esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
At minimum and maximum values of the SMF, the measurement results are as expected: at 10 nT, water samples at C12, C30, and C50 dilutions differ minimally from the control (intact water) and do not differ from each other (\(p>0.05\)); at 20,000 nT,  
Figure 4: The effect of water samples previously incubated at different magnetic field parameters on ROS production by neutrophils. Pre-incubation of water samples was performed under the geomagnetic field (GMF) conditions, but on different days (panels (**a**–**i**), 3 right bars) or under static magnetic field (SMF) conditions from 10 to 20,000 nT (panels (**a**–**i**), 3 left bars). The measurements of normalized chemiluminescence are presented as Mean \(\pm\) Standard deviation. Normalized chemiluminescence was calculated by dividing the maximum chemiluminescence value measured for a particular sample by the maximum value obtained for water. The value of chemiluminescence for “Water” sample (Control) was taken as 100% (not shown on the graph). \({}^{*}\)\(p<0.05\) (\(t\)-test with Holm correction for multiple comparisons). the differences in the samples from the control are much more pronounced (above 50%) (Figure 4i, all values around 150%), but all three samples (C12, C30, C50) similarly affect the intensity of cellular chemiluminescence (\(p>0.53\)). Thus, one of the main results is that the biophysical properties of water samples at C12, C30, and C50 dilutions prepared under the intermediate SMF component (varying from 500 nT to 900 nT) significantly differ between each other (\(p<0.05\)).
## 5 Deployment of PLC for Smart Metering and Smart Grid Systems
### NB PLC Systems for \(LV\) Grid Communications  
The most common use PLC technology for Smart Metering [16] is NB PLC. Although some tests and pilots have been performed using BB PLC technologies, and even real deployments with UNB PLC technologies exist, NB PLC is the prevalent choice for Smart Metering including both meter reading and real-time nature applications such as remote connection-disconnection of meters [133,134], and remote firmware upgrade capabilities to guaranty that new features can be taken to the field over the existing infrastructure. BB PLC systems have been deployed over electricity grids for commercial Internet access [135,136] and on the in-home segment for easy local area network (LAN) connectivity [74,79]. UNB PLC systems [33,34,35] have been used sometimes as a last resource option for really low-density and disperse areas, where no other technology options were viable from the economic standpoint.  
The information available on the results of NB PLC big scale deployments in literature is limited, as can be checked in [10]. Widespread information on deployments is based on the technical aspects documented by manufacturers (both proprietary and non-proprietary systems), but unfortunately this information is often biased by marketing. Referring to information provided on the results of standard PLC systems, references [36,99,100] are the most explicit references found, and even if they just provide information of samples of the results, they are a valuable source of knowledge as Chapter 6 will show with references to related studies. On the academic side, research has traditionally been focused on the basic aspects of the underlying technology in the devices (modulation, coding) and systems (channel modeling, noise models, medium access control strategies).  
The optimum deployment of PLC for Smart Metering requires previous knowledge of several specific features of the grid that will support this type of communications. These aspects will affect the PLC system behavior and, depending on their influence, different grid aspects and PLC parameters or functionalities should be adapted to improve the performance of the whole set. Details in this regard are given in next sections.
# Fault Investigation of Circulation Pumps to Detect Impeller Clogging
## 3 Results and Analysis
### Motor Current Signature Analysis--Impeller Clogging
#### 3.3.1 Constant Process Curve  
In this section, the results of the MCSA for the fault cases are shown, whereby the position of the shut-off valve remained in constant position during the measurement of the fault cases (see Section 2.3, second test run). As a result, the power consumption of the pump decreased as the number of blockages increases (see Table 1). Special focus was given to the fault frequencies \(f_{r,pump}\) and \(f_{BPF}\) described in theory. Figure 6 shows the MCSA around these fault frequencies in a waterfall diagram. All measurement results from healthy to seven clogged channels are shown. In addition, the MCSA around the frequencies 791.9 and 875.3 Hz are shown. These frequencies were the sideband frequencies of the 5 th harmonic.  
Figure 6 shows that at the frequencies 125.0 Hz (a), 208.4 Hz (b), 791.9 Hz (c) and at 875.3 Hz (d) the amplitude increase with increasing clogging. A maximum was reached at three or four clogged channels. From then on the amplitudes decrease with an increasing number of clogged channels. At the frequency 125.0 Hz the amplitude of \(-\)98.5 dB in healthy condition increases to \(-\)59.6 dB (+38.9 dB) for 3 clogged channels and \(-\)61.3 dB for 4 clogged channels. With complete clogging, the amplitude dropped to \(-\)90.6 dB (+7.9 dB). The variant with one clogged channel (\(-\)81.6 dB) behaved similarly to the variant with six clogged channels (\(-\)90.7 dB) and the variant with two clogged channels (\(-\)75.1 dB) behaved like the variant with five clogged channels (\(-\)69.2 dB). Considering the frequency 208.4 Hz, the amplitude increased from \(-\)109.9 dB to \(-\)79.6 Hz (+30.3 dB) for three clogged channels and to \(-\)84.0 dB for four clogged channels and decreases to \(-\)120.4 dB (\(-\)10.5 dB) for complete clogging. At the  
Figure 6: MCSA of a pump with different number of clogged impeller channels at a speed of 2500 rpm: (**a**) around \(f_{r,pump}\)\(-\) = 125.0 Hz. (**b**) Around \(f_{r,pump+}\) = 208.4 Hz. (**c**) Around 791.9 Hz. (**d**) Around 875.3 Hz. (**e**) Around \(f_{BPF}\) = 58.4 Hz.  
frequency 791.9 Hz the amplitude increased from \(-\)157.9 dB to \(-\)117.8 dB (+40.1 dB) for three clogged channels, which was the biggest increase. At the frequency 875.3 Hz the amplitude increased from \(-\)157.7 dB to \(-\)118.8 dB (+38.9 dB). If the BPF component was considered it can be seen that only for three clogged channels, there was an increase in the peak. The absolute value for the peak for three clogged channels is \(-\)155.2 dB and thus lower than the peaks of the other fault frequencies.
###### Abstract  
Using Internet of Things (IoT) applications has been a growing trend in the last few years. They have been deployed in several areas of life, including secure and sensitive sectors, such as the military and health. In these sectors, sensory data is the main factor in any decision-making process. This introduces the need to ensure the integrity of data. Secure techniques are needed to detect any data injection attempt before catastrophic effects happen. Sensors have limited computational and power resources. This limitation creates a challenge to design a security mechanism that is both secure and energy-efficient. This work presents a Randomized Watermarking Filtering Scheme (RWFS) for IoT applications that provides en-route filtering to remove any injected data at an early stage of the communication. Filtering injected data is based on a watermark that is generated from the original data and embedded directly in random places throughout the packet's payload. The scheme uses homomorphic encryption techniques to conceal the report's measurement from any adversary. The advantage of homomorphic encryption is that it allows the data to be aggregated and, thus, decreases the packet's size. The results of our proposed scheme prove that it improves the security and energy consumption of the system as it mitigates some of the limitations in the existing works.  
Internet of Things (IoT); wireless sensor network (WSN); data integrity; watermark; data injection attack +
Footnote †: journal: Sensors  
18
## 5 Experimental Results
### Measured Experiment
In addition, the focusing performance of the moving target is shown in Table 6. The theoretical range resolution is \(0.886(c/2B)=0.0738\) m. According to the system parameters, the Doppler bandwidth in the synthetic aperture time is about 300 Hz. So, the theoretical azimuth resolution is \(0.886(v/B_{a})=0.2017\) m. As can be seen from the table, the resolution obtained by the proposed method is closer to the theoretical value than that obtained by the KT-CICPF method. The azimuth resolution of the KT-CICPF method has a greater broadening. It is proved that the proposed method has better focusing performance. The radial velocities of T1-T3 estimated by the proposed method are 1.3156 m/s, 1.6851 m/s and \(-0.5725\)m/s, respectively. The distances of the three targets to the sensor \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Target** & **Resolution (m)** & **Proposed Method** & **KT-CICPF** \\ \hline \multirow{2}{*}{T1} & Range & 0.0918 & 0.0913 \\  & Azimuth & 0.2317 & 0.6078 \\ \multirow{2}{*}{T2} & Range & 0.0926 & 0.0924 \\  & Azimuth & 0.2590 & 0.4479 \\ \multirow{2}{*}{T3} & Range & 0.0867 & 0.0824 \\  & Azimuth & 0.2143 & 0.3383 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The Resolution of measured data. Figure 12: Moving-target focus results. (**a–c**) Magnified images of moving targets without processing of GMT (defocused). (**d–f**) Refocused results by KT-CICPF. (**g–i**) Refocused results by proposed method. are 7339.3228 m, 7631.5370 m and 7736.4643. In accordance with Equation (37), the three targets are shifted in azimuth by 140.7934 m, 187.5168 m and \(-64.5833\) m, respectively. After azimuth shift correction, the positions of the three targets are indicated by pentagrams in Figure 10a. The targets are basically positioned on the road. The accuracy of the estimated parameters is verified again.
# Multimode Physics in the Mode Locking of Semiconductor Quantum Dot Lasers
## 2 Multimode Mode-Locking Approach  
### Conditions for Frequency Comb and Pulse Train Formation  
For this discussion, we describe the intracavity laser field as a superposition of the passive cavity modes \(\ u_{n}(z)\):  
\[E(z,t)=\sum_{n}E_{n}(t)u_{n}(z)cos[\psi_{n}(t)] \tag{1}\]  
where \(E_{n}(t)\) and \(\psi_{n}(t)\) are the mode amplitude and phase, and the summation is over all lasing modes. One has a frequency comb when:  
\[\psi_{n}(t)=(v_{0}+n\Delta)t+\phi_{n} \tag{2}\]where \(v_{0}\) is a reference frequency, \(\phi_{n}\) is the time-independent part of \(\psi_{n}\) and the frequency differences between adjacent lasing modes are equal and given by the beat-note \(\Delta\) (Figure 1a). One has a mode-locked pulse train (Figure 1b) when in addition in Equation (2), and the phases are equal, i.e.,  
\[\phi_{0}=\phi_{1}=\phi_{3}=\cdots \tag{3}\]  
Note that Equation (3) is sufficient but not necessary. The passive cavity resonance satisfies \(\Omega_{m}=mc/(2 Ln_{B})\), where m is the mode number, c is the speed of light in vacuum, \(L\) is the cavity length and \(n_{B}\) is the background refractive index. A useful quantity for characterizing material dispersion and for optical design is the derivative \(dn_{B}/d\lambda_{n}\), where the wavelength \(\lambda_{m}=2L/m\)[24]. We found from Equation (1) that a constant \(dn_{B}/d\lambda_{n}\) may also result in a transform-limited pulse train.
# Application of the KINEROS 2 Model to Natural Basin for Estimation of Erosion
## 3 Erosion Estimation Models Used
### Usle
#### 3.1.3 Determination of Length-Slope Factor (\(Ls\))  
\(LS\) is the \"Length-Slope\" factor or \"Topographical\" factor, which is the influence of topography on the quantity of soil lost. It is important for two reasons:  
* The angle of the slope modifies water permanency time that flows along basin surface.
* The length and the angle of the slope affects the kinetic energy that water that circulates on its surface will reach, therefore it will affect to erosive power.  
There are several methods to obtain the \(LS\) factor, depending on the characteristics of the study basin. [PERSON] and [PERSON] (1978) and [PERSON] et al. (1994) proposed the following Equation (5) for determining \(LS\) under conditions having a maximum slope of 20% and a maximum length of 300 m [13,18,19].  
\[LS=L^{*}S^{*}=\left(\frac{X}{22.13}\right)^{m}\left(\frac{0.43+0.3s+0.043s^{2} }{6.613}\right) \tag{5}\]  
where \(L^{*}\) is the length factor and \(S^{*}\) is the slope factor, \(X\) is the hillside length (m) in its horizontal projection, \(m\) is the non-dimensional constant depending on an average slope \(s\) (%). The \(m\) values are 0.2 for \(s<1\%\), 0.3 for 1% \(\leq s<\)3%, 0.4 for 3% \(\leq s<\) 5%, and 0.5 for 5% \(\leq s\).  
The first formulation proposed by [PERSON] and [PERSON] (1978) is limited for slopes lower than 20%. For conditions having slopes between 20-50% and lengths exceeding 300 m, Equation (6) is used to calculate the \(LS\) value [13].  
\[LS=L^{*}S^{*}=\left(\frac{\lambda}{22.1}\right)^{0.3}\left(\frac{s}{9}\right) ^{1.3} \tag{6}\]  
where \(\lambda\) is the hillside length (m) in its horizontal projection.  
The global \(LS\) value is obtained by determining the weighted average of every segment of \(LS\) values. Refs. [13,20]  
For slopes exceeding 50%, the USLE \(LS\) factor is out of range because these slopes typically have no soil. Therefore, determination of the \(LS\) value is not possible.
## 5 Conclusions  
This paper has presented a framework for optimising buffer allocation in inter-facility material transfer and outbound logistics processes. The study has demonstrated the significance of strategic buffer placement and sizing in improving supply chain efficiency and performance. By utilising finite queueing networks and the generalised expansion method (GEM), the framework allows for the modelling, analysis, and optimisation of buffer allocations in manufacturing systems.  
Resource utilisation rates are crucial in identifying bottlenecks and improving system efficiency. Evaluating server utilisation at different nodes allows managers to allocate resources effectively by employing process subdivision or workload consolidation strategies.  
This study's significance lies in its pioneering approach to buffer allocation problems, considering both ends of logistics processes. While previous studies focused on single logistics processes, this research integrates inter-facility transfer and outbound logistics, paving the way for a holistic examination of buffer allocation in complex setups. Future research can explore integrated analyses encompassing inbound, intra, and outbound logistics to understand buffer allocation across the entire logistics network comprehensively.  
The study has specific limitations that require acknowledgement. Firstly, it concentrates on a single-server environment, overlooking the intricacies of multi-server setups. Secondly, assuming homogeneous jobs or customers may only partially represent real-world situations where variations exist. Thirdly, the study adopts a first-come, first-serve queue discipline, neglecting potential priority-based service considerations. Lastly, the assumption of uniform buffer costs at all stations disregards the possibility of varying costs at different locations. These limitations indicate potential areas for future research to enhance the applicability of buffer allocation models.  
In conclusion, this study provides valuable insights into the buffer allocation process for manufacturing companies to optimise inter-facility material transfer and outbound logistics. The proposed framework offers opportunities for improving operational efficiency and overall performance in manufacturing supply chains.  
Conceptualization, M.A. and L.K.; methodology, M.A. and L.K.; software, J.M.S.; validation, L.K., J.M.S. and A.E.; formal analysis, M.A. and L.K.; investigation, L.K. and J.M.S.; resources, L.K. and A.E.; writing--original draft preparation, M.A.; writing--review and editing, L.K. and J.M.S.; visualization, M.A. and A.E.; supervision, L.K. and A.E.; project administration, L.K. and A.E. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Open Access funding provided by the Qatar National Library.  
The authors declare no conflict of interest.
Figure A2: Optimisation run window—scenario 3.
# A Parameter Communication Optimization Strategy for Distributed Machine Learning in Sensors
## 2 Related Work
### The Distributed Machine Learning System  
Large-scale applications in distributed systems have promoted the development of distributed machine learning. At present, a notable amount of research has been devoted to distributed machine learning systems in both academia and industry.  
[PERSON] developed a multi-language machine learning class library called mix-net (MXNet) [18], which is an open source deep learning framework. It can quickly train deep learning models, supports flexible programming models and multiple languages. The MXNet library is portable and lightweight, scalable to multiple Graphics Processing Units (GPUs) and machines. [PERSON] implemented a framework for a deep learning algorithm training method called Caffe [19], which achieves the separation of definition and realization of the model, which greatly simplifies the model training processes [20]. In addition, Caffe supports GPU Compute Unified Device Architecture (CUDA) programming, which further accelerates the model training processes. [PERSON] designed and implemented a multi-GPU clustered distributed deep learning system called Commodity Off-The-Shelf High Performance Computing (COTS HPC) [21] that is based on a Message Passing Interface (MPI). [PERSON] proposed a novel Machine Learning Interface (MLI) [22], which is designed to accelerate data-centric distributed machine learning. [PERSON] designed a collaborative location-based regularization framework called Colbar [23].  
Baidu built a multi-machine GPU training platform called Parallel Asynchronous Distributed Deep Learning (Paddle) [24]. It distributes the data to different machines, coordinates the machine training through PS, and supports data parallelism and model parallelism. Tencent built a deep learning platform called Mariana [25], which includes three frameworks: the deep neural network GPU data parallel framework, the deep convolution neural network GPU data parallel and model parallel framework, and the deep neural network CPU cluster framework. Google developed a large-scale distributed depth learning system called DistBelief [12] to support speech recognition and 2.1 million categories of image classification [26]. On the basis of DistBelief, Google Research implemented and opened up a second-generation large-scale machine learning on heterogeneous distributed systems called TesorFlow [27; 28], which has great performance in high-level machine learning calculations with better flexibility and scalability.  
In order to ensure consistency, many distributed machine learning systems adopt BSP as the training strategy, which loses the advantages of distributed system performance. A small part of the distributed machine learning systems use completely asynchronous updates as the training strategy, but the model convergence cannot be guaranteed. Thus, a distributed machine learning system for iterative-convergent machine learning algorithms, which is based on the DFFT of machine learning, is still in the initial stage.
## 3 Results
### Cell Morphology and Distribution of WJ-MSC Analyzed by FE-SEM and CLSM and Flow Cytometry  
Primary WJ-MSC derived from umbilical cord were grown as adherent cells [29]. WJ-MSC were labeled with PKH26, a lipophilic dye that stably integrates into the cell membrane, seeded in the SF scaffolds and observed at different time points by FE-SEM. After 24 days, PKH26-WJ-MSC were distributed along the scaffold and showed a good level of surface adhesion highlighted by the numerous filopodia produced by the cells strictly interconnected both with the scaffold and with each other (see arrows in Figure 5D). The complexity of the cell interconnections was clearly evident in the cellular multi-layers observed in the scaffold alveoli (Figure 5D-F). In some cases, a large invasive capacity of the cells was shown by the tendency to occupy the volume space of the alveoli, creating a very complex organization and connectivity level (Figure 5B,C).  
Afterwards, distribution and cellular behavior of cultured PKH26-WJ-MSC in the scaffolds were assessed by fluorescence microscopy--CLSM and flow cytometer. Cells were seeded (5 \(\times\) 10\({}^{5}\) cells) on the top of the SF, and at Day 5, they adhered to the inner side and grew as a monolayer. From Day 5 to Day 21, PKH26-WJ-MSC significantly penetrated the inner pores of the scaffold, filling the spaces between the trabeculae of the sponge (Figure 6A). Thus, over 21 days, cells migrated from the top of SF, colonized and distributed evenly throughout the scaffold. Flow cytometry analysis, performed with the aim of verifying the labeling of the WJ-MSC with PKH26 over time, indicated that WJ-MSC maintained the PKH26 fluorescence during the 21 days of the 3D culture (Figure 6B).  
Figure 4: Silk–fibroin scaffold (SSF) morphology analyzed by FE-SEM. (**A**) SSF after the fabrication process displaying the sponge morphology of the sample characterized by high level of porosity and both the smoothness and coherence of the trabecular surfaces. (**B**,**C**) high magnification of the same marked area found, respectively, in (**A**,**B**).  
Figure 5: Silk–fibroin scaffold distribution of WJ-MSC analyzed by FE–SEM. **(A)** SSFc after 18 days of 3D culture. (**B,C**) high magnifications of the same marked area, found, respectively, in (**A,B**), showing the significant invasion ability of the cells in occupying the volume of an open space. (**D–F**) high magnifications of the same marked area in (**A**) that reveal the development of MSCs coating of the scaffold smooth surface. A significant cell–surface and cell–cell interconnectivity were shown (head arrows).  
Figure 6: WJ-MSC, labelling with the fluorescent dye PKH26, grown in 3D culture on silk scaffold analyzed by CLSM (**A**) and flow cytometer (**B**). (**A**) Cells grown on SF on the 5 th and 21 st days. (**B**) PKH26-WJ-MSC after being detached from scaffolds and analyzed at different times of culture.