esa-sceva/satcom-chunk-collection huggingface dataset:
# Time-Dependent Alternative Route Planning: Theory and Practice +
## 3 The TDAG Algorithm
### Analysis of TDAG's Preprocessing Phase and Query Algorithm
This is because of the facts that \(G\) is a sparse graph, and moreover the Dijkstra-Rank and the minimum-travel-time metrics are correlated within polylogarithmic factors (a consequence of Assumption 3). The expected time for constructing all the landmark-to-vertex paths in step 7 is \(\mathcal{O}(n^{1+\gamma})\), since there exist \(\mathcal{O}(n^{\gamma})\) leaves in the BFS ball, and each path consists of, at most, \(n-1\) edges. The overall running time of TDAGsimple is dominated by step 7 and is, therefore, \(\mathcal{O}(n^{1+\gamma})\). As for the approximation guarantee, within the constructed alternative graph \(H\), the earliest-arrival-time at \(d\) when departing from \((o,t_{o})\) is \(D_{H}[o,d](t_{o})\leq\bar{t}_{d}\). To see this, consider the leaf-vertex \(v\) in \(RBFS_{d}\), which also belongs to the approximating \(\ell d\)-path \(\bar{\pi}[\ell,d]\), that reaches \(d\) at time (at most) \(\bar{t}_{d}\), when departing from \((\ell,t_{\ell})\). The path \(\bar{\pi}[\ell,d]\) is not necessarily a subgraph of \(H\). Nevertheless, its subpath \(\pi[\ell,v]\) exists in \(H\). Moreover, the optimal path \(\hat{\pi}[v,d]\) also exists in \(H\). By following \(\pi[\ell,v]\), we reach \(v\) at time (at most) \(\bar{t}_{v}\). It also holds that \(\hat{t}_{v}\geq\bar{t}_{v}\), because \(\hat{t}_{v}\) is the latest-departure-time from \(v\) that reaches \((d,\bar{t}_{d})\). Therefore, if from \((\ell,t_{\ell})\) we follow the path \(\bar{\pi}[\ell,v]\oplus\hat{\pi}[v,d]\), then we can be sure that the arrival time at \(d\) is, at most, \(\bar{t}_{d}\), essentially due to the FIFO property in the travel-times metric:  
\[\bar{t}_{v} \geq\bar{t}_{v}=t_{\ell}+\overline{D}[\ell,v](t_{\ell})\] \[\bar{t}_{v}+D[\hat{\pi}[v,d]](\bar{t}_{v}) \leq\bar{t}_{v}+D[\hat{\pi}[v,d]](\bar{t}_{v})=\bar{t}_{v}+D[v,d]( \bar{t}_{v})=\bar{t}_{d}\]  
It is already known (cf. Theorem 1) that the approximate travel-time \(\bar{t}_{d}-t_{o}\) is an \((1+\epsilon+\psi)\)-approximation of the minimum-travel-time in \(G\), \(D[o,d](t_{o})\), and thus this approximation guarantee also holds for \(D_{H}[o,d](t_{o})\).
# A Survey on Memory Subsystems for Deep Neural Network Accelerators
## 5 Nonvolatile Memories (NVMs)  
Despite the high degree of parallelism offered by GPUs, DNN workloads have massive memory footprints and work to further worsen the memory bottleneck of the system. As a solution in [20], nonvolatile memories (NVMs) such as STT-RAM [30] show more promise than conventional SRAM, but due to NVM technology's absence from the commercial market, there is a need for a design space for further exploration of it. To this end, DeepNVM was developed to provide guidelines in order to analyze, distinguish, and optimize NVM-based caches for GPU architectures. It has the ability to assess the power, performance, as well as the area of NVMs in application in the last-level cache in the GPU.  
Figure 27: Block description of GraphR architecture proposed in [29].  
NVSim, another modeling tool for NVM technologies, was incorporated into the DeepNVM framework to develop additional functionality. Since NVSim disregards the architecture-level analysis and applications' specific memory behavior, it cannot perform cross-layer analysis on NVM technologies. Thus, NVSim is integrated with DeepNVM's advanced architecture-level ISO capacity and area, as well as area analysis flow to enable the investigation of the design space of both the latest NVM caches as well as the SRAMS such as STT-RAMs.  
This paper performs an evaluation in regards to the spin-transfer torque MRAM (STT-MRAM) in addition to the spin-orbit torque MRAM (SOT-MRAM) using the developed framework in a GPU-based DNN accelerator and makes the following notable findings when compared against the baseline performance of SRAM:  
* STT-MRAM consumes 2.17\(\times\) dynamic energy and SOT-MRAM uses 1.02\(\times\) more dynamic energy
* STT-MRAM and SOT-MRAM have 6.6\(\times\) and 8.5\(\times\) lower leakage energy respectively.
* In general, STT-MRAM and SOT-MRAM possess an energy reduction of 5.6\(\times\) and 7.7\(\times\) respectively.
* Lastly, both STT-MRAM and SOT-MRAM supply a decrease in area of 2.4\(\times\) and 3.0\(\times\) respectively.  
Summary and Discussion  
This section reviews scope of nonvolatile memory-based DNN accelerators. Recent works reflect that deepNVM is one such structure, which analyzes performance parameters for NVMs used as last-level caches (LLCs) in GPGPU architectures.
## 3 Principals of Routing Protocols and Their Breakdowns
### Geographical Information-Based Routing Protocols
#### 3.2.6. LETR [50]  
The authors of [50] suggest a new protocol, namely a location error-resilient transmission range adjustment-based protocol (LETR) for void avoidance in UWSN. The transmission strength of each sensor node is separated into k levels in the LETR protocol, and a depth adjustment technique is utilized to recover from node communication vacuum areas in extreme situations when nodes cannot detect any nearby nodes within their maximum transmission range levels. The forwarder is chosen based on packet progression and node priority rating. The LETR protocol uses programmable depth adjustment and transmission power modification to improve data transfer and network lifespan.  
* Comparison  
Here, most of the protocols do not use Hello protocols, but all of them follow layering routing protocols. Only RD uses a single sink, and the rest uses multiple sinks. The differences in the protocols are illustrated in Table 11.  
Figure 5: Trend shift in the computation of holding time in RD protocol.  
* Location-Based Routing Protocol  
To use location-based routing algorithms, sensor nodes in UWSN need to know their precise geographic position information. The best path confirmation technique based on geographical location data is to create a route utilizing the nodes' position data, such as angle and distance. The source node may pick the best neighbor node as the next-hop node to transfer data after collecting the destination node's unique geographical location information. Data packet flooding consumes a lot of network energy; therefore, the nodes may effectively avoid it and enhance data transmission efficiency. The following is a detailed description of several location-based routing methods.
## 2 Geological Setting and Geochemical Data  
### Regional Geological Background  
The western part of the Gangdese belt is located in the south-central part of Tibet. The tectonic structure belongs to the Gangdese-Nyainqentanglha landmass in the Gangdese-Himalayan tectonic area, which is between the Yarlung Zangbo River and the Bangong Lake-Nujiang Belt [24]. The basement consists of the Nyainqentanglha group from Presinian. The Cambrian and low-middle Triassic strata are generally absent, with more complete development observed in the Ordovician stratigraphy. The Ordovician-Permian strata are mainly distributed in the northern part of the study area, and the Upper Triassic-Jurassic strata are distributed in the northern part of the study area, while the Cretaceous-Neogene strata are distributed in the whole area (Figure 1). The region exhibits a significant presence of magmatic rocks, characterized by extensive chronological distribution, large exposed areas, wide distribution ranges, complex origins, and diverse types. The intrusive and volcanic rocks have been extensively developed since the Triassic, mainly formed in the Cretaceous-Paleogene, with the latest volcanic magma activity in the Miocene. [PERSON] et al. [25] classified the tectonic unit of the Gangdese orogenic belt. By comparing these tectonic units, the study area can be divided into four magmatic belts consisting of intermediate-acidic magmatic rocks, including South Gangdese, Longgeer-Nyainotanglha, Ze long, and Ban-Bengcuo. These four magmatic belts are closely associated with the distribution of the porphyry copper-molybdenum-gold deposits. The porphyry copper polymetallic deposits in the study area are mainly distributed in the Gangdese magmatic arc, including Zhunuo, Jiru, Xiongcun, Dongga, etc. Additionally, deposits can be found in the uplifted area of the Longgeer-Nanmulin arc-back belt, which includes Ria in Cuoqin, as well as in the island arc belt of Cuoqin-Xainza, consisting of Balazha, Gaerqiong, and others. The detailed chronology and tectonic setting of the typical porphyry deposits mentioned above have been studied by many authors. The results suggest that the formation environment and metallogenic characteristics of the porphyry deposits in this area are formed in three tectonic settings: island arcs, syn-collisional environments, and post-collisional extensional environments, occurring during four metallogenic periods: the Early Middle Jurassic, Late Cretaceous, Eocene, and Miocene. The porphyry deposits in island arcs such as Dongga and Xiongcun are only distributed in the south of the southern margin of the Gangdese magmatic arc zone. The porphyry deposits in syn-collisional and post-collisional extensional environments such as Zhunuo and Jiru are mainly distributed in the southern Gangdese magmatic arc. Additionally, a few porphyry deposits, such as Balazha and Gaerqiong, are distributed in the northern part of the Cuoqin-Xainza Island arc and the Bangor magmatic arc belt. All these insights greatly extend the geochemical prospecting of porphyry copper polymetallic ore in the western Gangdese.
## 4 Analysis of Research Variables
### RRC.ConnReq.Att and RRC.ConnReq.Succ  
Radio resource control (RRC) is a multirole network protocol that defines communication between an eNodeB and a mobile device. From a base station perspective (eNodeB), a mobile device in a cell can be found in two basic states [40]: _Idle Mode_ and _Connected Mode_.  
The independent variable _RRC.ConnReq.Att_ represents the number of attempts by the UE to establish a connection with an eNodeB, i.e., to start the RRC connection setup procedure, as shown in Figure 4 under number 1. Each time the procedure is started, this variable is incremented by one. In this case, the number of repeated connection-retransmission is not counted. _RRC.ConnReq.Succ_ represents the number of confirmation messages about the completion of the Connection Setup procedure that the eNodeB receives from the user equipment in the cell and is incremented by one each time the eNodeB receives this message. In Figure 4, this step is marked under number 3.
# An Improved Lightweight Dense Pedestrian Detection Algorithm
## Proposed Algorithm
### SloU Loss Function
#### 3.2.2 SIoU Loss  
The preceding loss function ignores the direction between the real and anticipated frames, resulting in a slow convergence rate. SIoU introduces the vector angle between the real and predicted frames and redefines the related loss function, which consists of four parts: angle cost, distance cost, shape cost, and IoU loss.  
1. Angle loss, defined as follows: \[\Lambda=1-2\times\sin^{2}(\arcsin(\frac{\text{c}_{\text{h}}}{\sigma})-\frac{ \pi}{4})\] (6)  
\[\sigma=\sqrt{\left(\text{b}_{\text{c}_{\text{x}}}^{\text{gt}}-\text{b}_{\text {c}_{\text{x}}}\right)^{2}+\left(\text{b}_{\text{c}_{\text{y}}}^{\text{gt}}- \text{b}_{\text{c}_{\text{y}}}\right)^{2}} \tag{7}\]  
\[\text{c}_{\text{h}}=\text{max}(\text{b}_{\text{c}_{\text{y}}}^{\text{gt}}, \text{b}_{\text{c}_{\text{y}}})-\text{min}(\text{b}_{\text{c}_{\text{y}}}^{ \text{gt}},\text{b}_{\text{c}_{\text{y}}})\] (8) where c\({}_{\text{h}}\) is the height difference between the actual frame's center point and the predicted frame's center point, and \(\sigma\) is the distance between the real frame's center point and the predicted frame's center point. \(\text{b}_{\text{c}_{\text{x}}}^{\text{gt}},\text{b}_{\text{c}_{\text{y}}}^{ \text{gt}}\) are the coordinates of the center of the real frame, and \(\text{b}_{\text{c}_{\text{x}}},\text{b}_{\text{c}_{\text{y}}}\) are the coordinates of the center of the predicted frame.
2. Distance loss, defined as follows: \[\Delta=2-\text{e}^{-\gamma\rho_{\text{x}}}-\text{e}^{-\gamma\rho_{\text{y}}}\] (9)  
\[\rho_{\text{x}}=\left(\frac{\text{b}_{\text{c}_{\text{x}}}^{\text{gt}}-\text{b} _{\text{c}_{\text{x}}}}{\text{c}_{\text{w}}}\right)^{2},\rho_{\text{y}}=\left( \frac{\text{b}_{\text{c}_{\text{y}}}^{\text{gt}}-\text{b}_{\text{c}_{\text{y}} }}{\text{c}_{\text{h}}}\right)^{2},\gamma=2-\Lambda \tag{10}\]  
\(\text{c}_{\text{w}},\text{c}_{\text{h}}\) are the width and height of the minimum outer rectangle of the real box and the predicted box.
3. Shape loss, defined as follows: \[\Omega=\left(1-\text{e}^{-\omega_{\text{w}}}\right)^{\theta}+\left(1-\text{e}^{ -\omega_{\text{h}}}\right)^{\theta}\] (11)\[\omega_{\rm w}=\frac{|{\rm w}-{\rm w}^{\rm gt}|}{{\rm max}({\rm w},{\rm w}^{\rm gt })},\omega_{\rm h}=\frac{|{\rm h}-{\rm h}^{\rm gt}|}{{\rm max}({\rm h},{\rm h}^{ \rm gt})} \tag{12}\]  
w,h,wgt,hgt are the width and height of the predicted and real frames, respectively, and \(\theta\) controls the degree of attention to shape loss.
4. The SIOU loss function is defined as follows:  
\[{\rm Loss}_{\rm SIOU}=1-{\rm IoU}+\frac{\Delta+\Omega}{2} \tag{13}\]
## 2 Materials and Methods
### Epileptogenic Zone (EZ), Seizure Onset Zone (SOZ), and Post-Surgical Outcome  
The EZ is the area of the brain that is indispensable for the generation of epileptic seizures [32]. It is a theoretical concept, meaning that it cannot be directly measured but only deduced postoperatively: if the patient is seizure-free after surgery, we conclude that the EZ must have been included in the resected brain area [32]. Thus, in our cohort, we defined the EZ as the area of the brain that was resected during the surgery (see Figure 1a) since all the patients became seizure free postoperatively. To segment the resection cavity in the patient's brain, we co-registered pre-surgical and post-surgical MRI and used the MATLAB-based app developed by our group to outline the resection [33; 34], followed by manual correction (3D Slicer software, v4) when needed. We measured the distance of each iEEG contact from the EZ as the Euclidean distance of the contact center from the closest resection margin. We then labeled each iEEG contact as within the EZ when having a distance below 5 mm (to account for post-operative brain shift and co-registration inaccuracies due to the child's brain growth).  
The SOZ is the area of the cortex from which clinical seizures are actually generated, as opposed to the EZ, which is the area of the cortex that is indispensable for seizure generation. Therefore, the SOZ can be actually measured during the pre-surgical evaluation in order to obtain an estimate of the EZ. At our institution, the SOZ is determined during each patient's long-term iEEG monitoring: independently from this study, pediatric epileptologists reviewed all the patients' seizures throughout the recording period and identified the SOZ as the iEEG contact/s showing the earliest change in activity associated with clinical or subclinical seizures [35; 36]. We thus defined the SOZ in the same way for this study, based on the patient's clinical records. Although the SOZ is regarded as the best estimate of the EZ, there may be parts of the SOZ that do not need to be resected to gain seizure freedom as well as parts of the EZ that are not measured as SOZ but are still able to generate seizures.  
Based on the criteria above, 797 out of 1928 iEEG contacts (41%) were labeled as within the EZ (mean: 40 contacts per patient), while 285 contacts (15%) were labeled as within the SOZ (mean: 14 contacts per patient). The post-surgical outcome was evaluated by a board-certified pediatric epileptologist (J.B.) from the most recent follow-up visit using the Engel scale [37].
# Feedback Stabilization of First Order Neutral Delay Systems Using the Lambert W Function
###### Abstract  
This paper presents a new approach to stabilize the first order neutral delay differential systems with two time delays. First, we provided a few oscillation and non-oscillation criteria for the neutral delay differential equations using spectrum analysis and the Lambert W function. These conditions were explicit and the real roots were analytically expressed in terms of the Lambert W function in the case of non-oscillation. Second, we designed a stabilizing state feedback controller for the neutral delay differential systems with two time delays, wherein the proportional and derivative gains were analytically determined using the results of the non-oscillation criteria. A few examples are given to illustrate the main results.  
neutral delay systems; oscillation; stabilization; feedback control; Lambert W function +
Footnote †: journal: Applied
## 3 Grassmann MTS Clustering
### Extrinsic Grassmann Clustering
#### 3.2.1 Embedding MTS Points on the Manifold into a Euclidean Space  
Since the Grassmann manifold \(\mathcal{G}\) (3) is differentiable, a derivative can be defined at any point \(\mathbf{U}\in\mathcal{G}\). Let \(\alpha(t)\) be a curve on the manifold, then its derivative \(\alpha^{\prime}(t)\) at a point \(\mathbf{U}\) is called a tangent space \(T_{\mathbf{U}}\mathcal{G}\) to \(\mathcal{G}\) at \(\mathbf{U}\). Note that the tangent space is equivalent to a real vector space. For every point \(\mathbf{U}\), a unique geodesic starting from \(\mathbf{U}\) in every direction can be defined in the form of an exponential map \(Exp_{\mathbf{U}}:T_{\mathbf{U}}\mathcal{G}\rightarrow\mathcal{G}\) such that \(d(\mathbf{U},Exp_{\mathbf{U}}(\mathbf{V}_{T}))=\|\mathbf{V}_{T}\|_{\mathbf{U}}\) for every \(\mathbf{V}_{T}\) in \(T_{\mathbf{U}}\mathcal{G}\) (see Figure 3 for an illustrative description). The inverse map to \(Exp_{\mathbf{U}}(\mathbf{V})\) can be defined by the logarithm \(Log_{\mathbf{U}}(\cdot)\) only in a certain neighborhood of \(\mathbf{U}\)[49].  
With forward mapping \(Exp_{\mathbf{U}}(\cdot)\) and inverse mapping \(Log_{\mathbf{U}}(\cdot)\), a geodesic distance measure, namely, tangent norm \(d_{T}(\mathbf{U},\mathbf{V})\) between \(\mathbf{U},\mathbf{V}\in\mathcal{G}\), can be defined as follows [49]:  
\[d_{T}^{2}(\mathbf{U},\mathbf{V})=\langle\mathbf{V},\mathbf{V}\rangle_{\mathbf{U }}=\|Log_{\mathbf{U}}(\mathbf{V})\|_{F}^{2}, \tag{7}\]  
where \(\langle\,\ \rangle\) denotes the inner product and \(\|\cdot\|_{F}\) is the Frobenius norm. Algorithms 3 and 4, respectively, describe the process of computing both mappings \(Log_{\mathbf{U}}(\cdot)\) and \(Exp_{\mathbf{U}}(\cdot)\)[49]. Figure 3 depicts the relations of the forward and reverse mappings between the Grassmann manifold and its tangent space.  
From the perspective of mapping points on the nonlinear manifold \(\mathcal{G}\) onto the tangent space, as depicted in Figure 3, it can be seen that the manifold is locally embedded into a Euclidean space [20]. In other words, such mappings are essentially equivalent to flattening the manifold via local diffeomorphisms. On the tangent space with mapped MTS data points, general-purpose clustering algorithms designed for Euclidean space can be deployed.  
However, it is worth noting here that such manifold mapping may lead to some problems. The exponential map \(Exp_{\mathbf{U}}(\cdot)\) is onto but only one-to-one in a neighborhood of \(\mathbf{U}\). The \(Log_{\mathbf{U}}(\cdot)\) is restrictively defined only around a small neighborhood of \(\mathbf{U}\). It is impossible to define global coordinates that make the whole manifold resemble Euclidean space [48].
# Utilizing Duplicate Announcements for BGP Anomaly Detection
## 3 Proposed Methodology
### Deep Learning Techniques  
#### 3.6.1 Long Short-Term Memory (LSTM)  
This is a type of neural network that was created to solve the disappearing slope problems. The backpropagation error associated with it rises and falls exponentially. This behavior of LSTM makes it suitable for creating a prototype for subordination in the long run. The network of LSTM is made up of different layers, and every single layer forms a set of periodically linked blocks, as shown in Figure 3. These periodically linked blocks are called cells [46]. There are three multiplicative units associated with every single cell called gates. The first one is called the forget gate, the second one is called the input gate, and the third one is called the output gate. These gates provide different operations in order to perform different functions, such as resetting the cell, reading the cells, and lastly, writing in the cells. The different gate outputs are denoted as Equation (4), Equation (5), and Equation (6), respectively.  
\[f_{t}=sigmoid\left(W_{f}|h_{t-1},x_{t}|+b_{f}\right) \tag{4}\]  
\[i_{t}=sigmoid(W_{i}|h_{t-1},x_{t}|+b_{i}) \tag{5}\]  
\[O_{t}=sigmoid(W_{o}|h_{t-1},x_{t}|+b_{o}) \tag{6}\]  
The newly created state of the cell \(C_{t}\) is given by Equations (7) and (8).  
\[\dot{C}_{t}=tanh(W_{c}|h_{t-1},x_{t}|+b_{c}) \tag{7}\]  
\[C_{t}=f_{t}oC_{t-1}+i_{t}o\dot{C}_{t} \tag{8}\]To the next cell the output is given by Equation (9),  
\[h_{t}=O_{t}otanh(C_{t}) \tag{9}\]  
In the above equations \(W_{f}\), \(W_{i}\), \(W_{O}\)_and_\(W_{C}\) denotes the weight matrices while \(b_{f}\), \(b_{i}\), \(b_{O}\)_and_\(b_{C}\) represents the bias vectors, respectively. The time series dataset is partitioned to each collector for testing and training purposes. The observation is utilized for training purposes, and the prototype reads the usual behavior of the time series. The parameters that cover the architecture of the network batch size are selected utilizing cross-validation on the validation dataset. The output layer is made up of a fully connected intense layer with a rectified linear unit (ReLU) as an activation function.
# Processing of EMG Signals with High Impact of Power Line and Cardiac Interferences
### EMG Signal with High Impact of 50 Hz and Higher-Order Components  
As expected, there is a high impact of environmental power line parasitic signal sources. It has been experimentally confirmed that the electrical contact between skin and the surface electrodes has the dominant importance in reducing this parasitic impact. The examples showing the contribution of 50 Hz and the higher spectral components are presented in Figure 2.  
Figure 1: An example of the raw EMG signal of the right abdominal muscle with high impact of 50 Hz power line and ECG disturbances, (**a**) raw signal, (**b**) after removing low-frequency spectral range using 3 rd order Butterworth HP filter, \(f_{c}\) = 2 Hz.
# Topological Navigation and Localization in
## 3 Topological Navigation and Localization for LHD
### Self-Localization
#### 3.3.3 Global Localization  
This module knows in each moment the node in which the LHD is located. The module also receives the current LHD's pose in the current node's coordinate frame. Using this information it evaluates if the LHD is still in the current node, or if it has moved to a neighbor node. If this is the case, then the estimation of the LHD's pose is transferred into the neighbor node's coordinate system. Each node has the geometric transformation to adjacent nodes so the transition is smooth.