esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Delivery Service Sharing (DSS)
#### 2.2.2 Customer-Oriented DSS  
Customer-oriented sharing has been proposed as a promising means for improving DSS applications (e.g., ridesharing), where multiple customer requests can be shared and severed by a vehicle simultaneously [3; 4; 5]. A mixed integer programming can be used to model the static customer-oriented DSS where the customer requests are known in advance, and a time-consuming Lagrangian decomposition approach is used to return the exact solution [8]. To scale up the customer-oriented DSS, [PERSON] et al. [9] propose a bipartite matching-based approximation algorithm. By considering the advantage of tip sharing with partners locating in proximal locations, a community-based trip-sharing paradigm is proposed, where customers are first clustered into local communities and optimal intra community trip sharing can be found by the integer programming [22]. By utilizing the offline partial paths, online nearby customer requests can be constructed and inserted by a local search in a real-time manner [23; 24; 25]. On the other hand, in ridesharing, the sharing paths should also satisfy the monetary constraints where passengers will not pay more compared with the scenario without ridesharing and taxi drivers will also make money for all the detour distance due to ridesharing [10]. Compared to the above customer-oriented DSS scenarios where customers have the fixed pickup/drop-off window, we study the more practical and flexible DSS variant where customers have time windows. Moreover, we design customer-oriented DSS algorithms with the systemic understanding of the power of customer sharing.
## 5 Conclusions
### Nomenclature  
Indices  
\(T\)  
Hourly index of the time  
\(M\)  
Monthly index of the time  
\(Ph\)  
Index of forecasted peaks  
\(Q\)  
Five coincident peaks for Ontario province, Canada  
\(W\)  
five-segments for the linearized depth of discharge (DOD) curve  
Parameters  
\(p^{DR}\)  
Demand response power (decision variable)  
\(Rsp_{t}\)  
Spot price at t ($/MWh)  
\(\overline{NC}\)  
Battery's rated cycle life  
\(NC\)  
Battery actual cycles life  
\(\overline{IC}\)  
Allowable maximum budget for the battery  
\(R_{PB}\)  
Battery cost factor for power  
\(R_{EB}\)  
Battery cost factor for energy  
\(\overline{PD_{PS}}\)  
Peak demand load for Ontario province  
\(C_{rate}\)  
Battery \(C_{rate}\) (\(\overline{PB}\) / \(\overline{EB}\))  
\({P_{t}}^{T}\)  
Total exchange power  
\({P_{t}}^{D}\)  
Power demand at t  
\({R_{m}}^{GAC}\)  
Charges for global adjustment per month  
\({R_{m}}^{DC}\)  
Demand charges per month  
\(\eta^{+}\)  
Battery efficiency (discharging)  
\(\eta^{-}\)  
Battery efficiency (charging)  
\(E^{0}\)  
Initial BESS energy value  
\(KDR\)  
Demand response contract ($/Mw)  
\(KD\)  
Battery loading coefficient during the coincident peak hour  
R  
Annual interest rate  
\(N_{y}\)  
Total number of operational years  
\(\gamma_{w}\), \(\beta_{w}\)  
Linearization parameters of DOD curve  
Variables  
\(ACC_{B}\)  
Batter capital cost per year ($/year)  
\(AEC\)  
Total energy cost per year ($/year)  
\(AGAC\)  
Cost of global adjustment per year ($/year)  
\(ADCC\)  
Cost of the demand charge per year ($/year)  
\(ARV_{EPA}\)  
Energy price arbitrage revenue per year ($/year)  
\(ARV_{DR}\)  
Demand response revenue per year ($/year)  
\(\overline{EB}\)  
Battery maximum energy (MWh) (decision variable)  
\(\overline{PB}\)  
Batter maximum power (MW) (decision variable)  
\(T\)  
Time step  
\(DOD\)  
Depth of discharge of the battery.  
\(PB_{t}\)  
Hourly Battery power at t  
\({P_{t}}^{B+}\)  
Discharging battery power at t (decision variable)  
\({P_{t}}^{B-}\)  
Charging battery power at t (decision variable)  
\(PDF\)  
Peak demand factor  
\(\Omega_{5 PH}\)  
Set of the five peaks for the Ontario Jurisdiction  
\(\Omega_{PH}\)  
Set of the forecasted peaks  
Integer variables  
\(IU\)  
Integer of the 5-segements of the linearized DOD curve  
\(u\)  
Integer of battery discharging
## 4 Strength Model of Inclined Pillar
### Verification of Inclined Pillar Shear Strength Model
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & **Author** & **Formula** & \(S_{I}\) & **Remarks** & **Eq.** \\ \hline
1 & [PERSON] et al. [36] & \(S_{p0}=S_{I}\frac{w^{0.46}}{h^{0.66}}\) & 7.2 & Coal pillar & (19) \\
2 & [PERSON] et al. [37] & \(S_{p0}=S_{I}\left(0.12+0.88\frac{w}{h}\right)\) & 2.7 & Coal pillar & (20) \\
3 & [PERSON] et al. [7, 38] & \(S_{p0}=S_{I}h^{-0.36}+\left(\frac{H}{250}+1\right)\left(\frac{w}{h}-1\right)\) & 0.27\(\sigma_{0}\) & Coal pillar & (21) \\
4 & [PERSON] et al. [39] & \(S_{p0}=S_{I}\frac{w0.5}{h^{0.75}}\) & 132.9 & Hard rock pillar & (22) \\
5 & [PERSON] et al. [40] & \(S_{p0}=S_{I}\frac{w}{h}\) & 0.42\(\sigma_{0}\) & Hard rock pillar & (23) \\
6 & [PERSON] et al. [13] & \(S_{p0}=S_{I}\cdot LDF\cdot\frac{w^{0.30}}{h^{0.59}}\)\(LDF\in\left(0,1\right)\) & 0.65\(\sigma_{0}\) & Hard rock pillar & (24) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Strength formulas of partial vertical pillars (coal pillars and hard rock pillars). Figure 9: Shear load vs. shear strength of failed and stable inclined pillar cases.
## 6 System Evaluation
### Search Precision  
Figure 8 evaluates the level of search precision to which SEPIM conforms under variable dimension reductions. This formula employs MAP, which was introduced in Section 3.2, and demonstrates SEPIM acquiring a similar precision in search as plaintext image search schemes [21; 22; 24; 25], with just 2% lower precision. SURF shows better performances as compared with SYBA and BRIEF approaches. Such approaches did not perform well when there is a large viewpoint change, and their lack of invariance to rotation is prominent. Because BRIEF and SYBA, unlike SURF, does not correct for orientation.  
The figure renders clear the negative relation between the MAP of SEPIM and a dimension reduction over PCA-256, the stability of which runs counter to when it is lesser than PCA-256. From these results, it appears that PCA-256 is the ideal setting required to balance both the precision of the search and its efficiency.
## 3 Problem Statement
### QoS Factors  
Latency: End-to-end latency \(L\) for a packet transmitted from a source node \(S\) to the BS can be calculated as the sum of transmission delays \(T_{trans}\) on each link \((i,j)\) in the path and queuing delays \(T_{queue}\):  
\[L=\sum_{(i,j)ePath}\bigl{(}T_{trans}+T_{queue}\bigr{)} \tag{5}\]  
where \(T_{trans}\) is the time for each transmission, and \(T_{queue}\) depends on traffic load and buffer capacity.  
Packet Delivery Ratio (PDR): \(PDR\) reflects the ratio of successfully received packets \(P_{recv}\) at the BS to the total sent packets \(P_{sent}\) by the source nodes:  
\[PDR=\frac{P_{recv}}{P_{sent}} \tag{6}\]  
This metric is vital for assessing the reliability of data transmission across the network.  
Throughput: Throughput \(T\) represents the amount of data successfully delivered to the BS per unit of time, where \(B_{ij}\) is the bandwidth capacity of each link \((i,j)\) and \(P_{success}\) is the probability of successful delivery:  
\[T=\sum_{(i,j)ePath}\bigl{(}B_{ij}.\ P_{success}\bigr{)} \tag{7}\]  
Higher throughput indicates an effective and efficient data transmission process.  
Link Reliability: Reliability (\(R\)) measures the probability of successful packet transmission without loss, where \(P_{loss,i,j}\) is the probability of packet loss on link \((i,j)\):  
\[R=\prod\
olimits_{(i,j)ePath}\Bigl{(}1-P_{loss,ij}\Bigr{)} \tag{8}\]  
This metric captures the robustness of the selected path by considering link quality and overall network reliability.  
The \(CF\) assesses the load on each link, calculated as the ratio of data flow \(Data\ Flow_{ij}\) to link capacity \(Link\ Capacity_{ij}\):  
\[CF=\frac{\sum_{(i,j)ePath}Data\ Flow_{ij}}{\sum_{(i,j)ePath}Link\ capacity_{ij}} \tag{9}\]  
Lower \(CF\) implies that traffic is balanced, and congestion is minimized, essential for maintaining smooth data flow across the network. Figure 4 illustrates the application architecture for congestion control in WSNs, emphasizing congestion detection, notification, and management while optimizing energy consumption and ensuring reliable data transmission. The different colored arrows in the figure represent various data flows within the network. The blue arrows indicate the transmission of sensor data from individual sensor nodes to cluster heads, which aggregate and forward the data. The purple arrows represent congestion notifications and control messages sent from cluster heads to the base station, ensuring efficient congestion management.
# Area of Interest Tracking Techniques for Driving Scenarios Focusing on Visual Distraction Detection
## 4 Discussion
* Algorithmic Suitability: The data suggests that the 'BAIT' may be more suitable for applications requiring precise attention detection on windshields, while the 'Pupil Labs software' may be favored for tasks that demand consistent dashboard focus measurements. The BAIT algorithm is a significant advancement in ID tag identification and has potential for practical applications. Its robustness and precision make it suitable for integration into research studies in both controlled environments and naturalistic driving scenarios. The BAIT algorithm can significantly enhance the accuracy and reliability of visual distraction measurements in the automotive industry, particularly in semi-naturalistic studies conducted under varying environmental conditions and custom cockpit setups. The study is limited by its specialized focus and environmental settings, and it only used ET data from 10 participants, each with 8 ID tags. The custom algorithm, tailored for driving scenarios and targeting specific areas like the FOV and UI in cars, may not perform as well in non-driving contexts. Its effectiveness is primarily in driving scenarios used in CDS, limiting its utility in managed settings. Reliance on ET technology and its tuning to specific vehicle interiors and IVIS also constrain its applicability. Furthermore, its focus on visual distraction in driving restricts broader usage beyond driver behavior analysis, impacting its generalizability.
# FTUC: A Flooding Tree Uneven Clustering Protocol for a Wireless Sensor Network
## 7 Conclusions  
Due to the uneven energy consumption in wireless sensor networks, a flooding tree uneven clustering protocol (FTUC) is proposed based on the conception of an unequal cluster protocol.  
FTUC is designed to obtain the global minimum and maximum distance values of the nodes to the base station through a tree type sub-network, and introduces referenced circles to elect CHs more evenly throughout the network. Through the weight value and competition radius, the CHs are  
Figure 12: Network alive node numbers depending on time using LEACH, UCR, and FTUC.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **LT-1 (s)** & **LT-2 (s)** & **Improved of LT-1 (\%)** & **Improved of LT-2 (\%)** \\ \hline LEACH & 5420 & 34,510 & 186.3 & 130.6 \\ UCR & 13,380 & 63,810 & 16.0 & 24.7 \\ FTUC & 15,520 & 79,510 & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Evaluation of lifetime LT-1 and LT2 for all three algorithms.  
Figure 13: Variance of residual energy for each network. FTUC enables one to have a better repartition over time, increasing the network lifetime.  
evenly distributed and unequal clusters are built. In order to avoid long-distance communications, an inter-cluster route is constructed according to the distance needed to reach the BS and the remaining energy of CHs. Therefore, FTUC has a lower energy consumption compared with LEACH and UCR in the cluster formation stage and during the inter-cluster communication stage. Furthermore, FTUC solves the energy consumption balance problem for each node. The simulation results show that the FTUC protocol reduces the energy consumption of the network effectively and prolongs the lifetime of the network.  
As FTUC uses a very efficient strategy to compute the competition radius, we are working on its evolution to support dynamic networks. In this model, new nodes can appear (or disappear) during the network lifetime and should be managed efficiently. In such a dynamic network, security is now a really important property that must be taken into account. In that way, new strategies need to be integrated, even in FTUC. Our work can then benefit from using new approaches like the one presented in [23], and this requires further work.  
The authors would like to thank the editor and anonymous referees for their valuable comments to improve the quality of this paper. This work was also supported by the Institut d'Electronique et de Telecommunications de Rennes (IETR) on Embedded System Laboratory.  
The work presented here was carried out in collaboration between all authors. [PERSON], [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments and analyzed the data; [PERSON] wrote the paper; and [PERSON] modify the paper.  
The authors declare no conflict of interest.
# Research Progress on Flexible Oxide-Based Thin Film Transistors
## 3 Approaches to Flexible Oxide-Based TFTs
### Flexible TFTs on Different Kinds of Substrates
#### 3.1.3 Polyimide (PI) Flexible Substrate
For example, TFTs have been employed in integrated circuits, like ring oscillators, amplifiers, logic gates, radiofrequency identification (RFID) systems and so on [39; 53; 55; 59; 90; 94; 95; 163; 164; 165; 166; 167; 168; 169]. It is necessary for TFTs to enhance their maximum power gain frequency and to reduce the parasitic capacitance between electrodes. [PERSON] et al. used a self-alignment process to realize a BCE  
Figure 9: (**a**) and (**b**), schematic of BCE TFT and ESL TFT, (**c**) and (**d**), transfer characteristic of BCE TFT and ESL TFT, (**e**) and (**f**) application of BCE TFT and ESL TFT [161; 162]. structure with a channel with a length of 0.5 \(\upmu\)m [163]. There, the maximum oscillation frequency (maximum power gain frequency) \(\mathrm{f_{max}}\) was extracted to be \(>\)300 MHz, giving hope that the device can be applied to high-frequency applications. Bending tests, also conducted in the study, showed that \(\mathrm{f_{max}}\) was reduced by 3.6% when a TFT was bent to a tensile radius of 3.5 mm. A flexible-skin-like sensor, biosensor and other kinds of sensors based on oxide-based TFTs are receiving wide interest [170; 171; 172]. [PERSON] and his co-workers presented a room temperature gas sensitive TFT, as shown in Figure 10[172]. Figure 10a shows the schematic fabrication flow of the a-IGZO TFT whose channel produces a chemical reaction when it is exposed to the oxygen, NO\({}_{2}\) or H\({}_{2}\)O. The experimental results show that there was almost no hysteresis in the response to NO\({}_{2}\) in the investigated range of 2-5 ppm with 20% background oxygen at room temperature. When combined with analog or logical circuits, this sensor could be realized for actual applications. So far, PI is one of the most appropriate substrates for flexible electronic devices. Actually, some electronic devices with PI-substrate displays have already been marketed. PI with its high process temperature, good compactness and mechanical strength has been widely studied for experimentation and commerce.
## 3 Results and Discussion
### Linear Expansion Test (Swelling) (LP) Test
#### 3.2.2 Effects of M-GGBS on Swelling  
Figures 9-14 present the vertical volume change (swelling) of gypseous soil treated with the M-GGBS stabilizer. The maximum vertical volume change was 7.293%, obtained after applying a 7.5M-2.5 GGBS ratio of 3:1 over 7 days of curing. However, all other specimens exhibited low swelling. For example, the swelling of soil samples stabilized with 5M-15 GGBS and 2.5M-17.5 GGBS was 0.001% and 0.005% after 90 days of curing, with swelling magnitude almost wholly suppressed to 99.95% and 99.77%. The reduction in the swelling values was because of the use of GGBS, which exhibited superior sulfate resistance along with its denser structure and lower presence of calcium ions [6; 38].
# A Review of Multimodal Interaction in Remote Education: Technologies, Applications, and Challenges
## 4 Keyword and Correlation Analysis
### Multimodal Interaction Across Educational Levels  
#### 4.3.1 K-12 Education  
In K-12 education, students' attention levels directly affect learning outcomes. Researchers use eye-tracking technology with speech recognition to track students' attention in real time during class. Fixation points, gaze duration, and verbal responses help educators adjust teaching strategies based on data. This improves classroom engagement  
Figure 4: Correlation of “Application”.  
Figure 5: Correlation of “Feedback”.  
and learning effectiveness. [PERSON] et al. [59] showed that eye-tracking technology can measure students' reading behaviors and cognitive processes, providing strong support for educational research.
## 6 Experimental Evaluation and Discussion  
In this part, we employ the novel defense given in this paper against several state-of-the-art WFP attacks and evaluate the defense's efficiency and reliability. Due to the design of the reliable multi-path transmission, we assume that an attacker can compromise one of the **m** circuits from which it can obtain and record encrypted traffic into the outgoing relay. Furthermore, we assume that the adversary knows the multi-path transmission and redundant splitting scheme used in this defense and that they have enough resources to obtain the website fingerprints generated when a web page is loaded, and can collect the patterns of subcircuits in the multi-path and categorize them as loading the same website. At the same time, the attacker will use a state-of-the-art deep learning classifier to match the traffic patterns and determine whether the traffic patterns composed of sub-tracks have the same flow as the website fingerprinting. To compare the classification accuracy, we calculate the evaluation metrics for the classifier test results, which are the true positive rate (TPR) and false positive rate (FPR), where the TPR indicates the percentage of actually visited pages distinguished by the classifier and the FPR indicates the percentage of pages incorrectly identified by the classifier.  
In Section 6, we first focus on the effectiveness of our defense. We first identify the defense that maximizes the difference by testing a different number of slices **s**, slicing strategies, and a different number of entry ORs **m**, then evaluate the effectiveness of the defense in the open world and real networks, respectively, and finally contrast this with other state-of-the-art defenses. In this section, we demonstrate the capability of our defense, SMART, successfully to reduce the accuracy of the most advanced WFP classifiers from over 98% to less than 12% without adding any padding packets and interval delay. Secondly, we focus on the reliability of this defense, and we show that using a redundant slice strategy increases the data recovery rate to close to 100% when using the same splitting strategy, compensating for the shortcoming of having to arrive sequentially to recover completed content. Thirdly, we test the network overhead of the entire scheme and can show that we provide a security boost to the Tor anonymous communication network at a very low overhead.
# Directional Support Vector Machines
## Appendix A Kernelized Symmetric Directional SVM  
### Kernel Validity: Proof of Theorem 1  
From the definition of \(g\), we may verify that it is an even function:  
\[g(-x)=\int_{0}^{T}h(t)h(t-x)\,dt=\int_{-x}^{T-x}h(x+\tau)h(\tau)\,d\tau=g(x). \tag{11}\]  
Because \(g\) is also periodic with period \(T\), it may be expressed in a Fourier series of the form:  
\[g(x)=\sum_{n=0}^{\infty}a_{n}\cos\left(\frac{2n\pi x}{T}\right). \tag{12}\]  
Thus,  
\[\begin{split} g(x-z)&=\sum_{n=0}^{\infty}a_{n}\cos \left(\frac{2n\pi(x-z)}{T}\right)\\ &=\sum_{n=0}^{\infty}a_{n}\cos\left(\frac{2n\pi x}{T}\right)\cos \left(\frac{2n\pi z}{T}\right)+\\ &+\sum_{n=0}^{\infty}a_{n}\sin\left(\frac{2n\pi x}{T}\right)\sin \left(\frac{2n\pi z}{T}\right)\\ &=\boldsymbol{\phi}(x)^{\intercal}\boldsymbol{\phi}(z),\end{split} \tag{13}\]  
where:  
\[\begin{split}\boldsymbol{\phi}(\cdot)&=\left(\sqrt{a _{0}},\sqrt{a_{1}}\cos\left(\frac{2\pi\cdot}{T}\right),\sqrt{a_{1}}\sin \left(\frac{2\pi\cdot}{T}\right),...,\right.\\ &\left.\left.\sqrt{a_{n}}\cos\left(\frac{2n\pi\cdot}{T}\right), \sqrt{a_{n}}\sin\left(\frac{2n\pi\cdot}{T}\right),...\right)^{\intercal}\end{split} \tag{14}\]  
Therefore, if \(\boldsymbol{\phi}(x)\) and \(\boldsymbol{\phi}(z)\) are real vectors, the product \(\boldsymbol{\phi}(x)^{\intercal}\boldsymbol{\phi}(z)\) is an inner product, and so, \(g(x-z)\) is a kernel function. Clearly, \(\boldsymbol{\phi}(x)\) and \(\boldsymbol{\phi}(z)\) are real vectors if and only if \(a_{n}\geq 0\), \(\forall\,n\), which can be proven to be true, concluding the proof:  
\[\begin{split} a_{0}&=\frac{2}{T}\int_{0}^{T}g(x) \,dx\\ &=\frac{2}{T}\int_{0}^{T}\int_{0}^{T}h(t)h(t+x)\,dt\,dx\\ &=\frac{2}{T}\int_{0}^{T}h(t)\int_{t}^{t+T}h(\tau)\,d\tau\,dt\\ &=\frac{2}{T}\left(\int_{0}^{T}h(t)\,dt\right)^{2}\geq 0,\end{split} \tag{15}\]and, for \(n\geq 1\),  
\[\begin{split} a_{n}&=\frac{2}{T}\int_{0}^{T}g(x)\cos \left(\frac{2n\pi x}{T}\right)dx\\ &=\frac{2}{T}\int_{0}^{T}\int_{0}^{T}h(t)h(t+x)\cos\left(\frac{2n \pi x}{T}\right)\,dt\,dx\\ &=\frac{2}{T}\int_{0}^{T}\int_{0}^{T}h(t)h(t+x)\cos\left(\frac{2n \pi(t+x)}{T}\right)\cos\left(\frac{2n\pi t}{T}\right)\,dt\,dx\\ &+\frac{2}{T}\int_{0}^{T}\int_{0}^{T}h(t)h(t+x)\sin\left(\frac{2n \pi(t+x)}{T}\right)\sin\left(\frac{2n\pi t}{T}\right)\,dt\,dx\\ &=\frac{2}{T}\int_{0}^{T}h(t)\cos\left(\frac{2n\pi t}{T}\right) \int_{t}^{t+T}h(\tau)\cos\left(\frac{2n\pi\tau}{T}\right)\,d\tau\,dt\\ &+\frac{2}{T}\int_{0}^{T}h(t)\sin\left(\frac{2n\pi t}{T}\right) \int_{t}^{t+T}h(\tau)\sin\left(\frac{2n\pi\tau}{T}\right)\,d\tau\,dt\\ &=\frac{2}{T}\left(\int_{0}^{T}h(t)\cos\left(\frac{2n\pi t}{T} \right)\,dt\right)^{2}\\ &+\frac{2}{T}\left(\int_{0}^{T}h(t)\sin\left(\frac{2n\pi t}{T} \right)\,dt\right)^{2}\geq 0.\end{split} \tag{10}\]
# Nationwide Flood Monitoring for Disaster Risk Reduction Using Multiple Satellite Data
## 5 Conclusions  
This study confirmed that time-series SfWi can be a key tool for pattern change analysis of spatio-temporal distribution of nationwide flood extent using time-series multiple satellite data. Many improvements should be made to fill the gap between data and algorithms as well as to achieve a rapid response of risk information for more accurate and efficient mega-flood change detection under the framework of nationwide flood monitoring. Through time-series monitoring of a flood event on the floodplain, it is possible to understand dynamic processes defining flood characteristics. Dynamic change in a flooded area can be integrated into physical hazard, exposure, and risk based on actual characteristics of spatio-temporal flood distribution. With near-real-time big data related to disasters, a nationwide flood monitoring and mapping system will help provide more credible risk information for stakeholders via web and mobile GIS applications not only to support evidence-basedpolicy making at local, regional, national, and international levels, but also to implement international cooperation for disaster preparedness and disaster risk reduction.  
This research was supported by the Japan Society for the Promotion of Science (JSPS) KAKENHI Grant-in-Aid for Scientific Research B: 15H05136. I thank [PERSON] for supporting an improved flood mapping algorithm and also thank [PERSON]. [PERSON] for sharing hydrological data.  
[PERSON] led this research as the principal investigator of Grant B: 15H05136. He designed the whole study from a conceptual framework to the development of a flood mapping algorithm using multiple satellite data.  
The authors declare no conflict of interest.