esa-sceva/satcom-chunk-collection huggingface dataset:
# Stabilisation of Soft Clay, Quick Clay and Peat by Industrial By-Products and Biochars
## 4 Discussion
### Stabilisation Effect for Different Binders in Clays
For example, BC2 contained a considerable amount of CaO in a calcite mineral structure, originating from the addition of limestone to the sludge prior to pyrolysis. However, this had no stabilisation effect since the CaO was not available for pozzolanic reactions with the CEM. This was also observed previously when QL and calcite (CaCO\({}_{3}\)) were used for the stabilisation of Tiller-Flotten clay [6]. The stabilisation effects of the calcium-based IBPs are shown in Figure 7b. Most of the IBPs had a positive stabilisation effect. However, there are a few exceptions where no effect was observed, specifically for FA, LS1 and LS2. Ladle slags in general are known for their relatively low effectiveness and often need to be alkali-activated [65; 94], which appears not to have been achieved with the added quantity of CEM. The BA and PSA performed well, with an increased strength of 50% to 150%. This indicates that the CaO in these binders was available for chemical reactions with pozzolans in the binder or soil. In fact, CaO in free-form in the BA and PSA at 16% and 4.8%, respectively, was also noted by the XRD analyses (Table 5). However, these contents were not directly proportional to the relative strength increase. Typically, for calcium-based products such as the ashes and ladle slags used herein, the CaO-content is correlated to the amount of cementitious products obtained in the soil-binder mixture. This, however, depends on the amount of CaO available for chemical reactions with the soil particles, also referred to as the active or free CaO content. This should be considered in further work.
## 4 Simulation Results  
This section describes the simulation results stored in the knowledge base for testing the real-time embedded system and provides the test validation of the proposed framework. The instances of simulators were developed in MATLAB to simulate the synchronization bugs that were stored in the knowledge base.  
Tx sends the data continually and Rx receives the without any disruption. This is the ideal case for communication. Figure 10 shows the simulation results of the normal correspondence between Tx and Rx.  
Figure 9: Emulators with multi interfaces.  
Delay refers to the time required for data to be transmitted from one processor to another. Communication delays can influence the performance of an embedded system overall. Delay can occur at the beginning, anywhere in the middle, and in both cases during the communication from Tx to Rx, shown in Figure 11.  
Figure 12 shows the results of simulations where the transmitter is sending the data continuously while the receiver is not receiving the data due to some fault in the communication system.  
Figure 11: Delay at both ends (Tx and Rx). (**a**) Delay Tx (at the beginning), delay Rx (at the beginning) (**b**) delay Tx (in between), delay Rx (in between), (**c**) delay Tx (both ((**a**),(**b**) cases)), delay Rx (both ((**a**),(**b**) cases)).  
Figure 10: Normal communication (Tx to Rx).  
Tx does not send data so Rx is unable to receives any data. Figure 13 shows the scenario where Tx does not send the data to the receiver (Rx). Figure 14 shows that Tx sends the data continuously but Rx overrides some packets of data during communication (i.e., the 3 rd packet of data overrides the 2\({}^{\text{nd}}\) packet of data), as detailed in Table A1. Figure 15 shows that data are not received in a sequence (out of harmony). Such data loss can happen in real-time systems when one or more packets of data fail to reach their destination.
# Systematic Review of Anomaly Detection in Hyperspectral Remote Sensing Applications
## 6 Structured Background Models
### Cluster or Mixture-based Models
#### 6.2.2 Cluster or Segmentation Based Models  
Cluster [35] or segmentation [66; 120] based anomaly detection methods generally perform unsupervised clustering (classification) or automatic segmentation of the hyperspectral image and then analyze the spectral distances from PUT to cluster or segment centroids. In the unsupervised clustering step, hard clustering techniques like K-means [121] or soft clustering like Fuzzy C-means are used [122; 123; 124]. Hard clustering techniques assign an integer value that corresponds to the membership of a pixel to a certain cluster, while soft clustering methods assign a value \(m_{kl}\) for the membership of the observed pixel to each of the clusters \(m_{kl}\in[0,1]\). Of soft clustering methods, fuzzy-based techniques [125; 126] may show potential for anomaly detection applications. Once the cluster statistics have been determined (usually cluster mean vector and cluster covariance matrix), a distance can be computed, which can be, for example, the Euclidean or Mahalanobis distance between the PUT and each cluster centroid. As the detection statistic, the minimum distance (distance to the spectrally closest cluster centroid) is used. If the squared Mahalanobis distance is chosen, it will lead to the so-called class-conditional GLRT [11]:  
\[\begin{array}{rl}&H_{1}\\ D_{CBAD}(\underline{x}|\mathcal{B})&=-\log\Bigl{\{}p_{\mathcal{N}}( \underline{x}\big{|}\underline{\hat{\mu}}_{i},\underline{\hat{\Sigma}}_{i}) \Bigr{\}}&>\\ &<\\ &H_{0}\\ &H_{1}\\ &\sim\Bigl{(}\underline{x}-\hat{\underline{\mu}}_{i}\Bigr{)}^{T}.\underline{ \hat{\Sigma}}^{-1}.\Bigl{(}\underline{x}-\underline{\hat{\mu}}_{i}\Bigr{)}&> \\ &<\\ &H_{0}\\ \end{array} \tag{44}\]  
where \(i\) denotes the index of the cluster to which the PUT \(\underline{x}\) has been assigned (the spectrally nearest cluster).  
The performance of these detectors strictly depends on the selection of the appropriate number of background clusters [11]. If this number is underestimated, then pixels that naturally belong to a larger number of clusters will be \"squeezed\" into one cluster. That will result in a higher variance of the resulting clusters, which will adversely affect the probability of anomaly detection. If the number of background clusters is overestimated, there is a possibility that anomalies will create a separate class. In that case, it may not even be possible to detect anomalies. In order to determine the optimal number of clusters, Akaike or Bayes information criteria were proposed [127]. The application of artificial neural networks such as self-organizing fields (self-organizing maps) [128] has been investigated in hyperspectral anomaly detection [129; 130], and is less sensitive to the selection of the appropriate number of clusters.
# Analysis of Electromagnetic Field Characteristics of Wave Glider
## 3 Analysis of Electric Field Noise Characteristics
### Analysis of Electric Field Characteristics under Different Motion States
In the range of 0.5-1 Hz, electric field interference is mainly generated by the steering engine (the action cycle of the steering engine is about 2 s). Comparing states 2-3, 4-5, and 6, it can be seen that the greater the shaking degree, the more obvious the steering engine action, and the greater the electric field interference;
2. According to Table 1, the peak-to-peak value of the axial frequency electric field is about 0.5-0.7 mV/m. At a depth of 50 m, the peak-to-peak value of the electric field in the same frequency band of the electromagnetic buoy is about 0.0001 mV/m. It can be seen that installing an electric field sensor directly at the bottom of the wave glider at this depth (<2 m) will cause significant noise interference, and the detection performance will be significantly reduced;
3. The peak value of the electrostatic field at the bottom of the glider is about 0.2-0.4 mV/m. The peak value of the electrostatic field measured directly by installing an electric field sensor at the bottom of the wave glider is relatively large. This is partly due to the shallow depth of the seawater and partly due to the bare metal leakage at the bottom of the glider; the induced electric field generated is relatively large. In summary, to reduce the peak value of the static electric field of the wave glider, it is possible to consider changing the distance between the underwater towed body and the surface parent body, as well as changing the metal structure of the towed body. To reduce the axial frequency electric field, the connection between the floating body on the water surface and the underwater towed body can be changed to reduce the significant change in direction, thereby reducing the change in rudder angle of the towed body and reducing the amplitude of the axial frequency electric field. According to the power spectrum analysis, it can be seen that the interference electric field generated during the motion of the wave glider is mainly concentrated within 1.5 Hz, and for some of the underwater moving bodies, the frequency of the axial-frequency electric field signals is concentrated in the range of 0.5-30 Hz, so that the use of the axial-frequency electric field of the underwater moving bodies can be considered to be detected. \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Status Number** & **Peak to Peak Value of Electrostatic Field (mV/m)** & **Peak to Peak Value of Shaft-Rate Electric Field (mV/m)** & **Hz Signal PSD (Power Spectral Density)** & **0.5–1 Hz Signal PSD (Power Spectral Density)** \\ \hline
1 & 0.2 & 0.2–0.3 & 0.003 & 0.016 \\
2 & 0.2 & 0.5 & 0.002 & **0.04** \\
3 & 0.2–0.3 & 0.6–0.7 & 0.0025 & **0.045** \\
4 & 0.3–0.4 & 0.8 & 0.05 & **0.08** \\
5 & 0.2–0.3 & 0.6–0.7 & 0.005 & **0.07** \\
6 & 0.14 & 0.2 & 0.008 & 0.0045 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of electric field characteristics in different motion states.
## 4 Results
### The Validation of PCRI's Effectiveness Using the inD Dataset
In the rear-end conflict, TTR and TRSD demonstrate a certain level of rationality. The change trends of both in crossing conflict are shown in Figure 15. From Figure 15a, a sudden change in TTR occurs around frame #30040. However, there are still many moments after frame #30040 with small TTC values. This indicates that relying solely on TTR might miss the identification of moments with higher potential conflict risk. In Figure 15b, the minimum value of TRSD occurs around frame #29960, but through video analysis, the most dangerous moment is identified at frame #29992, showing a significant discrepancy  
Figure 13: Comparison of different variates with TTC. from the TRSD detection result. Therefore, relying solely on TRSD is also insufficient for accurately identifying the most dangerous moment. Based on the frame-by-frame analysis methods described above, for the four intersections in the inD dataset, two videos with the highest number of conflict events are selected. The accuracy of PCRI and TTC in identifying conflict risk is compared for these selected videos. These videos contain 175 conflict events, out of which 88 conflicts exhibit significant differences in identifying the most critical moments between the two measures. For conflicts in determining the highest risk moment with differences, the deceleration of the subject vehicle is utilized to determine the critical moments of risk. The results reveal that PCRI significantly outperformed TTC in identifying dangerous moments in rear-end and lane-changing scenarios, with 12 and 32 instances more accurately identified, respectively. In crossing scenarios, PCRI (15 instances more accurate) also slightly outperformed TTC (10 instances more accurate). Additionally, there are a few cases where the identification results based on PCRI and TTC thresholds showed differences, but the frames are close to  
Figure 14: SSMs’ changes in conflict between vehicle #527 and vehicle #528. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Frame** & **Deceleration (m/s2)** & **Frame** & **Deceleration (m/s2)** \\ \hline
29996 & 1.4200 & 30066 & \(-0.3856\) \\
**29997** & **1.4204** & 30067 & \(-0.4021\) \\
29998 & 1.4193 & 30068 & \(-0.4159\)  
the ground truth. These instances are not included in the count of accurate conflict moment identifications for either variate. According to these comparisons, PCRI exhibits a lower missing rate of conflict identification and performs better than TTC in identifying critical moments for different conflict types. Furthermore, a single variate from PCRI cannot accurately reflect changes in conflict risk, while the comprehensive measures PCRI provide a better representation of conflict risk variations.
# Symptom Burden and Dietary Changes Among Older Adults with Cancer: A Cross-Sectional Study
## 3 Results
### Dietary Change and Cancer Diet
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Age Cohort** & \
\begin{tabular}{c} **Avoid Specific Food** \\ **Yes** \\ \end{tabular} &
\begin{tabular}{c} **Avoid Specific Food** \\ **No** \\ \end{tabular} & \(p\)**-Value** \\ \hline \(\geq\)65-74 years & 83 & 107 & \\
75–84 years & 35 & 62 & 0.2540 \\ \hline \(\geq\)85 years & 35 & 62 & \\ \(\geq\)85 years & 4 & 9 & 1.0000 \\ \hline \(\geq\)65-74 years & 83 & 107 & \\ \(\geq\)85 years & 4 & 9 & 0.4032 \\ \hline \multirow{2}{*}{**Age Cohort**} & \
\begin{tabular}{c} **Prefer Specific Food** \\ **Yes** \\ \end{tabular} &
\begin{tabular}{c} **Prefer Specific Food** \\ **No** \\ \end{tabular} & \(p\)**-Value** \\ \hline \(\geq\)65-74 years & 33 & 157 & \\
75–84 years & 16 & 81 & 1.0000 \\ \hline \(\geq\)85 years & 0 & 13 & 0.2084 \\ \hline \(\geq\)65-74 years & 33 & 157 & \\ \(\geq\)85 years & 0 & 13 & 0.1331 \\ \hline \multirow{2}{*}{**Age Cohort**} & \
\begin{tabular}{c} **Use of Additional** \\ **Supplements** \\ **Yes** \\ \end{tabular} &
\begin{tabular}{c} **Use of Additional** \\ **Supplements** \\ **No** \\ \end{tabular} & \(p\)**-Value** \\ \hline \(\geq\)65-74 years & 65 & 125 & \\
75–84 years & 31 & 66 & 0.7916 \\ \hline \(\geq\)85 years & 10 & 3 & **0.0040** \\ \hline \(\geq\)65-74 years & 65 & 125 & \\ \(\geq\)85 years & 10 & 3 & **0.0050** \\ \hline \multirow{2}{*}{**Age Cohorts**} & \
\begin{tabular}{c} **Cancer Diet** \\ **Yes** \\ \end{tabular} &
75–84 years & 4 & 93 & **0.3179** \\ \hline \(\geq\)85 years & 3 & 10 & 0.0346 \\ \hline \(\geq\)65-74 years & 14 & 175 & \\ \(\geq\)85 years & 3 & 10 & 0.0836 \\ \hline \multirow{2}{*}{**Sex**} & \begin{tabular}{c} **Avoid Specific Food** \\ **Yes** \\ \end{tabular} & \begin{tabular}{c} **Avoid Specific Food** \\ **No** \\ \end{tabular} & \(p\)**-Value** \\ \hline \multirow{2}{*}{\begin{tabular}{c} m \\ f \\ \end{tabular} } & 72 & 93 & \\  & 50 & 85 & 0.2878 \\ \hline \multirow{2}{*}{\begin{tabular}{c} m \\ f \\ \end{tabular} } & **Prefer Specific Food** & **Prefer Specific Food** & \(p\)**-Value** \\ \cline{2-2}  & **Yes** & **No** & \\ \hline \multirow{2}{*}{\begin{tabular}{c} m \\ f \\ \end{tabular} } & 24 & 141 & \\  & 25 & 110 & 0.4328 \\ \hline \multirow{2}{*}{\begin{tabular}{c} m \\ f \\ \end{tabular} } & \begin{tabular}{c} **Use of Additional** \\ **Supplements** \\ **Yes** \\ \end{tabular} & \begin{tabular}{c} **Use of Additional** \\ **Supplements** \\ **No** \\ \end{tabular} & \(p\)**-Value** \\ \hline \multirow{2}{*}{\begin{tabular}{c} m \\ f \\ \end{tabular} } & 59 & 106 & 0.1904 \\ \hline \multirow{2}{*}{\begin{tabular}{c} f \\ \end{tabular} } & 47 & 88 & \\ \hline \multirow{2}{*}{\begin{tabular}{c} **Cancer Diet** \\ **Yes** \\ \end{tabular} } & \
\begin{tabular}{c} **Cancer Diet** \\ **No** \\ \end{tabular} & \(p\)**-Value** \\ \hline \multirow{2}{*}{
\begin{tabular}{c} m \\ f \\ \end{tabular} } & 10 & 154 & 0.5046 \\ \cline{2-2}  & 11 & 124 & \\ \hline \end{tabular}
\end{table} 
Table 3: Dietary changes sub-group analysis of age cohorts, sex, and cancer entity.
# Designing Hydrogel-Based Bone-On-Chips for Personalized Medicine
## 3 Hydrogels
### Synthesis of Hydrogels  
Hydrogels for tissue engineering applications must fulfill a number criteria, such as physical parameters (e.g., degradation and mechanics), mass transport parameters (diffusion requirements), biological performance parameters and biological interaction requirements for each specific application (e.g., cell adhesion) [67]. In addition, those specifically designed to encapsulate cells need to be non-toxic, gelled without damaging the cells, allow the diffusion of nutrients and support its handling during experiments. All these factors have to be considered in order to create a \"custom-made\" biomaterial and will determine the type of gelation utilized in hydrogel synthesis.  
Hydrogels can be synthesized via various techniques, that include chemical cross-linking, physical cross-linking, polymerization grafting, and radiation cross-linking [68]. Attending the most common gelation methods utilized in hydrogel synthesis, it is normally distinguished between chemically or physically cross-linked hydrogels, based on the nature of the covalent bonds made within the matrix. Stable junctions or covalent bonds control the degree of swelling of the hydrogel based on their polymer-water relationship and the degree of reticulation in the matrix. On the other hand, physically cross-linked hydrogels result from ionic bonding, hydrogen bonding, physical interactions or molecular entanglements, which are reversible by application of force or other environmental changes [69; 70]. Due to the absence of toxic cross-linking agents, physical cross-linking has gained greater importance over the last years compared with chemical cross-linking. Nevertheless, it is difficult to control the material properties of the hydrogel, such as gelation time, pore size, chemical functionalization, and degradation time due to lack of a cross-linking agent (Figure 2B,C) [71].  
Figure 2: Hydrogels for bone-on-a-chip applications. (**A**) Hydrogel mechanical properties as a function of network concentration. Scheme of a collagen hydrogel in the rheometer (left). Stress sweeps of five collagen hydrogels with different collagen concentration (right) after 24 h of polymerization (image from [64]). (**B**) Hydrogel structural properties as a function of network concentration. Concentration dependence of the average pore size of fibrin (red) and collagen (black) gels (image from [65]). (**C**) Reconstituted collagen-based hydrogels within a microfluidic chip. Maximum intensity projection in the Z direction of a 2 mg/mL and a 4 mg/mL 3D collagen type I network structure. Fiber images obtained with Nikon D-Eclipse C1 equipped with a 40X oil objective (image from [66]). (**D**) Effect of cell encapsulation within a collagen hydrogel. 3D reconstruction of collagen fibers in the vicinities of the cell or far from the cell (middle figures). Changes on average fiber length for the regions near the cell and far from the cell (right) (image from [66]). Images taken with permission from publishers.
# LSANet: Lightweight Super Resolution via Large Separable Kernel Attention for Edge Remote Sensing
## 4 Implementation Details
### Ablation Study
#### 4.4.2 Effectiveness of the Feature Processing Part of the Module  
The experimental results in Table 4 demonstrate that in contrast to the CCA-only experiment (d), the experiment incorporating LSKA (g) leads to an increase in PSNR by 0.03 dB. Regarding the number of parameters, there is a rise of 13.4%, and the computational complexity increases by 12.3%. This indicates that LSKA can boost the feature extraction ability. On this foundation, when the RFEM is introduced in experiment (h), PSNR and the relevant performance are further enhanced. The PSNR goes up by 0.05 dB compared to experiment (g), while the number of parameters and computational amount stay the same, thus verifying the effectiveness of RFEM. As the input dimension (Inp. Dim.) increases from 48 (in experiment h) to 60 (in experiment k), the PSNR climbs by 0.14 dB, and the computational amount (FLOPS/G) increases by 54.4%. Considering the overall performance and computational cost, the experiment with an input dimension of 52 (i) is chosen as the final scheme.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Number** & **CCA** & **CCA+LSKA** & **RFEM** & **Inp. Dim.** & **Params/K** & **FLOPS/G** & **PSNR** & **Times/s** \\ \hline (d) & ✓ & & & 48 & 365 & 17.1 & 26.25 & 0.74 \\ (g) & & ✓ & & 48 & 414 & 19.2 & 26.28 & 0.82 \\ (h) & & & ✓ & 48 & 414 & 19.2 & 26.33 & 0.85 \\ (i) & & & ✓ & 52 & 463 & 21.4 & 26.39 & 0.90 \\ (j) & & & ✓ & 56 & 506 & 24.8 & 26.42 & 0.96 \\ (k) & & & ✓ & 60 & 594 & 29.6 & 26.47 & 1.02 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of different feature processing modules and different input dimensions.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Number** & **SRB** & **RLSKAB** & **LSKA Size** & **Params/K** & **FLOPS/G** & **PSNR** & **SSIM** & **Times/s** \\ \hline (a) & ✓ & & – & 550 & 26.3 & 25.99 & 1.29 & – \\ (b) & & ✓ & (3, 5) & 334 & 15.4 & 26.19 & 0.7866 & 0.80 \\ (c) & & ✓ & (5, 5) & 346 & 16.1 & 26.22 & 0.7875 & 0.86 \\ (d) & & ✓ & (5, 7) & 365 & 17.1 & 26.25 & 0.7884 & 0.91 \\ (e) & & ✓ & (7, 7) & 387 & 18.2 & 26.21 & 0.7871 & 0.94 \\ (f) & & ✓ & (7, 9) & 396 & 19.3 & 26.23 & 0.7890 & 0.99 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of different feature extraction modules and different LSKA sizes.
## 3 System Model
### MAC Layer Model  
In Figure 4, the transmission buffer is a first in first out (FIFO) queue. The transmitter receives and stores \(\mathrm{ln}\) packets in the finite buffer,then sends some packets from the buffer in the \(\mathrm{n}\)-\(\mathrm{th}\) time slot,. We assume that the traffic arrival distribution follows an independent and identical distribution (i.i.d) during each slot and the packets arrival follows a Poisson process with rate \(\lambda\). Hence, when one packet arrives, the probability density is denoted as:  
Figure 3: FSMC model.  
(4)  
Afterwards, we define that the backlog is denoted by b \(\in\) [0, B] at the transmitter buffer. B is the capacity of the finite buffer. Each packet contains L bits.When the buffer is full, the arriving packets will be dropped. The packet zn will be sent in slot n at the transmitter, where zn \(\in\) {0, 1,..., B}. The packets are affected by the Bit Error Ratio (BER) and smaller than zn, _i.e._, fn(BERn, zn)\(\leq\)zn when received at the receiver. fn is represented by a binomial distribution when packet losses are independent:  
\[p^{f}(f_{n}\mid BER_{n},z_{n})=bin(z_{n},1-PER_{n}) \tag{6}\]  
where \(PER\) is the packet error ratio, which meets \(PER_{n}=1-(1-BER_{n})^{l_{n}}\)\(b_{int}\) is the initial buffer state. \(b_{n}\) is the buffer state at the \(n\)-th slot. Therefore, the buffer state can evolve recursively at the transmitter as follows:  
\[\begin{cases}b_{0}=b_{int}\\ b_{n+1}=\min(b_{n}-z_{n}+l_{n},B)\end{cases} \tag{7}\]
## 4 Proposed Architecture
### Cloud Integration Layer  
As messages move beyond the hospital network, they enter the Cloud Integration Layer, which comprises a cloud-hosted MQTT broker capable of supporting large-scale distributed systems. Before transmission to the cloud, data is re-encrypted using stronger end-to-end encryption protocols such as AES-256. The cloud broker is integrated with identity and access management services, enabling dynamic control over who can access specific patient data. In addition, cloud-native services, such as intrusion detection systems and data logging modules, provide a robust layer of protection and traceability for transmitted information.