esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### SS Removal and Head Loss  
The measured SS concentrations at the influent were 165.8 \(\pm\) 2.8, 171.6 \(\pm\) 5.1, and 161.8 \(\pm\) 4.6 mg/L; those of the effluent from the sedimentation chamber (and, hence, the influent to the filtration chamber) were 38.0 \(\pm\) 4.0, 43.9 \(\pm\) 5.8, and 45.8 \(\pm\) 6.0 mg/L; and those of the effluent from the filtration chamber were 2.3 \(\pm\) 0.3, 2.8 \(\pm\) 1.6, and 5.1 \(\pm\) 2.6 mg/L; for filtration velocities of 20, 30, 40 m/h, respectively, as reported previously [20]. This indicates that the SS removal in both the sedimentation chamber and filtration chamber was excellent. However, the results in Figure 2 indicate that the SS removal in each chamber varied widely with the variation in filtration velocity. Thus, the C/C\({}_{0}\) ratio of the sedimentation chamber is 0.185 \(\pm\) 0.057, 0.204 \(\pm\) 0.041, and 0.206 \(\pm\) 0.019, while that of the filtration chamber is 0.141 \(\pm\) 0.068, 0.126 \(\pm\) 0.070, and 0.113 \(\pm\) 0.033, for filtration velocities of 20, 30, and 40 m/h, respectively. Meanwhile, the relative standard deviation of the C/C\({}_{0}\) ratio for the sedimentation chamber is 0.310, 0.201, 0.092, while that of the filtration chamber is 0.484, 0.555, and 0.297, at 20, 30, and 40 m/h, respectively. The more significant fluctuation of the C/C\({}_{0}\) ratio in the filtration chamber relative to that in the sedimentation chamber can be attributed to the variation in the SS removal in the latter. Moreover, the C/C\({}_{0}\) ratio at each operation time was not correlated with the filtration velocity at either the sedimentation chamber or the filtration chamber. This might be due to the irregular re-dispersion and breakage of the deposited SS [37].  
The measured total head loss of the clean filtration chamber (i.e., at time \(t\) = 0) was 3.0, 4.2, and 5.6 cm, and gradually increased to maximum values of 5.3, 6.5, and 7.9 cm at \(t\) = 300 min for filtration velocities of 20, 30, and 40 m/h, respectively, as reported in previous work [20]. This indicates that, although the re-dispersion and breakage of the deposited SS are expected to increase with increasing filtration velocity, a higher inlet SS load results in a higher head loss.  
Figure 2: The time evolution of SS concentration in the outlets of (**A**) the sedimentation chamber and (**B**) the filtration chamber.
# A Bayesian Modeling Approach to Situated Design of Personalized Soundscaping Algorithms
## 3 Experimental Validation
### Preprocessing  
All signals were first resampled to 16 kHz, since most of the speech intelligibility information is located below 7 kHz. Furthermore the computational load increases sharply for higher sampling frequencies, which is incompatible with the ultra-low power demands of hearing aids. After resampling the speech and noise signals are centered around 0. The speech signal is power-normalized to 0 dB and the noise signal is power-normalized to obtain the desired SNR for the experiments.  
Next, the signal is processed by a frequency-warped filter for two reasons, for detailed discussions see [53]. First, we would like to obtain a high frequency resolution for source separation to be more efficient. Extracting the frequency coefficients directly using the short-time Fourier transform would require processing longer blocks of data for obtaining a higher frequency resolution. This leads to the second reason: we would like to limit the processing delay of the hearing aid. A large processing delay leads to coloration effects when the hearing-aid user is talking, which is experienced as \"disturbing\" by the user [53]. From this it becomes evident that we need to compromise between both goals when directly using the STFT. The frequency-warped filter achieves both goals by warping the linear frequency scale to a perceptually more accurate frequency scale, also known as the Bark scale [54]. For the same block size it achieves a perceptually higher frequency resolution in comparison to the STFT.  
The frequency-warped filter consists of a series of first-order all-pass filters. The Z-transform for a single all-pass filter is given by  
\[A(z)=\frac{z^{-1}-a}{1-az^{-1}}, \tag{26}\]  
with warping parameter \(a\). At a sampling frequency of 16 kHz, the warped frequency spectrum best approximates the Bark scale for \(a=0.5756\)[55]. The frequency-warped spectrum can be obtained by calculating the fast Fourier transform (FFT) over the outputs of each all-pass section, often referred to as taps. Because of conjugate symmetry in the obtained frequency coefficients, about half of the coefficients is discarded to limit computational complexity. From the remaining coefficients the input of the source modeling stage is formed. Importantly, the frequency-warped filter will also be used for reconstructing the signal by adding an FIR compression filter to it, as done in [53]. Here the \"Gain calculation\" block in ([53], Figure 3) will encompass the source separation and soundscaping stages in our framework. Throughout all experiments we will use a frequency-warped filter of length \(F=32\), which yields 17 distinct frequency coefficients, with a step size of 32 for reduced computations.
# An Image Hashing-Based Authentication and Secure Group Communication Scheme for IoT-Enabled MANETs
## 6 Conclusions  
Mobile ad hoc networks (MANETs) have an essential role to play in future smart environments involving the Internet of Things (IoT) for managing node mobility. In IoT-based systems, they provide self-formation and self-connectedness of networks. Since any node can freely join or leave the network in MANETs, it renders the networks and applications vulnerable to security attacks. Therefore, the provision of secure communication is an essential requirement for nodes in MANETs.  
This paper presents a node joining and node migration algorithm for war situations. It involves effective authentication using an image hashing-based security approach which involves a timestamp to guard against a replay attack. The scheme protects against the intruder's attack during the mobility of the node to join the target group. To allow joining, the GH receives the security credentials and hash of images from the node and verifies them with the credentials received from the NA. This reduces the infiltration of vulnerable nodes in the group for war situations or troop-based operations. Moreover, the age of the message is also crosschecked to guard against bit alteration attacks. The proposed work was validated through extensive simulations using NS 2.35. Results elucidate that the proposed scheme dominates its counterparts in terms of computational cost and communication cost for the GH and NA. Moreover, the failure cost of cluster authentication and reliability of malicious nodes is also presented. Results prove the supremacy of our proposed scheme. For the authentication of a node within a group, IHA provides 33% and 30% lower computational costs than SRIH and ESIH, respectively. Moreover, IHA provides 22% and 17% lower communication costs than ESIH and SRIH, respectively.  
In the future, we plan to manage two-group fusion and security credential handling under one elected GH with more member nodes.  
This research received no external funding.  
**Data Availability Statement:** Not Applicable, the study does not report any data.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# High-Quality Video Watermarking Based on Deep Neural Networks for Video with HEVC Compression +
## 4 Results
### Watermarking Research Results
This is due to the properties of the HEVC codec, which performs operations on subsquares in this resolution, which causes significant changes in the values for individual pixels and directly translates into the loss of the possibility of correct recovery of the watermark. A possible solution to improve the possibility of complete recovery of the watermark is the use of error-correcting codes (ECC). ECC involve the addition of sufficient redundant data to the main information, therefore, the use of such a solution reduces the capacity of the watermark. In the case of the proposed method using ASPA, the use of ECC methods like Turbocodes [55; 56] or Polar codes [57; 58] would significantly increase the potential recovery of the watermark even when BER values are not equal to 0. The acceptable value of BER (when BER > 0) for which the watermark could be retrieved will be depended on the used ECC method and the size of the correction code. However, it will reduce the capacity of the watermark because subsquares that could be used for main information will be occupied by error correction codes. The introduction of ECC schemes for ASPA purposes will increase the probability of full watermark recovery even when BER > 0 after the decoding process with higher compression levels of HEVC. It is also worth underlining that adding a neural network to the learning process compression channel to retrieve embedded watermark creates own ECC mechanisms, which are based on learnt filters. By improving the architecture of DNN encoder and decoder, the probability of full watermark recovery will be increased. A preview of the encoder and decoder results is shown in Figure 7.  
The following characteristics (Figures 8-10) show the encoder and decoder training process. These elements were trained on an autoencoder basis for 500 training epochs.
## 2 Materials and Methods
### Importance Analysis Based on Random Forest Model  
To ascertain the precise and direct influence of each environmental factor on WUE changes, in this study, the random forest model was employed for importance analysis, as this method allows for the direct estimation of the importance of each variable for the prediction results and is not affected by additional inputs, thereby ensuring accuracy and the ability to identify the importance of the factors considered [35]. The inter-annual trend in WUE was employed as the dependent variable, with four main drivers serving as the independent variables: temperature, precipitation, the multi-year mean of saturated water vapor pressure deficit, and the multi-year mean of soil moisture.
## 5 Discussion
With respect to scalability and creating large projects, the PWCT2 approach is based on organizing large projects into folders, subfolders, and different visual source files, where a visual source file can load other files and use the functionality provided by them, such as functions and classes. However, more improvements are required in this area to provide visualizations that highlight information from different visual source files. The development and evaluation of PWCT2, the proposed research prototype and successor to PWCT1, bring several noteworthy limitations to light. First and foremost, PWCT2 supports only the Ring programming language, whereas PWCT1 provided visual components for various textual programming languages, including Harbour, C, Supernova, Python, and C# [17]. This limitation restricts the versatility of PWCT2 and may reduce its appeal to users who require support for multiple programming languages. Secondly, PWCT2 is not compatible with PWCT1 in terms of visual component design or visual source file formats. This incompatibility necessitates the development of tools to convert projects from PWCT1 to PWCT2, which could pose challenges for users looking to transition their existing projects. Additionally, PWCT2 is currently distributed as a desktop tool rather than a web-based application. The lack of a web-based version limits accessibility and convenience, highlighting a potential area for future development to enhance user experience and broaden the tool's reach. Since PWCT2 is based on the Qt framework, which supports WebAssembly, and the Ring language also supports the WebAssembly platform, we plan to develop PWCT2 for WebAssembly in the future. At this stage, we have an online version of the Form Designer, and other components will be ported as well. While PWCT2 includes the Ring2 PWCT feature, which allows it to accept Ring code generated by large language models, the process currently requires a copy-and-paste operation (e.g., from Copilot to PWCT2) [77]. A fully integrated solution, where writing a natural language prompt directly generates a visual representation without needing manual transfer, would be more efficient and user-friendly. This enhancement could significantly streamline the workflow and improve usability. Moreover, despite providing 39 instructional videos to explain PWCT2 features, using PWCT2 still demands a general understanding of programming and specific knowledge of the Ring language [85]. To achieve broader adoption, it may be necessary to expand educational resources, including tutorials and documentation, to support users with varying levels of expertise. In summary, while PWCT2 advances the capabilities of its predecessor and offers a robust platform for developing Ring-based applications, addressing the limitations identified could enhance its functionality, user experience, and appeal to a broader audience. Future work should focus on expanding language support, ensuring compatibility with PWCT1, developing a web-based version, improving integration with natural language processing tools, and enhancing educational resources to support a diverse user base.
## 4 Experiment and Results  
### Datasets  
To assess the effectiveness of DEPANet, experimental evaluations are conducted on the SD-saliency-900 dataset [27], a widely used benchmark for surface defect segmentation in strip steel manufacturing. This dataset contains 900 annotated samples with pixel-accurate \(200\times 200\) masks, partitioned into 540 training images and 360 test images. As illustrated in Figure 4, numerous instances of various defect categories are presented. During training, input images are resized to \(256\times 256\) pixels and standardized using Z-score normalization. During testing, the model processes \(256\times 256\) resized inputs, with the generated segmentation output upscaled to the original \(200\times 200\) resolution using bilinear interpolation for quantitative evaluation. This preprocessing pipeline maintains dimensional consistency while preserving defect characteristics, ensuring accurate performance evaluation.
# Diversity and Functional Evolution of Terpene Synthases in Rosaceae
## 3 Discussion
In addition, we observed that early diverged TPS-e clade is conserved in function and most of their members were all predicted as ent-kaur-16-ene synthases with exceptions in _M. domestica_. Despite TPS-e and TPS-f being sister clades that clustered together, we found that their function had undergone differentiation, in contrast to TPS-e that mainly participated in diterpenoid biosynthesis, most TPS-f members are involved in the monoter biosynthesis pathway. The expansion of TPS-f in _P. persica_ and their product, S-linalool synthase, is the essential aromatic substance in peach fruits [21]. Our findings indicated that the TPS family in Rosaceae species possesses remarkable function diversity; different clades expand in different lineages by gene duplication and divergence. The generation of altered subcellular localization, and new substrate specificities of TPSs, is a dynamic process that specialized the trait differentiation. However, experimental data like metabonomics, enzyme assays, are also needed to verify these observations. The expression profiling results on the _TPS_ gene family in three Rosaceae species (_P. persica_, _M. domestica_, and _F. vesca_) showed that most identified TPSs were expressed in at least one tissue. Most of the TPSs were specifically expressed in one certain tissue; the expressed TPSs in ripe fruits are rare. For each TPS clade, the expression pattern also varied among species, such as TPS-f genes, which demonstrated a high expression in _P. persica_, but a lower expression in _M. domestica_. We also found that many paralogs exhibited divergent expression patterns either in tissues or expression abundance, suggesting that expression divergence might significantly contribute to gene survival and function differentiation after gene expansion. It was worth noting that among the expressed TPSs, certain putative TPSs did not have both domains. In contrast, many TPSs with both domains were not expressed, and the finding suggested that in the complexity of TPSs expression, not all of the complete TPSs were functional, and some functional ones may lose activity in either one domain. These putative TPSs without both domains were assumed to be triggered by partial duplication and assumed to be pseudogenes for the loss of original function. A previous study found that a total of 12% of the pseudogenes still contained detectable open reading frames and were effectively expressed. The generated transcripts may contribute to the synthesis of small interfering RNA species that regulate parent transcripts [26]. Fast-evolving families involved in ubiquitination and secondary metabolism families always contain the highest number of pseudogenes [26; 27]. Hence, the functionality of the expressed \"fragmental\" TPSs in our study still needs further investigation.
# A Secure and Scalable Authentication and Communication Protocol for Smart Grids
## 3 Methodology
### Proposed Protocol Design
#### 3.2.4 Symmetric Key Establishment  
The client performs verification by generating the HMAC \(h^{{}^{\prime}}_{y}\) of the received message \(y_{C_{i}}\) and comparing it with the received HMAC \(h_{y}\). Following successful verification, the client decrypts the message and verifies the nonce and server ID for authenticity. Upon confirming these elements, the client securely stores \(k_{sym}\) for use in future communication.
# Three-Dimensional Assessment of Upper Limb Proprioception via a Wearable Exoskeleton
## 2 Materials and Methods
### Data Analysis  
Trajectories were saved at 100 Hz frequency. Recorded joints' positions were filtered offline using a third order Savitzky-Golay low-pass filter (cut-off frequency of 10 Hz).  
Proprioceptive performance was computed by using three kinematic indicators evaluated on the N repetition across each experimental condition (S) and MJ):  
1. The \"Matching Error\", which analyses performance accuracy, by computing the average of the absolute error between the proprioceptive target position \(\theta_{target}\) and the arm configuration \(\theta_{i}\): \[\text{Matching Error}=\frac{1}{\text{N}}\ \times\ \sum_{i=1}^{N}|\theta_{i}-\theta_{target}\ |\] (1)
2. The \"Error Bias\" [4] evaluates the overshoot and undershoot during the matching task by considering the signed error between the presented proprioceptive target location (\(\theta_{target}\)) and the final position (\(\vartheta_{i}\)) at the end of each trial [41]. For consistent interpretation, we transformed the signed Error Bias to a measure of a signed overshoot: \[\text{Error Bias}\ \text{os}=\frac{1}{\text{N}}\ \times\ \text{sign}(\theta_{target})\ \times\ \sum_{i=1}^{N}(\vartheta_{i}-\vartheta_{target}).\] (2)In this outcome, negative values represent an undershoot, and positive values represent an overshoot independently of the sign of the presented proprioceptive target.  
Both metrics have been expressed as percentage (%) of the distance between the Initial Position and the Proprioceptive Target which varied for each DoF considered (\(\Delta\vartheta\) = 50\({}^{\circ}\) for AA\({}_{\text{Shoulder}}\), \(\Delta\vartheta\) = 75\({}^{\circ}\) for FE\({}_{\text{Shoulder}}\) and \(\Delta\vartheta\) = 30\({}^{\circ}\) for FE\({}_{\text{Blbow}}\)). The target amplitudes were selected to reproduce in the MJ condition a typical functional gesture of daily activities, maintaining a percentage of about 30% of the total functional ROM for each DoF.
## 3 Linear Kalman Filter and Derivatives
\[K_{k}=P_{k}^{-}H_{k}^{T}\Big{(}H_{k}P_{k}^{-}H_{k}^{T}+R_{k}\Big{)}^{-1} \tag{12}\]  
where \(P_{k}^{-}\) is the covariance of the prediction stage estimation error defined as:  
\[P_{k}^{-}=E\Big{\{}e_{k}^{-}.\big{(}e_{k}^{-}\big{)}^{T}\Big{\}} \tag{13}\]  
The update of the error covariance matrix \((P_{k})\), that is the variance of the correction stage, is calculated as in (14). \[P_{k}=(I-K_{k}H_{k})P_{k}^{-} \tag{14}\]  
where \(I\) is the identity matrix. The value of the state at the future instant, using the current values \((\hat{x}_{k+1}^{-})\), is obtained in Equation (15) as follows:  
\[\hat{x}_{k+1}^{-}=\Phi_{k}\hat{x}_{k} \tag{15}\]  
Finally, the estimation of the error value that will be obtained \((e_{k+1}^{-})\) is calculated in (16). This value is used to estimate the covariance of the future error \((P_{k+1}^{-})\) in (17). \[e_{k+1}^{-}=x_{k+1}-\hat{x}_{k+1}^{-}=(\[PERSON]_{k}x_{k}+w_{k})-\big{(}\[PERSON]_{k} \hat{x}_{k}^{-}\big{)}=\[PERSON]_{k}e_{k}+w_{k} \tag{16}\]  
\[P_{k+1}^{-}=E\Big{[}e_{k+1}^{-}*e_{k+1}^{-}{}^{T}\Big{]}=E\Big{[}(\[PERSON]_{k}e_{ k}+w_{k})*(\[PERSON]_{k}e_{k}+w_{k})^{T}\Big{]}=\Phi_{k}P_{k}\Phi_{k}^{T}+Q_{k} \tag{17}\]  
Once the equations and matrices are defined, the recursive algorithm of the LKF can be initiated. Figure 4 shows the flow chart of the recursive algorithm. \(\Phi_{k}\) is the system's state matrix, and it must match the system model, meaning that to adequately size this matrix, the number of harmonic components must be known, corresponding to Equation (5). The accuracy of the estimation then depends on the LKF model [30]. The LKF will converge if the model contains a number of states equal to the harmonic components of the signal to be estimated. The LKF will attempt to adjust the developed model to the real value of the signal, aiming to adjust the output of each component so that the result is as close as possible to the actual value. This approach may result in the overall output value of the LKF being exactly equal to the desired signal, but the obtained components may differ from the actual components [30].