharidesamilies in PLM an exclusion size fractionation onto Biogel P2 was used.  
As observed in Figure 3, different families of oligosaccharides were fractionated from dp 2 to dp 10 according to Mw calibration [34].  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Mw (a)** & **Mn (b)** & **Polydispersity** & \multicolumn{8}{c}{**Monosaccharides (d) (mol, \%)**} \\ \cline{4-10}
**(kDa)** & **(kDa)** & **Mw/Mn** & **Rha** & **Ara** & **Gal** & **Glc** & **Xyl** & **GalA** & **GalA/Rha** \\ \hline
66.00 \(\pm\) 2.36 & 38.30 \(\pm\) 3.68 & 1.72 \(\pm\) 0.18 & 3.17 & 9.88 & 8.02 & 21.50 & 2.05 & 55.38 & 17.47 \\ \hline \hline \end{tabular}  
* Mn: number-average molecular mass was measured by SEC-MALLS-DRI. Each assay was conducted in duplicate, and data are expressed as means (\(\pm\)SEM). [ENDFOOTNOTE]
* Monosaccharides composition was measured by HPAEC. Rha: Rhamnose; Ara: Arabinoose; Gal: Gal: Galacose; Glc: Glucose; Xyl: Xylose; GalA: GalA: Galacturonic acid. All analyses were run in duplicate and the relative standard deviations were \(<\)5%.  
\end{table}
Table 2: Characterization of PWPs extracted from _P. major_ L. leaves.
# Boundary-Aware Hashing for Hamming Space Retrieval
###### Abstract  
Hamming space retrieval is a hot area of research in deep hashing because it is effective for large-scale image retrieval. Existing hashing algorithms have not fully used the absolute boundary to discriminate the data inside and outside the Hamming ball, and the performance is not satisfying. In this paper, a boundary-aware contrastive loss is designed. It involves an exponential function with absolute boundary (i.e., Hamming radius) information for dissimilar pairs and a logarithmic function to encourage small distance for similar pairs. It achieves a push that is bigger than the pull inside the Hamming ball, and the pull is bigger than the push outside the ball. Furthermore, a novel Boundary-Aware Hashing (BAH) architecture is proposed. It discriminatively penalizes the dissimilar data inside and outside the Hamming ball. BAH enables the influence of extremely imbalanced data to be reduced without up-weight to similar pairs or other optimization strategies because its exponential function rapidly converges outside the absolute boundary, making a huge contrast difference between the gradients of the logarithmic and exponential functions. Extensive experiments conducted on four benchmark datasets show that the proposed BAH obtains higher performance for different code lengths, and it has the advantage of handling extremely imbalanced data.  
image retrieval; deep hashing; Hamming space retrieval; boundary-aware penalization +
Footnote †: journal: Applied
# An Instrument Error Correlation Model for Global Navigation Satellite System Reflectometry
## 2 Materials and Methods
### Verification Techniques  
One of the primary challenges in constructing the CYGNSS Level 1 correlated error model \(R_{mod}\) is identifying a plausible validation scenario. In practice, it can be difficult to disentangle the various sources of correlated observation error structure, e.g., those caused by the inherent behavior and calibration of the instrument, by the geophysical retrieval and inversion process, and by the representativity errors imposed when observations are gridded and ingested into models. Further, the choice of ground truth may impose an additional source of correlated error, such as when using reanalysis data or another observation source.  
In an effort to disentangle these correlated errors and isolate only those due to instrumental sources, this work validates the correlated error structure \(R_{mod}\) by matching up near-simultaneous collocated observations made by two different observatories at nearlyidentical geometries, generating modeled NBRCS \(\sigma^{o}_{mod}\) from reanalysis data, and single- and double-differencing the results.
# Rogue Node Detection Based on a Fog Network Utilizing Parked Vehicles
## 5 Simulation Experiment
However, when the number of mobile vehicles is more than 75, the running time of the F-RouND scheme is one third slower than that of the proposed scheme. The reason is mainly because it dynamically forms the fog network using the moving vehicles on the road, which consumes a lot of time. However, the fog network formed by roadside parked cars in our scheme is much more stable. The scheme based on RSUs has the longest running time because of its complex algorithm. Theoretically, since neither the algorithm in this paper nor the F-RouND algorithm in [10] has nested loops, the time complexity of these two algorithms is O(n). In contrast, the RSU-based strategy algorithm in literature [7] has three layers of loops, with a high time complexity of O(n\({}^{5}\)). In general, the algorithm in this paper has a simple structure and has a good exploration of the computing power of each vehicle and RSU equipment. It can complete the detection of rogue nodes in a relatively short time.
## 3 Proposed Method
### Data Embedding Process  
As a basic assumption, the frame length is known. We consider two forms of embedding process in the frame processing: front-side and back-side. Figure 3a,b show the embedding process of the security code in the front-side and back-side forms, respectively.  
The host signal is divided into \(K\) frames by a short-term analysis using a fixed frame length. The even-numbered frame was set to \(k\) and the host signal of this frame was set to \(x_{k}(n)\) in the embedding process of the front-side form. In this case, the LP residue \(r_{k-1}(n)\) in the (\(k-1\))th frame is obtained by LP analysis from the host signal \(x_{k-1}(n)\) in the odd-numbered frame. The security code \(m(n)\) is spread-spectrum modulated using the LP residue \(r_{k-1}(n)\) in the (\(k-1\))th frame and embedded in the host signal \(x_{k}(n)\) of the even-numbered frame. The watermarked signal is \(y_{k}(n)\). The watermarked signal \(y_{k-1}(n)\) in the even-numbered frame is the same as the host signal \(x_{k-1}(n)\) in the even-numbered frame. To embed the security code robustly, the embedding strength \(a\) is controlled by the embedding-strength level \(L_{\text{SSL}}\) (as in the LP-DSS method) using Equation (6).  
Similarly, the embedding process (back-side form) can be done by switching the even/odd-numbered frames for LP residue calculation and embedding, as shown in Figure 3b.  
Figure 2: Block diagram of proposed method: (**a**) embedding process and (**b**) detection process with frame synchronization.
# Analytic Expressions for Radar Sea Clutter WSSUS Scattering Functions +
## 2 Results
### Important Special Cases
#### 2.5.2 Side-Looking Antenna, Level Flight Path
\begin{split} S_{h}^{\text{(side)}}(\tau,\rho)&=\frac {\lambda^{2}\sigma^{0}}{16\pi^{3}c^{2}\tau^{3}}\frac{1}{\rho_{X}^{\prime}}\int _{-\infty}^{\infty}\underline{G}^{2}(\beta|\tau)\underline{b}(\rho-\beta|\tau )d\beta\\ &=\frac{\lambda^{2}\sigma^{0}(\tau)}{16\pi^{3}c^{2}\tau^{3}} \frac{1}{\rho_{X}^{\prime}(\tau)}\left[\underline{G}^{2}(\rho|\tau)\ast \underline{b}(\rho|\tau)\right].\end{split}  \tag{26}\]  
If we note that the coefficients outside the brackets only depend on \(\tau\), we can simplify Equation (27):  
\[S_{h}^{\text{(side)}}(\tau,\rho)=f(\tau)\left[\underline{G}^{2}(\rho|\tau) \ast\underline{b}(\rho|\tau)\right]. \tag{27}\]  
The relationship in Equation (27) is a common model of the relationship between ICM spectrum, antenna pattern, and observed Doppler spectrum used in side-looking airborne radars (e.g., [54]); this derivation explicitly shows the conditions that are required for this model to be accurate. We can also define a baseband analogue to the characteristic function of Equation (16) as:  
\[\underline{k}(\Delta t|\tau)=\mathcal{F}_{\rho}^{-1}\left[\underline{b}(\rho|\tau) \right], \tag{28}\]  
and note that for a side-looking radar:  
\[ \begin{split} A_{h}^{\text{(side)}}(\tau,\Delta t)&= \mathcal{F}_{\rho}^{-1}\left[S_{h}^{\text{(side)}}\right]\\ &=f(\tau)\mathcal{F}_{\rho}^{-1}\left[\underline{G}^{2}(\rho| \tau)\ast_{\rho}\underline{b}(\rho|\tau)\right]\\ &=f(\tau)\mathcal{F}_{\rho}^{-1}\left[\underline{G}^{2}(\rho| \tau)\right]\mathcal{F}_{\rho}^{-1}\left[\underline{b}(\rho|\tau)\right]\\ &=A_{h}^{\text{(platform)}}(\tau,\Delta t)\,\cdot\,\underline{k}( \Delta t|\tau),\end{split}  \tag{29}\]  
where \(A_{h}^{\text{(platform)}}\) is the autocorrelation due to platform motion only. Because \(\underline{k}\) serves the function of windowing the correlation function (which in this case is also a covariance because the random process is zero mean) in \(\Delta t\), it is referred to in the STAP literature as a covariance matrix taper (CMT) [27; 28].
## 3 Proposed Model
### Serial Detection with Noise Prediction  
To implement the serial detection, we also need a buffer to get the \(j\times k\) data because the horizontal and vertical detectors process the 2D data in different directions. The output of the equalizer \(z[j,k]\) is fed into the horizontal detector (inner detector). When the VA is applied with the horizontal interference coefficients, the output signal \(s[j,k]\) of the inner detector has six levels. Consequently, \(s[j,k]-\epsilon[j,k]\) is used to obtain the soft output signal \(r[j,k]\), where the signal \(\epsilon[j,k]\) is the predicted signal of the signal \(e[j,k]\) in the testing process. Finally, the signal \(r[j,k]\) goes to the vertical detector and restores the signal \(\epsilon[j,k]\).  
In [19], the authors used a feedback line to reduce the down-track interference and added the remaining cross-track interference to the hard output of the inner detector to create the soft output signal \(s[j,k]\). This preserves the noise information to be correct in the next detector. However, this does not achieve the full characteristics of the nonlinear interference. Therefore, in this study, we used a neural network to predict the noise signal, because predicting the noise signal from the received signal is a nonlinear problem, and the neural network is therefore more appropriate than a linear equalizer.