esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Swelling Ability and Degradation  
Compared to dry wounds, a moist environment has been proved to initiate epidermal cell migration and fasten the wound re-epithelialization rate [33]. The swelling ability of the scaffolds in water was measured, and the results are presented in Figure 3B. The scaffolds prepared with 1% chitosan/RHC solution exhibited rapid swelling of 4000%, which further increased to ca. 6000 and 4800% after 0.5 and 1 mL of short nanofibers were added, respectively. This swelling behavior was much greater than that of scaffolds prepared by organic polymers [34]. The high-interconnected porosity of the scaffolds along with the hydrophilicity of RHC and chitosan molecules could be responsible for the superior water absorbability. This significant increase in swelling ability might be also produced by the  
Figure 3: Characterization of the different scaffolds. (**A**) Porosity. (**B**) Swelling ability. (**C**) Degradation rate in a lysozyme solution. (**D**) Spectra obtained from RHC and 3D nanofibrous scaffold in FT-IR tests. * \(p\) < 0.05, *** \(p\) < 0.001, compared with pure chitosan/RHC scaffold, *** \(p\) < 0.01. Bars represent mean \(\pm\)SD, number of repeats \(n=3\).  
Figure 2: SEM images of the 3D nanofibrous scaffolds. (**a–d**) Scaffolds of 1%-0.5 mL, 1%-1 mL, 2%-1 mL, and 1%-0 mL at 500\(\times\). (**A–D**) Scaffolds of 1%-0.5 mL, 1%-1 mL, 2%-1 mL, and 1%-0 mL at 5000\(\times\).  
high specific surface area of the nanostructures. However, the addition of the nanofibers might result in partial coverage of small pores on the scaffolds and a slight decrease in porosity, decreasing swelling ability as a result. When the chitosan/RHC concentration was increased to 2%, swelling ability decreased to 3300%, which is consistent with the low porosity of the scaffolds prepared with 2% chitosan/RHC solution.  
The degradation of a scaffold is one of the most important features in biomaterials selection and design, and it is a guarantee for the long-term success of tissue-engineered construction [35]. The degradation rates of different scaffolds were measured in a lysozyme solution for over 30 days (Figure 3C). All the scaffolds were observed to degrade with time, and the pure chitosan/RHC scaffold exhibited the highest degradation rate. The incorporation of short nanofibers significantly increased the stability of the scaffolds against the lysozyme solution; the degradation of the 1%-0.5 mL and 1%-1 mL groups was as low as ca. 30%. The primary degradation of the scaffolds was mostly caused by the breakages of the chitosan chain. However, nanofibers crosslinked in situ were very stable, hindering the contact between the scaffolds and the enzyme. In addition, the mechanical properties as well as the structural stabilities of the porous scaffolds were largely improved by the presenting of nanofibers; thus, the degradation process of the scaffolds was largely decreased [27; 31]. Moreover, increasing the concentration of the chitosan/RHC solution was found to further improve the stability of the scaffolds (ca. 19% after 30 days).
## 5 Conclusions  
In this study, we explored investigation of applicability of impact factors to estimate solar irradiance using climatic elements including temperature, humidity ratio, the amount of total cloud cover, the amount of middle and low-layer cloud cover, wind velocity and direction, and air pressure. The strategies were based on four machine learning algorithms: linear regression, SVM, MLNN, and LSTM. We evaluated the performance of the four models using eight years of weather data as a training set, and one year of data as a test set. The estimation results were then compared with real solar irradiance values. The results showed that the ANN models provided more accurate estimations of solar irradiance than conventional techniques (such as linear regression and SVM). Among the two ANN models, LSTM provided better performance, improving estimation accuracy by 31.7% compared to MLNN. To investigate the effect of each climate factor on the solar irradiance in a city,we also carried out impact factor analysis with the four estimation models. The results of the impact factor analysis revealed that temperature and the amount of total cloud cover are the dominant factors affecting the solar irradiance, and the amount of middle and low-layer cloud cover is also an important factor. The results from this work demonstrate that ANN models, especially those based on LSTM that consider time correlations in the data, can accurately estimate local solar irradiance using weather data without installing and maintaining on-site solar irradiance sensors. Hence, they can provide a cost-effective method of accurately estimating solar power generation, thermal heat gains, and thermal energy consumption rates in buildings and urban and rural areas. In future works, a study could define the differences of weather conditions between a meteorological station and a local place using ANNs methodologies, and then we could know how local surrounding environments such as high-rise buildings and mountains near a building located impact on solar irradiance. Additionally, we could denote how much air pollutants, such as PM 2.5, PM 10, could impact on solar irradiance using ANN methodologies.  
Conceptualization, M.K.K. and J.C.; methodology, J.C. and M.K.K.; software, J.C. and M.K.K.; validation, S.L. and K.S.K.; formal analysis, M.K.K. and J.C.; investigation, J.C. and M.K.K.; resources, M.K.K.; data curation, J.C., M.K.K. and K.S.K.; writing--original draft preparation, J.C. and M.K.K.; writing--review and editing, S.L. and K.S.K.; visualization M.K.K.; supervision, M.K.K. and S.L.; project administration, M.K.K. and S.L. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
Data Availability StatementData sharing needs permissions.  
This work was supported by Oslo Metropolitan University and part by Xi'an Jiaotong-Liverpool University Centre for Smart Grid and Information Convergence (CeSGIC).  
The authors declare no conflict of interest.  
References
## 3 Quasi-Steady Assumption-Based Communication Port Impedance
Thus, if the flow rate follows a sinusoidal function over time [13], the following applies:  
\[R_{\text{LA,3}}=\frac{bkv}{C_{\text{D,0}}^{2}A_{\text{A}}}+\frac{4}{3\pi}\frac{ \left(a^{2}k^{2}+b^{2}\right)\widetilde{M}_{\text{A}}}{\rho C_{\text{D,0}}^{2} A_{\text{A}}^{2}}+\frac{2}{\pi}\frac{\rho k^{2}v^{2}}{C_{\text{D,0}}^{2} \widetilde{M}_{\text{A}}} \tag{5}\]  
When the amplitude of oscillatory mass flow rate in the communication port approaches zero, the local loss of the communication port flow gradually decreases, while the energy loss (ideally equal to the dynamic pressure of fluid inside the main pipe) caused by the injection phenomenon plays a dominant role, regardless of the charging or discharging conditions. According to ref. [2], it can be calculated as follows:  
\[R_{\text{LA,1}}=\frac{v}{C_{\text{D,0}}A_{\text{A}}}\sqrt{K_{T}\frac{2}{3\pi}} \tag{6}\]  
where \(K_{T}\) is the proportional constant between the oscillatory flow pressure amplitude and the supply pipeline dynamic pressure, which can ideally be taken as 1. By utilizing Equations (4)-(6), assuming that the transition is continuous, we can calculate the quasi-steady assumption-based linear resistance \(R_{\text{QS}}\) of the communication port. Regarding the inertance of the communication port, some extended analysis can be conducted. [PERSON] proved that the inertance of an orifice can be calculated as follows [15]:  
\[L_{\text{O}}=\frac{l_{\text{E}}}{A_{\text{O}}}=\frac{0.29d}{A_{\text{O}}}+1.04 \int_{0}^{l}\frac{dx}{A(x)} \tag{7}\]  
where \(l_{\text{E}}\) is the effective orifice length, \(A_{\text{O}}\) is the orifice flow area, \(d\) is the orifice diameter, and \(A(x)\) is the cross-sectional area at distance \(s\) through the orifice. According to orifice flow theory, flow and pressure fluctuations can be assumed to be constant across the radius, leading to the derivation of a theoretical constant effective orifice length, even though this assumption is not strictly accurate [20]. [PERSON] demonstrated that the effective length for various orifices remains relatively constant over a range of Reynolds numbers examined [15]. Consequently, previous studies often assumed that the inductance of the communication ports can be ignored or is a constant value solely dependent on the geometry of communication ports. However, even at low oscillatory flow frequencies, the fluid flow pattern within the communication port undergoes significant changes. With an increase in mass flow rate in the communication port, the jet flow might become narrower, leading to a reduction in the cross-sectional area through the communication port and subsequently increase in inertance.
# Two-Way Multi-Antenna Relaying with Simultaneous Wireless Information and Power Transfer
## 4 Test Protocol
### Ts-Af Scheme  
For the MA phase, the description of the signal transmissions from the end nodes T\({}_{1}\) and T\({}_{2}\) to the relay node R can be done as in the TS-DF scheme. In the BC phase, the relay node amplifies and forwards the information signal as expressed in Equation (25), where:  
\[\begin{array}{ll}\left\|\widetilde{\mathbf{y}}_{\text{r}}\right\|=\sqrt{P_{ 1}\left\|\mathbf{h}_{1}\right\|^{2}+P_{2}\left\|\mathbf{h}_{2}\right\|^{2}+M \sigma_{\text{a}}^{2}+M\sigma_{\text{c}}^{2}}\end{array} \tag{42}\]  
and the sampled received (baseband) signal at the end node T\({}_{i}\) (\(i=1,2\)) is given by:  
\[\begin{array}{ll}y_{i}&=\sqrt{P_{\text{r}}}\mathbf{h}_{i}^{\text{T}}\frac{ \widetilde{\mathbf{y}}_{\text{r}}}{\left\|\widetilde{\mathbf{y}}_{\text{r}} \right\|}+n_{i}^{\left[\text{a}\right]}+n_{i}^{\left[\text{c}\right]}\\ &=\frac{\sqrt{P_{\text{r}}P_{\text{r}}}}{\left\|\widetilde{\mathbf{y}}_{\text{r }}\right\|}\mathbf{h}_{i}^{\text{T}}\mathbf{h}_{1}x_{1}+\frac{\sqrt{P_{\text{r }}P_{\text{r}}}}{\left\|\widetilde{\mathbf{y}}_{\text{r}}\right\|}\mathbf{h}_{ i}^{\text{T}}\mathbf{h}_{2}x_{2}+\frac{\sqrt{P_{\text{r}}}}{\left\| \widetilde{\mathbf{y}}_{\text{r}}\right\|}\mathbf{h}_{i}^{\text{T}}\mathbf{n}_{ \text{r}}^{\left[\text{a}\right]}+\frac{\sqrt{P_{\text{r}}}}{\left\|\widetilde{ \mathbf{y}}_{\text{r}}\right\|}\mathbf{h}_{i}^{\text{T}}\mathbf{n}_{\text{r} }^{\left[\text{c}\right]}+n_{i}^{\left[\text{a}\right]}+n_{i}^{\left[\text{c} \right]}\end{array} \tag{43}\]  
where \(n_{i}^{\left[\text{a}\right]}\) and \(n_{i}^{\left[\text{c}\right]}\) are defined below Equation (11). Assuming that \(P_{\text{r}}\), \(\left\{h_{1,m}\right\}_{m=1}^{M}\), \(\left\{h_{2,m}\right\}_{m=1}^{M}\), and \(\left\|\widetilde{\mathbf{y}}_{\text{r}}\right\|\) are known at T\({}_{i}\), an estimate of the intended signalLet \(\gamma_{ji}\) denote the instantaneous SNR of \(\hat{x}_{j}\) at T\({}_{i}\). It is straightforward to show that:  
\[\gamma_{ji}=\frac{P_{\mathrm{r}}P_{j}\bigg{|}\sum\limits_{m=1}^{M}h_{i,m}h_{j,m }\bigg{|}^{2}}{(\sigma_{\mathrm{a}}^{2}+\sigma_{\mathrm{c}}^{2})\bigg{(}P_{ \mathrm{r}}\sum\limits_{m=1}^{M}|h_{i,m}|^{2}+P_{1}\sum\limits_{m=1}^{M}|h_{1,m}|^{2}+P_{2}\sum\limits_{m=1}^{M}|h_{2,m}|^{2}+M\sigma_{\mathrm{a}}^{2}+M \sigma_{\mathrm{c}}^{2}\bigg{)}} \tag{45}\]  
and the corresponding end-to-end BER, \(\overline{P}_{\mathrm{e}}\), is the average BER associated with \(\gamma_{ji}\). Unfortunately, it is difficult, if not impossible, to find the PDF of \(\gamma_{ji}\). Therefore, the end-to-end BER has no closed-form expression and is obtained by means of simulations.
## 4 Discussion
### Justifications for Low Cohen's Kappa Values  
It should be noted that several characteristics' Cohen's Kappa values are below 0.7. Regarding IN-3 PACIFIC, four of its characteristics have a Cohen's Kappa below 0.7, namely Narrative (0), Self Presence (0.426), Agency or Control (0.504), and Identity (0.471). However, all of these can be attributed to their high subjectivity which we previously described. In addition, Narrative's Cohen's Kappa of 0 is a result of all agreements being in favor of rejection but at least one disagreement existing. Regarding C-SHARP, four of its characteristics have a Cohen's Kappa below 0.7, namely Place (0.652), Time (0), Learner Empowerment (0.504), and Novel Experience (0.631). Learner Empowerment and Novel Experience were already stated to have high subjectivity, while Place and Time had a few disagreements that drastically lowered the Cohen's Kappa. Place had all but one agreement be in favor of rejection, and it had only one disagreement, which was for Alpha-Guess [47], about whether the display of a castle was enough to count as a setting. All of [PERSON]'s agreements were in favor of rejection, and there were only two disagreements. One was for [PERSON], about whether the display of a castle implied a medieval time period, and the other was for [PERSON] [73], about whether the display of astronauts implied a relatively modern time period. Despite these characteristics' low Cohen's Kappa values, the characteristics should still not be disregarded, especially with their decent to exceptional agreement percentages; Narrative, Agency or Control (also called Learner Empowerment), and Identity all have 78.1%, Self Presence has 75%, Place has 96.9%, Time has 93.8%, and Novel Experience has 81.3%.
# Formation Control Technology of Fixed-Wing UAV Swarm Based on Distributed Ad Hoc Network
## 7 Conclusions  
This paper proposed a method of fixed-wing UAV swarm formation control based on distributed ad hoc networks. This method included the formation switching of UAVs, formation obstacle avoidance, and handling of abnormal conditions during flight.  
Simulation results showed that in formation switching, the ad hoc network, high-dynamics, fixed-wing UAV could form formation switching only by position information. The method was not sensitive to the initial position information of the UAV, which could eliminate errors during flight, and handle temporarily interrupted communication topologies and UAV drop, as well as other abnormal flight situations. In formation obstacle avoidance, the UAVs could be clustered into multiple subformations to pass through the obstacle avoidance area, and could be reconstructed as required formation. The formation technology was designed based on the waypoints, which was versatile, simple, and reliable, and is easy to realize in engineering.  
Compared with the classic consistent formation control algorithm and obstacle avoidance algorithm, it was shown that the formation technology method proposed in this paper had lower complexity and higher timeliness, and is suitable for online formation flying of highly dynamic UAVs in an ad hoc network. However, the route planned by the method did not consider optimality, and it was only suitable for high-speed and constant-speed formation missions. For obstacle avoidance algorithms, the obstacle model is too simple, and there may be scenarios for obstacle avoidance that are not covered by the algorithm. Next, we will consider the optimal route planning problem and extend the algorithm to three-dimensional flight scenes to increase its practicability.
## 2 Results  
### FAH Protein Expression in Human HCC Tumor Tissues  
Aberrations in the _FAH_ gene cause HT1, resulting in reduced activity and subsequently leading to hepatic failure, cirrhosis, renal dysfunction and HCC [12]. Thus, we wished to investigate the mRNA and protein expression levels of FAH in human tissues using a bioinformatics database. The Human Protein Atlas database suggested that the protein expression of FAH was detected in limited organs; high expression levels were confined to the liver and renal tissues (Supplementary Figure S1A). Additionally, several datasets such as the consensus dataset, HPA dataset, GTEx datasetand FANTOM5 dataset showed high mRNA expression of the _FAH_ gene only in the liver and adipose tissue (Figure S1B-E). These in silico data demonstrate that the expression pattern of FAH is dominant in the liver. Thus, we wished to analyze the expression of FAH in liver cancer and normal tissues. To this end, we clinically examined liver cancer and normal tissues from 26 liver cancer patients obtained from the ISU ABXIS cohort. Our results showed that out of 26 liver cancer patients, 14 patients showed significantly high expression of FAH in tumor tissues when compared to the normal tissues (Figure 1A). 7 patients showed moderate expression of FAH in tumor tissues compared to that in normal tissues (Figure 1B) and 5 patients showed non-significant changes in the expression of FAH protein between tumor and normal tissues (Figure 1C). Our results indicate that the expression of FAH protein is elevated in patients suffering from HCC. To support our results, we analyzed the expression levels of FAH using The Cancer Genome Atlas (TCGA) data in liver hepatocellular carcinoma (LIHC), for which normal-tumor matched RNAseq expression data were available. The results showed slight upregulation of the _FAH_ gene in tumor tissue when compared with normal tissues (Supplementary Figure S2). Considered together, our results showed that the expression of FAH protein but not the mRNA expression of FAH, is significantly upregulated in HCC, indicating the importance of factors affecting FAH protein turnover.  
Figure 1: Fumarylacetoacetate hydrolase (FAH) protein expression in human hepatocellular carcinoma (HCC) tumor tissues. (**A**) Representative immunohistochemical staining of endogenous FAH in 14 patient-derived normal and tumor tissues showing high expression of FAH in tumor samples. (**B**) Representative immunohistochemical staining of endogenous FAH in 7 patient-derived normal and tumor tissues showing moderate expression of FAH in tumor samples. (**C**) Representative immunohistochemical staining of endogenous FAH in 5 patient-derived normal and tumor tissues showing comparable expression of FAH in tumor samples (scale bar = 30 \(\upmu\)m).
# Memory and Cache Contention Denial-of-Service Attack in Mobile Edge Devices
## 3 Memory and Cache Contention Denial-of-Service (DOS) Attack
### Malicious Code Examples  
Figure 4 demonstrates code snippets of our example malicious codes for memory and cache contention DoS attack. Simple memory data copy operations are repeatedly performed in the infinite loop, as shown in Figure 3(a). When copying the data, the malicious code only loads 4-byte (one integer element) within the 64-byte cache line. It eventually leads to faster issuing of data fetching requests to the shared resources (L2 cache and main memory) while preventing the L1 data cache hits for the remaining 15 integer elements that reside in the same cache line. Because our malicious code shows a streaming behavior with the fixed stride (64-byte), the stride-prefetcher employed in the L2 cache also generates a number of prefetching requests (default degree: 8) to the main memory, as we explained in the previous subsection. This, in turn, gives further bandwidth pressure to the main memory. For memory footprint of our malicious code, we touch 32 MB (it actually copies 2 MB of the data, as we only copy 4 bytes in a 64-byte cache line) of the memory address space for each source (_sender_ in Figure 4) and destination array (_receiver_ in Figure 4), which is much larger than the L2 cache size. This eventually incurs a large number of malicious L2 and main memory access requests.  
Because there could be a number of variations (i.e., derivatives) for possible malicious codes, we also show more malicious code examples. A variety of malicious codes can be derived, as shown in Figure 3(b)-d. The overall shapes of the malicious codes are similar to the codes that are shown in Figure 3(a), while there is a slight difference inside of the main loop. In Figure 3(b)-d, integer-type addition, integer-type multiplication, and float-type addition are performed, respectively. Because they also contain consecutive memory accesses (load and store), they also have an effect of the memory and cache contention DoS attack, although the program behaviors and characteristics (e.g., functional unit usage) are different from other types of the malicious codes. In this case, if we try to detect the malicious codes by referring to the functional unit usage patterns (e.g., by sampling the performance counters), the detection of the memory and cache contention DoS attack will be difficult, as they can utilize a variety of the functional units in the CPU.  
Although one may perform assembly code-level optimizations for more effective attack, we do not perform any code optimization from our C-code example. One of the main threats of our memory and cache contention DoS attack is that a simple and naive malicious code can also lead to a successful attack, which implies that it could be very widely exploited.
## 4 Proposed Method  
In this section, we design a two-stage node deployment optimization method to address the coverage enhancement problem. The first stage expands the coverage range by adjusting the sensing direction of the stationary sensor. The second stage repairs coverage holes with less moving distance and achieves coverage enhancement. The principle and framework of the node deployment optimization method proposed in this article are shown in Figure 1. This method mainly includes the following steps: adjusting sensing direction, searching for the MEP, calculating the required number of mobile sensors, and scheduling mobile sensors.