esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Model  
We consider an asynchronous distributed system having Byzantine processes which are processes that can misbehave [17]. A correct process behaves exactly as specified by the algorithm whereas a Byzantine process may deviate arbitrarily from its protocol by exhibiting arbitrary behaviour at any point during the execution. A Byzantine process cannot impersonate another process or spawn new processes.  
The distributed system is modeled as an undirected graph \(G=(P,C)\). Here, \(P\) is the set of processes communicating asynchronously in the distributed system. Let \(|P|=n\). \(C\) is the set of FIFO (logical) communication links over which processes communicate by message passing. A process is interchangably used with a node in the graph.  
The distributed system is asynchronous, i.e., there is no fixed upper bound \(\delta\) on the message latency, nor any fixed upper bound \(\psi\) on the relative speeds of processors [18].  
In this paper, we consider only distributed algorithms to solve various problems. A _distributed algorithm_ is one in which each process has access only to its local variables and incident edge parameters; its local variables are not accessible to any other process/node. Exchange of variable values can be carried out explicitly through message-passing. The adjacent process may be Byzantine and hence, information received from it can corrupt local variables.  
Let \(e_{i}^{x}\), where \(x\geq 1\), denote the \(x\)-th event executed by process \(p_{i}\). An event may be an internal event, a message send event, or a message receive event. Let the state of \(p_{i}\) after \(e_{i}^{x}\) be denoted \(s_{i}^{x}\), where \(x\geq 1\), and let \(s_{i}^{0}\) be the initial state. The _execution_ at \(p_{i}\) is the sequence of alternating events and resulting states, as \(\langle s_{i}^{0},e_{i}^{1},s_{i}^{1},e_{i}^{2},s_{i}^{2}\ldots\rangle\). The _execution history_ at \(p_{i}\) is the finite execution at \(p_{i}\) up to the current or most recent or specified local state. The _happened before_[5] relation, denoted \(\rightarrow\), is an irreflexive, asymmetric, and transitive partial order defined over events in a distributed execution that is used to define causality.  
**Definition 1**.: _The happened before relation \(\rightarrow\) on events consists of the following rules:_  
1. _Program Order:_ _For the sequence of events_ \(\langle e_{i}^{1},e_{i}^{2},\ldots\rangle\) _executed by process_ \(p_{i}\)_,_ \(\forall\)__\(x,y\) _such that_ \(x<y\)_, we have_ \(e_{i}^{x}\to e_{i}^{y}\)_._
2. _Message Order:_ _If event_ \(e_{i}^{x}\) _is a message send event executed at process_ \(p_{i}\) _and_ \(e_{j}^{y}\) _is the corresponding message receive event at process_ \(p_{j}\)_, then_ \(e_{i}^{x}\to e_{j}^{y}\)_._
3. _Transitive Order:_ _If_ \(e\to e^{\prime}\wedge e^{\prime}\to e^{\prime\prime}\)_, then_ \(e\to e^{\prime\prime}\)_._  
**Definition 2**.: _The causal past of an event \(e\) is denoted as \(CP(e)\) and is defined as the set of events in \(E\) that causally precede \(e\) under \(\rightarrow\)._
## 3 Our Approach
### Delay-Resilient Remote Memory Attestation
#### 3.3.6 Step 6: Determination  
The process of the determination consists of the following steps:  
* Step 6.1: If checksums are the same, the verifier will compute the time spent in the entire challenge response. \[\Delta T_{Verifier}=Tr_{Verifier}-Ts_{Verifier}\] (24) where \(Tr_{Verifier}\) is the time when the verifier receives the response and \(Ts_{Verifier}\) is the time when the verifier sends the challenge request.
* Step 6.2: The verifier receives the reports from the relay nodes and computes the delay on each hop on the path. \[\left\{\begin{array}{l}D_{1}=\frac{\Delta T_{1}-\Delta T_{Verifier}}{2},\\ D_{i}=\frac{\Delta T_{i}-\Delta T_{i-1}}{2}\qquad(1<i\leq n-1)\end{array}\right.\] (25) where \(\Delta T_{i}\) is the time difference reported by relay node \(i\).
* Step 6.3: After eliminating the exceptional data by the Pauta criterion [28], the average delay in the network can be estimated by the sample mean: \[\bar{d}=\frac{1}{n^{\prime}}\sum\limits_{i=1}^{n^{\prime}}D_{i},\qquad(\mid D _{i}-\bar{D}\mid\leq 3s)\] (26) where \(n^{\prime}\) is the number of samples after eliminating the exceptional data and \(\bar{D}\) is the sample mean.
* Step 6.4: According to the derived transmission delay, the verifier obtains the time spent on computing the checksum as follows: \[T^{\prime}_{checksum}=\Delta T_{Verifier}-n\cdot\bar{d}\] (27) where \(\Delta T_{Verifier}\) is the time difference between the challenge sent and the response received by the verifier.
* Step 6.5: The verifier computes the posterior probability and the conditional risk based on the minimum risk Bayesian classifier [28]. \[P(\upomega_{j}|\mathbf{x})=\frac{p(\mathbf{x}|\upomega_{j})P(\upomega_{j})}{\sum \limits_{i=1}^{2}p(\mathbf{x}|\upomega_{i})P(\upomega_{i})},\qquad j=1,2\] (28) \[R(c_{i}|\mathbf{x})=\sum\limits_{j=1}^{2},\uplambda(c_{i},\upomega_{j})P(\upomega _{j}|\mathbf{x}),\qquad j=1,2\] (29) where the observation \(\mathbf{x}\) is the difference between the received checksum computing time \(T_{checksum}\) and the verifier's computing time \(T^{\prime}_{checksum}\). The _a priori_ probability \(P(\upomega_{j})\) is the probability of the node being compromised, which can be determined by the compromised history of the network. The class conditional probability density \(p(\mathbf{x}|\upomega_{j})\) can be estimated by the response time distribution of both the compromised nodes and the normal nodes with the experiment. The decision function \(\uplambda(c_{i},\upomega_{j})\) is determined by the damage caused by the wrong decisions according to the importance of the nodes.  
* Step 6.6: Comparing the conditional risk \(R(c_{i}|\mathbf{x}),i=1,2\), the verifier will find the decision that minimizes the conditional risk, as follows: \[R(c|\mathbf{x})=\min_{i=1,2}R(c_{i}|\mathbf{x})\] (30) where \(c\) is the minimum risk Bayes decision, \(\mathbf{x}\) is the observation and the \(c_{i}\) is the corresponding decision. Then, the verifier can confirm whether the remote node is compromised according to \(c\).
## 3 Constrained Adaptive Tracking Control  
### Wiener Model Formulation for SOFC  
The Wiener model can approach the nonlinear function arbitrarily, so it is designed to identify the dynamic model. In some literature, the general Wiener model can be composed of the linear dynamic part and the nonlinear static part [32]; this structure chart of the Wiener model is depicted in Figure 4.  
For the SOFC control system (7), the linear dynamic block of the Wiener model can be defined as:  
\[x(k)=\frac{B(q^{-1})}{A(q^{-1})}u(k)+\frac{C(q^{-1})}{A(q^{-1})}d(k) \tag{15}\]  
where:  
\[A(q^{-1}) =1+a_{1}q^{-1}+a_{2}q^{-2}+\cdots+a_{n_{a}}q^{-n_{a}}\] \[B(q^{-1}) =b_{0}+b_{1}q^{-1}+b_{2}q^{-2}+\cdots+b_{n_{b}}q^{-n_{b}}\] \[C(q^{-1}) =c_{1}q^{-1}+c_{2}q^{-2}+\cdots+c_{n_{c}}q^{-n_{c}}\]  
Therein, \(a_{i}\), \(b_{j}\) and \(c_{l}\) denote the parameters of the orders, \(i=1,\cdots,n_{a}\), \(j=0,1,\cdots,n_{b}\) and \(l=1,\cdots,n_{c}\).  
In this paper, the polynomial forms are utilized to describe the dynamic characteristics of the nonlinear static block [33]. The polynomial function is defined as:  
\[y(k)=\sum_{m=1}^{p}\sigma_{m}x^{m}(k) \tag{16}\]  
where \(\sigma_{m}\) is the parameter of the nonlinear static block and \(m=1,\cdots,p\); therein, \(m\) and \(p\) are the power of the intermediate variable \(x(k)\) and the degree of the polynomial function, respectively.
## 3 Results and Discussion
### Effect of Biochar and Humic Acid on the Content, Characterization and Component of DOM in Soil  
#### 3.2.1 Soil DOM Content  
The effects of different additions of biochar and HA on DOM content in soil varied, with the results in Figure 3 indicated significant increases in DOM content by 44.2%, 88.45%, 69.22%, and 103.83% (\(p<0.05\)), respectively, in treatments YB3, YB6, YB3H, and YB6H compared to treatment Y. Specifically, the DOM contents in YB6 and YB6H treatments were as high as 8.56 mg L\({}^{-1}\) and 9.25 mg L\({}^{-1}\), respectively. The results indicated that the DOM content in the soil increased with both the addition of biochar and HA. However, biochar had a more significant effect on enhancing the soil DOM content than HA. Some studies have shown that biochar can release DOM in soil, which is the main reason for increasing soil DOM content [45; 46].
# SMYOLO: Lightweight Pedestrian Target Detection Algorithm in Low-Altitude Scenarios
## 3 Proposed Method
### Low-altitude Pedestrian Detection Network Architecture: SMYOLO
#### 3.1.1 Depth Separable Convolution and Point Convolution  
SMYOLO is formed by stacking with depth separable convolution while using point convolution to increase the depth of the network structure, making the SMYOLO network \"narrow and deep\". In conventional convolution, assuming that the input feature dimension is \((H_{W},H_{W},M)\), the convolution kernel is \((N,H_{X},H_{X},M)\), and the output feature map is \((H_{K},H_{K},M)\). The calculation process is shown in the Figure 3.  
The amount of calculation required for conventional convolution to complete this process is shown in Equation (1).  
\[Calculation_{comp}=H_{K}\times H_{K}\times H_{X}\times H_{X}\times N\times M \tag{1}\]  
Figure 2: Low-altitude pedestrian detection algorithm flow chart.  
In-depth separable convolution is composed of depth convolution (Depthwise) and point convolution (Pointwise). Deep convolution works on each convolution channel, and point convolution integrates the convolution output of each channel. The calculation process is shown in Figure 4.  
SMYOLO's depth separable convolution requires the amount of calculation to complete this process is shown in Equation (2).  
\[Calculation_{dw,env}=H_{K}\times H_{K}\times H_{X}\times H_{X}\times M+H_{K} \times H_{K}\times N\times M \tag{2}\]  
The SMYOLO compression ratio is the ratio of the parameter amount of the conventional convolution to the parameter amount of the depth separable convolution, namely:  
\[Ratio=\frac{H_{K}\times H_{K}\times H_{X}\times H_{X}\times M+H_{K}\times H_{ K}\times N\times M}{H_{K}\times H_{K}\times H_{X}\times H_{X}\times N\times M}= \frac{1}{N}+\frac{1}{{H_{X}}^{2}} \tag{3}\]  
Figure 4: Depth separable convolution calculation process.  
Figure 3: Conventional convolution calculation process.  
It can be seen from the above formula that the parameter amount of the depth separable convolution is less than that of the conventional convolution, so replacing the conventional convolution with the depth separable convolution can reduce the amount of calculation to a certain extent. The basic structure of the deep separable convolutional network is shown in Figure 5. It will use deep convolution (Depthwise) and point convolution (Pointwise) instead of conventional convolution. In the SMYOLO target detection model, we not only replaced the conventional convolution in the head module of YOLOv4 with a depth separable convolution, but also modified the 3 \(\times\) 3 conventional convolution in the PANet module in YOLOv4. We replaced it with depth separable convolution, which will reduce the number of parameters to a certain extent and thus reduce the amount of calculation deployed on drones' onboard equipment.
# Efficient Opportunistic Routing Protocol for Sensor Network in Emergency Applications
## 3 Proposed Opportunistic Routing Protocol  
The proposed opportunistic density cluster-based routing (ODCR) protocol uses density-based clustering as well as the opportunity to forward or broadcast its stored data to the nearby or closest node to the destination, as the nodes are mobile and may loss the network connectivity. The following subsections present the proposed ODCR protocol. Section 3.2 defines some terminologies that are used in the proposed ODCR protocol. Sections 3.2-3.4 present different phases of the ODCR protocol, such as cluster formation, routing and cluster restoration. Figure 2 illustrates the flowchart of the working principle of the proposed ODCR protocol.
# Navigating the Maps: Euclidean vs. Road Network Distances in Spatial Queries
## 2 Related Work
### Spatial Queries in Road Network Distance
#### 2.2.2 Range Queries in Road Network Distance  
In the paper by [16], it is asserted that range queries, rooted in defining a region according to a specified radius or distance, can be more broadly and flexibly referred to as \"region queries\". These queries involve three essential components: the object of interest, the query point (representing the user's query location), and the designated region. [PERSON] and [PERSON] [16] categorized region queries into six types: traditional region queries, approximate region queries, constrained region queries, outer/inner fence object queries, and inverse range queries.  
Traditional regions establish boundaries to extract candidate objects. Approximate region queries address situations where users may inaccurately specify the query radius due to unfamiliarity with the area. A constrained region query incorporates additional constraints, such as spatial, temporal, kNN, or others, along with the query radius. Clustered objects region queries consider objects with spatial relationships, thereby aiming to identify clusters or groups of objects within a specified region. Inverse range queries involve multiple query points or objects and seek candidate objects that contain all query points within a specified range.  
Regarding range queries in road networks, two approaches are identified: restriction and extension [19]. The restriction approach for range queries is called range Euclidean restriction (RER). This method conducts a range query to find objects within a specified Euclidean distance from the query point. While maintaining the Euclidean lower bound property, it reduces false hits by performing a single network expansion. The algorithm efficiently refines results using sorted lists, segment checks, and comparisons, thereby considering boundary segments that may exceed the query threshold. On the other hand, the expansion approach for range queries is called range network expansion (RNE). This algorithm identifies qualifying segments within a specified network range from the query point and retrieves associated data entities. It optimizes by dividing qualifying segments (QSs) into sets corresponding to R-tree entries, thereby minimizing comparisons as it descends the tree. The algorithm ensures I/O optimality and handles scenarios where the QSs exceed memory capacity through efficient methodologies and optimizations.  
A study in 2.1.2 [17] explored the continuous range search within mobile navigation, thus explicitly focusing on user mobility. The study proposed a second approach emphasizing network distance over Euclidean distance, thereby aiming for enhanced efficiency in practical applications. The outlined method involves selecting an arbitrary path from a road map, segmenting it, and utilizing range network expansion (RNE) to identify entities within a specified range while considering Euclidean distance.
# Methods for Assessing Human-Machine Performance under Fuzzy Conditions
## 6 Grey System Theory
### Grey Numbers  
A _Grey Number (GN)_ is a number with known range but unknown position within its boundaries. If \(R\) denotes the set of real numbers, a GN A can be expressed mathematically by  
\[\text{A}\in[\text{a},\text{b}]=\{\text{x}\in R\text{: a }\leq\text{x}\leq\text{b}\}.\]  
If a = b, then A is called a _white number_ and if \(\text{A}\in(-\infty.+\infty)\), then A is called a _black number_. A GN may enrich its uncertainty representation with respect to the interval [a, b] by a whitening function g: [a, b] \(\rightarrow\) [0, 1] defining a _degree of greyness_ g(x) for each x in [a, b]. The closer is g(x) to 1, the greater the probability for x to be the representative real value of the corresponding GN.  
The well-known arithmetic of the real intervals [39] has been used to define the basic arithmetic operations among the GNs. More explicitly, if \(\text{A}\in[\text{a}_{1},\text{a}_{2}]\) and \(\text{B}\in[\text{b}_{1},\text{b}_{2}]\) are given GNs and k is a real number, one defines:  
* _Addition_ by \(\text{A}+\text{B}\in[\text{a}_{1}+\text{b}_{1},\text{a}_{2}+\text{b}_{2}]\).  
* _Subtraction_ by \(A-B=A+(-B)\in[a_{1}-b_{2},a_{2}-b_{1}]\), where \(-B\in[-b_{2},-b_{1}]\) is defined to be the _opposite_ of B.
* _Multiplication_ by \(A\times B\) [\(min\{a_{1}b_{1},a_{1}b_{2},a_{2}b_{1},a_{2}b_{2}\}\), \(max\{a_{1}b_{1},a_{1}b_{2},a_{2}b_{1},a_{2}b_{2}\}\)].
* _Division_ by \(A\): \(B=A\times B^{-1}\in[min\{\frac{a_{1}}{b_{1}},\frac{a_{1}}{b_{2}}\frac{a_{2}}{b_{1 }},\frac{a_{2}}{b_{2}}\}\), \(max\{\frac{a_{1}}{b_{1}},\frac{a_{1}}{b_{2}}\frac{a_{2}}{b_{1}},\frac{a_{2}}{b _{2}}\}]\). with \(b_{1}\), \(b_{2}\
eq 0\) and \(B^{-1}\in[\frac{1}{b_{2}},\frac{1}{b_{1}}]\), which is defined to be the _inverse_ of B.
* _Scalar multiplication_ by \(kA\in[ka_{1},\,ka_{2}]\), if \(k\geq 0\) and by \(kA\geq[ka_{2},\,ka_{1}]\), if \(k<0\).  
The white number whose value represents the GN \(A\in[a,b]\) is denoted by \(W(A)\). The process of determining \(w(A)\) is called _whitening of \(A\)_. When the distribution of \(A\) is unknown, i.e., no whitening function has been defined for \(A\), one usually utilizes the equally distant whitening by taking  
\[W(A)=\frac{a+b}{2}. \tag{16}\]  
For general information on GNs, we refer to [40].
## 1 Introduction
Halloysite is an aluminosilicate clay Al\({}_{2}\)O\({}_{3}\)-2 SiO\({}_{2}\)-nH\({}_{2}\)O, with a naturally aluminosilicate rolling numerous nanotubes formed. Those nanotubes have an average length of 0.5-2 \(\upmu\)m and diameter of 50-80 nm, in addition these nanotubes have hollow lumen with an average diameter of 10-15 nm [20]. Halloysite nanotubes (HNTs) are cyto- and bio-compatible [21]. They have attracted increasing attention in biomedical research due to its physicochemical stability, the potential for chemically modification, and ease of doping substances within its lumen, including therapeutic agents [22; 23; 24; 25], enzymes [26], nucleic acid [27], and metal nanoparticles [28]. In addition, HNTs have been proven to enhance mechanical properties for numerous materials, such as alginate [26], chitosan [29] epoxy [30], nylon [25], rubber [31], and calcium phosphate [32]. Furthermore, HNTs have also been reported to chemotactically attract pre-osteoblasts [33] and enhance osteogenic differentiation [32; 34; 35]. Here, we used halloysite due to its known ability to improve polymer material properties and release bioactive agents in a sustained manner. HNTs were loaded with zinc nanoparticles. Zinc is one of the essential minerals that play an essential role in bone health. It affects multiple enzyme activities, collagen synthesis [36], and DNA synthesis [37], and it has been demonstrated to stimulate bone metabolism [38]. Zinc oxide nanoparticles are also a known and potent agent, that disintegrates bacterial cell membranes and accumulates in the cytoplasm leading to apoptotic cell death [39; 40]. Therefore, zinc was selected as a coating for HNTs and then mixed with PLA for 3D printing. Fetal bovine serum (FBS) and NaOH were used to improve the surface hydrophilicity of a 3D printed scaffold. Scaffold mechanical properties and cell-material interactions were studied. We also coated the 3D printed scaffold with an antibiotic, gentamicin, to prevent contamination and assessed the drug efficiency after three weeks. This study aims to generate a 3D printed scaffold to support bone regeneration and prevent bacterial contamination, which may be potentially used for bone defect therapy in the clinic.
## 4 Evaluation and Analysis  
### Datasets  
To evaluate the performance of the proposed system in response to the appearance-changing conditions for LCD, we chose three widely used datasets as the test set (i.e., Gardens Point [50], Nordland [51], and St. Lucia [52] datasets). The details of these three datasets are shown in Table 2, and the appearance variations of some examples are shown in Figure 6.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & \begin{tabular}{c} **Image Resolution** \\ \& **Frequency** \\ \end{tabular} & **Image Type** & **Environment** & \
\begin{tabular}{c} **Appearance** \\ **Variation** \\ \end{tabular} &
\begin{tabular}{c} **Viewpoint** \\ **Variation** \\ \end{tabular} \\ \hline Gardens Point [50] & 1920 \(\times\) 1080, 30 Hz & RGB & Campus & Day-Night & Moderate \\ Nordland [51] & 1920 \(\times\) 1080, 25 Hz & RGB & Train journey & Winter-Summer & Small \\ St. Lucia [52] & 640 \(\times\) 480, 15 Hz & RGB & Suburban & Morning-Afternoon & Moderate \\ \hline \hline \end{tabular}
\end{table}
Table 2: Description of the Testing Loop Closure Detection Datasets with Changing Environmental Conditions.  
The details of the datasets are as follows:  
Gardens Point dataset is collected on the campus of Queensland University of Technology while crossing the walkways during the day (along both sides) and at night (along the right side only). The day-right and night-right pairs are adopted as DAY and NIGHT to test the performance under significant illumination variation. The ground truth is created manually according to the location.  
Nordland dataset is produced from a TV documentary that chronicles a train journey covering the changing appearance of four seasons. After arranging images from different seasons following their positions, the ground truth can be constructed based on image indexes. Images taken in winter and summer (denoted as WINTER and SUMMER) that contain significant variations in appearance are selected as part of the test set.  
St. Lucia dataset is captured in the suburbs at five different times (8:45, 10:00, 12:10, 14:10, and 15:45), with significant appearance changes due to the illumination changes. The ground truth is obtained from GPS logs. For the intense contrasts, SL0845 and SL1410 (collected at 8:45 and 14:10) are selected to form the test set.