esa-sceva/satcom-chunk-collection huggingface dataset:
# Reduced Overhead Routing in Short-Range Low-Power and Lossy Wireless Networks +
## 4 Reduced Overhead Routing Protocol
### Point to Multi-Point Reduced Overhead Routing
#### 4.2.1 Choice of the Hash Functions  
Typically, nodes in a short-range low-power and lossy wireless network possess limited resources, such as memory, and processing capabilities. Therefore, for the research domain considered here, we need hash function(s) with low memory complexity. To limit the packet forwarding delay the hash functions(s) should be simple enough so that hashes can be computed quickly in resource constraint nodes. Moreover, to be used in a BF such a hash function should also possess the following properties: \((i)\) uniformity, \((ii)\) applicability, \((iii)\) versatility, and \((iv)\) efficiency. We choose the Shift-Add-XOR (SAX) hash function class due to its simplicity and it also possesses the above mentioned properties [22]. Moreover, it works with any type of data with a similar efficiency. A BF uses \(k\) hash functions each producing a hash value of \(log_{2}(m)\) bits. To simplify this, a single hash function can be used to produce \(k\times log_{2}(m)\) bits, and afterwards the output can be split into \(k\) equal partitions, as suggested in [14].  
To analyze the feasibility of using BF for compressing the source routing information in a packet header, we need to determine a BF's false positive rate using the SAX hash function. Hence, we carried out a number of experiments. In our experiments, we analyzed the following two BF configurations: \((i)\)\(m=128\) bits, \(k=4\) and \((ii)\)\(m=256\) bits, \(k=2\). We varied \(n\) from 1 to 20 and used IEEE 802.15.4 MAC addresses. To determine the BFs' false positive rate, we generated 1000 random IEEE 802.15.4 MAC address in each experiment and applied SAX hash function to obtain \(k\) positions in the BF's m-bit array. The values at those \(k\) positions then determine whether any of the addresses was present in the BF. We repeated each experiment 30 times and in order to obtain confidence intervals (CI) around the mean we used the t-distribution with the sample size of 30. Figure 5 shows the comparison of SAX false positive rate (using the two configurations discussed above) with the theoretical bound. Figure 5 reveals that there is a very close fit between SAX false positive rate and the theoretical bound, hence SAX hash function based bloom filter is a good choice for compressing the source routing information in the packet header.
# Ground-Borne Vibration Model in the near Field of Tunnel Blasting
## 2 Dimensional Analysis
### Propagation Mechanism and Influencing Factors of Ground Vibration  
Explosive detonation will have an intense impact on the geotechnical medium around the blast hole, which is essentially a dynamic process of the propagation and disturbance of the impact stress wave in the geotechnical medium. This phenomenon can be described as a very rapid release of energy in a very short time and the produced high stresses are attenuated with increasing distance and time. According to the energy attenuation rate and consumption, the amplitude, properties, waveform, and action time of the stress wave are constantly changing in this process, and the rock crushing, cracking, vibration, and other damages to varying degrees are caused from the center of the explosive to the outside.  
The magnitude of these damages, especially the amplitude of ground vibration, will show different changes with time, geological conditions, initiation mode, distance from the detonation center, and charging structure. The factors affecting vibration propagation can be roughly divided into three categories. These parameters and their dimensions in each category are as follows [16]:  
In terms of explosives: maximum charge per delay \(Q\left[\mathrm{M}\right]\), explosive density \(\rho_{c}\left[\mathrm{M}\cdot\mathrm{L}^{-3}\right]\), charge density \(\rho\left[\mathrm{M}\cdot\mathrm{L}^{-3}\right]\), expansion coefficient of detonation \(\gamma_{c}\), energy released by unit explosive \(E_{c}\left[\mathrm{M}\cdot\mathrm{L}^{2}\cdot\mathrm{T}^{-2}\right]\), initiation time per delay \(T\left[\mathrm{T}\right]\), detonation velocity \(C_{c}\left[\mathrm{L}\cdot\mathrm{T}^{-1}\right]\), detonation time \(t\left[\mathrm{T}\right]\), propagation velocity of shock wave in rock \(C\left[\mathrm{L}\cdot\mathrm{T}^{-1}\right]\), initial pressure of detonation gas \(P_{0}\left[\mathrm{M}\cdot\mathrm{L}^{-1}\cdot\mathrm{T}^{-2}\right]\), shock wave pressure \(P\left[\mathrm{M}\cdot\mathrm{L}^{-2}\cdot\mathrm{T}^{-2}\right]\), etc.  
Rock: system stiffness (force required to compress rock unit displacement) \(k\left[\mathrm{M}\cdot\mathrm{T}^{-2}\right]\), initial density \(\rho_{r}\left[\mathrm{M}\cdot\mathrm{L}^{-3}\right]\), elastic modulus \(E\left[\mathrm{M}\cdot\mathrm{L}^{-1}\cdot\mathrm{T}^{-2}\right]\), [PERSON]'s ratio \(\mu\), yield limit \(Y\left[\mathrm{M}\cdot\mathrm{L}^{-1}\cdot\mathrm{T}^{-2}\right]\), minimum resistance line \(W\left[\mathrm{L}\right]\), longitudinal wave velocity \(c_{r}\left[\mathrm{L}\cdot\mathrm{T}^{-1}\right]\), rock wave impedance \(R_{r}\left[\mathrm{M}\cdot\mathrm{L}^{-2}\cdot\mathrm{T}^{-1}\right]\), etc.  
Charge structure (geometric parameters): distance from the detonation center \(R\left[\mathrm{L}\right]\), radius of blast hole \(b\left[\mathrm{L}\right]\), radius of charge loaded into the blast hole \(a\left[\mathrm{L}\right]\), decoupling coefficient \(b/a\), elevation difference \(H\left[\mathrm{L}\right]\), explosive spacing \(d\left[\mathrm{L}\right]\), density coefficient of blast hole \(m\), etc.
## 3 Related Works  
### Major Evaluation Metrics  
Due to different characteristics of the approaches taken in the literature, studies on mask-face detection methodologies use a variety of indicators to evaluate their performance. In this section, we introduce these evaluation metrics used frequently in our investigated research, and the corresponding formulas.  
We first provide the definitions of the required statistics. True Positive (\(T_{P}\)) indicates that the positive sample is predicted correctly. False Negative (\(F_{N}\)) refers to the positive sample being incorrectly predicted to be of negative class. True Negative (\(T_{N}\)) defines cases of a negative sample being correctly classified into the negative class. Additionally, False Positives (\(F_{P}\)) are the result of negatives samples incorrectly classified into the positive class.  
Recall that Equation (1) indicates how many positive samples are correctly detected in the prediction result. The precision shown in Equation (2) is defined as evaluating the proportion of correctly predicted samples. Since the values of precision and recall are inversely proportional to each other, sometimes there are contradictions. Thus, it is necessary to use \(F1\)-_score_ to weight and average these two indicators, as expressed in Equation (3).  
\[Recall=TPR=\frac{T_{P}}{T_{P}+F_{N}} \tag{1}\]  
\[Precision=\frac{T_{P}}{T_{P}+F_{P}} \tag{2}\]  
\[F1\text{-}score=2\times\frac{precision\cdot recall}{precision+recall} \tag{3}\]  
The True Positive Rate (\(TPR\)) has the same definition as recall (1). The False Positive Rate (\(FPR\)) shown in Equation (4) refers to the proportion of wrongly predicted negative samples with respect to all negative samples. \(TPR\) and \(FPR\) are often used as a pair of evaluation indicators. Accuracy, expressed in Equation (5), is the most common indicator which indicates the proportion of correctly classified samples with respect to the overall sample space. In general, a higher accuracy suggests better classification performance.  
\[FPR=\frac{F_{P}}{T_{N}+F_{P}} \tag{4}\]  
Figure 2: The overall mask-face detection review structure.  
\[Accuracy=\frac{T_{P}+T_{N}}{T_{P}+F_{P}+T_{N}+F_{N}} \tag{5}\]  
In order to measure the overlap degree between the predicted bounding box and the ground truth, the Intersection over Union (_IoU_) was utilized as shown in Equation (6). By adjusting the value of _IoU_, a corresponding set of recall and precision values can be obtained. The Average Precision (_AP_), expressed in Equation (7), is the area enclosed by this set of values. The mean Average Precision (_mAP_) is calculated by averaging all _AP_ values of \(N\) categories as defined in Equation (8).  
\[IoU=\frac{Area\ of\ Overlap}{Area\ of\ Union} \tag{6}\]  
\[AP=\int_{0}^{1}p(r)dr \tag{7}\]  
\[mAP=\frac{1}{N}\sum_{i=1}^{N}AP_{i} \tag{8}\]
## Materials and Methods
### PEF Processing  
Chilled and frozen samples of different lamb cuts were processed with a lab scale PEF system (Elcrack-HPV 5, DIL Quakenbruck, Germany). PEF treatment chamber (6 cm height \(\times\) 4 cm width \(\times\) 6 cm length) was used to hold the samples and stainless-steel parallel electrodes with a distance gap of 4 cm. The meat was carefully cut using a special cutting mould to fit the dimensions of the PEF chamber and then placed between two electrodes within the PEF chamber.  
For the present study, chilled and frozen lamb cuts were studied in two different groups; control-chilled and control-frozen-thawed (non-PEF treated sample), and PEF-chilled and PEF-frozen-thawed: (PEF treated sample). The different muscles were cut parallel to the direction of fibre, which was perpendicular to the electric current. The recorded weight of sample was approximately 62 \(\pm\) 5 g. PEF processing for each lamb muscle was conducted on 6 independent replicates. After processing, samples were vacuum packed in polyethylene plastic bags and were stored at 4 \({}^{\circ}\)C for two different storage periods (0 and 7 days). Afterwards, the samples were stored at \(-\)20 \({}^{\circ}\)C for approximately 2 months prior to further analysis.  
Chilled and frozen samples were treated at a specific energy of 88-109 kJ kg\({}^{-1}\), frequency of 90 Hz, the electric field strength of between 1-1.4 kV cm\({}^{-1}\), pulse number of 964, and pulse width of 20 \(\upmu\)s. During PEF treatment, pulse shape (square wave bipolar) was observed online through the use of an oscilloscope (Model UT2025C, Uni-Trend Group Ltd., Hong Kong, China). The initial temperature was maintained at 4 \({}^{\circ}\)C, and temperature of the meat cuts before and after PEF treatments was determined using temperature loggers (Grant Squirrel SQ800, Cambridgeshire, UK).
###### Abstract  
Conventional subsoiling has the problem of excessive draught resistance, which has long been a major concern. A great reduction in draught is urgently required to make better use of the subsoiling technique with many agronomic benefits. In this study, an attempt was made to test the draught reduction performance of a series of tools with continuous belt motions. Comparative simulations between regular tools and belt tools were carried out using the discrete element method (DEM) to investigate the effects of belt motion on tool force, soil disturbance characteristics, particle velocity distribution, and soil deformation and movement patterns. The results indicated that the belt motion reduced the draught force by 13.18%, 25.21%, 37.98%, and 44.64% for the rake angles of 30\({}^{\circ}\), 45\({}^{\circ}\), 60\({}^{\circ}\), and 75\({}^{\circ}\), respectively, and caused acceptable increases in downward vertical force. The changes in soil disturbance, particle velocity distribution, and soil deformation and movement patterns contributed to the reduction in draught force. Negative effects on soil disturbance were acceptable considering the 13.18% to 44.64% reductions in draught force. The present study suggests that the belt motion has a great application potential for draught reduction in subsoiling. Also, this can serve as a theoretical foundation for future studies.  
subosiler; discrete element method (DEM); continuous belt; draught force; soil disturbance; particle velocity; specific resistance; soil movement; soil deformation Article
## 2 Related Works
### Routing Based on Clustering  
Although the protocols with history information work well enough to be able to achieve high message delivery ratios, messages are delivered with a high latency. To reduce the delay, other properties such as mobility and the relationship among mobile nodes have been considered to make routing decisions. In some particular DTN environments, clustering with hierarchical structures has been proposed to reduce the end-to-end delay. In [6], a hierarchical forwarding mechanism has been proposed to group the nodes according to their encounter frequency. Initially, each node is considered as a cluster consisting of a single node. And the operation which combines the two best clusters to form a new cluster, determined by the distance function, is repeated until the cluster with all nodes is finally formed. Similar works using clustering technology can be found in [7; 8; 9]. In [7], the maximum diameter is used for the clustering and link similarity has been adopted in [9]. In [8], similar mobility pattern is used for the clustering and a new cluster-based routing protocol has been proposed. By the proposed protocol, different routing schemes have been adopted for the intra-cluster and the inter-clusterrouting. For the intra-cluster routing, direct transmission is used while gateway nodes are used to exchange messages for the inter-cluster routing. By these protocols, the mobile nodes with frequent contact and the mobile nodes with less contact will be differently treated, thus improving the message transmission efficiency. However, the messages' destinations may not be able to receive all the messages due to the unreliability of the wireless communication channels.
## 6 Applications of Antimicrobial Peptides in Active Packaging
### Challenges in Incorporation of AMP into Food Packaging Material  
The incorporation of antimicrobial peptides into the packaging material has several challenges when it has to be commercialised. Some of the main issues are discussed further:  
(i) Compatibility between the packaging matrix and AMP: The impregnation of AMP into packaging material requires a certain level of compatibility to prevent the destruction of the AMP and ensure proper binding to the packaging material. Direct incorporation of antimicrobial compounds affects the release rate and durability [100]. [PERSON] reported that covalent bonding between the antimicrobial peptide and packaging matrix helps to counter-blast the release but has a negative impact on the stability of the antimicrobial activity's efficiency [101].  
(ii) Compatibility between AMP-impregnated package and food: This factor can be assessed by migration tests and extraction experiments [102; 103]. It should be ensured that AMP-impregnated material does not release any toxic or undesirable compounds due to chemical reactions.  
(iii) Stability on treatment: Packaging material has to undergo several high-temperature and pressure treatments. Additionally, in some cases, after the placement of the food inside the packaging, it is passed through preservation treatments, such as UHT, aseptic packaging and modified atmosphere packaging. Therefore, AMP should be capable of resisting changes during such treatments. High-temperature treatments during thermal-mechanical transformation processes reduce the antimicrobial activity of the packaging material [104].  
(iv) Release System: Once the AMP is introduced into the packaging material, it has to have a controlled release rate throughout the expiry of the food product to ensure the food safety, which is quite challenging. Crystalline regions create difficulty in releasing compounds (or AMPs) from packaging matrix [100].  
(v) Complications during harvesting and its production cost: Techniques known for harvesting AMPs are tedious, which makes it difficult to be performed at a large scale. The production cost of AMPs needs to be reduced with improved production efficiency. [PERSON]\(\tilde{\text{n}}\)as, in an extensive review of peptide development, claimed that synthesis of high-molecular-weight AMPs is complicated, has lesser yield and a higher production rate [105].
## 4 Materials and Methods
### LC-MS Conditions  
Chromatographic separation was performed on the Dionnex UHPLC system. Urine extracts, obtained as described above, were injected at a volume of 10 \(\mu\)L on a reversed phase pentafluorophenyl (PFP) column (Discovery HS F5 100 mm \(\times\) 2.1 mm, 3 \(\upmu\)m (Sigma-Aldrich, Poznan, Poland)). Autosampler and column temperatures were set to 4 \({}^{\circ}\)C and 25 \({}^{\circ}\)C, respectively. The flow rate was 0.3 mL/min. The mobile phase A was water with formic acid (99.9:0.1; \(v/v\)) and mobile phase B was acetonitrile with formic acid (99.9:0.1; \(v/v\)). The total time of analysis per sample was 40 min. The starting mobile phase conditions were 0% B from 0 to 3.0 min, followed by a linear gradient to 90% B from 3.0 to 25.0 min, an isocratic hold at 90% B until 34.0 min, and a 6 min column re-equilibration time [40]. Total ion chromatograms of QC samples in both ionization modes are shown in Supplementary Information Figure S1.  
The analyses were performed in both positive and negative electrospray ionization modes in separate runs on a Q Exactive Focus Orbitrap mass spectrometer (Thermo Fisher Scientific). In positive ionization mode, the following HESI ion source parameters were used: spray voltage 1500 V, capillary temperature 300 \({}^{\circ}\)C, sheath gas 40 a.u., aux gas flow rate 15 a.u., probe heater temperature 300 \({}^{\circ}\)C, and S-Lens RF level 55%. For negative ionization mode, HESI ion source parameters were as follows: spray voltage 2500 V, capillary temperature 256 \({}^{\circ}\)C, sheath gas 48 a.u., aux gas flow rate 11 a.u., probe heater temperature 413 \({}^{\circ}\)C, and S-Lens RF level 55%. Scan range was set on \(m/z\) 80-1000 with resolution 70,000. The instrument was calibrated using external calibration immediately before the analysis and every 48 h, resulting in mass accuracy <2 ppm. Data acquisition was performed with Xcalibur software v. 4.0.  
All samples were analyzed in one randomized sequence and QC samples were run periodically (8-10 injections) to verify instrument performance. QC samples were prepared by mixing 20 \(\mu\)L of each of the 48 urine extracts.  
The putative identification of compounds was confirmed based on Full MS/dd-MS2 mode. Fragmentation parameters were as follows. Mass resolution: 35,000 full width at half maximum (FWHM), AGC target: 2E4, minimum AGC: 8E3, intensity threshold: auto,  
Figure 3: The high throughput 96-semi-automated SPME system used for analysis of urine samples (**A**) and a set of 96 SPME devices coated with different types of extraction phases (the PS-DVB coating selected for the study is represented in rows 3 and 4 from the right) (**B**).  
maximum IT: auto, isolation window: 3.0 \(m/z\), stepped collision energy: 10 V, 20 V, 40 V, loop count: 2, dynamic exclusion: auto. Fragmentation spectra were confirmed with online databases such as LIPID MAPS, HMDB, METLIN, and mzCloud.
## 2 Problem Formulation
### Dynamic Cooperative Path Planning Constraints of UAVs
#### 2.1.3 Four-Dimensional Coordination Constraints  
The four-dimensional coordination constraints consist of spatial coordination constraints and time coordination constraints. In this paper, it is assumed that any \(\text{UAV}_{i}\) in \(\text{UAV}_{N}\) flies at a uniform speed in any speed interval \(\left[\mathcal{V}_{\text{min}},\mathcal{V}_{\text{max}}\right]\); that is, the estimated time of arrival (ETA) interval of \(\text{UAV}_{i}\) is \(\left[t_{\text{min}}^{i}=\sigma_{i}/\mathcal{V}_{\text{max}},t_{\text{max}}^ {i}=\sigma_{i}/\mathcal{V}_{\text{max}}\right]\).  
**Definition 4 (four-dimensional coordination constraints).**_The cooperative path of all UAVs in \(\text{UAV}_{N}\) must meet the following requirements:_1. _At moment_ \(t\)_, the distance_ \(D_{i,j}(t)\) _between any two UAVs is:_ \[D_{i,j}(t)=\left\|\sigma_{i}(t)-\sigma_{j}(t)\right\|_{2}\geq D_{\text{min}}\; \text{i},\text{j}=1,2,\ldots,k,i\
eq j\] (7) _where_ \(D_{\text{min}}\) _is the minimum safe distance._
2. _The set_ \(T\) _of ETA of all UAVs is:_ \[T=\underset{i=1}{\overset{n}{\cap}}\left[t_{\text{min}}^{i},t_{\text{max}}^{i} \right]\
eq\varnothing\] (8)
## 3 Background and Overview of LEACH
### First Order Radio Model  
Low energy radio model is considered as an important factor to communicate effectively over a network. In our work, simple energy consumption first order radio model is presumed. However, energy consumption in transmitter and receiver modes will affect and compensate the performance of routing protocols. In transmitter and receiver mode electronic circuitry dissipate \(E_{Tx-dec}\)= \(E_{Rx-dec}\)= \(E_{dec}\)= 50 nJ/bit to run different operations of a given radio model. At the transmitter end the signal is amplified to \(\in\)= 100 pJ/bit/m\({}^{2}\) for achieving acceptable bit to noise ratio \(E_{b}/N_{0}\) at the receiver end, as shown in Table 2. Generally, this model describes the energy consumption while transmitting and receiving a packet to neighbor node or to an end station. Equations (1) and (2) show the mathematical model for energy dissipation when transmitting a \((l)\) bit packet to remote distance \((d)\).  
\[E_{Tx}(l,d)=E_{Tx-dec}(l)+E_{Tx-emp}(l,d) \tag{1}\]  
\[E_{Tx}(l,d)=E_{dec}\times l+\ \varepsilon_{amp}\ \times l\times d^{2} \tag{2}\]  
Equations (3) and (4) describe the energy dissipation induced by the reception of \((l)\) bits packet. In this model, the receiver consumes less energy than the transmitter [15] because it does not need to further amplify the signal for forwarding the received information, but instead will only process it.  
\[E_{Rx}(l)=E_{Rx-dec}(l) \tag{3}\]  
\[E_{Rx}(l)=E_{dec}\times(l) \tag{4}\]  
Symmetric radio channel is assumed in this model, in which the energy required to transfer information from a node \(X\) to a node \(Y\) is similar to that needed to transmit the same information from node \(Y\) to node \(X\). Alternatively, the clustering protocols are designed in such a way that consumes minimum amount of energy for each transition. Additionally, transmit distance should also be reduced for improving the energy consumption for transmission and receiving operation, especially in this model [4; 16; 17; 18].