esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 MSC Interaction with NP Cells
This physiological mechanism of MSCs recruitment or homing may play a role in promoting the repair of different musculoskeletal tissues [57]. Interestingly, recent studies investigated the cross-talk between MSCs and NPCs through trophic factors [58]. [PERSON] et al. investigated paracrine interactions in a co-culture system by isolating MSCs, AF and NPCs from the same donor. The characterization revealed an increase in mRNA levels expression of ECM genes (SOX9, COL2A1 and ACAN), the downregulation of MMPs and ADAMTS, as well as the inhibition of the production of pro-inflammatory factors compared to monocultures [59]. [PERSON] et al. alsoemployed a co-culture system to understand the influence of BM-MSCs on TNF-\(\alpha\)-induced NPC degeneration and reported an increase in cell proliferation and type II collagen production, a decrease in MMP-9 expression, and reduced TGF\(\beta\)/NF-kB signaling in pro-senescent NPCs. Premature senescence and aging of NPCs were restored in the co-culture system [60]. Even though the regenerative, anabolic, and inflammatory-modulating properties of MSCs have been demonstrated in several investigations, many aspects are still unclear. The harsh microenvironment with the absence of vasculature, hypoxia, low glucose concentration, acidity, hyperosmolarity and continuous mechanical stimulation plays a crucial role for cell survival and their regenerative potential [61]. Furthermore, MSC implantation may further impair the balance between the nutrient supply and demand of resident disc cells, thus causing cell death. [PERSON] et al. investigated the effectiveness of a conditioned medium (CM) derived from the culture of MSCs in a rat IDD model. MSC-derived CM has shown immunomodulatory and anti-inflammatory properties in a detrimental environment by down-regulating the expression levels of pro-inflammatory cytokines such as IL-1\(\beta\), IL-6, IL-17 and TNF [62]. Although MSC-based therapy may be considered an attractive and a safe option, there are still unsolved issues. In addition to the abovementioned shortcomings, MSCs require a long and expensive process for in vitro expansion according to the minimum standard to guarantee controlled conditions in production processes and clinical procedures [63].
# From Brown-Field to Future Industrial Networks, a Case Study
## 3 Case Study: Iggesund Paperboard
### Data Collection and Data Set  
The network traffic was captured by enabling mirroring of the traffic recorder port connected to one of the switches in the production network. The reason for limiting the data collection to one switch was due to the physical distance between control rooms. Consequently, the captured data does not represent all the traffic of the production network, but the communication of one of the machinery. The captured traffic contains both primary and secondary networks traffic, not simultaneously, for a duration of almost 12 h. The result of the data collection was 60 files, and each file contains more than 3 million records and is 0.99 GB in size. The initial captured traffic flows are huge files containing packet dumps from the network, in _.pcap_ format. After a preliminary packet-level investigation, to create a suitable format for further exploratory traffic analysis and characterization in python [19], each file was converted to.csv format. Each of the files consists of header fields, and payload data of more than 3 million packet and each record was formatted as [_Timestamp\({}_{ps}\)_, \(VLANID\)_, \(IP_{source}\)_, \(IP_{destination}\)_, \(Protocol\)_, \(Length\)_, \(Hardware\)_, \(Hardware\)_, \(Port\)_source_, \(Port_{destination}\)_, \(info\), \(delta\)).  
Production networks work in cycles expanding over time. The network traffic dynamics can be captured more accurately when it is studied over several operational cycles. Hence, 10 consecutive files were selected for the analysis and experiments carried out in this study. The final data set consists of more than 30 million packet records over 90 min. Due to the resource-intensiveness of the processing the huge data set, and to enhance the clarity of illustrations in this study, a simplified data set with record tuples of [_Timestamp\({}_{ps}\)_, \(VLANID\)_, \(IP_{source}\)_, \(IP_{destination}\)_, \(Protocol\)_, \(Length\)_] are selected.
# Study on the Population Balance Dynamics Simulation of Grinding under Impact Crushing
## 3 Results and Discussion
### The Population Balance Dynamics Simulation of Grinding under Impact Crushing of PyrrIhotite  
2.1 Calculate the Cumulative Particle Size Distribution under Sieve \(t_{xx}\) for Any Sieve Size Based on the Drop Weight Test Results  
Based on the data obtained from the drop weight test in Section 2.2.1, the calculated \(xx\) value is used as a subscript for the cumulative particle size distribution under the sieve, and the results shown in Table 3 of Section 3.1.1 are obtained.  
According to Section 2.2.1, based on the pyrrhotite drop weight test data, we used the interpolation method to obtain the points for the cumulative particle size distribution curve under the sieve and calculated the \(t_{xx}\) values listed in Table 3 of Section 3.1.1. On this basis, we drew a scatter relationship diagram between the \(t_{xx}\) value and the corresponding groups of \(t_{10}\) values, as shown in Figure 6. Using the Origin polynomial-fitting method, the functional relationship between \(t_{10}\)-\(t_{xx}\) of the pyrrhotite under impact is obtained, as shown in Table 7.  
Like in Table 4, it can be seen from Table 7 that the \(R^{2}\) values of all \(t_{xx}\) are close to 1, indicating that the test data have a high level of reliability. According to Sections 2.2.1 and 2.2.3, the calculated results of the \(E_{\rm cs}\) and \(t_{10}\) values for each particle size are shown in Table 8.  
From Table 8, it can be seen that the \(E_{\rm cs}\) of the 25 mm steel ball medium for 2.8118 mm pyrrhotite particles is 0.81 kWh/t, and its \(t_{10}\) reaches 55.27%. When the particle size of pyrrhotite is 1.0015 mm, its \(E_{\rm cs}\) is 17.84 kWh/t, which can lead to its \(t_{10}\) reaching the limit value of 76.59%. As the particle size continues to decrease, the \(E_{\rm cs}\) of the particles increases, but the \(t_{10}\) does not increase. This correlation is attributable to the fact that finer particle sizes demand greater energy inputs for crushing and there is a reduced chance of contact with the steel ball, reducing the probability of being crushed. Therefore, its \(t_{10}\) no longer  
Figure 6: The relation curves of \(t_{10}\)-\(t_{xx}\) for pyrrhotite.  
increases with the increase in \(E_{\text{cs}}\). Compared to the quartz particles, the pyrrhotite particles of the same size have a lower \(E_{\text{cs}}\), but their \(t_{10}\) is larger and their crushing degree is higher. This means that minerals with lower hardness require less energy to be crushed by impact, which conforms to the mineral crushing principle.
## 2 Structural Principle, Detection Mechanism, and Performance Indicators
### Structural Principle of Optical Waveguide Refractive Index Sensing
#### 2.1.3. Refractive Index Sensing Principle of MMI Structure  
MMI utilizes the self-imaging principle of light to periodically replicate the input field profile at specific positions [20]. A typical MMI consists of three parts: input waveguides, a multimode waveguide region, and output waveguides. The width of the multimode waveguide region is wide enough to support multiple high-order waveguide modes. Using waveguide mode theory, the beat length of the two lowest-order modes is:  
\[L_{\pi}=\frac{\pi}{\beta_{0}-\beta_{1}}\approx\frac{4n_{eff}W^{2}}{3\lambda_{0 }}, \tag{4}\]  
where \(W\) approximates the width of the multimode waveguide region, and \(n_{eff}\) is the effective refractive index of the multimode interferometer region. Furthermore, it can be used to derive the expression for the propagation constant of the \(m\)th-order mode:  
\[\beta_{m}=\beta_{0}-\frac{m(m+2)\pi}{3L_{\pi}}. \tag{5}\]  
When an input field profile \(\psi(x,0)\) imposed the multimode waveguide region at \(z=0\), it is decomposed into the intrinsic modes of the multimode waveguide region:  
\[\psi(x,0)=\sum_{m}c_{m}\psi_{m}(x), \tag{6}\]  
where \(c_{m}\) is the field excitation coefficients. When light propagates along the \(z\)-axis for a certain distance, the amplitude of each order mode remains unchanged, while the phase changes. Formula (6) can be expressed as:  
\[\psi(x,z)=\sum_{m}c_{m}\psi_{m}(x)\times exp\bigg{(}i\frac{m(m+2)\pi}{3L_{\pi} }z\bigg{)}. \tag{7}\]  
Although the accumulated phase of different-order modes is different, when phase matching condition are met, interference occurs among these modes, and the input field profile is reproduced periodically, which is called the self-imaging effect.
# Secure Inter-Frame Space Communications for Wireless LANs
## 2 Periodic Interference Detection Techniques
### Signal Power and Pulse Width  
It is common to measure the power of the received signal and compare it with a threshold to determine if a signal exists. In addition to this, if pulse width is considered, most radar pulses are very short compared with WLAN frames. For example, many weather radar systems use 1-\(\upmu\)s or even shorter pulses. If a power above a power threshold appears for less than a time threshold, it can be a radar pulse [13].  
This approach needs fine tuning to decrease the false alarm probability. A large power signal can appear through other sources instead of the target periodic interference. Although an impulse noise may exceed a power threshold, it should not be determined as a periodic interference signal. One method of fine tuning is to match the received power with the known pulse power characteristics, such as constant power and abrupt appearance and disappearance, as well as short width. It should be noted that some radar signal pulses have a pulse width of over 100 \(\upmu\)s. In this case, it is difficult to find an appropriate period threshold for the \"short\" pulse.
## 3 Results and Discussion
### Effect of Interaction  
#### 3.4.1 Effect of Interaction of Factors on Lr  
In order to observe certain curvature regions and interactions among the process parameters, the response surfaces of Equation (4) were modeled through robust parameter design. Lr was presented  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Source** & **SS** & **Df** & **MS** & **F-Value** & **p-value** & \\ \hline Model & 23.58 & 9 & 2.62 & 7.91 & 0.0017 & ** \\ Ds & 11.26 & 1 & 11.26 & 34.01 & 0.0002 & *** \\ Fr & 1.09 & 1 & 1.09 & 3.30 & 0.0993 & \\ Cc & 6.19 & 1 & 6.19 & 18.68 & 0.0015 & ** \\ Ds-Fr & 5.164E-003 & 1 & 5.164E-003 & 0.016 & 0.9031 & \\ Ds-Cc & 0.17 & 1 & 0.17 & 0.50 & 0.4944 & \\ Fr-Cc & 0.20 & 1 & 0.20 & 0.60 & 0.4577 & \\ Ds\({}^{2}\) & 2.30 & 1 & 2.30 & 6.94 & 0.0250 & * \\ Fr\({}^{2}\) & 1.23 & 1 & 1.23 & 3.73 & 0.0824 & \\ Cc\({}^{2}\) & 0.68 & 1 & 0.68 & 2.06 & 0.1814 & \\ Residual & 3.31 & 10 & 0.33 & & & \\ Lack of Fit & 2.51 & 5 & 0.50 & 3.12 & 0.1186 & \\ Pure Error & 0.80 & 5 & 0.16 & & & \\ Cor Total & 26.89 & 19 & & & & \\ \hline \end{tabular} SS = sequential sum of squares; Df = degrees of freedom; MS = mean squares; F = Fischer’s variance ratio; p = probability value; * Significant (p \(<\) 0.05); ** Very significant (p \(<\) 0.01); *** Very significant (p \(<\) 0.001).  
\end{table}
Table 4: Analysis of variance for damage rate.  
in Figure 6 on the experimental region, with hold values specified in each graph. From the contour map, it was found that the central color of Figure 6a was obviously darker than those of Figures 6b and 6c, and the trend of change was stronger, as was the p-value of each item in Table 3; this indicated that the interaction was more significant.  
Figure 6: Response surface diagrams of interaction: (**a**) Interaction of Ds-Fr; (**b**) interaction of Ds-Cc. (**c**) Interaction of Fr-Cc.
###### Abstract  
Hybrid ion exchangers (HIXs) containing fine Cu\({}_{2}\)O and Cu\({}^{0}\) particles were subjected to thermal analysis in order to determine their hygroscopic water content (with regard to their anomalously low porosity) and to determine the effect of the oxidation state of the copper atom in the deposit on the thermal properties of composite materials. Commercially available anion exchangers, Amberlite IRA 900C1 (macroticular, M) and Amberlite IRA 402 OH (gel-like, G), were used as supporting materials. M/Cu\({}_{2}\)O, G/Cu\({}_{2}\)O, M/Cu and G/Cu, containing 4.3-8.4 wt% Cu, were subjected to thermal analysis under respectively air and N\({}_{2}\). TG/DTG curves revealed that dry M/Cu and G/Cu contained as little as 7.2% and 4.3% hygroscopic water, while M/Cu\({}_{2}\)O and G/Cu\({}_{2}\)O contained respectively 10.6% and 9.4% (Cu\({}^{0}\) was a stronger water repellent than Cu\({}_{2}\)O). The oxidation state of the copper atom in the deposit was found to affect the amount of the forming char, and also Cu\({}^{0}\) was found to contribute to the formation of more char than in the pyrolysis of the pure resin (the anion exchanger with no copper deposit). Under air the two kinds of particles transformed into CuO, while under N\({}_{2}\) metallic copper and char (from the resin phase) made up the solid residue. This means that in the pyrolysis of the HIXs the inorganic phase participated in char formation and it also transformed itself (undergoing reduction when possible). The above findings provide a basis for in-depth research aimed at the innovative use of copper-containing HIXs and at obtaining usable composite materials with a designed (organic-inorganic) composition.  
hybrid ion exchanger; cuprous oxide; zero valent copper; thermal analysis; pyrolysis Article
## 3 Use Case. CEDER-CIEMAT Microgrid
### Identification of Elements
#### 3.1.2 Dispatchable Renewable Generation Sources  
The CEDER microgrid includes different renewable generation systems, with a total installed capacity of over 400 kW:  
* Photovoltaic: There are 11 distributed photovoltaic systems throughout the centre, with a total installed capacity of 181.9 kW. They are non-dispatchable from the resource point of view.
* Wind: There are five small-scale wind turbines with a total installed capacity of 162.2 kW. They are non-dispatchable from the resource point of view.
* Hydraulic Turbine: There is a 60 kW mini Pelton turbine. It is dispatchable.  
Photovoltaic modules and small wind turbines are not dispatchable from a resource point of view, but we can modify their output by sending commands to their inverters if there is sufficient radiation or wind speed, respectively.
## 3 Results
#### Direct Application of an Anode Enrichment Consortium Reduces Significantly Startup Times  
Because it appeared that certain _Desulfomonas_ strains were associated with efficient MFC startup, next, we attempted to enrich those strains from a working MFC and directly apply them to new anodes (experiment 2016-E). An anode sample was taken from an efficient MFC that had been running for forty days. This sample was placed directly on an agar plate containing BCM as the basic medium with acetate as a single electron donor source, and fumarate as the electron acceptor. Once bacterial colonies started to grow, five suspected colonies were tested using the _Desulfurononas_ dechlorinator-targeted primers (Supplementary Figure S1d).  
Figure 4: PCR products obtained with the specific 16S-_Desulfuronodaceae_ primers for efficient and non-efficient MFCs. Relative abundance of _Desulfuronodaceae_ according to amplicon sequencing is shown. Last lane from the left is a 100 bp size ladder.  
Figure 3: PCR products obtained with the specific _Desulfurononas_ dechlorinator-targeted primers of representative MFCs as indicated on the gel for experiments 2015 and 2016. _Desulfurononas_ pure DNA was purchased from DSMZ for _Desulfurononas acetxigens, DSM 1397_ and performed as a positive control. First lane from the left is a 100 bp size ladder.  
Phylogenetic analysis of this colony revealed that additional bacterial species were present (Supplementary Figure S3), therefore, we referred to it as a highly enriched electroactive consortium. Then, the colonies were applied on clean anodes by spreading the colony onto the anode surface, and then these anodes were inoculated inside MFCs [30]. Figure 5 compares the lag time and time to reach steady-state current density for all experimental sets conducted in this study. From these experiments, it is evident that lag time was significantly reduced when directly applying a highly enriched electroactive consortium. MFCs inoculated with only wastewater (i.e., no enrichment of the anode, experiments 2015, 2016, and 2016-2), had a mean lag time of five days and a mean startup time to reach stable current production of 16 days. These times are significantly higher than those obtained by MFCs that were pretreated with an electroactive consortium (lag times of 6 h, and startup time to reach steady-state voltage of 2.9 days). In contrast to the reduced startup times, no difference was observed with regards to the maximal current density obtained by the different experimental sets (Supplementary Figure S4).
## 3 Performance Analysis  
When it comes to the performance of BER in the system, two channels are studied: one is the AWGN channel; the other the multipath Rayleigh fading channel. As all \(n\) parts of parallel information packets are similar but independent, we only need to analyze the error probability of one of them.  
Here, it is assumed that the largest multipath time delay is less large than the symbol duration, i.e., \(0<\tau_{L}<<\beta\). Therefore, the ISI--the inter-symbol interference--is insignificant [17; 18]. Moreover, during one symbol duration, it is regarded that the channel slowly fades and that the channel parameters are stable. The signal received can be represented as follows:  
\[p_{r}(t)=\sum_{l=1}^{L}\alpha_{l}p(t-\tau_{l}T_{c})+n(t) \tag{4}\]  
where \(\tau_{l}\) is the chip delay of the \(l\)th path. \(\alpha_{l}\) is the propagation gain of the \(l_{\text{th}}\) path. \(n(t)\) is the AWGN with variance of \(\frac{N_{0}}{2}\) and zero mean.  
We assume that both the chip and the sinusoidal carrier synchronization are perfectly achieved, and the output of the average block is the averaged reference signal in Figure 4, which can be obtained as  
\[y_{ave}(j)=\frac{1}{n}\sum_{i=1}^{n}\left(\sum_{l=1}^{L}\alpha_{i,l}Rx_{j-\eta} +\sum_{k=0}^{R-1}\left((W_{r,k+1}\otimes I)\cdot n_{i,RN_{c}+j}\right)\right) \tag{5}\]  
Figure 4: Block diagram of the MC-MDCSK-MCS receiver.  
where \(I\) is a one-sequence with \(N_{c}\)-length. \(x_{j-\tau_{1}}\) presents the chaos signal after multipath fading channel, \(\alpha_{ij,l}\) is the transmission gain of the \(l\)th path of the \(i\)th information packet, and \(n_{i,RN_{c}+j}\) is the AWGN of the \(i\)th information packet.  
Similarly, the information that bears signals of the \(m\)th in-phase branch and quadrature branches obtained from the \(i\)th subcarrier can be given as  
\[y_{2m-1,i}(j) =\sum\limits_{l=1}^{L}\alpha_{i,l}Ra_{m,i}x_{j-\tau_{1}}+\sum \limits_{k=0}^{R-1}\left((W_{2m-1,k+1}\otimes I)\cdot n_{i,RN_{c}+j}\right) \tag{6}\] \[y_{2m,i}(j) =\sum\limits_{l=1}^{L}\alpha_{i,l}Rb_{m,i}x_{j-\tau_{1}}+\sum \limits_{k=0}^{R-1}\left((W_{2m,k+1}\otimes I)\cdot n_{i,RN_{c}+j}\right) \tag{7}\]  
where \(1\leq j\leq N_{c}\).  
In Figure 4, based on the diagram block of the receiver, the judgement variable can be obtained as  
\[\left\{\begin{array}{l}z_{m,i}^{a}=\sum\limits_{j=1}^{N_{c}}y_{ave}(j)\cdot y _{2m-1,i}(j)\\ z_{m,i}^{b}=\sum\limits_{j=1}^{N_{c}}y_{ave}(j)\cdot y_{2m,j}(j)\end{array}\right. \tag{8}\]  
Equations  
When \(L=1\) and \(\alpha_{I}=1\), which means there is only one path, it is just the AWGN case.
# A New Improved Multi-Sequence Frequency-Hopping Communication Anti-Jamming System
## 5 Simulation Results and Analysis
### Simulation Result Analysis
Even under low SJR conditions and despite the IMSFH system having an SNR significantly lower than that of the other systems, it still demonstrates  
Figure 8: BER performance of IMSFH under different JSR. Figure 9: The frequency-detection accuracy. superior performance. This is because compared with the MPFH and WGMPFH proposed in references [25; 26], we do not avoid jamming, but actively utilize reactive jamming to improve communication performance and achieve good anti-jamming effects. As mentioned earlier, without considering encoding, DFH uses broadband reception and performs worse than MSFH using narrowband reception in the face of non-tracking jamming. Similarly, in this case, the anti-jamming performance of IDFH in reference [19] is also weaker than that of IMSFH. We have verified that in complex jamming environments (reactive jamming, multi-tone jamming, frequency sweeping jamming), multi-tone jamming and frequency sweeping jamming are non-tracking non-intelligent jamming. To demonstrate the superior performance of IMSFH, we conducted relevant experiments to compare IDFH with IMSFH and analyze their performance differences in the same complex jamming environment. From Figure 11, we can observe that under the same signal-to-noise ratio (SNR), when JSR = \(-3\), the bit error rate gain of IMSFH is close to 3 dB compared to IDFH. Similarly, with the increase in JSRs, as other non-intelligent jamming powers increase, the reactive jamming power also increases. When the JSR is large, IMSFH can more effectively utilize reactive jamming to achieve anti-jamming communication. When the JSR = 5, the bit error rate gain of IMSFH is close to 9 dB. It can be seen that in a complex jamming environment, when the JSR is high, IMSH has a better utilization effect on reactive jamming than IDFH. Through the analysis of simulation results, it can be seen that the signal-to-noise ratio has a significant impact on the IMSFH system. For IMSFH, FH/BFSK, WGMPFH, and MPFH, IMSFH is based on the idea of jamming utilization. The higher the reactive jamming power, the better the jamming utilization efficiency and anti-jamming performance. For IDFH and IMSFH, they are both based on the idea of using reactive jamming to improve communication performance. In complex jamming environments, except for reactive jamming, other forms of jamming are unfavorable factors for communication. By comparing the simulation results of IDFH and IMSFH, it can be seen that IMSFH performs better. Therefore, when anti-reactive jamming occurs in more complex jamming environments, IMSFH is a better choice. Figure 10: The BER performance.
# DFT-Spread Spectrally Overlapped Hybrid OFDM-Digital Filter Multiple Access IMDD PONs
## 4 Upstream DFT-Spread Hybrid OFDM-DFMA PON Performance
### Impacts of Digital Filter Parameters on Maximum Aggregated Upstream Transmission Rates
Figure 7e shows that the SNRs are similar for CH1 when \(\alpha\) = 0 and \(\alpha\) = 0.2; however, there is an obvious improved SNR for subcarrier indices 12-14 of CH2 when \(\alpha\) = 0.2. Figure 7 also shows that the SNR curve for CH1 is almost constant with respect to the subcarrier index for the optimal \(\alpha\) value. In comparison with the Q-phase channel for both cases, the high SNR observed for the I-phase channel is mainly a result of the boost in the magnitude response, as shown in Figure 7c. The obtained results indicate that there is an optimal \(\alpha\) factor value that minimizes digital filter impairments and, subsequently, maximizes the upstream transmission rate of both channels. Figure 6: Aggregated rate versus excess bandwidth over a 25 km SSMF IMDD PON transmission system when a 7-bit resolution is considered.