esa-sceva/satcom-chunk-collection huggingface dataset:
# Fault Tolerant DHT-Based Routing in MANET
## 5 Simulation and Results Analysis
### Normalized Overhead  
Normalized overhead defines network scalability and shows transmission overhead per lookup request successfully entertained. In general, the operations carried out by a protocol produce overhead, but factors such as network size, mobility, etc., are directly associated.  
Therefore, we provide an overhead lookup comparison using varying network sizes and nodes' moving speed. As shown in Figure 9, FTDN gains are significant as compared to VCP and DART (the Figure 9a-d show the results at speed 0.5 m/s, 1.0 m/s, 1.5 m/s and 2 m/s respectively). The dominant factors in overhead reduction are the dynamic replication of mapping information and alternate paths avoiding faulty nodes. These measures make possible resolutions of lookups with minimum possible overhead in the presence of faulty nodes (i.e., AN failures, network partition) since nodes can find mapping data at nearby replicas despite forwarding lookup requests too far away distant ANs. However, the case is different in VCP and DART; for example, deploying and locating replicas (i.e., ring search) in VCP need extra transmissions and cause more overhead. Similarly, in DART, lookup queries are resolved far away from actual AN (i.e., no replication); that is why DART produces more overhead than FTDN. Moreover, growing network size and increasing mobility produce more overhead in all protocols, but FTDN still performs well than VCP and DART. A slight increase in FTDN, with an increase in mobility, is due to the recurrent execution of LS recovery, LIDs reassignment, and merging processes; similarly, growing network size increases an average number of hope counts to reach AN; eventually, this increases overhead per lookup request. However, this impact is lower in FTDN as compared to VCP and DART as depicted in Figure 9.  
Figure 8: E2E delay results, as boxplots, against different node moving speeds with varying network sizes.  
Boxplot analysis comparison, with varying network sizes and speed, shown in Figure 10, shows consistent gains in FTDN as compared to VCP and DART.  
Figure 9: Normalized overhead as a function of network size and speed.
## 3 Results and Discussions
### Analysis of Petrochemical Excess Sludge Disposal
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Element**} & \multirow{2}{*}{**Dried Cq Sludge (mg/kg)**} & \multirow{2}{*}{**Cq Sludge Ash (mg/kg)**} & \multicolumn{3}{c}{**Limit Value**} \\ \cline{5-6}  & & & **Land Improvement [51]** & **Mixed Landfill Mud [50]** & **Greening Mud [52]** \\ \hline Be & 0.877 & N.D. & — & — & — \\ B & 8.35 & 28.23 & 100 & — & 150 \\ Al & 643.48 & 15,344.93 & — & — & — \\ Sc & 2.65 & 2.73 & — & — & — \\ Ti & 290.439 & 1065.99 & — & — & — \\ V & 34.274 & 122.62 & — & — & — \\ Cr & 24.71 & 96.24 & 600 & 1000 & 600 \\ Mn & 164.189 & 518.72 & — & — & — \\ Fe & 6060.09 & 2404.98 & — & — & — \\ Co & 4.824 & 18.86 & — & — & — \\ Ni & 41.61 & 150.44 & 100 & 200 & 100 \\ Cu & 18.33 & 67.70 & 800 & 1500 & 800 \\ Zn & 628.958 & 2300.92 & 2000 & 4000 & 2000 \\ Ge & 1.345 & 4.08 & — & — & — \\ As & 86.723 & 301.03 & 75 & 75 & 75 \\ Se & 598.41 & 719.43 & — & — & — \\ Mo & 3.427 & 13.20 & — & — & — \\ Sb & 53.207 & 175.39 & — & — & — \\ Ag & 0.188 & 0.56 & — & — & — \\ Cd & 0.287 & 0.99 & 5 & 20 & 5 \\ Hg & 3.452 & 0.27 & 5 & 25 & 5 \\ Pb & 9.715 & 38.69 & 300 & 1000 & 300 \\ Th* & 0.50 & 1.77 & — & — & — \\ U* & 1.59 & 5.90 & — & — & — \\ \hline \hline \end{tabular} Note * radiouclides  
\end{table} 
Table 5: Element content of Cq sludge sample.
## 2 Modeling of UAV-Assisted Communication
### UAV Air-to-Ground Channel Model
The UAV and valid moving ground users are constrained in the area of earthquake-stricken cells, which means one user turns invalid as soon as he/she runs out of the exit. When optimizing the trajectory, the height of UAV is fixed in each experiment. The FDMA (frequency division multiple access) technique is applied; thus, there is no interference between ground users and between ground users to drones. To build up this UAV-assisted communication model, specifically defined parameters, formulas, and functions are introduced as follows. Briefly speaking, air-to-ground communication quality is used as an evaluation index and expressed in terms of system throughput. Communication throughput depends on the arrival rate of bi-directional links. Therefore, transmission power of uplink and downlink channels in A2G communication can be calculated, followed by the corresponding SINR (Signal to Interference plus Noise Ratio). \(P_{ug}\) denotes received power at ground users in downlink communication, with \(P_{gu}\) defined for received power at UAV in uplink mode. Taking the link parameter into consideration, \(P(u,g_{j})\) describes transmission power between link \(j\), in which \(u\) refers to UAV and \(g_{j}\) refers to \(j^{th}\) ground user. The probabilistic LoS/NLoS space power propagation model is clarified in (3) and (4), which defines transmission power \(P\), received gain \(G\), euclidean distance \(d\), and environmental loss parameter \(k_{0}\). \(P_{LoS}\) and \(P_{NLoS}\) refer to the occurrence possibility of LoS and NLoS channel, while \(\phi_{LoS}\) and \(\phi_{NLoS}\) refer to the corresponding shadow parameters. \[P_{ug}(u,g_{j})=\frac{P_{u}G(d_{j})}{(k_{0}d_{ij})^{n}(p_{LoS}\phi_{LoS}+p_{NLoS }\phi_{NLoS})}. \tag{3}\]  
\[P_{gu}(u,g_{j})=\frac{P_{g}G_{0}}{(k_{0}d_{ij})^{n}(p_{LoS}\phi_{LoS}+p_{NLoS }\phi_{NLoS})}. \tag{4}\]  
\[SINR_{ug}=\frac{P_{ug}(u_{i},\epsilon_{j})}{\sigma^{2}},SINR_{gu}=\frac{P_{gu} (u_{i},\epsilon_{j})}{\sigma^{2}}. \tag{5}\]  
Finally, the throughput is calculated through [PERSON]'s law as follows, as the considered communication occurs in the channel with AWGN (Additive White Gaussian Noise). \[C=B\times log_{2}(1+SINR). \tag{6}\]
## 4 Methodology  
In this section, the adopted methodology for short-term load forecasting using the different models is discussed. Figure 8 shows the basic flow of the methodology used for forecasting. The method includes four major steps: (1) the collection of data, (2) the analysis and selection of the data and factors affecting load, (3) model development using time series data for a time series model, and (4) FF-ANN result evaluation and model adjustment. The historic data were thoroughly analyzed to identify the factors affecting the change in demand as discussed in Section 2. The selected data were used to develop the model with multiple iterations and adjustments in the parameters of the model. In our case,  
Figure 6: Correlation with the previous six load lags.  
Figure 7: Correlation plot for 30 lag days.  
the data were studied and selected for the time series model and compared with a final FF-ANN model. Each model was tested for accuracy and made acceptable by adjusting the model parameters under unacceptable scenarios.  
The electricity demand data were observed every hour, and hence there were 24 observations in a day. With the collection of one year of data (i.e., 8760 total observations) as discussed in Section 2, the data were subdivided into 365 sets for each day. Based on analysis performed on the data, they showed a high correlation with the lag load, as benchmark forecasting was performed using various conventional time series methods and a comparative analysis conducted with the proposed ANN model. Initially, the data were fed to conventional time series models and forecasting was performed for two different days: weekends and weekdays. The load showed good correlation with the lag data from Figure 6. Hence, models such as the moving average, weighted moving average, exponential smoothing, and [PERSON]'s model, which used lag relation, were used to set the benchmark for our model.
# BA-YOLO for Object Detection in Satellite Remote Sensing Images
## 1 Introduction
ACmix combines the advantages of CNNs and transformers, allowing it to simultaneously capture local and global semantic information. Convolutional layers can capture local features, while self-attention can capture global dependencies. ACmix can handle both local and global semantic information, so we replaced the C2f module in the YOLOv8 backbone network with C2 fAC, thereby improving the performance of remote sensing image object detection tasks. The modified YOLOv8 is named BA-YOLO. Experimental results demonstrate that BA-YOLO, proposed by us, surpasses the performance of standard YOLOv8 in detecting targets in the DOTA dataset. Contributions:  
1. Designed a new feature fusion layer that combines the advantages of BiFPN. 2. Added the C2 fAC module to enhance the model's feature extraction capability. 3. Implemented effective training strategies, including multi-scale training and testing, and data augmentation. 4. Achieved a mean average precision (mAP) of 0.722 on the DOTA dataset.
# A Coupled Darcy-Forchheimer Flow Model in Fractured Porous Media
## 2 Materials and Methods
### Seepage Equations in Fracture  
Under a high hydraulic gradient, groundwater flows faster in fractures, which can be described by Forchheimer's law. The relationships between the pressure gradient and the flow velocity of the fluid in the fracture are as follows [30],  
\[(1+\beta|\mathbf{u}_{f}|)\mathbf{u}_{f}=-\frac{k_{f}}{\mu}\
abla p_{f} \tag{3}\]  
\[\
abla\mathbf{u}_{f}=q_{f} \tag{4}\]  
where \(\mathbf{u}_{f}\) is flow velocity in the rock fracture, \(k_{f}\) is the permeability coefficient of the rock fracture, \(p_{f}\) is the fluid pressure of the rock fracture, \(q_{f}\) is the fluid source of the rock fracture, and \(\beta\) is the Forchheimer coefficient.  
To consider the flow exchange of the porous media and the fractured media, the exchange function \(\Psi\) is introduced to show their mutual effect. The strength of flow exchange between porous media and fractured media is related to fluid characteristics and fracture connectivity. Therefore, the exchange function of pressure can be defined as follows:  
\[\Psi^{fm}=CI\times\frac{k_{m}}{\mu}\times\frac{p_{f}-p_{m}}{L} \tag{5}\]  
\[\Psi^{fm}=CI\times\frac{k_{f}}{\mu}\times\frac{p_{m}-p_{f}}{A} \tag{6}\]where \(CI\) is the connectivity of the fractures, and \(L\) and \(A\) are the area (length) of the fracture and the volume (area) of the pore, respectively. Connectivity \(CI\) was defined by [31] and expressed as,  
\[CI_{ij,k}=\frac{A_{ij,k}}{\overline{d}_{ij,k}} \tag{7}\]  
where \(A_{ij,k}\) is the total length of fracture \(k\) in pore matrix element \(ij\), and the average distance between fracture element \(k\) and pore matrix element \(ij\), as shown in Figure 1.  
\[CI_{ij,k}=\frac{A_{ij,k}}{\overline{d}_{ij,k}} \tag{8}\]  
\[\overline{d}_{ij,k}=\frac{\int\int_{k}dA}{A_{ij}} \tag{9}\]  
By expanding this formula, the double integral expression is obtained,  
\[\overline{d}_{ij,k}=\frac{\iint\sqrt{\left(x^{\prime}-x^{\prime}{}_{ko}\right) ^{2}+\left(y^{\prime}-y^{\prime}{}_{ko}\right)^{2}}dx^{\prime}dy^{\prime}}{ \iint dx^{\prime}dy^{\prime}} \tag{10}\]  
where, \(\left(x^{\prime}{}_{ko},y^{\prime}{}_{ko}\right)\) is the central coordinate of fracture element \(k\) in the local coordinate system. Equation (10) generally has analytical expressions in a small part of cases, while most cases must be solved by numerical method. [PERSON] et al. (2011) [31] gave analytic expressions in some cases.  
By substituting the pressure exchange function into Equations (1)-(4), the governing equations of pore and fracture seepage can be obtained,  
\[q_{m}+\
abla(\frac{k_{m}}{\mu}\
abla p_{m})+\Psi^{mf}=0 \tag{11}\]  
\[\
abla\frac{k_{f}}{\mu}\
abla p_{f}+(1+\beta|\mathbf{u}_{f}|)q_{f}+\Psi^{fm}=0 \tag{12}\]  
Equations (11) and (12) describe Darcy flow in porous media and Forchheimer flow in fractured media under high water pressure. The relations to two flows use the pressure exchange function.  
Figure 1: Schematic diagram of fractured porous media.
# The Debris Flow Risk Prediction Model Based on PCA-Elman
## 2 Materials and Methods
### Pca  
The core concept of Principal Component Analysis (PCA) is to employ orthogonal transformation to recombine correlated debris flow risk assessment indicators into a new set of linearly independent composite indicators, known as principal components. This method effectively captures most of the information contained in the original data while reducing the dimensionality of the debris flow sample data, making it highly suitable for studies involving multiple correlated evaluation indicators, such as debris flow risk prediction models [15]. Among all extracted principal components, the first principal component has the largest variance, with each subsequent component's variance decreasing. During analysis, one can select principal components with larger variances to replace the indicators in the original sample data based on the magnitude of variance and the cumulative contribution rate, thereby gaining an intuitive understanding of the amount of  
Figure 1: Spatial Geographic Location and Terrain Slope of Yunnan Province.  
information contained in the original evaluation indicators within the composite indicators. In this study, the Principal Component Analysis function in IBM SPSS Statistics 29 software was utilized to dimensionally reduce the original debris flow risk assessment indicators and synthesize new linearly independent composite indicators, which were then used as input nodes for the Elman Neural Network to predict the risk levels of debris flows.
# Backdoor Attacks on Deep Neural Networks via Transfer Learning from Natural Images
## 2 Materials and Methods
### Transfer Learning
#### 2.2.1 Pneumonia Classification from Chest X-ray Images  
This task comprised detecting pneumonia cases from chest X-ray images. We obtained a dataset of chest X-ray images classified as normal or exhibiting pneumonia from previous studies [3; 10]. The dataset consisted of 1800 training images (900 images per class) and 540 testing images (270 images per class).
## 5 Approximately Optimal Steiner Tree Algorithm
### Shortest-Path Forest  
After the APSP distances and routing tables are found, we construct a _shortest-path forest_ (SPF) based on the terminals of the Steiner tree.  
**Definition 9**: _(Shortest-path forest): For a weighted, undirected graph \(G=(V,E,W)\) together with a given set of terminal nodes \(Z=\{z_{1},\ldots,z_{k}\}\), a subgraph \(F=(V,E_{F},W)\) of \(G\) is called a shortest-path forest if it consists of \(|Z|\) disjoint trees \(T_{z}=(V_{z},E_{z},W)\) satisfying_  
1. \(z_{i}\in T_{z_{j}}\) _if and only if_ \(i=j\)_, for_ \(i,j\in[k]\)_._
2. _For each_ \(v\in Z_{i},d_{G}(v,z_{i})=\min_{z\in Z}d_{G}(v,z)\)_, and a shortest path connecting_ \(v\) _to_ \(z_{i}\) _in_ \(G\) _is contained in_ \(T_{z_{i}}\)__3. _The_ \(V_{z_{i}}\) _form a partition of_ \(V\)_, and_ \(E_{z_{1}}\cup E_{z_{2}}\cdots\cup E_{z_{k}}=E_{F}\subset E\)__  
In other words, an SPF is a forest obtained by gathering, for each node, a shortest path in \(G\), connecting it to the closest Steiner terminal node.  
For a node \(v\) in a tree, we will let \(par(v)\) denote the parent node of \(v\) in that tree, \(s(v)\) the Steiner terminal in the tree that \(v\) will be in, and \(ID(v)\in[n]\) the ID of node \(v\in V\). Let \(\mathcal{Q}(v):=\{z:d_{G}(v,z)=\min_{z\in\mathcal{Z}}d_{G}(v,z)\}\) be the set of Steiner terminals closest to node \(v\). We make use of the following procedure for the SPF:  
DistributedSPF  
Input: For each node \(v\in G\), APSP distances and the corresponding routing table \(R_{v}\).  
Output: An SPF distributed among the nodes.  
Each node \(v\) sets \(s(v):=\operatorname*{argmin}_{z\in\mathcal{Q}(v)}ID(z)\) using the APSP information.  
Each node \(v\) sets \(par(v):=R_{v}(s(v))\), \(R_{v}\) being the routing table of \(v\), and sends a message to \(par(v)\) to indicate this choice. If \(v\) receives such a message from another node \(u\), it registers \(u\) as its child in the SPF.  
Step 1 in DistributedSPF requires no communication since each node already knows the shortest-path distances to all other nodes, including the Steiner terminals, meaning it can be executed locally. Each node \(v\) choosing \(par(v)\) in step 2 can also be conducted locally using routing table information and, thus, step 2 requires 1 round of communication in \(n-|Z|\) classical messages, since all non-Steiner nodes send one message.  
**Claim 2**: _After executing the DistributedSPF procedure, the trees \(T_{z_{k}}=(V_{z_{k}},E_{z_{k}},W)\) with \(V_{z_{k}}:=\{v\in V:s(v)=z_{k}\}\) and \(E_{z_{k}}:=\{v,par(v)\}:v\in V_{z_{k}}\}\) form an SPF._  
(i) holds since each Steiner terminal is closest to itself; (iii) is immediate. To see that (ii) holds, note that for \(v\in V_{z_{k}}\), \(par(v)\in V_{z_{k}}\) and \(\{v,par(v)\}\in E_{z_{k}}\) as well. Then, \(par(par(\ldots par(v)\ldots))=z_{k}\) and the entire path to \(z_{k}\) lies in \(T_{z_{k}}\).  
Hence, after this procedure, we have a distributed SPF across our graph, where each node knows its label, parent, and children of the tree it is in.
# An Event-Driven Serverless ETL Pipeline on AWS
## 6 Discussion  
### Reliability  
The official AWS documentation states that S3 events will most likely deliver an event once, but it is possible that an event will be delivered multiple times, or not at all. The documentation does not clearly quantify how often this may occur and, in order to cater for this, we recorded any such occurrences through the experiments. Judging by our results, S3 delivered the event notification successfully 100% across all experiments (71,280 events). Nonetheless, we noticed two unexplained failed invocations when transferring a total of 100 MB at 1 event per minute using 128 MB of lambda memory (See Figure 3). The functions logs indicated that those functions timed-out when attempting to send the output data chunks to writer SQS queue. Although it is not clear why this time-out occurred, it is likely that this could have been a minor outage of that particular SQS queue, which leads to the conclusion that it is always necessary to establish failover strategies for production environments. With our proposed architecture, this was picked up by the triggering dead letter queue.
## 2 Literature Review
### Fundamental Limit on Minimum Supply Voltage  
According to the analysis in Section 2.1, a minimal supply voltage exists when an inverter loses its binary switching capability. Many research papers [15; 16; 17; 18; 19; 20] have focused on this issue. Based on [PERSON]'s fundamental limit [16], this minimum operation voltage \(V_{dd\_limit}\) is given as follows in (2):  
\[V_{dd\_limit}=2V_{T}\times\ln[1+S_{S}/\left(\ln 10\times V_{T}\right)] \tag{2}\]  
where \(V_{T}\) is the thermal voltage and \(S_{S}\) is the sub-threshold slope.  
To obtain a more intuitive understanding, \(S_{S}\) is affected by the channel depletion region capacitance per unit area (\(C_{d}\)) and the gate oxide capacitance per unit area (\(C_{ox}\)), as indicated in (3):  
\[S_{S}=\ln 10\times V_{T}\times(1+C_{d}/C_{ox}) \tag{3}\]  
For a more advanced process node, as the depth of the gate oxide layer is thinner, \(C_{ox}\) is larger and the corresponding \(S_{S}\) is smaller. Thus, the circuits implemented in an advanced process node can work under a lower supply voltage regardless of the leakage current. It is worth noting that, for a certain process node, \(C_{ox}\) is fixed and \(V_{T}\) is a physical constant. Thus, only \(C_{d}\) is controlled by the biasing voltage.  
However, the leakage current is inevitable. Thus, with the reduction of VDD, the swing of the output signal will be deteriorated due to the decreased ratio of \(i_{on}\)/\(i_{off}\). For the large signal analysis of a general inverter, during the low-to-high transition at the output node, the final output voltage (\(V_{H}\)) is decided by the leakage current. Assume that the on-resistance of PMOS is \(R_{on}\), which is inversely proportional to the on-current \(i_{on}\), and the off-resistance of NMOS is \(R_{off}\), which is inversely proportional to the off-current \(i_{off}\). Thus, \(V_{H}\) at a fully charged condition can be expressed as in (4):  
\[V_{H}=\frac{R_{off}/R_{on}}{1+R_{off}/R_{on}}\times V_{DD}\propto\frac{i_{on}/ i_{off}}{1+i_{on}/i_{off}} \tag{4}\]  
Similarly, during the high-to-low transition at the output node, the final voltage (\(V_{L}\)) at a fully discharged condition is obtained as in (5):  
\[V_{L}=\frac{1}{1+R_{off}/R_{on}}\times V_{DD}\propto\frac{1}{1+i_{on}/i_{off}} \tag{5}\]  
According to (4) and (5), it is obvious that the swing of the output voltage (\(V_{swing}=V_{H}-V_{L}\)) is influenced by the ratio of \(R_{off}\)/\(R_{on}\) or \(i_{on}\)/\(i_{off}\). Larger \(R_{off}\)/\(R_{on}\) or \(i_{on}\)/\(i_{off}\) indicates an increased V\({}_{\text{swing}}\). However, with the reduction of VDD and the scaling down of process, both \(R_{off}\) and \(i_{on}\) decrease, and both \(R_{on}\) and \(i_{off}\) increase; thus, \(R_{off}\)/\(R_{on}\) or \(i_{on}\)/\(i_{off}\) becomes smaller. Finally, \(V_{swing}\) also shrinks.  
Therefore, to maximize the output voltage swing, \(R_{off}\)/\(R_{on}\) or \(i_{on}\)/\(i_{off}\) is required to maximize. As the influence of \(R_{off}\)/\(R_{on}\) and \(i_{on}\)/\(i_{off}\) on \(V_{swing}\) is almost equivalent, \(i_{on}\)/\(i_{off}\) is used in the following discussion to simplify the analysis.
# Comprehensive Evaluation on an ID-Based Side-Channel Authentication with FPGA-Based AES
## 5 Evaluation of the Authentication Speed  
In this section, we describe the evaluation of the authentication speed using our experiment setup. Table 2 represents the difference in authentication time between the straightforward Challenge-S approach [1] and the ID-based \(n\)-round Challenge-S approach. As for the acquisition time, our setup takes 43 s to measure 1000 EM traces and 0.5 s to measure a 1000-round EM trace. This shows that the \(n\)-round approach could largely reduce the data acquisition time.  
As for the data processing time, both the \(n\)-round AES and the ID system have advantages. In the data processing of the straightforward Challenge-S approach, 5 AES rounds have to be calculated for each EM trace to estimate the side-channel information. Since there are, in total, 1000 traces, 5000 AES rounds must be calculated using 1000 different plaintexts. In contrast, the 1000-round AES calculates 1000 intermediate values in total. Moreover, the 5000 AES rounds of calculation need to be performed for each register key without the ID system. Using the ID system, only the claimed register key is compared with the observed side-channel information. As shown in Table 2, the data processing time is \(0.34\cdot q\) for the straightforward Challenge-S approach, while the ID-based \(n\)-round Challenge-S approach requires less than 0.1 s. For both the HD model and the XOR model, the leakage profile is prepared before the processing the measurement. Therefore, both models will be able to authenticate in a short time period.  
It is reasonable to expect the acceleration of authentication when the ID system is applied to side-channel authentication. With the performed experiments and the time measurements, the acceleration can be understood more clearly since both the decomposition of the consumed time and the contributions of each techniques are clear.  
Note that, the time required for the pre-authentication processes is similar for both authentication schemes. The pre-authentication processes consist of the key registration part and the leakage profiling part. As for the key registration part, the ID-based side-channel authentication scheme is the same as the conventional scheme except that a device ID is additionally registered together with the secret key. For the leakage profiling part, only the scheme using the XOR model requires the leakage profiling, which has negligible time consumption, since the profile only needs to be performed once for each type of prover device.