esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Statistical Analysis
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Kicking Performance** & \multicolumn{2}{c}{**Ball Velocity**} & \multicolumn{2}{c}{**Ball Spin**} \\ \hline \hline \multicolumn{2}{c}{VIF Range} & \multicolumn{3}{c}{1.39–2.66} \\ R\({}^{2}\)m & \multicolumn{2}{c}{0.247} & \multicolumn{2}{c}{0.169} \\ R\({}^{2}\)c & \multicolumn{2}{c}{0.394} & \multicolumn{2}{c}{0.295} \\ \multicolumn{2}{c}{Predictors} & Estimate (\(\beta\)) & 95\% CI & Estimate (\(\beta\)) & 95\% CI \\ \hline \multirow{8}{*}{Fixed Effects} & Hip Add-Abd ROM & 0.014 & \([-0.033,0.061]\) & 0.235 * & [0.003, 0.467] \\  & Hip Flex-Ext ROM & \(-0.007\) & \([-0.054,0.040]\) & 0.022 & \([-0.214,0.258]\) \\  & Hip IR-ER ROM & 0.048 & \([-0.011,0.107]\) & \(-0.262\) & \([-0.558,0.033]\) \\  & Peak Hip Add Vel & \(-0.175\) & \([-0.398,0.048]\) & 0.461 & \([-0.638,1.561]\) \\  & Peak Hip Abd Vel & 0.082 & \([-0.171,0.335]\) & \(-1.026\) & \([-2.301,0.250]\) \\  & Peak Hip Flex Vel & \(-0.029\) & \([-0.218,0.160]\) & \(-0.638\) & \([-1.587,0.310]\) \\  & Peak Hip Ext Vel & \(0.230\) * & \([0.017,0.444]\) & 0.218 & \([-0.844,1.281]\) \\  & Peak Hip IR Vel & \(-0.009\) & \([-0.101,0.082]\) & 0.385 & \([-0.074,0.844]\) \\  & Peak Hip ER Vel & \(0.024\) & \([-0.115,0.162]\) & \(-0.010\) & \([-0.698,0.678]\) \\  & Knee Flex-Ext ROM & \(0.017\) & \([-0.008,0.042]\) & \(-0.140\) * & \([-0.265,-0.014]\) \\  & Peak Knee Flex Vel & \(-0.056\) & \([-0.165,0.053]\) & 0.035 & \([-0.510,0.579]\) \\  & Peak Knee Ext Vel & \(0.193\) * & \([0.003,0.384]\) & \(1.057\) * & \([0.114,2.000]\) \\  & Ankle Pre-DF ROM & \(-0.001\) & \([-0.029,0.026]\) & 0.024 & \([-0.113,0.162]\) \\  & Peak Ankle DE Vel & \(-0.066\) & \([-0.171,0.039]\) & \(-0.353\) & \([-0.886,0.181]\) \\  & Peak Ankle PF Vel & \(0.074\) & \([-0.014,0.161]\) & 0.049 & \([-0.385,0.482]\) \\ Random Effect & Player Var & \multicolumn{2}{c}{0.588} & \multicolumn{2}{c}{11.647} \\ \hline \hline \end{tabular}  
* Add: add: addution; Abd: abduction; Flex: flexion; Ext: extension; PF: plantar flexion; DF: dorsiflexion; Vel: velocity; *: \(p\) < 0.05.  
\end{table} 
Table 3: LMM of lower joint parameters and kicking performance for preferred leg kicks.
# Secure Quantum Communication Technologies and Systems: From Labs to Markets
## 4 QKD Applications
### Aerospace Communication  
In the aerospace segment, due to difficulties with physical access to devices, the sensitiveness of data transmitted, and also due to the huge amount of investment needed, QKD technologies appear to be a short-term solution to avoid cyber-attacks. QKD could be used to encrypt: (1) critical ground-to-space communication, (2) satellite-to-satellite links, and by combining the two, (3) ground-to-ground communication over satellite networks. Quantum systems may be employed in both low and medium earth orbit (LEO/MEO), where the relative proximity to the earth surface guarantees acceptable losses due to diffraction of light, while their high speed in respect to the ground represents the main drawback for pointing the laser. Applications could also involve geostationary earth orbit (GEO) networks,  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Devices** & **Protocols** & **Medium** & **Cost** & **Distance** & **Application** \\ \hline \multirow{2}{*}{Integrated} & DV-QKD & \multirow{2}{*}{In Air} & \multirow{2}{*}{Low} & Short & Internet of Things, \\  & CV-QKD & & & (1–10 m) & Contactless payment \\ \hline \multirow{2}{*}{Bulkly/Integrated Weak pulses} & DV-QKD & \multirow{2}{*}{In Air} & \multirow{2}{*}{Medium} & High & Space \\  & Decoys states & MDI & & (200–35000+ & \\  & Pol. Enc. & & & km) & Communications \\ \hline \multirow{2}{*}{Bulkly/Integrated Weak pulses} & DV-QKD & \multirow{2}{*}{In Fiber} & \multirow{2}{*}{Medium} & Medium & Inter Database \\  & Decoys states & & & (10–100 km) & Short reach meshed \\  & MDI & & & & networks \\ \hline \multirow{2}{*}{Bulkly Cryogenic Temp.} & DV-QKD & \multirow{2}{*}{In Fiber} & \multirow{2}{*}{High} & Medium/high & Critical \\  & MDI & & In Air & (10–100 km) & infrastructure \\  & One Time Pad & & & & management \\ \hline \multirow{2}{*}{Bulkly/Integrated Weak pulses} & DV-QKD & \multirow{2}{*}{In Fiber} & \multirow{2}{*}{Medium} & Medium & Wide area network \\  & CV-QKD & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Technology features versus different application domains.  
where instead there is huge distance from the ground to the satellite, but the link can be maintained continuatively [126]. In aerospace, DV-QKD is easier to adopt compared to CV-QKD due to the high variability of the atmospheric channel, the changing positions of objects, and the long distances, increasing the complexity of channel estimation. Polarization encoding is adopted in a straightforward manner, since the polarization state can travel unaffected by rotation in air. The high distances and the size of the equipment suggest the usage of bulky, high detection efficiency devices supported by low temperature operation. Further, the absence of a wired infrastructure between communicating devices in earth-to-satellite scenarios makes a possible network implementation based on point-to-point links without quantum switches and routers difficult to implement.
# Secure Beamforming Design for UAV-Empowered Integrated Sensing and Communication
## 3 Joint Optimization Algorithm
### UAV Position Optimization  
After determining the transmitting beamforming \(\mathbf{\omega}_{k}^{c},\forall k\) and \(\mathbf{\omega}_{n}^{r},\forall n\), the optimization subproblem for the UAV position \(\mathbf{q}\) can be formulated as:  
\[\begin{array}{l}\text{P5}:\underset{\mathbf{q}}{\text{min}}\underset{j,k}{\text{ max}}\Big{(}\text{SINR}_{j,k}^{c}\Big{)}\\  
\[\log_{2}\eta\big{(}\mathbf{W}_{k}^{c},d\big{(}\mathbf{q},\mathbf{e}_{j}\big{)} \big{)}\] \[-\log_{2}\left(\sum\limits_{i=1,j+k}^{K}\eta\left(\mathbf{W}_{i}^{c},d \big{(}\mathbf{q},\mathbf{e}_{j}\big{)}\right)\right.+\sum\limits_{n=1}^{N}\mu\big{(} \mathbf{W}_{n}^{c},d\big{(}\mathbf{q},\mathbf{e}_{j}\big{)}\big{)}\right.+\frac{{\sigma_{e} }^{2}}{\beta}d^{2}\big{(}\mathbf{q},\mathbf{e}_{j}\big{)}\bigg{)}\leq\log_{2}(B)\] (20) where \[\eta\big{(}\mathbf{W}_{k}^{c},d\big{(}\mathbf{q},\mathbf{e}_{j}\big{)}\big{)} =\big{|}\mathbf{a}^{\mathrm{H}}(\mathbf{q},\mathbf{e}_{j})\mathbf{\omega}_{k}^{c} \big{|}^{2} \tag{21}\] \[=\sum\limits_{a=1}^{N}\big{[}\mathbf{W}_{k}^{c}\big{]}_{a,a}+2\sum \limits_{a=1}^{N}\sum\limits_{b=a+1}^{N}\Bigl{|}\big{[}\mathbf{W}_{k}^{c}\big{]}_{ a,b}\Big{|}\cos\left(\theta_{a,b}^{\mathbf{W}_{k}^{c}}+\pi(b-a)\frac{H}{d(\mathbf{q},\mathbf{e}_{j} )}\right)\] \[\mu\left(\mathbf{W}_{n}^{c},d\big{(}\mathbf{q},\mathbf{e}_{j}\big{)}\right) =\big{|}\mathbf{a}^{\mathrm{H}}(\mathbf{q},\mathbf{e}_{j})\mathbf{\omega}_{n}^{c} \big{|}^{2}\] (22) \[=\sum\limits_{a=1}^{N}\big{[}\mathbf{W}_{n}^{c}\big{]}_{a,a}+2\sum \limits_{a=1}^{N}\sum\limits_{b=a+1}^{N}\bigl{|}\big{[}\mathbf{W}_{n}^{c}\big{]}_{ a,b}\big{|}\cos\left(\theta_{a,b}^{\mathbf{W}_{n}^{c}}+\pi(b-a)\frac{H}{d(\mathbf{q},\mathbf{e}_{j} )}\right)\] The proofs for (21) and (22) can be referred to in reference [11]. At this point, the left-hand side of the inequality constraint (20) exhibits non-convexity with respect to the optimization variable \(\mathbf{q}\). We implement the SCA algorithm. In the \(l\)-th iteration, the left-hand side of the inequality is linearized through a first-order Taylor expansion at the local point \(\mathbf{q}^{(l)}\), and the inequality constraint is subsequently approximated as follows: \[C_{j,k}^{(l)}+\mathbf{D}_{j,k}^{(l)}\mathrm{H}\Big{(}\mathbf{q}-\mathbf{q}^{(l)}\Big{)} \leq\log_{2}(B)\] (23) where \(C_{j,k}^{(l)}\) is obtained from (20) during the \(l\)-th iteration, while \(\mathbf{D}_{j,k}^{(l)}\) is derived from the \(l\)-th iteration of the derivative of (20) with respect to the variable \(\mathbf{q}^{(l)}\). \(\mathbf{\phi}\big{(}\mathbf{W}_{k}^{c},d\big{(}\mathbf{q}^{(l)},\mathbf{e}_{j}\big{)}\big{)}\) and \(\delta\big{(}\mathbf{W}_{n}^{c},d\big{(}\mathbf{q}^{(l)},\mathbf{e}_{j}\big{)}\big{)}\) respectively denote the derivatives of \(\eta\left(\mathbf{W}_{k}^{c},d\big{(}\mathbf{q}^{(l)},\mathbf{e}_{j}\big{)}\right)\) and \(\mu\Big{(}\mathbf{W}_{n}^{c},d\big{(}\mathbf{q}^{(l)},\mathbf{e}_{j}\big{)}\big{)}\) with respect to the variable \(\mathbf{q}^{(l)}\).
# mDARAL: A Multi-Radio Version for the DARAL Routing Algorithm
## Appendix A mDARAL Changes  
This appendix lists the changes in _mDARAL_ with respect to _DARAL_, covering the additional control messages in the new version of the routing algorithm and the new parameters included to configure the new features. This appendix is based on Appendixes A and B of our previous work [5].  
Figure 8: Average convergence time of _Daral_ (D) and _mDaral_ (MD) for small-, medium- and large-size scenarios under different propagation conditions and node densities (ND5, ND10 and ND15).  
Operation code represents the control message identification used by _DARAL_ and _mDARAL_, which counts 19 different messages, 15 from _DARAL_ and an additional four from _mDARAL_, which are described below:  
* CHANGE_CHANNEL_REQUEST: it fires the channel migration process.
* CHANGE_CHANNEL_ACK: it confirms the reception of a _CHANGE_CHANNEL_REQUEST_ frame.
* VPAN_CONFIG_REQUEST: it sends a request to the VC of a certain sub-network, requesting the configuration information of its sub-network (LC and vIDs).
* VPAN_CONFIG_ACK: it confirms the reception of a _VPAN_CONFIG_REQUEST_ frame, broadcasting the sub-network configuration details through the CC.  
The following list presents the additional parameters of _mDARAL_, with respect to _DARAL_:  
* _rrsiPtclst_: This parameter defines the maximum percentage of link quality, in terms of LQI/RSSII, that the link can worsen. In addition, 100% of the signal is set by the initial LQI measurement used during the connection with the VC.
* _ratSnngAckRd_: This ratio sets the limit where the VC can start with the migration channel process. After receiving enough answers to the _CHANGE_CHANNEL_REQUEST_ from the nodes in its sub-network, the VC changes its LC.
* _channelAliveTime_: This timer defines the time that a node can wait without any answer once the migration channel process has been fired. After this time, the node requests the configuration of the sub-network to the VC, using a _VPAN_CONFIG_REQUEST_ through the CC.  
All of these parameters could be tuned, as in _DARAL_, by intelligent systems, making feasible their application to diverse scenarios, not only for Smart Cities, but also for IoT.
## 4 Results
### Evaluation of Performance of Key Features  
Three key features are used in the proposed LDS U-Net model-light dense blocks, multi-scale paths, and selection gates. At the onset, it is important to assess whether these features play an important role in achieving the objectives. Hence, during the ablation study, models were made by isolating the desired key features and comparing the semantic segmentation performance. At first, both the multi-scale paths and selection gates were removed from the main model. The model is named as the light dense (LD) model and it successfully segments the thoracic and lumbar bony features. Next, the selection gates are added with the LD model, and named as the light convolution selection (LCS) model. As an alternate improvement to the LD model, the conventional shortcut connection was replaced with the inception skip path to form the light dense inception (LDI) model. Finally, all of the above models were implemented together as the light dense selection U-Net (LDS U-Net) model.
## 3 Theoretical Background and Problem Formulation
### STPA as Component of a Decision Making Tool
#### 3.2.1 Markov Decision Processes  
An HRC system in the operational phase can be considered as a dynamic scheme of states led by specific actions. Thus, we can conveniently model our problem as a Markov Decision Process (MDP). Following the MDPs formulation, we define a set of system states, a set of actions, the effects of the actions and the immediate value of each action. The state is the way the ecosystem (i.e., the environment, the human and the robot) currently exists, and an action will have the effect of changing the state of this ecosystem. The objective is to select the best action for a particular state, since alternative actions have different outcomes and may transit the system to a different state. The automation of decision-making in MDPs is achieved by associating selected actions with a value. The transition is modeled by specifying the resulting next state for each starting state and action. However, there is a certain limitation in classical MDPs, where the main assumption is that the next state depends only upon the current state [30]. Although this assumption can be valid for certain applications, in safety system scenarios, where time evolution of the facts also plays a major role, a sequential combination of CA and UCAs advocate in the system's transition from safe related states to hazard states and eventually in states associated with losses [27]. However, based on the Markov assumption, the next state is solely determined by the current state (and current action) and, thus, the MDP model is not capable of modeling these situations directly.
# Monthly Precipitation over Northern Middle Atlas, Eastern Morocco: Homogenization and Trends
## 3 Results  
### Data Homogenization  
The study area is marked by some of the greatest rainfall variability in North Africa [51]. For this reason, and after reviewing the diagnostic plots of data generated by Climatol, we decided to re-tune the outlier detection threshold. Instead of \(\pm 5\) standard deviations, the threshold was set to \(-10\) and \(+14\) standard deviations. This was done to avoid suppressing or correcting real data reflecting extreme climate signals and/or microclimate singularities in the context of a high spatial variability of precipitation [40]. After this setting, only four outliers were detected at stations S1, S15, S17 and S21, and they were corrected.  
Missing data were estimated by Climatol from nearby stations by adapting the method of [PERSON] and [PERSON] [25]. In all, about 50% of the data were filled in.  
The values of the correlation coefficients of the monthly data, as a function of distance, show a remarkable variation between the raw series and the homogenized series. Indeed, before homogenization, the values are more dispersed compared to those after homogenization (Figure 3). Moreover, the two correlograms of the monthly correlations between the stations show relatively high correlations (>0.6) for the stations located within about 25 km of each other. The correlation coefficient decreases progressively with the distance, reaching a low value of 0.22 in the stations located at a distance of approximately 100 km (Figure 3). This is not surprising, given the strong variations in both the geographical location of each station in the topographic context and the exposure of the slopes to moist air masses.  
Orographic discontinuity can give opposite precipitation regimes over short distances.  
It should be noted that for the raw series, where the correlation coefficients reach negative values in stations located at a distance that does not exceed 50 km, the homogenized series' correlogram looks more physically plausible.
###### Abstract  
The cross-correlation function (CCF) is an established technique to calculate time-of-flight for ultrasonic signals. However, the quality of the CCF depends on the shape of the input signals. In many use cases, the CCF can exhibit secondary maxima in the same order of magnitude as the main maximum, making its interpretation less robust against external disturbances. This paper describes an approach to optimize ultrasonic signals for time-of-flight measurements through coded excitation sequences. The main challenge for applying coded excitation sequences to ultrasonic signals is the influence of the piezoelectric transducer on the outgoing signal. Thus, a simulation model to describe the transfer function of an experimental setup was developed and validated with common code sequences such as pseudo noise sequences (PN), Barker codes and chirp signals. Based on this model an automated optimization of ultrasonic echoes was conducted with random generated sequences, resulting in a decrease in the secondary positive maximum of the CCF to 56.6%. Based on these results, further empiric optimization leveraging the nonlinear regime of the piezoelectric transducer resulted in an even lower secondary positive maximum of the CCF with a height of 25% of the first maximum. Experiments were conducted on different samples to show that the findings hold true for small variations in the experimental setup; however, further work is necessary to develop transfer functions and simulations able to include a wider parameter space, such as varying transducer types or part geometry.  
ultrasound; cross-correlation function; coded excitation; time-of-flight Accepted: 8 June 2021  
16 June 2021
## 2 Material and Methods
### Specim Preparation  
The optimal ratio of raw materials for preparing gangue-based roadway shotcrete was ascertained on the basis of the results of initial trials, that is, the gangue/sand/cement/water/accelerator ratio was 44.5:44.5:25:16.5:1. On the basis of the ratio, gangue powder was used to replace 30%, 40%, and 50% of the cement. When preparing the specimens, the coarse aggregate, fine aggregate, and cement weighed in advance were firstly mixed and stirred for 5 min; then, the uniformly mixed accelerator and water were poured into the dry mixture, followed by stirring for 5 min. The stirred slurry was placed in three layers in molds measuring 100 mm \(\times\) 100 mm \(\times\) 100 mm. Then, the vibrating platform was used to vibrate the slurry (15-30 s); the vibration process should not only make the slurry inside the bubble completely escape but also prevent over-vibration to produce segregation. After smoothing the surface (localized depressions and bumps not exceeding 0.1 mm), the specimens were  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Materials** & \multicolumn{6}{c}{**Chemical Composition (wt.\%)**} \\ \cline{2-7}  & **SiO2** & **Al2O3** & **Fe2O3** & **CaO** & **MgO** & **K2O** & **SO3** & **Others** \\ \hline Gangue & 62.58 & 18.81 & 2.64 & 3.27 & 1.78 & 2.86 & 0.33 & 7.73 \\ Ordinary Portland cement & 22.53 & 5.22 & 3.53 & 61.04 & 1.73 & 0.56 & 2.31 & 3.08 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Main chemical composition of gangue and cement.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Material** & **Replaced Materials** & **Replacement Ratio** & **Results** \\ \hline Scrap glass & Sand & 0–100\% & Enhanced low-substitution efficiency \\ Tailings & Sand & 100\% & Mechanical compliance achieved \\ Ferrochromre slag & Natural aggregates & 50\% & Enhanced mechanical–durability performance \\ Tailings & Natural aggregates and sand & 100\% & Mechanical compliance achieved \\ Gangue & Natural aggregates & 100\% & Mechanical compliance achieved \\ Geopolymer & Cement & 100\% & Spray mechanical compliance achieved \\ Fly ash & Cement & 4–33\% & Mechanical compliance achieved \\ Red mud & Cement & 10–25\% & Mechanical compliance achieved \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of research advances in shotcrete production using waste materials.  
prepared. The prepared specimens were placed in a curing box for 1 d, 7 d, and 28 d at a temperature of (20 \(\pm\) 2) \({}^{\circ}\)C and a relative humidity of 95%. Thereafter, the specimens were removed to measure the mechanical performance of the shotcrete. The specimen preparation process is illustrated in Figure 1.
# Combating Fake News with Cryptography in Quantum Era with Post-Quantum Verifiable Image Proofs
## 8 Public Statements, Secret Witnesses, and Instance-Witness Relations  
In the context of verifying the authenticity and integrity of edited photos, the proof system relies on a public statement, a secret witness, and an instance-witness relation to ensure that the edited image is a valid transformation of an original, signed photo. The public statement consists of the edited photo \(x\), the editing function \(f\), and the camera's public key \(vk\). These elements are known to the verifier and provide the basis for the verification process. The secret witness, on the other hand, includes the original photo \(w\) and the camera's signature \(\sigma\) on \(w\). This information is kept private and is used by the prover to generate the proof.  
The proof system is defined by the instance-witness relation \(R\), which formalizes the conditions that must be satisfied for the proof to be valid. Specifically, the relation \(RR\) is defined as follows:  
\[R:=\{((\mathrm{vk},f,x);(w,\sigma));f(w)=x\wedge\mathrm{Vf}(\mathrm{vk},w, \sigma)=1\} \tag{6}\]  
This relation states that the edited photo \(x\) must be the result of applying the editing function \(ff\) to the original photo \(w\), and the signature \(\sigma\) must be valid under the camera's public key \(vk\). The proof system, thus, requires two distinct proofs--a transformation proof and a signature proof.  
The transformation proof demonstrates that \(f(w)=x\), meaning that the edited photo \(x\) is the result of applying the editing function \(f\) to the original photo \(w\). This proof ensures that the editing process was performed correctly and that the final image is a valid transformation of the original.  
The signature proof verifies that the signature \(\sigma\) is valid for the original photo \(w\) under the camera's public key \(vk\). This proof ensures that the original photo was indeed signed by the camera and has not been tampered with since its creation. Together, these proofs provide a comprehensive verification mechanism for edited photos. The transformation proof ensures the integrity of the editing process, while the signature proof ensures the authenticity of the original image. By combining these proofs, the system can guarantee that the edited photo is both a valid transformation of the original and derived from a trusted source.  
This proof system is particularly relevant in applications like Post-Quantum VerITAS, where the goal is to verify the provenance of edited images, while preserving privacy and security. The use of zero-knowledge proofs ensures that the secret witness (the original photo and signature) remains hidden, while the public statement and instance-witness relation provide a transparent and verifiable framework for ensuring the authenticity and integrity of digital content.
# Effective Data Utilization in the Context of Industry 4.0 Technology Integration
## 1 Introduction
### Big Data
#### 1.5.1 Data Collection and Data Mining  
Modern manufacturing systems are equipped with advanced sensors that collect sequential data from different processes. These data are low in volume when accessed individually, but as a whole, they generate a huge amount of data. To generate simulations, evaluations and predictions, it is fundamental that high-quality data collection becomes a priority goal for organizations in their transformation into smart organizations [17].  
Data mining is the process of analysing data to identify new patterns within one or more data sets. Data mining techniques use integrated data from a database by utilizing statistical and mathematical methods [35]. Some advanced data mining methods use clustering, classification, association rules, regression, prediction and variance analysis. By utilizing such methods, large amounts of dynamic and raw data can be organized and evaluated [16].