esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 CFD Methodology and Numerical Modeling
### Analysis of Data Indicators: Uniformity Assessment  
In previous studies, it has been shown that airflow rate, temperature, and relative humidity above the crop canopy all play an important role in crop growth, which directly affects crop production quality and yield [10; 17; 50]. In this research, the objective uniformity parameter (\(OUI\)) is used to evaluate the uniformity of airflow velocity distribution over the crop. The relative standard deviation (\(RSD\)) is employed to evaluate the uniformity of temperature and relative humidity. Relative standard deviation \(RSD\) is defined as the ratio of the standard deviation (\(SD\)) to the mean. The smaller the \(RSD\), the lower the variance and higher the uniformity of the evaluated variables. The objective uniformity parameter [28] is defined by the following:  
\[OU\ =\ \left(1-\frac{S_{U}}{2U_{0}}\right)\times 100\% \tag{12}\]  
\[S_{U}\ =\ \sqrt{\frac{1}{N-1}{\sum_{k=\ 1}^{N}}{\left(U_{k}-U_{0}\right)^{2}}} \tag{13}\]  
where \(S_{U}\) is the standard deviation of the flow distribution within the plant factory relative to the target velocity. \(U_{k}\) and \(N\) are the average air velocity (m\(\cdot\)s\({}^{-1}\)) at the upper surface of the crop canopy of the k-th cultivation trap and the number of cultivation trays in the room, respectively. \(U_{0}\) is the air velocity (m\(\cdot\)s\({}^{-1}\)) suitable for crop growth and it is taken as 0.4 m\(\cdot\)s\({}^{-1}\) in this study, and the value of this velocity is considered to facilitate photosynthesis in crops [51].  
The uniformity of air temperature and relative humidity distribution is assessed using the relative standard deviation (\(RSD\)), which is calculated as the ratio of the standard  
\begin{table}
\begin{tabular}{l l} \hline
**Domain** & **Dimensions: Length (L) \(\times\) Width (W) \(\times\) Height (H)** \\ \hline Room & 4.4 \(\times\) 2.2 \(\times\) 3.3 m\({}^{3}\) \\ \hline Cultivations & 1.6 \(\times\) 0.5 \(\times\) 0.15 m\({}^{3}\) \\ \hline Plant areas & 1.6 \(\times\) 0.5 \(\times\) 0.12 m\({}^{3}\) \\ \hline \end{tabular}
\end{table}
Table 2: Dimensions.  
Figure 1: Schematic of the computational domain.  
deviation to the mean. A smaller \(RSD\) indicates higher uniformity. Mathematically, it can be defined as follows:  
\[RSD_{T}\ =\ \frac{\sqrt{\frac{1}{\overline{N-1}\sum_{k=\,1}^{N}\left(T_{k}- \overline{T}\right)^{2}}}}{\overline{T}}\times 100\% \tag{14}\]  
\[RSD_{RH}\ =\ \frac{\sqrt{\frac{1}{\overline{N-1}\sum_{k=\,1}^{N}\left(RH_{k}- \overline{RH}\right)^{2}}}}{\overline{RH}}\times 100\% \tag{15}\]  
where \(RSD_{T}\) and \(RSD_{RH}\) are the relative standard deviation (%) of air temperature and relative humidity above the crop, respectively. \(T_{k}\) and \(RH_{k}\) represent the average air temperature (K) and relative humidity (%) above the kth cultivation tray crop, and \(\overline{\overline{\overline{\overline{\overline{\overline{\overline{\overline{ \overline{\overline{\overline{\overline{\overline{\overline{\overline{\overline{\overline{\overline{ \overline{\overline{\cdotcdotcdotcdotcdotcdotcdotcdotcdotcdotcdotcdot
# The Impact of Leveee Openings on Storm Surge: A Numerical Analysis in Coastal Louisiana
## 2 Storm Surge Model
### Model Setup  
As shown in Figure 2, the model domain focuses on the Louisiana coast covering Barataria Basin, the lower MR delta, Breton Sound and Lake Pontchartrain. The grid size is \(521\times 526\). The domain has a spatial resolution ranging from 2 km offshore to 56 m in the MR. The offshore water level and current boundaries were provided by a Gulf-Atlantic model (see model details in [2]). The MR discharge was added at Belle Chasse using the U.S. Geological Survey (USGS) observed data. In Barataria Basin, two Neumann boundaries were set at the north and west sides to allow water to freely flow out of the model domain. The USGS 5 m resolution Coastal National Elevation Database ([[https://www.usgs.gov/special-topics/coastal-national-elevation-database-applications-project](https://www.usgs.gov/special-topics/coastal-national-elevation-database-applications-project)]([https://www.usgs.gov/special-topics/coastal-national-elevation-database-applications-project](https://www.usgs.gov/special-topics/coastal-national-elevation-database-applications-project)), accessed on 23 October 2022) was used for the interpolation of topography and bathymetry. The crest width of levees is normally only a few meters, which cannot be resolved by the current model grids. The sub-grid structure of local weirs in Delft3D was activated to represent the MR&T leveee system. It treated levees as \"thin walls\" with limited heights along grid lines following the actual levee positions. When surface elevation at either side of the structure is larger than the leveee height, two grids previously blocked by the structure will be connected and the flux between them can be calculated by an empirical formula. The removal of levees can be achieved by setting the top of local weirs to zero (NAVD88). It should be noted that this removal operation does not alter grid cell bed levels at both sides of the structure. The leveee base and nearby topography resolved by model grids remain unchanged in the model. Two leveee removal locations (see Figures 1 and 2b) were considered in this study, that is, the upper-crossing location and the lower-crossing location. As shown in Figure 2b, green lines denote the removal parts of levees. At the upper-crossing location, two parts at each bank of the MR were removed. In addition, another part in Barataria was removed as well to allow water to flow into the basin. At the lower location, one part along the west bank of the MR was removed since there are no levees along the east bank, and one part in Barataria was removed as well. The width of each opening was set to 1 mile (1.6 km).
# To Cache or Not to Cache
## 1 Introduction
**FOMO**'s contribution is the development and evaluation of novel cache admission policies and not in the latency and/or throughput optimization of the I/O access stream to non-datapath caches as has been considered in complementary, recent work [15]. Consequently, we analyze and evaluate **FOMO** using three different eviction policies (LRU, ARC, and LIRS) against the current state-of-the-art non-datapath caching algorithms on a workload collection comprising of 176 workloads sourced from five different storage system workload repositories. For each workload, we evaluate against six different cache size configurations, each sized as a percentage of each workload's footprint, defined as the set of unique items accessed by the workload. **FOMO** improves upon state-of-the-art non-datapath caching algorithms, achieving improved hit rate consistency while also reducing cache writes significantly. The use of a Miss-History gives **FOMO** a more acute understanding of the workload behavior, compared to mARC, which depends exclusively on cache hit rate to make its decisions. When compared against state-of-the-art cache admission policies such as LARC, mARC, and TinyLFU, **FOMO** is able to see an average improvement of 2.64%, 1.16%, and 27.45%, respectively, while still reducing the writes made to the cache to levels similar to that of LARC.
## 3 Results  
### Good Operation Practice: Minimizing Wastestreams  
#### 3.1.1 Clarifier Sludge Drain  
For reliable system performance, sludge in the clarifier was continuously drained via a control valve. To optimize drainage, 54 experiments were conducted, testing six valve openings ranging from 13% to 23%. Each valve setting was randomly assigned per day, and the corresponding sludge flow rate was recorded. The objective was to minimize sludge volume while avoiding increases in clarified water turbidity and preventing drain clogging.  
Figure 3 presents the average, minimum, and maximum flow rates observed across experiments. Clogging emerged as a key issue, driven by sludge compaction over time, which reduced flow and eventually obstructed the drain, leading to critical clarifier failure. The 19% opening of the sludge drain valve, corresponding to experiments 30-39, was the lowest setting that allowed for consistent and reliable flow, equal to approximately 45 mL/s. The drawback of this setting is that it equals approximately 9% of the design throughput of the entire system, meaning that the sludge is highly diluted, and the pilot generates a large volume of wastewater compared to the drinking water produced.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Parameter** & **Unit** & **Mean** & **Median** & **P5** & **P95** & **Std Dev** \\ \hline Chemical oxygen demand & mg/L & 9 & 10 & 5 & 11 & 2.5 \\ Alkalinity & mgc\({}_{\text{ACO}}\)\({}_{\text{3}}\)/L & 120 & 118 & 63 & 168 & 30 \\ Total oxidized nitrogen & mgN/L & 1.07 & 1.06 & 0.33 & 1.95 & 0.49 \\ Total Kjeldahl nitrogen & mgN/L & 0.47 & 0.41 & 0.15 & 0.94 & 0.25 \\ Specific conductance & uS/cm & 354 & 359 & 213 & 470 & 83 \\ Soluble reactive phosphorus & uSP/L & 17.0 & 13.2 & 4.8 & 42.1 & 14.5 \\ Cl\({}^{-}\) & mg/L & 29.2 & 26.8 & 13.3 & 51.9 & 12.8 \\ Total coliform & CFU/100 mL & 13,100 & 8500 & 943 & 39,900 & 14,700 \\ _E. coli_ & CFU/100 mL & 869 & 160 & 20 & 4750 & 2050 \\ \hline \end{tabular}
\end{table}
Table 1: _Cont._
# Aerial Grasping of an Object in the Strong Wind: Robust Control of an Aerial Manipulator
## Appendix A  
Proof of Theorem 1.: We define \(h(e_{\Phi_{b}})=\left[\begin{array}{ccc}h_{x}&h_{y}&h_{z}\end{array}\right]^{T}=( ^{I}R^{B,d}-^{I}R^{B})e_{3}\), and attitude error \(e_{\Phi_{b}}=\left[\begin{array}{ccc}e_{\varphi}&e_{\theta}&e_{\psi}\end{array} \right]^{T}\). Combining with Equation (1) we have,  
\[\left\{\begin{array}{l}h_{x}=c\varphi s\theta c\psi+s\varphi s\psi-(c\varphi _{d}s\theta_{d}c\psi_{d}+s\varphi_{d}s\psi_{d})\\ h_{y}=c\varphi s\theta s\psi-s\varphi c\psi-(c\varphi_{d}s\theta_{d}s\psi_{d}- s\varphi_{d}c\psi_{d})\\ h_{z}=c\varphi c\theta-c\varphi_{d}c\theta_{d}\end{array}\right. \tag{10}\]  
For \(h_{x}\) in Equation (32), replacing \(\left[\begin{array}{ccc}\varphi&\theta&\psi\end{array}\right]\) by \(\left[\begin{array}{ccc}\varphi_{d}+e_{\varphi}&\theta_{d}+e_{\theta}&\psi_{ d}+e_{\psi}\end{array}\right]\) and utilizing the following trigonometric functions:  
\[\left\{\begin{array}{l}\sin(a+b)=\sin(a)+\sin(\frac{b}{2})\cos(a+\frac{b}{2 })\\ \cos(a+b)=\cos(a)-\sin(\frac{b}{2})\sin(a+\frac{b}{2})\\ \left|\sin(a)\right|\leq 1\\ \cos(a)\right|\leq 1\end{array} \tag{11}\]  
we can get:  
\[\left\{\begin{array}{l}|h_{x}|\leq 2\Big{|}s\frac{e_{\varphi}}{2}\Big{|}+ \Big{|}s\frac{e_{\theta}}{2}\Big{|}+2\Big{|}s\frac{e_{\varphi}}{2}\Big{|}+ \Big{|}s\frac{e_{\varphi}}{2}\Big{|}\cdot\Big{|}s\frac{e_{\theta}}{2}\Big{|}+ \Big{|}s\frac{e_{\varphi}}{2}\Big{|}\cdot\Big{|}s\frac{e_{\varphi}}{2}\Big{|} \\ \qquad\qquad\qquad+2\Big{|}s\frac{e_{\varphi}}{2}\Big{|}\cdot\Big{|}s\frac{e_{ \varphi}}{2}\Big{|}+\Big{|}s\frac{e_{\varphi}}{2}\Big{|}\cdot\Big{|}s\frac{e_ {\theta}}{2}\Big{|}\end{array}\right. \tag{12}\]  
Utilizing the following inequalities into Equation (11):  
\[\left\{\begin{array}{l}|u|\cdot|b|\leq\frac{1}{2}(|a|+|b|),\text{for}\ |a|\leq 1 \ \text{and}\ |b|\leq 1\\ |a|\cdot|b|\cdot|c|\leq\frac{1}{3}(|a|+|b|+|c|),\text{for}\ |a|\leq 1,\ |b|\leq 1 \ \text{and}\ |c|\leq 1\\ \sin(a)\Big{|}\leq|a|\end{array}\right. \tag{13}\]  
So we can get:  
\[|h_{x}|\leq\frac{10}{3}\Big{|}s\frac{e_{\varphi}}{2}\Big{|}+\frac{7}{3}\Big{|} s\frac{e_{\theta}}{2}\Big{|}+\frac{10}{3}\Big{|}s\frac{e_{\psi}}{2}\Big{|}\leq \frac{5}{3}\big{(}\big{|}e_{\varphi}\big{|}+|e_{\theta}|+\big{|}e_{\psi}\big{|} \big{)} \tag{14}\]  
Similarly, we can get the property of \(h_{y}\) and \(h_{z}\):  
\[\left\{\begin{array}{l}|h_{y}|\leq\frac{5}{3}\big{(}\big{|}e_{\varphi}\big{|} +|e_{\theta}|+\big{|}e_{\psi}\big{|}\big{)}\\ |h_{z}|\leq\frac{3}{4}\big{(}\big{|}e_{\varphi}\big{|}+|e_{\theta}|\big{)} \end{array}\right. \tag{15}\]  
So, with Equations (13) and (14), we can get:  
\[\|h(e_{\Phi_{b}})\|=\sqrt{h_{x}^{2}+h_{y}^{2}+h_{z}^{2}}\leq k_{1}\|e_{\Phi_{ b}}\| \tag{16}\]  
where \(k_{1}=\sqrt{13}\) For the hex-rotor we can assume the maximum value of the \(F_{t}\) is \(k_{2}\). So,  
\[\left\|\delta(e_{\Phi_{b}})\right\| \leq\|\frac{F_{t}}{m_{S}}\|\cdot\|h(e_{\Phi_{b}})\|\] (17) \[\leq\frac{k_{1}k_{2}}{m_{S}}\|e_{\Phi_{b}}\|\] \[=\|\Big{|}\ \ O_{3\times 3}\ \ \ O_{3\times 3}\ \ \ \sigma I_{3\times 3}\ \ \ O_{3\times 3}\
# Burst Channel Error Reduction Based on Interleaving for Efficient High-Speed Wireless Communication
## 2 Burst Error, OFDM, and Interleaving  
Adding redundancy to information sequences increases message delivery success rates in communication systems. Burst errors occur in a continuous portion of the received data. Burst errors represent a typical kind of interference in radio communications. For example, let u be an information sequence.  
u = ABC  
Let the error correction coding (ECC) duplicate each symbol as follows:  
v = A1A2A3B1B2B3C1C2C3  
These data are now wirelessly sent. For a three-symbol-length burst of interference during transmission, this yields an error stream (X):  
R = A1A2A3 XXXXC1C2C3  
Despite the redundancies, symbol B is now irrecoverable due to the loss of all copies. If the error exceeds the repeated symbols, the error-correcting algorithm fails to retrieve the original data sequence. Let the redundant symbols be scrambled randomly within the stream, for example:  
V = A2B3C2A1B1C3A2B2C1  
The same burst interference that results in the sequence can be as follows:  
R = A2B3C2 XXXA2B2C1  
As a result, we obtain some duplicates of all the symbols, from which the original symbols and information sequence may be reconstructed. Therefore, interleavers are responsible for the scrambling of the message information. The detailed concept of interleavers will be stated in later subsections.
# Efficient Parallel Processing of Big Data on Supercomputers for Industrial IoT Environments
## 2 Overview and Theoretical Foundations
### Pipelines Parallelism  
The efficient execution of industrial IoT workflows demands sophisticated parallelism strategies that account for the unique constraints of distributed manufacturing environments. Unlike traditional batch-oriented scientific pipelines, industrial applications must balance real-time responsiveness with computational efficiency across heterogeneous edge and cloud resources. This challenge has given rise to adaptive parallelism models that dynamically adjust to workload characteristics and infrastructure capabilities.  
Coarse-grained parallelism dominates industrial workflows where task dependencies enforce sequential execution patterns. A typical predictive maintenance pipeline exemplifies this approach, where raw sensor data undergoes staged processing: initial cleaning and normalization at edge nodes precedes feature extraction on fog-layer servers, with final inference executed in the cloud. Each stage depends on outputs from its predecessor, creating a task-sequential workflow that mirrors traditional HPC pipelines but with stricter latency constraints. This pattern proves particularly valuable in digital twin implementations, where physics-based simulations must await preprocessed inputs from distributed sensor networks.  
In contrast, fine-grained parallelism enables the high-throughput processing required for industrial streaming applications. Modern smart factories leverage data parallelism to distribute homogeneous operations--such as visual inspection or vibration analysis--across edge devices or GPU-equipped fog nodes. The emergence of 5G-enabled edge computing has further enhanced this capability, allowing microsecond-scale task scheduling that supports latency-critical applications like closed-loop robotic control. Advanced manufacturing plants now implement hierarchical parallelism models that combine both approaches. For instance, a distributed energy grid monitoring system might employ fine-grained parallelism for real-time phasor measurement analysis across edge devices, while reserving coarse-grained orchestration for downstream fault localization simulations that trigger only when anomalies exceed predefined thresholds. This dual approach exemplifiesthe industrial cloud-edge continuum, where parallelism strategies must continuously adapt to fluctuating workloads and resource availability [10].
## 5 Security Analysis  
We assume the existence of passive and active attackers. A passive attacker monitors communication channels without interfering, aiming to extract sensitive information through eavesdropping. An active attacker, on the other hand, can manipulate, inject, modify, or block messages, attempting to disrupt the protocol or deceive participants. Furthermore, the channel between the relying party and the verifier is assumed to be secure. As a consequence, we focus on protection of the communication between device and relying party. In particular, this includes protection against authenticity and integrity, such that the relying party sending the request is also the only one able to verify the request.  
The following informal security analysis evaluates the protocol's resilience against common threats. The analysis highlights key features and their effectiveness in mitigating these threats, as outlined below:  
1. **Man-in-the-Middle (MitM) Attack Prevention:** The protocol is designed to mitigate MitM attacks through the use of cryptographic techniques, particularly signing attestation tokens with private keys within a secure environment. Since only the secure environment and the verifier party have access to the private keys, the attestation tokens cannot be altered in transit without knowing the private key.
2. **Impersonation Protection:** The public key of the relying party is included in the attestation request and is used to encrypt the nonce. This makes it impossible for another relying party with knowledge of the model to impersonate the original relying party. If any other party intercepts the request, it cannot reproduce the necessary cryptographic responses, which prevents impersonation. Furthermore, the attestation token includes the originator of the request, represented as the hash of the relying party's public key (see Table 2). This ensures that a relying party cannot use attestation tokens generated from another relying party's attestation request to issue verification requests to a verifier entity.
3. **Replay Attack Prevention:** The device increments the nonce with each attestation request, which ensures that replaying previous requests will not work. The device can identify the replayed request and abort the process. The initial nonce is derived from a boot seed using HKDF function with an internal device-specific key. The boot seed is not used directly since it is included as part of the PSA attestation token (see Listing 1). Therefore, an attacker cannot deduce future nonce values.
4. **Device-side DoS Protection:** The device checks for valid attestation responses based on the nonce and the hash of the model. A zero-knowledge proof over the hash of the model weights enables the relying party to convince the device that has access to the model weights or at least to the hash of the model weights. This prevents attackers from manipulating the attestation responses to trigger failures in the device.
## 2 Circuit Topology of RF-Energy-Harvesting Integrated Circuit
### RF-DC Rectifier
Voltage was subsequently changed to \(V_{in}+(V_{in}-V_{TH})\) across the capacitor C\({}_{\rm 1}\) for RF\({}_{\rm in}\geq 0\). M\({}_{\rm 2}\) was the conducting MOSFET, and the voltage of C\({}_{\rm 2}\) was charged to \(2\times(V_{in}-V_{TH})\) by reducing a threshold voltage \(V_{TH}\) of M\({}_{\rm 2}\). In the second time cycle (2T), RF\({}_{\rm in}\) charged the capacitor C\({}_{\rm 2}\) to \(V_{in}+2\times(V_{in}-V_{TH})\) for RF\({}_{\rm in}\leq 0\), and a steady voltage of \(3\times(V_{in}-V_{TH})\) was passed through M\({}_{\rm 3}\) and stored in C\({}_{\rm 3}\). For RF\({}_{\rm in}\geq 0\), the voltage of C\({}_{\rm 3}\) was abruptly changed to \(V_{in}+3\times(V_{in}-V_{TH})\), and M4 was the conducting MOSFET. A steady voltage of \(4\times(V_{in}-V_{TH})\) was generated across the capacitor C\({}_{\rm 4}\). After the third time cycle (3T) was completed, a steady voltage of \(6\times(V_{in}-V_{TH})\) was generated without a body effect across the load capacitor C\({}_{\rm L}\). The ideal output DC voltage Vdc could be expressed as follows:  
\[V_{dc}(V)=6\times(V_{in}-V_{TH}), \tag{7}\]  
Equation (7) indicates that the larger the threshold voltage \(V_{TH}\) is, the smaller the DC output voltage \(V_{dc}\) is. Threshold voltage \(V_{TH}\) is highly correlational to a semiconductor process. A conventional CMOS has an inherent threshold voltage of approximately 0.45 V for the standard TSMC 0.18 process, whereas a low threshold voltage of 28 mV was obtained using a conventional MOS. The conventional MOS was used to not only develop a new RF-DC rectifier but also to enhance power efficiency, particularly for an ultra-low input power of less than \(-\)10 dBm.
# Improving Traffic Load Distribution Fairness in Mobile Social Networks
## 5 Simulation and Discussion  
### Simulation Setup  
The scenarios of simulations and evaluation metrics considered in the TraLDA's investigation are now discussed. We implement TraLDA and the algorithm benchmarks in the Opportunistic Network Environment (ONE) simulator [22]. For the simulations, we vary the total number of nodes and simulation time dependent of the mobility scenarios. A warm-up phase of 30% of the simulation duration is used to enable nodes to gather information about the network's states. We set the node buffer to 20 MB, while the message size and its TTL are set to 10 kB and 7 days, respectively. A new message is generated at a rate of 12 messages per hour at a random node, and is directed to a randomly selected destination. For each algorithm, the simulations are run five times with distinct random number seeds.  
For mobility scenarios, we use two realistic, long period of human encounter datasets, Reality [39] and Sassy [51]. In Reality, 100 mobile phones were carried by MIT staffs and students for nine months. The phones were running software that performed Bluetooth device discovery every 5 min, logging contacts with nearby Bluetooth-enabled devices. The dataset gathered device contacts in the campus over the given period. The traces were acquired in Sassy, however, utilising tMote invent devices carried by academics of University of St. Andrews. The invent devices were designed to broadcast beacons every 6.67 s to detect other devices within a 10-m radius. The experiment was conducted for 74 days, where they were asked to bring the devices at all times, whether in or out the town.  
For performance evaluation, we utilise the following evaluation metrics:  
1. Delivery ratio: the ratio of the number of messages delivered to the number of new messages created.
2. Delivery latency: the time it takes for a message to be created and forwarded to the intended recipient.
3. Message overhead ratio: the fraction between total overhead messages and total delivered messages. The total overhead messages is computed as the number of forwarded messages minus the number of messages successfully deliveredGINI index: this statistical dispersion measure [52] computes the disparity between values of a frequency distribution. Here, the GINI index is used to quantify the fairness level of traffic load distribution in the network: a value of \"0\" indicates that traffic is divided equally among network nodes, while a value of \"1\" indicates that all network traffic is processed by a single node.